Selecting factors of microphone
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There are many methods used to detect properties of electrical machines. Between diagnostic methods include too the measurement and analyze of noise, which generates electrical machines.
Itself the noise of electric machines is by product of the machine operation. The generation of noise is involved in many physical principles.
Noise of electrical machinery is generated by the vibration of machine parts. Gradual spread of vibration from the engine to the surroundings causes pulses of air with certain frequencies. This creates a sound wave generator, which can be within a certain frequency range, audible to humans.
The main sources of noise in electrical machines are time change of the electromagnetic fields, noise of bearings and other mechanical sources. Finally, the unwanted noise is creating too due to coolant flow or parts that come into contact with coolant in electric machines. Level of noise sources in electrical machines depends on the structural arrangement and the accuracy of engine design.
A major problem in measurement noise is interference environment. For a perfect suppression ambient noise is necessary to have a specialized laboratory. It should be also measured machine have isolated from vibration, which it may be transferred from storage.
The sound wave is generated by vibrating objects and can be defined as mechanical interference with the finite speed of advancing through the media. These waves have small amplitude, adiabatic oscillation are characterized by a wave speed, wavelength, frequency and amplitude. Sound has the character of longitudinal sound waves in the direction of propagation in the environment. In other words, it is the movement of individual particles of the medium in a direction parallel to the transmission of energy. Sound waves spread in three-dimensional environment from the source. It is same in all directions, if is the environment homogeneous. Sound waves can be polarized, they cannot have orientation. Non-polarized waves can oscillate in any direction in the plane perpendicular to the direction of propagation.
Sound amplitude can be measured as sound pressure level (SPL), sound intensity (SIL), sound power level (SWL) and the intensity of the acoustic energy (SED).The human ear can perceive sound waves of sufficient intensity and frequencies are ranging from 20 to 20,000 Hz. The Minimum sound intensity is different for different frequency and it is called the threshold of audibility. Range of sound intensity, which can capture the human ear, is 10-12 to 1
Concentration of particle of vibrating environment corresponds with increase or decrease pressure inside gasses and liquids. This means that the total pressure in the environment is changing and therefore fluctuates around the initial static value or barometric pressure. The acoustic pressure is then considered deviation of the total pressure from the static pressure. For acoustic pressure is valid relationship
Where
For effective sound pressure value is valid relationship
Acoustic pressure is a variable and it describing the noise source quantitatively. The measured level depends on the observer\'s distance from the source and the quality of the transmission environment Acoustic pressure level gives us information on the total sound pressure across a entire audible band. For sound pressure level is valid relationship
Where
Mechanical vibrations are transmitted in form of mechanical energy from the source through acoustic waves. Sound power level is called the energy that passes per unit time over surface. For sound output, we can write the relationship
Where
The sound power level depend on the the environment parameters and distance from the measurement point. The sound power level can be expressed as
Where
k… constant
Acoustic intensity is a vector quantity that describes the amount and direction of flow of acoustic energy in the environment. Vector of acoustic intensity is time-change of instantaneous sound pressure and it is corresponding instantaneous speed of vibrating particle environment in the same place
Where
I… Acoustic intensity
From the physical point of view, mechanical sound is waves in a flexible environment. The Frequency range of sound audibly for human ear is from 20 Hz to 20 kHz. The sound spreads in all directions from resources by transmitting acoustic wave energy. Division by frequencies of sound waves:
Infrasound - up to 20Hz
Low frequency - 20Hz to 40Hz
RF - 8kHz to 16kHz
Ultrasound - 20kHz over
Dividing the sound by timing:
Steady
variable
intermittent
pulse
The interest noise frequency is over 1000 Hz for induction machines. Noise of Electrical Machines is characterized as a set of sounds that are caused by rapid changes in air pressure. These changes cause most commonly:
Vibration of machine parts or the whole of its surface
Aerodynamic phenomena that lead to pulsation of pressure near the machine
Basic sources of noise are induction motors (see diagram):
Electromagnetic source
The mechanical source
The aerodynamic source
Division of noise sources in electrical machines
The noise from electromagnetic source is the most typical component noise of electrical machine. Its cause is the vibration of motor body, or other parts of the machine on which work the electromagnetic forces. Frequency Spectrum noise of the electromagnetic source has discrete character, while there is very distinct directional radiation characteristics of this component in many cases.
Determining the influence of this component on the overall noise of electric machine is often simply done so, that after switching off the machine from the network is observed decline in the acoustic signal in time. If is this decline immediately, then it is obviously a component of the noise of electromagnetic origin. Another method of investigation is the measurement of electromagnetic noise spectrum for different values of power - or even frequency.
Noise origin of ventilation is crucial observe especially in machines with high rotational speed. Detailed analysis of the fan noise shows that the main source in this case is very fan with its nearest surroundings. It is the decisive exceeds other sources of noise, which can be, for example rotor wings, radial or axial cooling channels in the machine, input and output caps and the like.
Frequency analysis of noise ventilation origin shows that the spectrum has a broadband character, either discrete or vice versa. In the first case, the aerodynamic noise is created from turbulent airflow near fan blade and near the entrance, but also the output edges of blades. These pulsations are uneven both in space and in time, so the frequency spectrum created of wind noise is broadband and contains all components of the audible band.
In contrast, discrete nature of the spectrum, sometimes the siren phenomenon can arise. This phenomenon arise if the fan or behind obstacles (such as a blade with these obstacles) is not profile of velocity uniform air flow around the wheel circumference, leading to periodic pulsation of pressure. Then the siren noise is produced naturally.
The noise of mechanical origin is primarily inflicted on roller bearings and unbalance of rotating machine parts. Rolling bearings can create multiple frequency components, which have their origin mainly in inequality as part of rolling paths of the bearing rings. In principle, the noise of mechanical origin has a mixed character.
The influence of magnetic induction in the air gap formed magnetic forces; these forces operate across various directions. They may also have various amplitude and frequency. Their work is split between the rotor and stator of electric machine. Their characteristics depend on the size and shape of the air gap and a number of other factors.
The construction of the rotor is the main radiator noise machine. If the frequency is close to the radial force or equal to one natural frequency of the stator system, resonance occurs which leads distorted stator system with vibration and noise. Magnetostriction noise electric machine can be neglected in most cases due low and high frequency 2f arrangement r = 2p of radial forces, where f is the fundamental frequency and p is the number of pole pairs. However, the radial forces due magnetostriction can reach up to 50% the radial forces produced in the air gap magnetic field.
Magnetic flux density wave
Stator:
Rotor:
Where
k,l…Variable (values 1,2,3,4,….)
For total wave of magnetic flux density can be write relationship
The magnetic stress wave has worked in radial directions on the stator and on active surfaces of rotor. This causing the deformation and subsequently cause the vibration and noise.
The mixed product of stator and rotor winding space harmonic create forces at frequencies
Where
n…. value n=0, ±1, ±2,… [-]
p… number of pole pairs [-]
s… slip
The mixed product of stator winding and rotor eccentricity space harmonics create forces with frequencies
The mixed product of stator winding and rotor saturation harmonics create forces at frequencies
The air gap width depends only on position (no on time) in the static eccentricity. We conclude that the magnetic field in the air gap is rotating synchronous speed. That is given by the mains frequency and with the number of pole pair’s induction machine. Modulation of magnetic field in one period is function, which is represented by a variable air gap, i.e. a function of its conductivity. Static eccentricity is defined as the rotor axis offset from the axis of the stator. The air gap has a variable character. There is stronger interaction of stator and rotor magnetic field at the point where the gap is smaller. Influence of the static eccentricity manifests as the emergence of side frequency bands, which are shifted from the mains frequency
Static eccentricity is straight-line. The frequency for static eccentricity is twice power frequency
The relative eccentricity
Where
R… Inner stator core radius
r… Outer rotor radius
e… Rotor eccentricity
g… Ideal uniform air-gap for e=0
Dynamic eccentricity occurs when the rotor failure or its affiliates. Ratios are complicated by the fact that the width of air gap is not just a function of position, but is also a function of time. The variable air gap is changing at the rotation of the rotor. There is emergence of side bands that appears in the frequency range of vibrations of electric machine.
Angular frequency for dynamic eccentricity
The frequency generated by the dynamic eccentricity
For frequency generated by eccentricity is true also relationship
Where
R…Number of grooves engine
s… Chute
p… Number of pole pairs
Rotor eccentricity
Aerodynamic noise arises most often around the fan, or in the vicinity of the machine that behaves like a fan. Noise can be created too on the necks stator slot windings or rotor. The aerodynamic noise sources can also include the noise produced by air flow inside and outside the design of electrical machines.
The main reason for the fan noise is formation of turbulent air flow around the blades. This noise is characterized by spectrum in a wide range, which has continuous character. Acoustic performance is increasing with the square of velocity. Siren noise can be eliminated by increasing the distance between the impeller and the stationary obstacle.
For the fan noise can write the relationship
Where
The vortex frequency is expressed by
The frequency of the pure tone due to the fan blades is given by relationship
Where
N…speed [rev/min.]
Sound power level of aerodynamic noise is
Where
p… Fan static pressure [Pa]
Q…Flow rate [
Reducing aerodynamic noise in electrical machines can be use the following ways:
Reducing the required amount of coolant used for ventilation of electrical machines
Optimal design of fan. Especially the number and shape of the fan blades has an impact on the noise generated by the electric machine.
To minimize the noise is needed to prevent vibration machine parts, which come into contact with a cooling medium.
Mechanical noise is mainly due with bearings, their defects, ovality, sliding contacts, bent shaft, rotor unbalance, shaft misalignment, couplings, U-joints, gears etc. In principle, the mechanical source of noise has a mixed character. The noise caused by unbalance of rotating parts and noise of bearing is spread after machine constructions very well. Dynamic balancing in production serves to reducing the noise of mechanical source. Especially for machines with high speed is necessary to perfect balance. Also, compliance with the manufacturing tolerances and technological processes, especially in the manufacture of small machines is the best solution to reduce the noise of mechanical source. Any change in noise from this source can mean failure of the mechanical parts inside the motor. For example, the bearings failure (damaged ball) is appear in the noise spectrum. There are specific frequencies by individual damage. The very faults of bearings and their effect on the noise spectrum of the electric machines are now well mapped.
Design of bearings can be either a sliding or rolling bearings. Rolling bearings can create multiple vibration frequencies, which have their origin mainly in the uneven parts or rolling themselves paths to the bearing rings. If bearing has mechanical damage, there is uneven movement of the whole system and thus increasing vibration and noise of the electric machine.
The main mechanical sources of the noise
Alignment
Inaccurate machining of parts
Running speed
Number of rolling elements carrying the load
Mechanical resonance frequency of the outer ring
Lubrication conditions
Temperature
The noise of rolling bearings depends on the type of bearing and its construction and accuracy of bearing parts. The increase in vibration and noise level of bearings, when the rotational speed changes from
Ball pass frequency – outer race
Where
D…Pitch diameter [m]
Ball pass frequency – inner race
Uneven journal
k=1, 2, 3, …
Axial groves
In certain cases, the vibrations and thus noise transmitted from the load, which is connected to the induction motor. In most cases, this occurs with wrong balance or bad connects of couplings. Uneven distribution load acting on the motor shaft or inappropriate use of gears may also affect noise machine. The only possible protection against these effects is the perfect balance of the whole set and if possible an even distribution of forces acting on the connecting elements. Noise arises too due to coupling of the machine with a load, e.g., shaft misalignment. Next noise arises from belt transmission, from cogwheels and couplings. It may also arise to noise due to mounting the machine on foundation or other structure.
For measurement noise of induction machines can be used several techniques. The basic method for the measurement noise is the sound meter. It is a device which measures sound pressure.
Measurement of noise can be divided into three main parts. The first part is data capture. For this purpose, the most commonly used microphones, or specialized equipment to measure noise (sound level meter). Their output is usually an analog signal, which must be further processed. When choosing of microphone is needed careful heed on certain parameters that can affect measurement accuracy. One of the most important parameter is the sensitivity of frequency. Worse microphones not recorded of the entire spectrum of the measured noise. Thanks to this complicates achieve it of accurate analysis results. Other parameters include the microphone sensitivity, which indicates the size of the output voltage (mV / Pa), depending on the pressure acting on the membrane. In addition, the structural dimensions of the measurement microphone and also the type of sound field that which is measured. Computers are most frequently use for Signal processing. For this reason it is necessary to convert from analog signal to digital form.
