The vehicle body parameter.
\r\n\tHydrogen gas is the key energy source for hydrogen-based society. Ozone dissolved water is expected as the sterilization and cleaning agent that can comply with the new law enacted by the US Food and Drug Administration (FDA). The law “FDA Food Safety Modernization Act” requires sterilization and washing of foods to prevent food poisoning and has a strict provision that vegetables, meat, and fish must be washed with non-chlorine cleaning agents to make E. coli adhering to food down to “zero”. If ozone dissolved water could be successively applied in this field, electrochemistry would make a significant contribution to society.
\r\n\r\n\t
\r\n\tOxygen-enriched water is said to promote the growth of farmed fish. Hydrogen dissolved water is said to be able to efficiently remove minute dust on the silicon wafer when used in combination with ultrasonic irradiation.
\r\n\tAt present researches on direct water electrolysis have shown significant progress. For example, boron-doped diamonds and complex metal oxides are widely used as an electrode, and the interposing polymer electrolyte membrane (PEM) between electrodes has become one of the major processes of water electrolysis.
\r\n\t
\r\n\tThe purpose of this book is to show the latest water electrolysis technology and the future of society applying it.
In recent years, the noise and vibration of cars have become increasingly important [20, 23, 29, 30, 35]. A major comfort aspect is the transmission of engine-induced vibrations through powertrain mounts into the chassis (see Figure 1). Engine and powertrain mounts are usually designed according to criteria that incorporate a trade-off between the isolation of the engine from the chassis and the restriction of engine movements. The engine mount is an efficient passive means to isolate the car chassis structure from the engine vibration. However, the passive means for isolation is efficient only in the high frequency range. However the vibration disturbance generated by the engine occurs mainly in the low frequency range [8, 19, 23, 30]. These vibrations are result of the fuel explosion in the cylinder and the rotation of the different parts of the engine (see Figure 2). In order to attenuate the low frequency disturbances of the engine vibration while keeping the space and price constant, active vibration means are necessary.
A variety of control techniques, such as Proportional-Integral-Derivative (PID) or Lead-Lag compensation, Linear Quadratic Gaussian (LQG),
On the other hand, wavelet theory is a relatively new and an emerging area in mathematical research [2]. It has been applied in a wide range of engineering disciplines such as signal processing, pattern recognition and computational graphics. Recently, some of the attempts are made in solving surface integral equations, improving the finite difference time domain method, solving linear differential equations and nonlinear partial differential equations and modelling nonlinear semiconductor devices [5, 6, 7, 13, 16, 17, 18, 21, 27].
Front axis of AUDI A 8 from [22, 30] (Werkbild Audi AG).
Chassis excited by the engine vibration.
Orthogonal functions like Haar wavelets (HWs) [13, 16], Walsh functions [7], block pulse functions [27], Laguerre polynomials [14], Legendre polynomials [5], Chebyshev functions [12] and Fourier series [28], often used to represent an arbitrary time functions, have received considerable attention in dealing with various problems of dynamic systems. The main characteristic of this technique is that it reduces these problems to those of solving a system of algebraic equations for the solution of problems described by differential equations, such as analysis of linear time-invariant, time-varying systems, model reduction, optimal control and system identification. Thus, the solution, identification and optimisation procedure are either greatly reduced or much simplified accordingly. The available sets of orthogonal functions can be divided into three classes such as piecewise constant basis functions (PCBFs) like HWs, Walsh functions and block pulse functions; orthogonal polynomials like Laguerre, Legendre and Chebyshev as well as sine-cosine functions in Fourier series [21].