Large numbers of types A/D converters is on the market. Some are stand-alone converters; others are integrated to the specialized measurement cards. In both cases, the measurement depends on the three main parameters. The first is the measuring range of the converter. It gives the minimum, respectively maximum, measurable value. Because the signal is weak from a microphone, there should be used an amplifier for its amplification. Another parameter of the A/D converter is the bit depth conversion. This parameter defines the limitations of this device.
Block diagram of measurement process
Factors to selecting a suitable type of microphone are as follows
Freely field for a closed chamber | Tolerance sensitivity | Noise level background |
An important range of sound pressure levels | Frequency distortion tolerance | Humidity |
An important frequency range | Phase distortion tolerance | Atmospheric pressure |
Tolerance of non-linear distortion | wind | |
Own noise tolerance | Strong electromagnetic fields | |
Mechanical shock |
Selecting factors of microphone
Sound level meteris an essential instrument for measuring sound pressure levels. This device consists of the following components: Microphone, preamp, overload detector, central Unit, weighing Network, filters, amplifier, RMS detector, Output and Display.
One of the basic parameters of sound level meter is range of frequency. The sound intensity I has broad frequency range. The dispersion of the frequencies is from lower
Where
Spectral intensity level (ISL)
Where
Similarly, the sound pressure level
Where
Fast Fourier Transformation is one of the most common mathematical functions, which is used for noise analysis of electrical machines. The Fast Fourier Transformation is applied in an increasing scale in science, engineering, and technology. The use of complex exponentials has often been convenient rather than fundamental. Most signals and functions used in real applications are real rather than complex. In areas such as digital filtering, convolution, correlation, image processing, and partial differential equations, the actual signals or functions, are real, but they are considered to be the real part of a complex quantity in order to be able to use the complex formulation of Fourier series and transforms. The complex Fourier transform (CFT) of a signal
The inverse complex Fourier transform (ICFT) is given by
The real Fourier transform (RFT) of
Where:
The inverse real Fourier transform (IRFT) is given by
Equation (3) and (5) can be written for
and
Equation (44) reflects the fact that
The inverse of (44) for
Equations (44) and (45) are very useful to convert from one representation to the other. When
then
Surrounding noise sources have an impact on the measurement of electrical machinery. It is not always possible to perform measurements in specialized laboratories, which are perfectly sound-insulated. To laboratory measurement can penetrate the noise from nearby sources (see Fig. 4), which is inaudible to the human ear. The interference from other sources can be created undesirable frequencies in the frequency band.
Noise measurement in the laboratory when the machine is switched off
Interference of other sources in the neighborhood of workplace cannot be directly prevented, but you can minimize their impact on analysis of the measured signal. Before the measurements it must be made measurement ambient noise before the main measurements. It is necessary to determine whether the background noise is random, or it is periodically repeated. In the case of random noise is preferable to wait to other time of measurement or it must count with errors in the measurement. In the event that can be measurement of noise repeated.Can be recorded the extent of spectral interference with which will be calculate when evaluating the measured results. From Spectral analyses of interference is possible to determine the proportion of individual harmonics. These harmonic then they can be the "subtracted" from the noise levels of electrical machines.
The next part of the measurement was performed on the induction motor which worked without a load. The electric motor was loosely placed on a foam board. This board was for suppression the transmission of vibrations from the surroundings. External vibrations are not desirable for accurate measurements.
Measurement noise of electric machine, that is run, is shown in Fig. 5. As seen from the measured values, that the noise level is constantly fluctuating.
Noise of induction machines - no load
Noise of induction machine – 1 rotation
On Fig. 7is an analysis of the measured noise using MATLAB. Specifically, was carried Fast Fourier Transform (FFT). Dominant frequency is 600 Hz. This frequency is multiple of power supply frequency. It is a frequency of radial forces. In measurement signal can be involved many harmonics frequencies of radial forces. Than we can write equation
Where
Fast Fourier Transformation of induction machine noise
It was done measurements eccentricity of rotor. Eccentricity of rotor is shown in Fig. 9. From the measured values it was found that the largest deviations occur in the range of approximately 120 degrees.
When comparing the noise of induction machines recorded on one rotation and values of rotor eccentricity can see a connection. In both cases (Fig. 8 and Fig. 9) appeared larger deflection in the range of 120 degrees. Extreme deviation is in a different quadrant in each graph. This is due to the different measurement principles. Noise measurements done digitally, while measuring the eccentricity was used mechanical method. It was therefore not possible to accurately determine the initial rotor position in both measurements.
it can be argued that the noise of induction machines is generated of the rotor who has eccentricity. Given that the, that machine is equipped with a ventilator, there are two sources of noise. The influence of the fan but will not cause displacement of only a specific part of one rotation.
Given that the measured induction motor was not equipped with cooling system (fan) can be assumed, that the vibration and thus the noise are produced only by electromagnetic source and mechanical source.
Noise envelope – 1 rotation
Rotor Eccentricity - Mechanical measurement
Analyze of noise was performed on the one rotation of rotor. The Fig 5 shows the noise levels depending on the position of the rotor. As the graph shows it is to generate greater levels of noise in the position of the rotor from 300 to 60 degrees (about 120 degrees).
Diagnosis of induction motors is a very complex issue that has many components. One of them is the analysis of motor noise. Noise measurement asynchronous machines are the commonly used diagnostic method. This method is relatively simple. You need to be near an electrical machine quality microphone and recording equipment. Analysis itself can be the performed on specialized software, either on the spot or later in laboratory.
Subsequent analysis of the signal can then indicate whether the machine operates as required, or whether there was damage to electrical equipment. Based on the fast Fourier analysis of noise can be determined which components of the signal are dominant. Based on knowledge of layout design of the engine is then possible to determine what is causing individual harmonics. According to the frequency it is possible to determine which there the main sources of noise are.
A major problem in measuring the noise may be interference from nearby sources. To avoid the external influence of external noise is possible only in specialized laboratories.
During measurements realized appeared possible link between noise and rotor eccentricity of electrical machinery. In the analysis of noise is dominant skew in the range of 120 ˚ in one rotation. In the same range (120 ˚) was measured the dominant deflection of rotor eccentricity this rotor machine. Given that the machine has not a cooling system, there is not source of aerodynamic noise; there are only two possible causes of this deviation. Source of electromagnetic noise would not cause deviation only at certain rotor position, but in the whole rotation. Displacement of noise in a certain position the rotor it cannot assign too resources source of mechanical noise. This group includes vibration bearings. During the measurement was verified that the bearings are not damaged. There are not larger deviations of movement in rotation of bearing.
As a source of noise is impact of rotor eccentricity on the running of the induction motor. Unfortunately, the verification of this theory would require accurate measurement with recording of the rotor position and size of air gap.This measurement is very difficult.
Research described in this paper was financed by the Ministry of Education of the Czech Republic, under project FR-TI3/073 Research and development of small electric machines; and the project of the Grant agency CR No. 102/09/1875 - – Analysis and Modeling of Low Voltage Electric Machines Parameters. The work was supported by Centre for Research and utilization of renewable energy - CZ.1.05/2.1.00/01.0014
Risk-based security is built around the premise that information obtained from observable aspects of human identity and possession and knowledge acquired about hidden aspects of human capability and intent can be intelligently combined to assess to some great extent of accuracy the threat a given individual poses to a security system, be it an airport or a border crossing point (BCP). Then, in turn, associating the estimated level of threat with a measure of risk by factoring in the cost that the assessed threat can represent to the system that is being secured by taking into account the impact and cost a given threat can have on a security system, a risk-based security approach can be designed and implemented, whereby security checks are tailored to be commensurate to the estimated risk each individual may pose, instead of being uniform irrespectively of the risk posed by each individual, as is the case today. Taking into account that less than 5% of all individuals can be a potential security risk, the savings in terms of time required to go through risk-based security systems with speedier tests for the 95% of low to no risk individuals can be significant, waiting times in security lines can be reduced and thus the level of comfort and customer satisfaction be drastically improved.
The concept of
The concept of risk-based security is indeed promising in terms of improving travelers’ experience by easing off security screening and reducing the overall time required to spend at a security check-point. However, the difficulty in implementing a risk-based security systems lies on: (a) developing and implementing non-intrusive, GDPR1 compliant technology and systems that can estimate the risk level of each traveler without inducing additional and cumulative delays; (b) testing such systems before rolling them out in operational environments; and (c) estimate their performance and efficacy under ideal conditions for obtaining performance bounds, calculating the cost of the required investment for implementing risk-based technologies; and (d) calculate the degradation in performance when moving away from the “ideal” operational conditions into realistic operational conditions.
The European Union (EU) and other international organizations promote this approach through various initiatives. The European Commission (EC) issued the “Smart Borders package” which aims to modernize the Schengen area’s external border management by improving the quality and efficiency of border crossing processes through the establishment of ‘Stronger and Smarter Information Systems for Borders and Security’ [1]. The International Air Transport Association (IATA) proposed a Checkpoint of the Future, designed to enhance security while reducing queues and intrusive searches at airports by using intelligence-driven risk-based measures [2]. Along these lines the EC funded the Research and Innovation project FLYSEC [3] has developed and demonstrated an innovative, integrated, and end-to-end airport security system facilitating risk-based screening with the introduction of novel intelligent technologies.
This chapter
Figure 1 shows a today’s conventional security check point whereby we distinguish two types of checks: (a) the “normal” check where all individuals in the security check area are treated uniformly by applying the same level of security for all; and (b) the “increased” security check point where travelers are channeled if they fail at normal security check point. It should be pointed out that in this security system of check points, currently implemented almost worldwide, the “increase inspection” is usually the outcome of randomized selection of travelers to be subjected to an increased level of inspection and is usually based on the principle of “importance sampling2” methods. These methods try to detect a probabilistic event, such as the existence of a suspect among travelers, with a certain degree of confidence by taking into account the probability of existence of such an event and possibly the range of values the event can assume. These methods are “blind,” that it they draw samples from the distribution indiscriminately and without takin into account any specific attributes of the samples, and thus, they are also GDPR compliant. As it will be pointed out further down in the chapter, risk-based methods need to pay special attention to comply with GDPR as they gather and use information and knowledge about individuals’ private data such as identity, possession, capability, and intent.
Today’s security check-point concept (curtesy of TRESSPASS).
Risk-based security associates the estimated risk for each traveler with a commensurate level of security scrutiny. Using prior information about each traveler and sensory data obtained while the traveler is within the security perimeter of a monitored area, a risk-based security system assigns a risk factor to each traveler and depending on the value of the risk factor, the traveler is mapped to a level of security scrutiny commensurate with the perceived risk. Although different number of levels can be associated with the estimated risk, for practical reasons, it is sufficient to associate the entire range of risk values into three different levels of security, Trusted/Registered (Green), Casual (Yellow) and Enhanced Security (Red), as shown in Figure 2 [5].
Association of three security scrutiny levels, namely “enhanced security,” “casual traveler,” and “trusted/registered” with the estimated level of risk for each traveler. These three levels have been introduced in the FLYSEC project [
In Figure 2,3,4 a number of GDPR-compliant technologies that can be used for and contribute to the risk assessment are shown in and include: mobile app way finding; dynamic travelers flow management; intelligent visual surveillance; Wi-Fi/Bluetooth localization; RFID mobile tracking; and behavioral analysis & risk-based security personnel mobile app.
Figure 3 represents a risk-based security check point that results from combining the three-level risk-based security screening of Figure 2 with the conventional security screening of Figure 1. As it can been seen from Figure 3, the need for assessing each traveler’s risk factor from various observable parameters requires measuring somehow these parameters, of course in a GDPR compliant way, and thus additional processing steps and capabilities that may induce additional delays in screening process. Thus, the fundamental premise of risk-based security as a means of providing the same, at least, level of security as conventional check points without inducing additional delays, seems to be in conflict with the additional delay induced by additional screening tests required for estimating each traveler’ risk index, unless the risk assessment process is done transparently while the travelers move from the entry to exit points in a BCP (Border Crossing Point).
Risk-based security check point: The standard (randomized scrutiny checks) security check point of
Figures 4 and 5 depict two block diagrams implementing the conventional security screening process of Figure 1 and the risk-based security screening process of Figure 3 respectively. From the two diagrams it is clear that additional screening stages are required for assessing the risk for each traveler in risk-based security. Each one of these additional risk assessing stages induce additional delays in the security screening process, that add up to an overall additional time required for risk-based security screening compared to the time required for security screening through a conventional security check point.
Configuration 1 (current BCP implementation).
Configuration 2 (risk-based BCP implementation).
Thus, it appears that risk-based security may require additional processing time for estimating risk that may offset the benefits from faster security screening for those travelers whose estimated risk classifies them in either the “trusted/registered traveler” or “casual travelers” categories for whom security screening is relaxed and thus faster than the time would be required to screen them in today’s conventional check points of Figure 1. Granted that over 90% of travelers fall within these two categories and will experience reduced delays at security screening, it remains to determine if the aggregate benefits from the reduced security screening at check points will trade off positively against the additional delays induced by the additional screening points for determining each traveler’s risk as in Figure 5.
In order to quantify the cost–benefit trade-offs between the efficiency of a risk-based security BCP and the delay induced by additional checks required for assessing risk, the following experiment was conducted using Fraunhofer’s FhG BCP Monte-Carlo agent-based simulator of a BCP configuration (curtesy of Fraunhofer Institute) [4].
For the simulation, we assumed a BCP with 1000 travelers, some exhibiting normal (no risky) behavior, whereas the rest exhibit suspicious behavior, with the following parameters:
Distribution of traveler types: [Normal, Suspicious]: [0.9,0.1]
Alarm threshold for each component: 0.5
Risk calculation: According to the script below:
Effectiveness calculation:
Ran over 10000 iteration with 100 travelers each time for both the configurations.
Using the above script for generating travelers with the above choice of parameters, 10.000 iterations with 100 travelers each time were run for each one of the two configurations of Figures 4 and 5, and the effectiveness (as defined above) of each configuration was calculated. The results regarding the effectiveness (as defined above) of each configuration are qualitatively summarized schematically in the graph of Figure 6.
Effectiveness calculation of a conventional BCP with random security checks determined by importance sampling versus risk-based BCP configuration: Effectiveness increases with the use of risk-based security in a BCP.
Effectiveness calculation of a conventional BCP with random security checks determined by importance sampling versus risk-based BCP configuration taking into consideration the additional delays induced by the additional risk assessment stages in configuration 2: Effectiveness increases with the use of risk-based security in a BCP, while induced delays increase as well.
From Figure 7 it is clear that there is a competing mechanism between effectiveness (another way of stating “comfort”) and delay induced by a risk-based security BCP versus a conventional BCP with randomized tests based on the theory of importance sampling5. The aim of the two EU-funded projects FLYSEC and TRESSPASS, coordinated by the author, is for FLYSEC to: (a) demonstrate that there is technology available or can be developed to implement risk-based security in a GDPR compliant way; (b) provide solid evidence of the risk-based security screening as an effective and non-instructive means of providing security with convenience to travelers; and for TRESSPASS to: (c) provide a comprehensive risk-assessment framework for calculating risk systematically in accordance with the TRESSPASS multi threat, multimodal that includes all
measures undertaken with third countries or service providers;
cooperation with neighboring countries;
border control and counter-smuggling measures;
control measures within the area of free move,
by taking into account estimates and information about.
Multi-modal, multi-tier TRESSPASS risk-assessment model.
Observable and hidden risk factors.
Thus, the aim of the two funded projects, namely FLYSEC and TRESSPASS, is to provide solid evidence and the means for moving the operating point (OP) of a risk-based BCP from the delay induced OP to the no-delay induced OP, or as close to it as possible without inconveniencing travelers and in a GDPR compliant way, as shown in Figure 10.
Moving the operating point of a risk-based BCP to minimizing security check delays is the objective of both FLYSEC and TRESSPASS EU-funded projects.
The greatest challenge in risk-based border management is the estimation of the risk for each individual traveler. In TRESSPASS, a framework for modeling risk and a systematic approach of quantifying risk are proposed as follows:
Risk indicators are accurately estimated from available data collected from background information.
The risk for each traveler is calculated.
Based on risk, the system adjusts the number and types of security checks required for each traveler, in order to maintain a desired security level while optimizing the security system performance in terms of efficiency, traveler satisfaction and operational cost reduction.
Figure 11 summarizes in a comprehensive visual depiction the risk-based framework used in TRESSPASS [4], and previously introduced in FLYSEC [3]. The framework for risk-based security consists of an extensive use of technologies to estimate risk from both Observable and Hidden risk indicators across all four security tiers and heavily tested, both in vivo and in vitro through simulation, in carefully designed pilots across all three BCP modalities: air, land and sea.
TRESSPASS comprehensive risk-based security framework.
Paramount to the design and testing alternative designs of risk-based security concepts, technologies and protocols, in order to achieve the increase in effectiveness of BCPs with the parallel reduction of delays, is the use of simulation. iCrowd is an agent-based simulator that can be used to implement and test different risk-based concepts and technologies in a flexible and realistic simulation environment [6]. Figures 12 and 13 show a photo-realistic virtual reconstruction of an airport used extensively in simulating security scenarios and policies for a variety of projects and pilot use-cases.
Photo-realistic, agent-based simulation using iCrowd.
Queue lanes in a risk-based security checking system: Photo-realistic simulation provided by iCrowd.
The iCrowd Simulator is an agent-based simulation platform capable of handling small-scale to large-scale crowds and calculating the change of the status of each participating component depending on dynamic interactions with other entities or the environment during simulation time [7, 8]. It can be utilized in any bounded area, i.e. building interiors and exteriors, stadiums, or any exterior area e.g. public places like squares, open-air festival etc. Currently, it is being used to simulate crowd movement and crowd interactions in general, with the graphical display being optional. As
iCrowd offers a fully operational flow simulation for travelers and personnel inside an airport, as displayed in Figures 12 and 13. It enables the user to define simulation scenarios, it is implementing a sophisticated crowd engine with collision avoidance6 with multiple, different behaviors that can co-exist inside the same simulation. It also supports distributed simulations, operating as an orchestrator. It has been integrated with the C2 Web Portal OCULUS Air to communicate data, such as displaying the position and the movement of simulated entities in real time, and the Fusion and Ingestion Server to update travelers’ status accordingly depending on their interactions with the airport’s hardware and other security technologies (i.e. Beacons, RFID scanners and RFID tags for carry-on luggage tracking), Figure 14(a)–(d).
(a): Aspect of third-person camera. (b): Path planning example: The green line indicates the path the selected agent (displayed are red) is following (c): Travelers enter the airport. The display of hold and hand luggage is turned on. (d): Travelers go through the check-ins. The display of hold and hand luggage is turned on.
This section of the chapter presents and discusses implementable means for assessing risk without inducing additional delays beyond what passengers experience with today’s screening process, but instead reduce the time it takes to go through the security screening process by adjusting the level scrutiny in accordance to the perceived risk.
If passenger trajectories at an airport, or any BCP by the same token, could be tracked from the moment they enter the airport or the BCP in general, one could conceivably be able to differentiate suspicious looking trajectories from trajectories that would be expected for a passenger and thus classified as normal. Differentiating, however, between normal and abnormal behaviors may be a difficult proposition by itself, let alone that it should be done in accordance with privacy and GDPR regulations.
In the work presented in [9, 10], those two issues were addressed as follows. To develop a privacy and GDPR compliant tracking method, we assumed that passengers are tracked using overhead cameras that identify passengers as point targets from their top-down footprints (silhouettes); the footprints are reduced to a point for each passenger and are tracked across the entire airport area or BCP. In the initial phase of the study in [9, 10], it was assumed that passengers tracking was perfect, i.e. that all passengers’ traces as they moved around the airport or BCP area are (anonymously) identifiable and traceable. i.e. that the tracking system has perfect knowledge of the position of each passenger at any time. Although the assumption of perfect knowledge is idealistic, it allows us to get upper bounds on the performance of the tracking system that can be used to make trade off calculations between cost of investment on cameras infrastructure versus the (theoretically) achievable accuracy of the risk calculation.
The difficulty in risk assessment based on trajectories stems from the difficulty in defining what constitutes an abnormal behavior and how it can analytically be described. In the approach in [9, 10] this has been overcome by defining what constitutes a normal (expected) behavior, training the AI (Artificial Intelligence) system to recognize normal behavior and test it with abnormal behaviors to reflect loitering, jittering, and other deviations from expected “normal” behaviors.
Figure 14(a) through (d) are snap shots from the native visualizer of the iCrowd simulator simulating an anomaly detection mechanism based on travelers’ tracking AI algorithm based on a Recursive Neural Network (RNN) [9, 10]. As discussed above, we assumed that travelers can be tracked anonymously using top-down view cameras in compliance with GDPR and ethics regulations. Based on a model of what constitutes a normal traveler route (trajectory) in an airport (or similarly any other BCP), a convolutional recursive neural network was trained with “normal trajectories” generated by the iCrowd simulator. Once the RNN is trained with “normal trajectories,” travelers with “suspicious behaviors” are generated among travelers with “normal behaviors” and the algorithm is tested if it could detect the “suspicious trajectories.” In Figure 14(b), the traveler with suspicious behavior is color-coded red. The risk assessment algorithm detects and identifies the suspicious traveler in Figure 14(d).
A complete technical description of the anomaly detection algorithm is given in the references [9, 10]. Next, we summarize the results in [9, 10] in order to demonstrate the possibility of implementing a risk-based security system that monitors traveler risk continually without additional delays that can offset the benefits of the risk-based approach.
The evaluation of the risk assessment system of [9, 10] is done using the Precision-Recall (PR) diagram, the Receiver Operating Characteristic (ROC) curve, the Confusion Matrix, the F1-score and the Total Accuracy, as defined next:
Precision = (# of true suspicious behaviors detected)/(# of total labeled suspicious behaviors)
Recall = (# of true suspicious behaviors detected)/(# of total suspicious behaviors)
Receiver Operating Characteristic (ROC) curve = Probability of detection versus false alarm probability diagram
Confusion Matrix = Normal versus abnormal confusion matrix
F1-score = 2*[(Precision * Recall)/[Precision + Recall] which is the harmonic average of Precision and Recall.
Total Accuracy = (# of Total Assessments)/(# of Total Cases)
Figures 15–17 illustrate the PR diagram, the ROC curve and the Confusion Matrix respectively. Eqs. 6 and 7 calculate F1-score and Total Accuracy respectively. It should be reminded that the values of all evaluation metrics are defined within the interval [0, 1]. The closer to 1 a value lies, the better the achieved performance. Table 1 summarizes the values of the recruited evaluation measures. The threshold score derived by the RNN architecture, by maximizing F1-score, is 3.7.
Precision-recall diagram.
ROC curve.
Confusion matrix.
Measure | Value |
---|---|
Average PR score | 0.66 |
ROC AUC (Area Under Curve) | 0.97 |
F1-Score | 0.78 |
Total Accuracy | 0.99 |
Values of the recruited evaluation measures.
Furthermore, the F1 score (i.e. the harmonic average between Precision and Recall, Eq. (1)), along with the Total Accuracy, Eq. (2), the ROC AUC (Area Under Curve), and Average PR score, are calculated in Table 1,
Most of the
Although the conditions the risk assessment algorithm was evaluated under assumed perfect knowledge of the traveler trajectories, relaxation of the assumption of perfect knowledge of the traveler trajectories by injecting noise in the position accuracy and/or assuming missing position data, did not have a considerable negative effect on the detection of abnormal trajectories as discussed next.
In order to assess the performance of the anomaly detection algorithm in realistic conditions we introduce noise in the data to emulate the uncertainty in passengers’ positions reports. The “noisy data” emulate the inaccuracy in the reports of the positions of the people in the space. Under realistic conditions, the tracking and risk assessment system will receive data from inaccurate sources, such as cameras, sensors, etc. used to estimate distances, mobile signal strength, etc.
In stark contrast, the iCrowd emulator produces people and their movements, and periodically reports the exact ones (so without noise) their positions in the risk assessment system. During the preprocessing of this data the possibility of the system to add Gaussian noise, the “volume” of which (parameter σ2 of Gaussian noise) is given by the user. This is obviously not intended to never be used in real application, and exists only for experimentation. For examining the behavior of the system under realistic conditions is required noisily data of different intensity.
Noise can enter the system in 2 cases: during training and during testing or actual application. It is known that when training any neural network, it is good to have variety in the data in which the network is exposed so that it is not over-trained. So, it is expected that training with Noisy data can improve the overall performance of the system. During testing or the actual implementation of the system would definitely be better to have perfect data, but unfortunately this is often impossible. In the context of the internship training and validation data were performed with Gaussian noise with σ2 from 0 to 1.9 with step 0.1, testing data with corresponding noise levels, and for each combination they were trained and evaluation of the neural network, and metrics were calculated for each of them. The metrics used were the Receiver Operating Characteristic curve (ROC curve), the Precision-Recall curve (PR curve), and the corresponding Area Under Curve (AUC) scores. These metrics give similar results, in the sense that they are defined in [0,1] with value range [0,1], so the minimum AUC value is 0 and the maximum is 1. Higher value means better true positive and true negative to false positive and false negative ratio. The two metrics generally return similar results, but in our case more weight is given in metric PR, as it offers a better estimate in cases that interest us more the positive class of results, or the results consist of significantly more elements of one of the two classes. Both features apply in the case of this risk assessment system.