In the present paper, we, for the first time, introduce a computational solution to the finite-time robust optimal control problem of the vehicle engine-body vibration system based on HWs. To this aim, mathematical model of the engine-body vibration structure is presented such the actuators and sensors used to investigate the robust optimal control are selected to be collocated. Moreover, the properties of HWs, Haar wavelet integral operational matrix and Haar wavelet product operational matrix are given and are utilized to provide a systematic computational framework to find the approximated robust optimal trajectory and finite-time
The rest of this paper is organized as fallows. Section 2 introduces properties of the HWs. Mathematical model of the engine-body vibration structure is stated in Section 3. Algebraic solution of the engine-body system is given in Section 4 and Haar wavelet-based optimal trajectories and robust optimal control are presented in Sections 5 and 6, respectively. Simulation results of the robust optimal control of the vehicle engine-body vibration system are shown in Section 7 and finally the conclusion is discussed.
The notations used throughout the paper are fairly standard. The matrices
Properties of HWs, which will be used in the next sections, are introduced in this section.
The oldest and most basic of the wavelet systems is named Haar wavelet that is a group of square waves with magnitude of.
and
The finite series representation of any square integrable function
where
Remark 1. The approximation error,
The matrix
where
The integration of the vector
where the matrix
represents the integral operator matrix for PCBFs on the interval
with
for
and
In the study of time-varying state-delayed systems, it is usually necessary to evaluate the product of two Haar function vectors [13]. Let us define
where
with
\n\t\t\t\tand
Moreover, the following relation is important for solving optimal control problem of time-varying state-delayed system:
where
with
The sketch of engine-body vibration system
A schematic of the vehicle engine-body vibration structure is shown in Figure 3. The actuator and sensor used to this control framework are selected to be collocated, since this arrangement is ideal to ensure the stability of the closed loop system for a slightly damped structure [26]. In our study, only the bounce and pitch vibrations in the engine and body are considered [35]. The engine with mass
The vehicle body with mass
where the states
The system Eq. (14) can be represented in the following state-space form
where
Taking displacement of the chassis
In this section, we study the problem of solving the second-order differential equations of the engine-body system (14) in terms of the input control and exogenous disturbance using HWs and develop appropriate algebraic equations.
Based on HWs definition on the interval time
Now by integrating the system above in an interval
By using the Haar wavelet expansion (2), we express the solution of Eq. (15), input force
where
Therefore, using the wavelet expansions (18)-(20), the relation (17) becomes
For calculating the matrix
Solving Eq. (24) for
where the matrices
Consequently, using (25) and (26) and the properties of the Kronecker product, the solution of system (15) is
and it is also clear that to find the approximated solution of the system, we have to calculate the inverse of the matrix
Displacement of the chassis respect to f(t) (a) and de(t) (b).
The control objective is to find the optimal control
where
where
From (15) and the relation
where
Remark 2. By substituting
and using (4), we read
By substituting the definition (31) in (33) and using the properties of the operator
where the matrices
and the matrices
It is clear that the cost function of
By considering
Then the wavelet coefficients of the optimal control law will be in vector form as
Consequently, the optimal vectors of
and
Finally, the Haar function-based optimal trajectories and optimal control are obtained approximately from Eq. (27) and
In this section, an optimal state feedback controller is to be determined computationally such that the following requirements are satisfied:
the closed-loop system is asymptotically stable;
under zero initial condition, the closed-loop system satisfies
The control objective is to find the approximated robust optimal control
It is well known that a sufficient condition for achieving robust disturbance attenuation is that the inequality
From (15), the Eq. (40) can be represented as
where
Using the relation
where
Moreover, according to Remark 2 in [18], the following relation is already satisfied between
By using the definition (44) in Eq. (45), we have
Using the property of the Kronecker product, i.e.
where the matrices
It is easy to show that the worst-case disturbance in Eq. (47) occurs when
By substituting Eq. (48) into Eq. (47) we obtain
Minimizing the right-hand side of Eq. (49) results in the algebraic relation between wavelet coefficients of the robust optimal control and of the optimal state trajectories in the following closed form
As a result we have
Consequently, if there exists positive scalar
then inequality (41) is concluded.