During the experiment, networks emerged that failed to find an acceptable solution to why either they were trapped in a local minimum or they encountered the phenomenon of exploding gradient. These cases appeared to be random and independent of the parameters noise, so the network was initialized differently and the training started from the beginning. The experiments were performed using 3 levels of congestion in space, low, moderate, and high, and for each of them 40 neural networks were created, one for each training/testing noise combination mentioned above. For every desired network, 4 independent trainings were conducted and the averages of metrics of interest were kept. The same test data, corresponding to low-to-medium congestion, were used for testing all tested models. The final results are presented below (Figures 18–20):
Training with low congestions data with different noise levels. Testing with low-to-medium congestion data.
Training with medium congestions data with different noise levels. Testing with low-to-medium congestion data.
Training with high congestions data with different noise levels. Testing with low-to-medium congestion data.
From the ROC AUC score graphs above, it is seen that the models that result in the highest performance correspond to the following noise level in the training data:
For low noise data, the best performing data with AUC = 0.91 corresponds to noise level σ2 = 0.8 in the training data.
For medium noise data, the best performing data with AUC = 0.96 corresponds to noise level σ2 = 1.6 in the training data.
For high noise data, the best performing data with AUC = 0.95 corresponds to noise level σ2 = 1.4 in the training data.
From the above results it is clear that the performance of the networks remains constant when we apply noise to the test data. This implies that, since training completed, the network remains robust and is not affected by data noise, so it can to be used in a real application. Of particular interest are variations that occur when present noise in education data. As mentioned above, training a neuron network usually benefits from the difference in training data, as it helps learn the patterns that appear in the data instead of the data itself. This obviously does not mean that the more noise the better. In every network and for every application there is some optimal noise level that offers the best performance. At cases with low and moderate congestion it seems that the Gaussian noise with σ2 ∼ 0.5–0.8 has the best performance, while for high congestion the training with noise performs better with σ2 ∼ 1.4. The variance has not yet been attributed to any of its specific features network, model, training method, or data.
The evaluation results from the performance of the risk assessment algorithm with the iCrowd simulator demonstrates that risk assessment can be done accurately and without necessarily inducing additional delays in the security screening process since the trajectory classification in normal or suspicious is done by overhead cameras while the travelers go about their normal check-in routine at the airport. To that extent, the proposed risk assessment method based on anomaly detection on traveler trajectories can be used to improve the security screening effectiveness while keeping the delay low (or moving the operating point in Figure 10 from high delay to low.).
Furthermore, the proposed method can be used as a financial investment tool for estimating the cost of acquiring the necessary equipment (in this case overhead cameras) for a certain level (probability of accuracy) before purchasing it, and for performing a trade-off analysis between the cost of acquisition of the necessary equipment and the expected performance improvement in risk assessment. This way, the risk assessment simulator allows to be used as a cost–benefit tool for the analysis of performance of a risk-based security system.
In its current form, the work in [9, 10] uses the time series of the coordinates of the trajectories of airport travelers for deep learning. In the future, additional features could be exploited. Such features are the velocity, acceleration and heading of the traveler. Moreover, alternative deep learning architectures could be tested such as the ones that account for contextual anomalies [11]. Furthermore, experiments on real-world data of human trajectories should be conducted. Such data are expected to contain more subtle and sophisticated anomalies. Finally, procedures that degrade data quality and emulate more realistic operational conditions are being implemented in order to test our system in the artificial presence of missing data, noisy data, data association issues, as is the case with data capturing devices operating under realistic operational condtions. Nevertheless, the present work and framework allow security investment decisions on tracking devices and infrastructure to be made by assessing the effectiveness of such an investment through the proposed risk assessment method that envelops the performance of any such system from above by considering ideal tracking conditions through perfect knowledge of all agents’ location. The proposed method and framework is currently being extended to cover other border security modalities, such as sea, land as well as multimodal crossing points in the context of the EU-funded TRESSPASS project [4].
In conclusion, a deep learning architecture for real-time risk assessment based on the trajectories of airport travelers as proposed in [9, 10] can be used for assessing risk without interrupting or delaying the flow of passengers at an airport or BCP at large. The architecture implements a deep RNN network and is fully automated. Thus, it is expected to be of great use to the human operators monitoring airport surveillance footages, reducing the potential errors and misjudges. The proposed risk assessment system is tested on a realistic, synthetic data set generated with the iCrowd simulator tailored to data sets representing traveler movements at the Luxembourg airport; however, any airport or BCP could have been modeled and used instead. The experimental results are very promising and they indicate that further security improvements at airport control points are achievable through risk assessment without inducing additional delays. This is due to the fact that the suspicious behavior threshold, derived by the deep learning procedure in [9, 10], lies at such a level so as to capture the malicious behavior while, at the same time, reducing false-positive alerts.
In [3] a GDPR compliant, mobile application was developed to allow security personnel on the floor of an airport, or any BCP, report in real time and with full respect to passengers’ anonymity, suspicious behaviors, such as nervousness, unjustifiable sweating, etc., while passengers stand in security check lines. The mobile app works in conjunction with Smart Queue, another enabler of risk-based security [5]. Smart Queue is system that works in conjunction with passengers’ ID documents; the system scans the passengers’ ID document upon their arrival at the airport, or entry in the BCP, and in any subsequent security queue. This way, Smart Queue not only does it count the number of passengers at a queue waiting to go through security screening, but knows in which position in the queue each passenger stands. This way, the security personnel that uses the security mobile app, needs to identify passengers only by their indexing number in the line they stand when reporting to the risk assessment back office system any suspicious behaviors about them. This way, anonymity of passengers and their personal data protection are maintained by the security mobile app. The information sent this way by the security personnel on the floor is then fused along with all other risk assessment reports about each passenger and the risk estimate is updated. The risk is reported to the security screening system and the passenger is classified in one of the three risk categories, namely green, yellow or red, as mentioned earlier.
In FLYSEC [3], a novel system architecture for Security and Safety surveillance systems that aims to identify adverse events or behaviors which may endanger the safety of people or their well-being has been introduced [12]. Through proper adaptations the system is applicable to a variety of monitoring systems for various critical infrastructures, border crossing points, and other places of interest (e.g., malls, mass transport systems). The proposed architecture depicts an Internet of Things (IoT) platform which comprises a sensing tier, a back – end processing and intelligence tier and a front end for visualization and user feedback tier. In further monitor and surveillance is performed mainly on the back – end intelligence component which consists of two modules: (a) the event detection module combined with a data fusion component responsible for the fusion of the sensors inputs along with relevant high level metadata, which are pre-defined features that are correlated with a suspicious event, (b) an adaptive learning module which takes inputs from security personnel about the correctness of the detected events, and uses it in order to properly parameterize the event detection algorithm. Moreover, a statistical and stochastic analysis component is incorporated which is responsible for specifying the appropriate features to be used by the event detection module. Statistical analysis estimates the correlations between the features employed in the study, while stochastic analysis is used for the estimation of dependencies between the features and the achieved system performance.
The system architecture is organized basically in three tiers: Sensing components, back–end components, and front – end devices. The sensing components are responsible for acquiring input which is either high or low level heterogeneous data coming from visual sensors (CCD, IR, etc.), biometric sensors (fingerprints, other), audio sensors (microphones), indoor localization equipment (Wi-Fi, beacons, RFID scanners, etc.), document scanners which provide information about visitors (for example travel documents in an airport, or purchase information recorded on personal discount electronic cards), or human reports via terminal devices (e.g. PDAs, mobile phones, tablets, etc.).
Front–end devices are responsible for visualizing information to end–users and assisting their operations (for example official authorities receiving information about detected incidents of great interest, or visitors getting navigation information inside an infrastructure, etc.). Front–end devices consist of official management terminal tools which manage the information collected and processed by the back–end and sensing components and assist personnel operations by providing alerts and notifications about significant events (Figure 21), visualizations of infrastructure’s layout along with real – time updates about essential points of interest (for example size of queues, sensors viability, crowd distribution, etc.) (Figure 22). Moreover, front – end devices include also mobile user devices which operate as a personal assistant to passengers at an airport or a BCP. These mobile devices may provide online and offline services regarding indoor navigation, recommendation services (for products, point of interests, etc.), notifications and alerts. Finally, via these devices each user may provide a feedback to the system about requests or reports, about incidents that may concern their safety, or public security, or interactions with the system in the context of system automatic personal servicing.
FlySec portal: - intelligent services visualization (upper); − automatic passenger classification (lower).
FlySec portal - layout visualization.
The back–end component contains the intelligence modules which process the input coming from sensing devices and produce high level intelligence and metadata which assist operational personnel, enhance end–users’ experience and content management services. These metadata are used either for further processing by fusion algorithms, or presented to end – devices via visualization methods on each end–device. Such metadata concerns directed paths for navigation services, fused high level visual information, or information regarding recommendations, detected incidents or notifications and alerts. Finally, the content management services enable efficient data storing and retrieving operations in a scalable way. The back – end component comprises a Message-oriented middleware in order to interconnect all the sensing and processing component, provides a REST API to front–end devices, supports web platforms interfaces (web – portal) and orchestrates the accurate functionality of the whole system, Figure 23.
Reference architecture of the FLYSEC security and safety risk-assessment surveillance system.
The core intelligence residing in the “Analytics, Data Fusion and Risk-based Security Server” is presented in Section 3.3. The Data protection, Legal Compliance and Ethics are important aspects that should be taken into consideration in the system architecting process and are analyzed in Section 3.4.
The proposed system is designed with the aim of enabling automated surveillance of large infrastructures such as airport, shopping malls, other. Such tasks incorporate massive monitoring of infrastructure visitors in real – time. Monitoring operation is based on an Internet of Things (IoT) installation architecture consisting of: (a) various types of sensing devices such as CCD surveillance cameras, QR/barcode scanners, localization equipment (NFC tags, WiFi beacons, etc), RFID scanners, etc., (b) processing units, both centralized and/or distributed, and (c) terminal devices such as mobile phones/tablets, computers, screens, electric signs, etc. (Figure 24).
Back-end intelligence system architecture.
Each sensor device may pre-process the acquired raw data (distributed processing) and the results are gathered on a central cloud-computing infrastructure consisting of independent but co-operative intelligent component each one dedicated for processing data and producing a specific intelligent response for the system. Moreover, the output is transferred to terminal devices. This processing procedure consists of the following steps (Figure 25).
Flow of data from sensors to the cloud or terminal devices.
The intelligent services are also responsible for automating the monitoring procedure and enhancing visitors’ experience. Therefore, we propose two types of services: (a)
These services aim at monitoring visitors’ behavior, profile, and interactions and provide information that could facilitate their purpose of visit and indicate services that act as added value to visitors and simultaneously promote each infrastructure expectations. Indicatively two representative use cases are Navigation services and Recommendation engine.
Navigation service corresponds to indoor localization and navigation of infrastructure visitors in order to assist them in reaching their desired points of interest (POI) not only as quickly as possible but also as efficient and desirable as possible by taking into account user requirements (e.g. disabilities, specific demands) and user location. Moreover, the service provide directions to each visitor via their mobile device to various POIs and informs the user in order to assist them reaching their goal of visit (for example provide information about location of various products in a supermarket, or shops in a mall, or provide information about flight departures or gates status in an airport)
Recommendation engine aims at providing suggestion of POIs or services that take place inside the infrastructure. The engine takes into account the user profile (information that each user provides optionally during account registration), user feedback (comments, rates), user location and contextual information (time, season, POI status) and create recommendations that are estimated to be assistive to user visiting experience but also promoting infrastructure and POIs expectations and benefits.
These services aim at monitoring visitors’ position and behavior and automatically detect incidents of significant interest such as malicious behavior, anomalous crowd trajectory flow etc. This solution is expected to enhance surveillance procedure for large-scale circumstances where it is demanded in real time, the accurate surveillance of a massive crowd. Indicatively we suggest two surveillance services: Suspicious unattended luggage incidents detection and suspicious visitor loitering detection.
Unattended luggage incidents detection aims at monitoring in parallel both visitors and the luggage they carry. Such monitoring could be approached either using CCD cameras and approximately detect abandoned luggage for a long period of time, or by tagging luggage (for example using RFID tags) where using RFID scanners in co-operation with visual sensors (CCD cameras) and human reports (official surveillance personnel), estimate potential unattended luggage incidents. Moreover, in order to monitor visitors’ position, we propose the use of indoor localization techniques using mobile devices in order to have an approximation of visitors’ location that willingly allow it, and in addition visual sensors and human reports as well, in order to increase system’s awareness of crowd location. Fusion of such information shall be exploited by machine learning algorithms, which result to a coarse grain estimation of visitors’ luggage abandonment.
Suspicious loitering detection aims at monitoring visitors’ location and in real–time detect anomalous visitors’ trajectories or positions that could be suspicious for malicious purposes. Such components may incorporate visual sensors (CCD cameras), human reports and mobile devices localization techniques (Wi-Fi beacons, NFC tags).
The Data Fusion unit inside the Analytics, Data Fusion and Risk-based Security Server aims to perform Hard and Soft fusion of heterogeneous data [13, 14, 15] available from disparate sources of information such as physical sensors (“hard” data) and human resources (“soft” data). Hard data fusion refers to the combination of raw information from multiple sources so as to achieve more accurate estimations of the desired parameters (position, speed, other). To this end, a variety of theoretical tools, such as Signal processing techniques, Kalman filters, Sequential Monte Carlo methods, etc., can be used. On the other hand, soft data fusion usually applies on textual information (e.g., from humans’ reports, social networks, Internet, other) which has to be further processed using methods such as Information retrieval, Natural Language processing, and Semantic knowledge representation. Moreover, in this unit, Decision level fusion techniques could be applied using Evidence theory [14, 15, 16], Fuzzy Logic [17], 2-tuple Linguistic representation models [18, 19], and reinforcement learning methods [20, 21].
The Risk-based assessment unit is responsible for the classification of events and individuals into security classes according to their risk severity level. The unit exploits behavior and event indicators and their corresponding weights estimated in the ALMS system, intelligence generated in the Back–end Intelligence component, and any useful information from the system’s data sources in order to generate alerts and notifications to the Command-and-Control (C2) center if the risk severity level exceeds predefined thresholds.
A security and safety monitoring system has to detect, evaluate, and classify, in an efficient and timely manner, behaviors and events of interest. To achieve this critical need, the algorithmic parameters used in the “Analytics, Data Fusion and Risk-based Security Server” have to be initialized and adaptively adjusted to handle changes in the monitoring environment. To this end, the use of an Adaptive Learning Management System (ALMS) which will exploit new and accumulated information is essential. An ALMS system can be applied for instance to iteratively adjust the Risk Assessment classification thresholds and the weights of the behavioral and event indicators or to recognize correlations between indicators, events, and behaviors in order to optimize the classification process and improve the efficiency of the system. An example of such an optimization approach could be the selection of a reduced number of indicators for event identification.
For the development of automated procedures able to estimate correlations, optimize selected parameters under certain criteria, and extract reduced dimensional feature vectors for Behavior and event detection the ALMS system demands efficient methodologies and algorithms. These methodologies and techniques can cover a wide area of theoretical tools including Machine Learning, Factor Component Analysis, Statistical methods, Time series analysis, Optimization theory, Sparse clustering, Fuzzy Logic, and other [18, 19, 20, 21].
As shown in Figures 23 and 26, the ALMS unit receives input from i) the system’s database which includes data from system’s data sources, outputs of Data Fusion, Analytics, and Risk assessment unit, and optimization criteria and constraints and ii) the Security personnel Mobile App which is then used for the training of the applied algorithms. The ALMS stores its output in the system database, making it accessible to other units, and creating a continuous feedback loop of information gathering, learning, and adapting to security threats as they evolve.
The adaptive learning management system architecture.
The Factor Component Analysis component performs Factor Analysis on features/indicators denoting individual characteristics which affect the categorization of individuals in security-threat levels. Factor analysis is used to reduce the dimensionality of a correlation matrix that contains features/indicators describing a specific event or behavior. Factor Analysis does that by producing new general variables, called “factors”, incorporating inside them, the initial features/indicators according to a condition of high inter-correlation between the newly emerged general variables (“factors”) and the initially presented specific variables [19, 22].
The system needs to analyze the input feed and result to its outcome, taking into account however, environmental factors regarding system’s efficiency, explicit policies that should by adopted or exceptions that should be applied, that are related to specific locations (e.g., restricted areas) or specific human profiles. Such information usually is returned to the system in the form of a generic asynchronous qualitative feedback (for example insisting user discards of system’s outcomes, or exception to system’s rules) that should be assimilated in real – time.
The system should be able to receive environmental feedback and adapt its operation to the current circumstances and requirements. Therefore, we propose a two-mode adaptation, an offline and an online. The offline adaptation regards a system initialization, responsible for translating human – understandable requirements to algorithms’ parameterization. The online adaptation should track environmental feedback for each action of set of actions (policies) produced by the system and adapt algorithms’ behavior in order to fulfill system’s requirements.
In this case we propose the implementation of reinforcement learning techniques where environmental feedback should be encoded to quantitative measures of rewards, Figure 27.
Online ALMS.
Security and safety management systems and their data fusion and intelligent analytics capabilities require substantial data collection and processing in order to offer the best possible awareness and decision support to C&C operators, field personnel and first responders. Especially in the context of homeland security, privacy and data protection is often seen through the typical trade-off model perspective, requesting the public to give up –in the best case knowingly- on particular rights over the control of their personal data. However, such systems should not be based and developed on exceptions or operate only in extraordinary circumstances, the latter being very inefficient. With the latest guidelines of EU General Data Protection Regulation (GDPR), principles of data minimization and privacy by design will shift from best practices into a much more regulated form.
The proposed system is in line with these principles, following a “by design approach” in terms of data protection and ethics. Data collected are structurally separated from identifiable information, and identification occurs only upon the logged and explicit intervention of a human operator when truly needed. By assessing risks on real time, the system itself has the advantage of performing data minimization through early elimination of lower risk cases. On the front end and field, privacy enhancing technologies and smart sensors are also preferred and selected. E.g. smart visual sensors with on-board processing capabilities can filter out data before sending it over the wire and to the server for processing. Moreover, the system has been designed to include specific safeguards to protect individuals against discrimination, stigmatization and unduly prohibition of access to goods and services. Defined in [23], the system adopts these definitions and extends them to all protected grounds as defined in the Charter and the Treaty of Amsterdam, taking also into account the proposal for the horizontal directive that extends the context of EU non-discrimination law and prohibits discrimination “on grounds of sex, racial or ethnic origin, age, disability, sexual orientation, religion or belief”. In this context, Fairness and bias detection algorithms are applied to the adaptive learning management system while the human operator remains in control of the final enforcement following any automated decision making process. Intelligent behavior analytics can further support the case where security risks are based and calculated on how a person acts on the scene and not any discriminatory background information.
A subject of past and current research, assessing the societal acceptance of surveillance and security solutions comes with its own challenges. Acceptance is based on multiple parameters, individual perceptions and sometimes misconceptions and individual practices which may not be in line with the expressed concerns [24]. The proposed system and the overall risk-based security paradigm, is based on the positive fact that the vast majority of people have no malicious intent. The system focuses on the unknown and high-risk cases, intending to shift the current practices from annoying horizontal and disruptive processes to seamless and unobtrusive security. The combination of privacy and ethics by design along with the ethical and unobtrusive treatment set the parameters for a system with high acceptance, positive public perception and trust.
In this chapter we discussed the concept of risk-based security, the possible trade-off between increased convenience for passengers from risk-based security and the delays induced by additional checks needed for establishing each passenger’s risk. We also presented a number of technologies, systems and applications that can be used for assessing risk at an airport or BCP without inducing additional delay as the discussed approaches estimate risk on-the-fly while passengers either walk around the airport or BCP from entrance to security check points or BCPs, or queue up in a security line awaiting to go through security checks. All methods discussed are GDPR and ethics compliant, thus they can be implemented in accordance to privacy and ethics regulations. Furthermore, the novel system architecture for Security and Safety monitoring systems introduced in [3] has been presented. The proposed system aims to identify adverse events or behaviors which may endanger the safety of people or their well-being having the ability to adapt in the surveillance environment changes. The dynamic adjustment of the algorithmic parameters adopted in various units of the system such as intelligence, and Risk assessment, makes it possible to monitor security threats as they evolve. Thus, the proposed scheme provides the potential of a high-performance system both in terms of the detection interval as well as in terms of the performance accuracy offering the capability of a timely and efficient response to abnormal events and behaviors.
The research described in this paper has been supported by the following research contracts:
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The author would also like to acknowledge the use of some material from the Refs. [5, 6, 7, 8, 9, 10, 12, 14]. He co-authored in collaboration with his colleagues whose names appear in these references.
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All published Book Chapters are licensed under a Creative Commons Attribution 3.0 Unported License. Monographs are licensed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license granted to all others. Our Copyright Policy aims to guarantee that original material is published while at the same time giving significant freedom to our Authors. IntechOpen upholds a flexible Copyright Policy meaning that there is no copyright transfer to the publisher and Authors hold exclusive copyright to their work.