From the relations above we obtain the robust optimal vectors of
and
Finally, the Haar wavelet-based robust optimal trajectories and robust optimal control are obtained approximately from Eq. (27) and
In this section the proposed computational methodology is applied to the vehicle engine-body vibration system (15) such the exogenous disturbance
Comparison of displacement of the chassis found by HWs at resolution level j=5 (solid) and by analytical solution (dashed).
To compare the approximate solutions
Comparison of input force found by HWs at resolution level j=5 (solid) and by analytical solution (dashed).
This paper presented the modelling of engine-body vibration structure to control of bounce and pitch vibrations using HWs. To this aim, the Haar wavelet-based optimal control for vibration reduction of the engine-body system was developed computationally. The Haar wavelet properties were introduced and utilized to find the approximate solutions of optimal trajectories and robust optimal control by solving only algebraic equations instead of solving the Riccati differential equation. Numerical results were presented to illustrate the advantage of the approach.
Appendix A
A1. Some properties of Kronecker product\n\t\t\t\t
Let
A2. Derivatives of inner products of Kronecker product\n\t\t\t\t
Let
A3. Chain rule for matrix derivatives using Kronecker product\n\t\t\t\t
Let
Parameters | Values |
1000 [kg] | |
810 [ | |
20000 [N/m] | |
300 [N/m/s] | |
2.5 [m] |
The vehicle body parameter.
Parameters | Values |
250 [kg] | |
8.10 [ | |
200000 [N/m] | |
200 [N/m/s] | |
0.5 [m] |
The engine parameters.
Zeros | Poles |
-6.23 | -6.2313 |
-0.97 | -1.09 |
0.03 | 0.14 |
-1.10 | -0.29 |
Pole-zero locations of the 8th -order model.
Theorem 1 (State Feedback) [9]. Consider dynamical system
under assumption
has a positive semi-definite solution
The knowledge of electrical properties of soils in physics and electrical engineering are important for many applications. The long-distance electromagnetic telegraph systems from 1820 are used, with two or more wires to carry the signal and the return currents. It was discovered that the earth could be used as a return path to complete the circuit, making the return wire unnecessary [1]. However, during dry weather, the earth connection often developed a high resistance, requiring water on the earth electrode to enable the telegraph to ring [1].
\nAn important radio propagation and engineering problem has been solved in 1909 by A. Sommerfeld. He has solved the general problem of the effect of the finite conductivity of the ground on the radiation from a short vertical antenna at the surface of a plane earth. The surface wave propagation is produced over real ground for the medium frequency AM radio service, where the attenuation of the electric field depends on the dielectric properties of the soil, mainly of the dielectric losses [2]. Considering the word “Soil” means the uppermost layer of the earth’s crust, it contains the organic as well as mineral matter. From 1936 up to 1941, Norton, Van der Pol, and Bremmer made the computation of the field strengths at distant points on the flat and spherical Earth’s surface [3, 4].
\nIn agriculture applications, the electrical resistivity methods have been introduced by Conrad Schlumberger in France and Frank Wenner in the United States, for the evaluation of ground electrical resistivity. In saline soils, the electric conductivity measured is high, and the effects of salinity are manifested in the loss of stand, reduced rates of plant growth, reduced yields, and in severe cases, total crop failure [5].
\nThe applications like the protection of electrical generating plant are necessary to provide earth connections with low electrical resistance. The radio transmitting and receiving stations for broadcasting is generally covered by radiation transmitted directly along the ground [6]. In electrical engineering, “ground” is the reference point in an electrical circuit from which voltages are measured.
\nFor archeology, geophysics, engineering, and military applications, the so-called ground-penetrating radar (GPR) is a technique widely used. The radar signal is an electromagnetic wave that propagates through the earth, and its signal is reflected when an object appears or there is a change in the properties of the earth. In order to determine the depth of an object under the ground, it is necessary to know the electrical properties of the soil [7].
\nThe equations that relate the electric field (E) and magnetic field (H) are based on the electromagnetic theory formulated by James Clerk Maxwell in 1864, whose validity has allowed great advances in diverse areas, such as telecommunications, electricity, electronics, and materials [8].
\nRegarding the behavior of the materials under the action of an electric field, in the conductive materials, the charges can move freely, meaning that the electrons are not associated with an atomic nucleus. In the case of dielectric materials, the charges are associated with an atom or specific molecule [9]. There are two main mechanisms where the electric field distorts the distribution of charge in a dielectric, stretching and rotation. The relationship between the electric dipole moment inducted under the action of an applied electric field is called atomic electric polarizability \n
In a material with an applied electric field, a convenient definition is to consider the contributions of the dipole moment per unit volume; this parameter is called polarization, which is a macroscopic definition instead of a molecular or atomic definition [9, 10]:
\nIt is evident that the contributions of the electric dipole moment in a volume element \n
And the polarization
\nIn Figure 1, the external electric field applied to a dielectric material and the resulting polarization can be observed.
\nPolarization applying external electric field E, to a dielectric material.
From the macroscopic point of view in most of the dielectric material, when the electric field is canceled, the polarization in the material will be nullified. In addition, the polarization of the material will vary as the electric field varies, i.e., \n
It is convenient to define the electric displacement, because it allows to relate by means of the Gaussian law with the free charges; therefore
\nThen
\nThe electrical permittivity is defined as the relationship between the electric displacement vector \n
Result
\nIt is convenient to define [11]:
\nResult
\nThe electric properties of the material are completely defined by means of \n
In problems with electromagnetic fields, four vectors are defined: E and B; D and H. These vectors are assumed to be finite throughout the entire field, and at all ordinary points to be continuous functions of position and time, with continuous derivatives [12]. The constitutive relations link the vectors of the fields \n
For the electromagnetic propagation in soils, the parameters \n
The electrical resistivity obtained by soil mapping exhibits a large range of values from \n
Table of electric resistivity \n\n\n\nΩ\n/\nm\n\n\n\n and electric conductivity \n\n\n\nσ\n/\nm\n\n\n\n of soils (Ref. Samoulian et al.) [13].
There are evidences that for compacted soils of clay, it exhibits an anisotropic behavior in the resistivity measured in the horizontal and vertical directions [14].
\nThe literature contains the measurement of the dielectric properties of soils at different frequencies with slotted lines and time-domain reflectometry (TDR) methods [15].
\nThe measured variations of the electric permittivity of soils with fractions of sand, silt, and clay and with volumetric moisture content have been studied for frequency of 440 MHz used by the radar observations [16].
\nThe coaxial probe technique terminated in the material under test has been used to measure the dielectric properties of the vegetation. The dielectric data reported are based on measurements of the amplitude and phase of the reflection coefficient of a coaxial probe [17, 18].
\nThe transmission line method has been used to measure the dielectric properties [19, 20]. These transmission lines are coaxial, quasi-coaxial, and two-wire transmission lines. Consider a transmission line with a homogenous dielectric material inside, and the propagation is transverse electromagnetic mode (TEM), where the electric and magnetic field are perpendicular to the propagation direction; this can be observed in Figures 3 and 4.
\nSection of the two-wire transmission line with the electric and magnetic fields.
Section of coaxial transmission lines and the electric and magnetic fields.
The separation between the conductive cylinders that form the coax transmission line should be much lower than the wavelength of the signal that propagates, so the transmission line will not be affected by the propagation modes of high orders, such as the TE\n11 [19].
\nCoaxial transmission lines are widely used for the transmission of radiofrequency signals and its application in radiocommunications and for broadcasting [21]. The transmission lines allow the connection between a generator or emitter and a load or antenna. The air coaxial transmission line consists of two cylindrical conductors, with air between both conductors. These metallic conductors are those that impose the boarder conditions that must comply with the electric and magnetic fields of the electromagnetic wave that travel inside the line. The coaxial transmission lines are used to measure the electrical properties of a dielectric material located inside the coaxial transmission line, as shown in Figure 4.