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On September, 29th 2006 he has won a post PhD fellowship from the university of Bologna (from October 2006 to October 2008), at the competitive examination he was ranked first in the industrial engineering area. He extensively served as referee for several international journals. He is author/coauthor of more than 100 research papers. He has been involved in some projects supported by MURST and European Community. 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Delac received his B.Sc.E.E. degree in 2003 and is currentlypursuing a Ph.D. degree at the University of Zagreb, Faculty of Electrical Engineering andComputing. His current research interests are digital image analysis, pattern recognition andbiometrics.",institutionString:null,institution:{name:"University of Zagreb",country:{name:"Croatia"}}},{id:"557",title:"Dr.",name:"Andon",middleName:"Venelinov",surname:"Topalov",slug:"andon-topalov",fullName:"Andon Topalov",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/557/images/1927_n.jpg",biography:"Dr. Andon V. Topalov received the MSc degree in Control Engineering from the Faculty of Information Systems, Technologies, and Automation at Moscow State University of Civil Engineering (MGGU) in 1979. He then received his PhD degree in Control Engineering from the Department of Automation and Remote Control at Moscow State Mining University (MGSU), Moscow, in 1984. 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Aalborg University has Two Satellite Campuses, one in Copenhagen (Aalborg University Copenhagen) and the other in Esbjerg (Aalborg University Esbjerg).\n· He is a member of prestigious IEEE (Institute of Electrical and Electronics Engineers), and IAENG (International Association of Engineers) organizations. \n· He is the chief Editor of the Journal of Software Engineering.\n· He is the member of the Editorial Board of International Journal of Computer Science and Software Technology (IJCSST) and International Journal of Computer Engineering and Information Technology. \n· He is also the Editor of Communication in Computer and Information Science CCIS-20 by Springer.\n· Reviewer For Many Conferences\nHe is the lead person in making collaboration agreements between Aalborg University and many universities of Pakistan, for which the MOU’s (Memorandum of Understanding) have been signed.\nProfessor Akbar is working in Academia since 1990, he started his career as a Lab demonstrator/TA at the University of Sussex. After finishing his P. hD degree in 1992, he served in the Industry as a Scientific Officer and continued his academic career as a visiting scholar for a number of educational institutions. In 1996 he joined National University of Science & Technology Pakistan (NUST) as an Associate Professor; NUST is one of the top few universities in Pakistan. In 1999 he joined an International Company Lineo Inc, Canada as Manager Compiler Group, where he headed the group for developing Compiler Tool Chain and Porting of Operating Systems for the BLACKfin processor. The processor development was a joint venture by Intel and Analog Devices. In 2002 Lineo Inc., was taken over by another company, so he joined Aalborg University Denmark as an Assistant Professor.\nProfessor Akbar has truly a multi-disciplined career and he continued his legacy and making progress in many areas of his interests both in teaching and research. 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With continued research, different topologies have emerged presenting less torque ripple, high efficiency, high power factor, and high power density. However, there has always been a trade‐off between gaining some of the advantageous and losing some with each new technology. In this chapter, various SRM topologies, design, principle of operation, and respective phase switching schemes are extensively reviewed, and their advantages and drawbacks are discussed. On the other hand, some of SRM limitations (such as excitation penalty, control complexity, noise, and vibration) have prompted research into the incorporation of permanent magnets into the basic SRM structure, and therefore, the chapter also includes discussion on a new class of SRM with permanent magnet assist (PM‐assist) called doubly salient permanent magnet (DSPMM). The DSPM motor incorporates the merits of both the PM brushless motor and the SRM.",book:{id:"6017",slug:"switched-reluctance-motor-concept-control-and-applications",title:"Switched Reluctance Motor",fullTitle:"Switched Reluctance Motor - Concept, Control and Applications"},signatures:"Mohammad Mahdi Bouiabady, Aliakbar Damaki Aliabad and\nEbrahim Amiri",authors:[{id:"175468",title:"Dr.",name:"Ebrahim",middleName:null,surname:"Amiri",slug:"ebrahim-amiri",fullName:"Ebrahim Amiri"},{id:"203202",title:"Mr.",name:"Mohammad Mahdi",middleName:null,surname:"Bouiabady",slug:"mohammad-mahdi-bouiabady",fullName:"Mohammad Mahdi Bouiabady"},{id:"203203",title:"Dr.",name:"Aliakbar",middleName:null,surname:"Damaki Aliabad",slug:"aliakbar-damaki-aliabad",fullName:"Aliakbar Damaki Aliabad"}]},{id:"55383",doi:"10.5772/intechopen.68911",title:"Switched Reluctance Motor Drives for Hybrid Electric Vehicles",slug:"switched-reluctance-motor-drives-for-hybrid-electric-vehicles",totalDownloads:3344,totalCrossrefCites:2,totalDimensionsCites:2,abstract:"Because of the ever‐increasing concerns on the energy utilization and environmental protection, the development of hybrid electric vehicles (HEVs) has become a hot research topic. As the major part of HEV technologies, the electric motor drives have to offer high efficiency, high power density, high controllability, wide‐speed operating range, and maintenance‐free operation. In particular, the switched reluctance (SR) motor drive can achieve most of these goals; therefore, this motor type has drawn much attention in the past. This chapter aims to serve as an overview of the latest developments of the SR motor drive, purposely for HEV applications. To be specific, the discussions on motor structures for torque density enhancement and torque ripple minimization are covered.",book:{id:"6017",slug:"switched-reluctance-motor-concept-control-and-applications",title:"Switched Reluctance Motor",fullTitle:"Switched Reluctance Motor - Concept, Control and Applications"},signatures:"Christopher H.T. Lee, James L. Kirtley, Jr. and M. Angle",authors:[{id:"201412",title:"Dr.",name:"Christopher H. T.",middleName:null,surname:"Lee",slug:"christopher-h.-t.-lee",fullName:"Christopher H. T. Lee"}]},{id:"55535",doi:"10.5772/intechopen.69228",title:"Four‐Quadrant Control of Switched Reluctance Machine",slug:"four-quadrant-control-of-switched-reluctance-machine",totalDownloads:1639,totalCrossrefCites:1,totalDimensionsCites:1,abstract:"This chapter illustrates modeling techniques and software simulation of the switched reluctance machine (SRM) machine models with controllers for efficient operation. The first model is based on torque and flux data generated through finite element analysis (FEA) and the second model is geometry-based machine model, which are used to develop the operation logic for four-quadrant control of an SRM. The results obtained from these models were used to develop a control strategy to adapt turn-on and turn-off (commutation) angles efficiently. Two digital controllers, namely the phase current controller for regulating current with a hysteresis band and the PI (proportional-integral) speed controller for regulating the speed, are developed to deliver the desired output torque. The controller is based on a negative feedback closed-loop control system.",book:{id:"6017",slug:"switched-reluctance-motor-concept-control-and-applications",title:"Switched Reluctance Motor",fullTitle:"Switched Reluctance Motor - Concept, Control and Applications"},signatures:"Sandeep Narla",authors:[{id:"201434",title:"M.Sc.",name:"Sandeep",middleName:null,surname:"Narla",slug:"sandeep-narla",fullName:"Sandeep Narla"}]},{id:"55678",doi:"10.5772/intechopen.69280",title:"Direct Instantaneous Torque Controlled Switched Reluctance Motor Drive for Fan Type Load and Constant Torque Load",slug:"direct-instantaneous-torque-controlled-switched-reluctance-motor-drive-for-fan-type-load-and-constan",totalDownloads:1544,totalCrossrefCites:1,totalDimensionsCites:1,abstract:"Switched reluctance motor (SRM) drives can be a good competitor to conventional induction and permanent magnet motors in variable speed applications because of advantages, such as simple construction, no rotor windings, high torque to inertia ratio, adaptability to hostile conditions, etc. Due to its high nonlinearity, the torque ripple is high in switched reluctance motor. The sophisticated direct instantaneous torque control (DITC) can maintain the torque developed by the motor within hysteresis band by suitably selecting the switching states of the converter. Hence, DITC controller minimizes the torque ripples and also provides fast response to the torque changes. The performance of DITC controlled SRM drive is analyzed through simulations during acceleration and also in steady state for two types of load torques namely fan type and constant torque. The variation of the switching frequency of the converter is analyzed by changing the torque hysteresis band. It has been observed that as the hysteresis band decreases, the switching frequency increases. So, the hysteresis band cannot be increased beyond a certain limit so as to ensure that the switching frequency of the device cannot increase beyond its operating limit.",book:{id:"6017",slug:"switched-reluctance-motor-concept-control-and-applications",title:"Switched Reluctance Motor",fullTitle:"Switched Reluctance Motor - Concept, Control and Applications"},signatures:"Srinivas Pratapgiri",authors:[{id:"193348",title:"Dr.",name:"Srinivas",middleName:null,surname:"Pratapgiri",slug:"srinivas-pratapgiri",fullName:"Srinivas Pratapgiri"}]},{id:"55557",doi:"10.5772/intechopen.69270",title:"Design, Power Electronics and Torque Control of Switched Reluctance Machines",slug:"design-power-electronics-and-torque-control-of-switched-reluctance-machines",totalDownloads:1754,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"In the last decade, increased tendency in the field of automotive industry was focused on the development of highly efficient and low-cost electric propulsion systems to replace the existing internal combustion solutions. The aim is to reduce the pollution due to carbon dioxide emissions into the air. Several electric machine topologies with their power electronics, control and supply units are continuously in the development process to reach the desired goal. One such machine is the switched reluctance machine (SRM), reaching increased power density, low cost and possibility of continuous operation despite fault occurrence. Designing the machine, choosing its power electronics and controlling the machine to diminish the negative effect of the torque ripples are key points in reaching the proper propulsion system. The main topics presented in detail in this chapter are managing the reader’s skills with an analytic design breviary, presenting the machine’s control strategies for instantaneous torque linearization and finally, showing a power converter topology with increased performances in low voltage applications. To be more close to such an application, the exampled machine is developed for a light electric vehicle for people with physical disabilities. Operational skills of the machine will be validated based on complex simulations.",book:{id:"6017",slug:"switched-reluctance-motor-concept-control-and-applications",title:"Switched Reluctance Motor",fullTitle:"Switched Reluctance Motor - Concept, Control and Applications"},signatures:"Mircea Ruba and Petre Dorel Teodosescu",authors:[{id:"190371",title:"Dr.",name:"Mircea",middleName:null,surname:"Ruba",slug:"mircea-ruba",fullName:"Mircea Ruba"},{id:"195867",title:"Dr.",name:"Petre",middleName:"Dorel",surname:"Teodosescu",slug:"petre-teodosescu",fullName:"Petre Teodosescu"}]}],mostDownloadedChaptersLast30Days:[{id:"55670",title:"Switched Reluctance Motor Topologies: A Comprehensive Review",slug:"switched-reluctance-motor-topologies-a-comprehensive-review",totalDownloads:4958,totalCrossrefCites:4,totalDimensionsCites:5,abstract:"Switched reluctance motor (SRM) is gaining much interest in industrial applications such as wind energy systems and electric vehicles due to its simple and rugged construction, high‐speed operation ability, insensitivity to high temperature, and its features of fault tolerance. With continued research, different topologies have emerged presenting less torque ripple, high efficiency, high power factor, and high power density. However, there has always been a trade‐off between gaining some of the advantageous and losing some with each new technology. In this chapter, various SRM topologies, design, principle of operation, and respective phase switching schemes are extensively reviewed, and their advantages and drawbacks are discussed. On the other hand, some of SRM limitations (such as excitation penalty, control complexity, noise, and vibration) have prompted research into the incorporation of permanent magnets into the basic SRM structure, and therefore, the chapter also includes discussion on a new class of SRM with permanent magnet assist (PM‐assist) called doubly salient permanent magnet (DSPMM). The DSPM motor incorporates the merits of both the PM brushless motor and the SRM.",book:{id:"6017",slug:"switched-reluctance-motor-concept-control-and-applications",title:"Switched Reluctance Motor",fullTitle:"Switched Reluctance Motor - Concept, Control and Applications"},signatures:"Mohammad Mahdi Bouiabady, Aliakbar Damaki Aliabad and\nEbrahim Amiri",authors:[{id:"175468",title:"Dr.",name:"Ebrahim",middleName:null,surname:"Amiri",slug:"ebrahim-amiri",fullName:"Ebrahim Amiri"},{id:"203202",title:"Mr.",name:"Mohammad Mahdi",middleName:null,surname:"Bouiabady",slug:"mohammad-mahdi-bouiabady",fullName:"Mohammad Mahdi Bouiabady"},{id:"203203",title:"Dr.",name:"Aliakbar",middleName:null,surname:"Damaki Aliabad",slug:"aliakbar-damaki-aliabad",fullName:"Aliakbar Damaki Aliabad"}]},{id:"55383",title:"Switched Reluctance Motor Drives for Hybrid Electric Vehicles",slug:"switched-reluctance-motor-drives-for-hybrid-electric-vehicles",totalDownloads:3344,totalCrossrefCites:2,totalDimensionsCites:2,abstract:"Because of the ever‐increasing concerns on the energy utilization and environmental protection, the development of hybrid electric vehicles (HEVs) has become a hot research topic. As the major part of HEV technologies, the electric motor drives have to offer high efficiency, high power density, high controllability, wide‐speed operating range, and maintenance‐free operation. In particular, the switched reluctance (SR) motor drive can achieve most of these goals; therefore, this motor type has drawn much attention in the past. This chapter aims to serve as an overview of the latest developments of the SR motor drive, purposely for HEV applications. To be specific, the discussions on motor structures for torque density enhancement and torque ripple minimization are covered.",book:{id:"6017",slug:"switched-reluctance-motor-concept-control-and-applications",title:"Switched Reluctance Motor",fullTitle:"Switched Reluctance Motor - Concept, Control and Applications"},signatures:"Christopher H.T. Lee, James L. Kirtley, Jr. and M. Angle",authors:[{id:"201412",title:"Dr.",name:"Christopher H. T.",middleName:null,surname:"Lee",slug:"christopher-h.