\nBy analyzing the circuit model of a transmission line, the currents and voltages that propagate along it can be determined, using the circuit theory [22]. The equivalent circuit model of a transmission line can be seen in Figure 5. According to the equivalent circuit model of a transmission line, the characteristic impedance \n
Distributed parameters of the transmission line.
where R is the series resistance per unit length \n
If the transmission line has no losses, it means that R = 0 and G = 0; then the characteristic impedance can be reduced as follows:
\nThe input impedance of a transmission line, with a material inside considering the material with dielectric losses, can be expressed thus [23]:
\nwhere \n
The time-domain reflectometry (TDR) is a well-known technique used to find the interruption point of the transmission lines in a CATV installation and is also useful to determine the dielectric permittivity (see Figure 6).
\nSetup of the dielectric measurement by the TDR method [24].
The time-domain reflectometry uses a step generator and an oscilloscope; a fast edge is launched into the transmission line under investigation, where the incident and reflected voltage waves on the transmission line are monitored by the oscilloscope. This method shows the losses and the characteristic impedance of the line: resistive, inductive, or capacitive [25]. The TDR method is based on the velocity of the electromagnetic wave that propagates through the soil, and the velocity of the wave depends on the water content of the soil. If a pulse is applied to a no-loss transmission line, the time domain graphic can be shown like in Figure 7. Considering the soil like a nonmagnetic media with low dielectric loss is [26, 27]:
\nPropagation of the pulses in the time domain graphic with dielectric air [24].
where \n
The time interval \n
Picture of the voltage as a function of time for the probe is in the soil [26].
where \n
Then
\nUsually the transmission line probes have a minimum length of 15 cm, because the incident electromagnetic wave takes a time of 1 ns in air in order to return to the input of the line. This time is too short to be measured.
\nThe conductivity of the soil can be determined computing the reflected pulses in the probe in the time domain graphic (see Figure 8) [26, 28]. Numerous methods have been proposed by researchers; one of these is the procedure of Dalton et al. (1984) [26]:
\nwhere \n
Also the conductivity can expressed thus [29]:
\nwhere:
\n\n\n
\n\n
Temperature correction \n
This method is based on the measurement of the reflection coefficient by means of the vector network analyzer (VNA) on the frequency domain of a coaxial transmission line in the soil; this can be observed in Figure 9 [30, 31, 32].
\nDielectric measurement by the coaxial transmission line method. (a) Setup of the measurement experiment; (b) section of the transmission line and the material under test [33].
The vector network analyzer can measure the scattering coefficient of a two-port passive network where the reflection coefficient in voltage \n
where \n
The impedance of the probe can be calculated thus:
\nwhere \n
Then the complex electric permittivity for frequencies lower than 50 MHz can be approximated thus [31]:
\nSome references of these measurement methods by means of characteristic impedance have been developed [35, 36]. This methods is shown in Figure 10.
\nInput impedance of the transmission line for \n\n\nZ\nL\n\n=\n0\n\n and \n\n\nZ\nL\n\n→\n∞\n\n.
The input impedance can be computed by Eq. (17) for two different loads’ impedance:
(a) Open circuit in the load \n
\n
(b) Short circuit in the load \n
where, in general, the material inside the transmission line could be a dielectric loss; the propagation constant can be written thus:
\nwhere \n
Using the relation between \n
The argument of the ln
\nReplacing the Ln
\nThen the propagation constant can be written thus:
\nBy these last equations, the attenuation constant and the phase constant can be calculated with \n
The propagation constant \n
Equating real and imaginary parts of \n
\n\n
Another expression of \n
Equating real and imaginary part of Eq. (43)\n
\nResults
\nThe series resistance of the conductor of the coaxial transmission line used is \n
\n
The input impedances are measured for a load impedance at short circuit \n
The attenuation constant \n
The electric permittivity \n
In this way, a practical method of measurement is available to determine the parameters of dielectric materials, using coaxial transmission lines, in the frequency range from 1 to 1000 MHz. A problem that appears when measuring dielectric materials is the connector that establishes the link between the coaxial transmission line and the vector impedance meter. A systematic error in the impedance measured is introduced.