-t.-lee",fullName:"Christopher H. T. Lee"}]},{id:"55535",title:"Four‐Quadrant Control of Switched Reluctance Machine",slug:"four-quadrant-control-of-switched-reluctance-machine",totalDownloads:1639,totalCrossrefCites:1,totalDimensionsCites:1,abstract:"This chapter illustrates modeling techniques and software simulation of the switched reluctance machine (SRM) machine models with controllers for efficient operation. The first model is based on torque and flux data generated through finite element analysis (FEA) and the second model is geometry-based machine model, which are used to develop the operation logic for four-quadrant control of an SRM. The results obtained from these models were used to develop a control strategy to adapt turn-on and turn-off (commutation) angles efficiently. Two digital controllers, namely the phase current controller for regulating current with a hysteresis band and the PI (proportional-integral) speed controller for regulating the speed, are developed to deliver the desired output torque. The controller is based on a negative feedback closed-loop control system.",book:{id:"6017",slug:"switched-reluctance-motor-concept-control-and-applications",title:"Switched Reluctance Motor",fullTitle:"Switched Reluctance Motor - Concept, Control and Applications"},signatures:"Sandeep Narla",authors:[{id:"201434",title:"M.Sc.",name:"Sandeep",middleName:null,surname:"Narla",slug:"sandeep-narla",fullName:"Sandeep Narla"}]},{id:"55557",title:"Design, Power Electronics and Torque Control of Switched Reluctance Machines",slug:"design-power-electronics-and-torque-control-of-switched-reluctance-machines",totalDownloads:1754,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"In the last decade, increased tendency in the field of automotive industry was focused on the development of highly efficient and low-cost electric propulsion systems to replace the existing internal combustion solutions. The aim is to reduce the pollution due to carbon dioxide emissions into the air. Several electric machine topologies with their power electronics, control and supply units are continuously in the development process to reach the desired goal. One such machine is the switched reluctance machine (SRM), reaching increased power density, low cost and possibility of continuous operation despite fault occurrence. Designing the machine, choosing its power electronics and controlling the machine to diminish the negative effect of the torque ripples are key points in reaching the proper propulsion system. The main topics presented in detail in this chapter are managing the reader’s skills with an analytic design breviary, presenting the machine’s control strategies for instantaneous torque linearization and finally, showing a power converter topology with increased performances in low voltage applications. To be more close to such an application, the exampled machine is developed for a light electric vehicle for people with physical disabilities. Operational skills of the machine will be validated based on complex simulations.",book:{id:"6017",slug:"switched-reluctance-motor-concept-control-and-applications",title:"Switched Reluctance Motor",fullTitle:"Switched Reluctance Motor - Concept, Control and Applications"},signatures:"Mircea Ruba and Petre Dorel Teodosescu",authors:[{id:"190371",title:"Dr.",name:"Mircea",middleName:null,surname:"Ruba",slug:"mircea-ruba",fullName:"Mircea Ruba"},{id:"195867",title:"Dr.",name:"Petre",middleName:"Dorel",surname:"Teodosescu",slug:"petre-teodosescu",fullName:"Petre Teodosescu"}]},{id:"55614",title:"Current‐Controlled SRM Fed by Three‐Phase Boost PFC",slug:"current-controlled-srm-fed-by-three-phase-boost-pfc",totalDownloads:1651,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"In this chapter, firstly, converter types of switched reluctance motor (SRM) are described. Current control structure of SRM, which has six stator and four rotor poles, over an asymmetric bridge converter, is also explained. While feeding SRM by an AC grid, grid voltages have to be converted to DC voltage; to realize this conversion, in order to obtain high power factor and sinusoidal grid current, power factor correction (PFC) circuits must be used. In this study, an asymmetric bridge converter of SRM is fed by three‐phase PFC boost converter that consists of uncontrolled diode rectifier and DC‐DC boost converter with high frequency operation. PFC boost converter is controlled by nonlinear control algorithm. By means of the simulations that are conducted by MATLAB/Simulink, grid voltage and current, current harmonics of each phase, three‐phase currents of phases, flux, and current of SRM are presented. Simulation results show that proposed SRM that is fed by three‐phase PFC boost converter system gives the desired performance, for both grid and SRM side.",book:{id:"6017",slug:"switched-reluctance-motor-concept-control-and-applications",title:"Switched Reluctance Motor",fullTitle:"Switched Reluctance Motor - Concept, Control and Applications"},signatures:"Erdal Şehirli and Meral Altınay",authors:[{id:"119997",title:"Mr.",name:"Erdal",middleName:null,surname:"Sehirli",slug:"erdal-sehirli",fullName:"Erdal Sehirli"},{id:"119998",title:"Dr.",name:"Meral",middleName:null,surname:"Altınay",slug:"meral-altinay",fullName:"Meral Altınay"}]}],onlineFirstChaptersFilter:{topicId:"743",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:89,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:104,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:32,numberOfPublishedChapters:318,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:12,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:141,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:129,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:113,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:106,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:5,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:15,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. 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Saxena is a vice dean and professor at King George's Medical University, Lucknow, India. His research interests involve understanding the molecular mechanisms of host defense during human viral infections and developing new predictive, preventive, and therapeutic strategies for them using Japanese encephalitis virus (JEV), HIV, and emerging viruses as a model via stem cell and cell culture technologies. His research work has been published in various high-impact factor journals (Science, PNAS, Nature Medicine) with a high number of citations. He has received many awards and honors in India and abroad including various Young Scientist Awards, BBSRC India Partnering Award, and Dr. JC Bose National Award of Department of Biotechnology, Min. of Science and Technology, Govt. of India. 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He obtained a Master’s degree in Public Health and PhD in Public Health and Epidemiology. He has a background in Clinical Medicine and has taken courses at higher diploma levels in public health from University of Transkei, Republic of South Africa, and African Medical and Research Foundation (AMREF) in Nairobi, Kenya. Dr. Kasenga worked in different places in and outside Malawi, and has held various positions, such as Licensed Medical Officer, HIV/AIDS Programme Officer, HIV/AIDS resource person in the International Department of Diakonhjemet College, Oslo, Norway. He also managed an Integrated HIV/AIDS Prevention programme for over 5 years. He is currently working as a Director for the Health Ministries Department of Malawi Union of the Seventh Day Adventist Church. Dr. Kasenga has published over 5 articles on HIV/AIDS issues focusing on Prevention of Mother to Child Transmission of HIV (PMTCT), including a book chapter on HIV testing counseling (currently in press). 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The preliminary objectives of the study are to understand and develop the evidence-based tools and interventions for the control and prevention of malaria in different sites of the INDIA. Alongside, with the help of next-generation genomics study, the team has studied the antimalarial drug resistance in India. Further, he has extended his research in the development of Humanized mice for the study of liver-stage malaria and identification of molecular marker(s) for the Artemisinin resistance. At present, his research focuses on understanding the role of B cells in the activation of CD8+ T cells in malaria. Received the CSIR-SRF (Senior Research Fellow) award-2018, FIMSA (Federation of Immunological Societies of Asia-Oceania) Travel Bursary award to attend the IUIS-IIS-FIMSA Immunology course-2019',institutionString:"Nirma University",institution:{name:"Nirma University",country:{name:"India"}}},{id:"334383",title:"Ph.D.",name:"Simone",middleName:"Ulrich",surname:"Ulrich Picoli",slug:"simone-ulrich-picoli",fullName:"Simone Ulrich Picoli",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/334383/images/15919_n.jpg",biography:"Graduated in Pharmacy from Universidade Luterana do Brasil (1999), Master in Agricultural and Environmental Microbiology from Federal University of Rio Grande do Sul (2002), Specialization in Clinical Microbiology from Universidade de São Paulo, USP (2007) and PhD in Sciences in Gastroenterology and Hepatology (2012). She is currently an Adjunct Professor at Feevale University in Medicine and Biomedicine courses and a permanent professor of the Academic Master\\'s Degree in Virology. She has experience in the field of Microbiology, with an emphasis on Bacteriology, working mainly on the following topics: bacteriophages, bacterial resistance, clinical microbiology and food microbiology.",institutionString:null,institution:{name:"Universidade Feevale",country:{name:"Brazil"}}},{id:"229220",title:"Dr.",name:"Amjad",middleName:"Islam",surname:"Aqib",slug:"amjad-aqib",fullName:"Amjad Aqib",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/229220/images/system/229220.png",biography:"Dr. Amjad Islam Aqib obtained a DVM and MSc (Hons) from University of Agriculture Faisalabad (UAF), Pakistan, and a PhD from the University of Veterinary and Animal Sciences Lahore, Pakistan. Dr. Aqib joined the Department of Clinical Medicine and Surgery at UAF for one year as an assistant professor where he developed a research laboratory designated for pathogenic bacteria. Since 2018, he has been Assistant Professor/Officer in-charge, Department of Medicine, Manager Research Operations and Development-ORIC, and President One Health Club at Cholistan University of Veterinary and Animal Sciences, Bahawalpur, Pakistan. He has nearly 100 publications to his credit. His research interests include epidemiological patterns and molecular analysis of antimicrobial resistance and modulation and vaccine development against animal pathogens of public health concern.",institutionString:"Cholistan University of Veterinary and Animal Sciences",institution:null},{id:"62900",title:"Prof.",name:"Fethi",middleName:null,surname:"Derbel",slug:"fethi-derbel",fullName:"Fethi Derbel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/62900/images/system/62900.jpeg",biography:"Professor Fethi Derbel was born in 1960 in Tunisia. He received his medical degree from the Sousse Faculty of Medicine at Sousse, University of Sousse, Tunisia. He completed his surgical residency in General Surgery at the University Hospital Farhat Hached of Sousse and was a member of the Unit of Liver Transplantation in the University of Rennes, France. He then worked in the Department of Surgery at the Sahloul University Hospital in Sousse. Professor Derbel is presently working at the Clinique les Oliviers, Sousse, Tunisia. His hospital activities are mostly concerned with laparoscopic, colorectal, pancreatic, hepatobiliary, and gastric surgery. He is also very interested in hernia surgery and performs ventral hernia repairs and inguinal hernia repairs. He has been a member of the GREPA and Tunisian Hernia Society (THS). During his residency, he managed patients suffering from diabetic foot, and he was very interested in this pathology. For this reason, he decided to coordinate a book project dealing with the diabetic foot. Professor Derbel has published many articles in journals and collaborates intensively with IntechOpen Access Publisher as an editor.",institutionString:"Clinique les Oliviers",institution:null},{id:"300144",title:"Dr.",name:"Meriem",middleName:null,surname:"Braiki",slug:"meriem-braiki",fullName:"Meriem Braiki",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/300144/images/system/300144.jpg",biography:"Dr. Meriem Braiki is a specialist in pediatric surgeon from Tunisia. She was born in 1985. She received her medical degree from the University of Medicine at Sousse, Tunisia. She achieved her surgical residency training periods in Pediatric Surgery departments at University Hospitals in Monastir, Tunis and France.\r\nShe is currently working at the Pediatric surgery department, Sidi Bouzid Hospital, Tunisia. Her hospital activities are mostly concerned with laparoscopic, parietal, urological and digestive surgery. She has published several articles in diffrent journals.",institutionString:"Sidi Bouzid Regional Hospital",institution:null},{id:"229481",title:"Dr.",name:"Erika M.",middleName:"Martins",surname:"de Carvalho",slug:"erika-m.-de-carvalho",fullName:"Erika M. de Carvalho",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/229481/images/6397_n.jpg",biography:null,institutionString:null,institution:{name:"Oswaldo Cruz Foundation",country:{name:"Brazil"}}},{id:"186537",title:"Prof.",name:"Tonay",middleName:null,surname:"Inceboz",slug:"tonay-inceboz",fullName:"Tonay Inceboz",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/186537/images/system/186537.jfif",biography:"I was graduated from Ege University of Medical Faculty (Turkey) in 1988 and completed his Med. PhD degree in Medical Parasitology at the same university. I became an Associate Professor in 2008 and Professor in 2014. I am currently working as a Professor at the Department of Medical Parasitology at Dokuz Eylul University, Izmir, Turkey.\n\nI have given many lectures, presentations in different academic meetings. I have more than 60 articles in peer-reviewed journals, 18 book chapters, 1 book editorship.\n\nMy research interests are Echinococcus granulosus, Echinococcus multilocularis (diagnosis, life cycle, in vitro and in vivo cultivation), and Trichomonas vaginalis (diagnosis, PCR, and in vitro cultivation).",institutionString:"Dokuz Eylül University",institution:{name:"Dokuz Eylül University",country:{name:"Turkey"}}},{id:"71812",title:"Prof.",name:"Hanem Fathy",middleName:"Fathy",surname:"Khater",slug:"hanem-fathy-khater",fullName:"Hanem Fathy Khater",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/71812/images/1167_n.jpg",biography:"Prof. Khater is a Professor of Parasitology at Benha University, Egypt. She studied for her doctoral degree, at the Department of Entomology, College of Agriculture, Food and Natural Resources, University of Missouri, Columbia, USA. She has completed her Ph.D. degrees in Parasitology in Egypt, from where she got the award for “the best scientific Ph.D. dissertation”. She worked at the School of Biological Sciences, Bristol, England, the UK in controlling insects of medical and veterinary importance as a grant from Newton Mosharafa, the British Council. Her research is focused on searching of pesticides against mosquitoes, house flies, lice, green bottle fly, camel nasal botfly, soft and hard ticks, mites, and the diamondback moth as well as control of several parasites using safe and natural materials to avoid drug resistances and environmental contamination.",institutionString:null,institution:{name:"Banha University",country:{name:"Egypt"}}},{id:"99780",title:"Prof.",name:"Omolade",middleName:"Olayinka",surname:"Okwa",slug:"omolade-okwa",fullName:"Omolade Okwa",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/99780/images/system/99780.jpg",biography:"Omolade Olayinka Okwa is presently a Professor of Parasitology at Lagos State University, Nigeria. She has a PhD in Parasitology (1997), an MSc in Cellular Parasitology (1992), and a BSc (Hons) Zoology (1990) all from the University of Ibadan, Nigeria. She teaches parasitology at the undergraduate and postgraduate levels. She was a recipient of a Commonwealth fellowship supported by British Council tenable at the Centre for Entomology and Parasitology (CAEP), Keele University, United Kingdom between 2004 and 2005. She was awarded an Honorary Visiting Research Fellow at the same university from 2005 to 2007. \nShe has been an external examiner to the Department of Veterinary Microbiology and Parasitology, University of Ibadan, MSc programme between 2010 and 2012. She is a member of the Nigerian Society of Experimental Biology (NISEB), Parasitology and Public Health Society of Nigeria (PPSN), Science Association of Nigeria (SAN), Zoological Society of Nigeria (ZSN), and is Vice Chairperson of the Organisation of Women in Science (OWSG), LASU chapter. She served as Head of Department of Zoology and Environmental Biology, Lagos State University from 2007 to 2010 and 2014 to 2016. She is a reviewer for several local and international journals such as Unilag Journal of Science, Libyan Journal of Medicine, Journal of Medicine and Medical Sciences, and Annual Research and Review in Science. \nShe has authored 45 scientific research publications in local and international journals, 8 scientific reviews, 4 books, and 3 book chapters, which includes the books “Malaria Parasites” and “Malaria” which are IntechOpen access publications.",institutionString:"Lagos State University",institution:{name:"Lagos State University",country:{name:"Nigeria"}}},{id:"273100",title:"Dr.",name:"Vijay",middleName:null,surname:"Gayam",slug:"vijay-gayam",fullName:"Vijay Gayam",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/273100/images/system/273100.jpeg",biography:"Dr. Vijay Bhaskar Reddy Gayam is currently practicing as an internist at Interfaith Medical Center in Brooklyn, New York, USA. He is also a Clinical Assistant Professor at the SUNY Downstate University Hospital and Adjunct Professor of Medicine at the American University of Antigua. He is a holder of an M.B.B.S. degree bestowed to him by Osmania Medical College and received his M.D. at Interfaith Medical Center. His career goals thus far have heavily focused on direct patient care, medical education, and clinical research. He currently serves in two leadership capacities; Assistant Program Director of Medicine at Interfaith Medical Center and as a Councilor for the American\r\nFederation for Medical Research. As a true academician and researcher, he has more than 50 papers indexed in international peer-reviewed journals. He has also presented numerous papers in multiple national and international scientific conferences. His areas of research interest include general internal medicine, gastroenterology and hepatology. He serves as an editor, editorial board member and reviewer for multiple international journals. His research on Hepatitis C has been very successful and has led to multiple research awards, including the 'Equity in Prevention and Treatment Award” from the New York Department of Health Viral Hepatitis Symposium (2018) and the 'Presidential Poster Award” awarded to him by the American College of Gastroenterology (2018). He was also awarded 'Outstanding Clinician in General Medicine” by Venus International Foundation for his extensive research expertise and services, perform over and above the standard expected in the advancement of healthcare, patient safety and quality of care.",institutionString:"Interfaith Medical Center",institution:{name:"Interfaith Medical Center",country:{name:"United States of America"}}},{id:"93517",title:"Dr.",name:"Clement",middleName:"Adebajo",surname:"Meseko",slug:"clement-meseko",fullName:"Clement Meseko",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/93517/images/system/93517.jpg",biography:"Dr. Clement Meseko obtained DVM and PhD degree in Veterinary Medicine and Virology respectively. He has worked for over 20 years in both private and public sectors including the academia, contributing to knowledge and control of infectious disease. Through the application of epidemiological skill, classical and molecular virological skills, he investigates viruses of economic and public health importance for the mitigation of the negative impact on people, animal and the environment in the context of Onehealth. \r\nDr. Meseko’s field experience on animal and zoonotic diseases and pathogen dynamics at the human-animal interface over the years shaped his carrier in research and scientific inquiries. He has been part of the investigation of Highly Pathogenic Avian Influenza incursions in sub Saharan Africa and monitors swine Influenza (Pandemic influenza Virus) agro-ecology and potential for interspecies transmission. He has authored and reviewed a number of journal articles and book chapters.",institutionString:"National Veterinary Research Institute",institution:{name:"National Veterinary Research Institute",country:{name:"Nigeria"}}},{id:"158026",title:"Prof.",name:"Shailendra K.",middleName:null,surname:"Saxena",slug:"shailendra-k.-saxena",fullName:"Shailendra K. Saxena",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRET3QAO/Profile_Picture_2022-05-10T10:10:26.jpeg",biography:"Professor Dr. Shailendra K. Saxena is a vice dean and professor at King George's Medical University, Lucknow, India. His research interests involve understanding the molecular mechanisms of host defense during human viral infections and developing new predictive, preventive, and therapeutic strategies for them using Japanese encephalitis virus (JEV), HIV, and emerging viruses as a model via stem cell and cell culture technologies. His research work has been published in various high-impact factor journals (Science, PNAS, Nature Medicine) with a high number of citations. He has received many awards and honors in India and abroad including various Young Scientist Awards, BBSRC India Partnering Award, and Dr. JC Bose National Award of Department of Biotechnology, Min. of Science and Technology, Govt. of India. Dr. Saxena is a fellow of various international societies/academies including the Royal College of Pathologists, United Kingdom; Royal Society of Medicine, London; Royal Society of Biology, United Kingdom; Royal Society of Chemistry, London; and Academy of Translational Medicine Professionals, Austria. He was named a Global Leader in Science by The Scientist. He is also an international opinion leader/expert in vaccination for Japanese encephalitis by IPIC (UK).",institutionString:"King George's Medical University",institution:{name:"King George's Medical University",country:{name:"India"}}},{id:"94928",title:"Dr.",name:"Takuo",middleName:null,surname:"Mizukami",slug:"takuo-mizukami",fullName:"Takuo Mizukami",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/94928/images/6402_n.jpg",biography:null,institutionString:null,institution:{name:"National Institute of Infectious Diseases",country:{name:"Japan"}}},{id:"233433",title:"Dr.",name:"Yulia",middleName:null,surname:"Desheva",slug:"yulia-desheva",fullName:"Yulia Desheva",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/233433/images/system/233433.png",biography:"Dr. Yulia Desheva is a leading researcher at the Institute of Experimental Medicine, St. Petersburg, Russia. She is a professor in the Stomatology Faculty, St. Petersburg State University. She has expertise in the development and evaluation of a wide range of live mucosal vaccines against influenza and bacterial complications. Her research interests include immunity against influenza and COVID-19 and the development of immunization schemes for high-risk individuals.",institutionString:'Federal State Budgetary Scientific Institution "Institute of Experimental Medicine"',institution:null},{id:"238958",title:"Mr.",name:"Atamjit",middleName:null,surname:"Singh",slug:"atamjit-singh",fullName:"Atamjit Singh",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/238958/images/6575_n.jpg",biography:null,institutionString:null,institution:null},{id:"333753",title:"Dr.",name:"Rais",middleName:null,surname:"Ahmed",slug:"rais-ahmed",fullName:"Rais Ahmed",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/333753/images/20168_n.jpg",biography:null,institutionString:null,institution:null},{id:"252058",title:"M.Sc.",name:"Juan",middleName:null,surname:"Sulca",slug:"juan-sulca",fullName:"Juan Sulca",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/252058/images/12834_n.jpg",biography:null,institutionString:null,institution:null},{id:"191392",title:"Dr.",name:"Marimuthu",middleName:null,surname:"Govindarajan",slug:"marimuthu-govindarajan",fullName:"Marimuthu Govindarajan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/191392/images/5828_n.jpg",biography:"Dr. M. Govindarajan completed his BSc degree in Zoology at Government Arts College (Autonomous), Kumbakonam, and MSc, MPhil, and PhD degrees at Annamalai University, Annamalai Nagar, Tamil Nadu, India. He is serving as an assistant professor at the Department of Zoology, Annamalai University. His research interests include isolation, identification, and characterization of biologically active molecules from plants and microbes. He has identified more than 20 pure compounds with high mosquitocidal activity and also conducted high-quality research on photochemistry and nanosynthesis. He has published more than 150 studies in journals with impact factor and 2 books in Lambert Academic Publishing, Germany. He serves as an editorial board member in various national and international scientific journals.",institutionString:null,institution:null},{id:"274660",title:"Dr.",name:"Damodar",middleName:null,surname:"Paudel",slug:"damodar-paudel",fullName:"Damodar Paudel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/274660/images/8176_n.jpg",biography:"I am DrDamodar Paudel,currently working as consultant Physician in Nepal police Hospital.",institutionString:null,institution:null},{id:"241562",title:"Dr.",name:"Melvin",middleName:null,surname:"Sanicas",slug:"melvin-sanicas",fullName:"Melvin Sanicas",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241562/images/6699_n.jpg",biography:null,institutionString:null,institution:null},{id:"337446",title:"Dr.",name:"Maria",middleName:null,surname:"Zavala-Colon",slug:"maria-zavala-colon",fullName:"Maria Zavala-Colon",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Puerto Rico, Medical Sciences Campus",country:{name:"United States of America"}}},{id:"338856",title:"Mrs.",name:"Nur Alvira",middleName:null,surname:"Pascawati",slug:"nur-alvira-pascawati",fullName:"Nur Alvira Pascawati",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Universitas Respati Yogyakarta",country:{name:"Indonesia"}}},{id:"441116",title:"Dr.",name:"Jovanka M.",middleName:null,surname:"Voyich",slug:"jovanka-m.-voyich",fullName:"Jovanka M. Voyich",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Montana State University",country:{name:"United States of America"}}},{id:"330412",title:"Dr.",name:"Muhammad",middleName:null,surname:"Farhab",slug:"muhammad-farhab",fullName:"Muhammad Farhab",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Agriculture Faisalabad",country:{name:"Pakistan"}}},{id:"349495",title:"Dr.",name:"Muhammad",middleName:null,surname:"Ijaz",slug:"muhammad-ijaz",fullName:"Muhammad Ijaz",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Veterinary and Animal Sciences",country:{name:"Pakistan"}}}]}},subseries:{item:{id:"20",type:"subseries",title:"Animal Nutrition",keywords:"Sustainable Animal Diets, Carbon Footprint, Meta Analyses",scope:"An essential part of animal production is nutrition. Animals need to receive a properly balanced diet. One of the new challenges we are now faced with is sustainable animal diets (STAND) that involve the 3 P’s (People, Planet, and Profitability). We must develop animal feed that does not compete with human food, use antibiotics, and explore new growth promoters options, such as plant extracts or compounds that promote feed efficiency (e.g., monensin, oils, enzymes, probiotics). These new feed options must also be environmentally friendly, reducing the Carbon footprint, CH4, N, and P emissions to the environment, with an adequate formulation of nutrients.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/20.jpg",hasOnlineFirst:!0,hasPublishedBooks:!0,annualVolume:11416,editor:{id:"175967",title:"Dr.",name:"Manuel",middleName:null,surname:"Gonzalez Ronquillo",slug:"manuel-gonzalez-ronquillo",fullName:"Manuel Gonzalez Ronquillo",profilePictureURL:"https://mts.intechopen.com/storage/users/175967/images/system/175967.png",biography:"Dr. Manuel González Ronquillo obtained his doctorate degree from the University of Zaragoza, Spain, in 2001. He is a research professor at the Faculty of Veterinary Medicine and Animal Husbandry, Autonomous University of the State of Mexico. He is also a level-2 researcher. He received a Fulbright-Garcia Robles fellowship for a postdoctoral stay at the US Dairy Forage Research Center, Madison, Wisconsin, USA in 2008–2009. He received grants from Alianza del Pacifico for a stay at the University of Magallanes, Chile, in 2014, and from Consejo Nacional de Ciencia y Tecnología (CONACyT) to work in the Food and Agriculture Organization’s Animal Production and Health Division (AGA), Rome, Italy, in 2014–2015. He has collaborated with researchers from different countries and published ninety-eight journal articles. He teaches various degree courses in zootechnics, sheep production, and agricultural sciences and natural resources.\n\nDr. Ronquillo’s research focuses on the evaluation of sustainable animal diets (StAnD), using native resources of the region, decreasing carbon footprint, and applying meta-analysis and mathematical models for a better understanding of animal production.",institutionString:null,institution:{name:"Universidad Autónoma del Estado de México",institutionURL:null,country:{name:"Mexico"}}},editorTwo:null,editorThree:null,series:{id:"13",title:"Veterinary Medicine and Science",doi:"10.5772/intechopen.73681",issn:"2632-0517"},editorialBoard:[{id:"175762",title:"Dr.",name:"Alfredo J.",middleName:null,surname:"Escribano",slug:"alfredo-j.-escribano",fullName:"Alfredo J. 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