\nTherefore, the study and correction of the mentioned error in the section will be carried out.
\nThree coaxial transmission lines of General Radio (GR) Type 874, with air dielectric, have been used with a length of 100, 200, and 300 mm. The main characteristics of the General Radio coaxial transmission lines, type 874, are the following:
\nCharacteristic impedance \n
Input and output connector GR874
\nIt is important to perform the correction of the impedance introduced by the connector of the transmission line used. This connector is shown in Figure 11, and it is composed by a dielectric of very low dielectric losses and has a length of 10 mm (Figure 12). The characteristic impedance of the connector is practically \n
N connector and its equivalent of a transmission line with dielectric of air.
Equivalent length of the transmission line of the connector GR874.
The input impedance to the connector can be written thus:
\nwhere \n
The electric permittivity of the dielectric of the connector is unknown; then it is easy to assume a transmission line with air equivalent to the connector with \n
Considering the connector with no losses
\nThen the input impedance of the connector with \n
where \n
The length \n
The experimental results of the electric conductivity and the dielectric permittivity measurement of the dry sand can be observed in Figures 13 and 14. In Figure 13, the electric conductivity as a function of the frequency, by means of the capacitive method, and the three types of transmission line lengths have been measured: L = 100, 200, and 300 mm; the convergence of all measurements are evident.
\nElectric conductivity as a function of the frequency for dry sand samples, using a capacitive method and three transmission lines: 100, 200, and 300 mm.
The relative dielectric permittivity as a function of the frequency for dry sand samples has been measured, using a capacitive method and three transmission lines: 100, 200, and 300 mm.
In Figure 14, the relative electric permittivity as a function of the frequency, by means of the capacitive method, and the three types of transmission line lengths have been measured: L = 100, 200, and 300 mm; there is a convergence of all measurements. It is important to note that the shorter transmission line has a wider bandwidth of measurement. The transmission line length of \n
The expected value of the dielectric permittivity measured for the dry sand by means of a parallel plate capacitor and the three transmission lines used are shown in Table 1. The standard deviation of the three measurements shows a good agreement up to the vicinity of the resonant frequency of each transmission line. In Table 2, the electric conductivity of the dry sand can be observed. These curves have the same slope and show a good convergence.
\nfreq.(MHz) | \nCapacitor | \nT. line 100 mm | \nT. line 200 mm | \nT. line 300 mm | \nExpected value | \nStd. dev | \n
---|---|---|---|---|---|---|
1 | \n2.80 | \n2.86 | \n2.65 | \n2.65 | \n2.74 | \n0.034 | \n
2 | \n2.60 | \n2.76 | \n2.62 | \n2.75 | \n2.68 | \n0.021 | \n
3 | \n2.60 | \n2.70 | \n2.60 | \n2.70 | \n2.65 | \n0.010 | \n
5 | \n2.50 | \n2.61 | \n2.57 | \n2.65 | \n2.58 | \n0.012 | \n
7 | \n2.50 | \n2.54 | \n2.55 | \n2.62 | \n2.55 | \n0.0075 | \n
10 | \n2.50 | \n2.51 | \n2.50 | \n2.60 | \n2.52 | \n0.0073 | \n
20 | \n2.40 | \n2.45 | \n2.47 | \n2.57 | \n2.47 | \n0.0153 | \n
30 | \n2.40 | \n2.42 | \n2.46 | \n2.57 | \n2.46 | \n0.0173 | \n
50 | \n2.40 | \n2.40 | \n2.46 | \n— | \n2.41 | \n0.0028 | \n
70 | \n— | \n2.37 | \n— | \n— | \n— | \n— | \n
100 | \n— | \n2.35 | \n— | \n— | \n— | \n— | \n
Relative electric permittivity of dry sand.
freq.(MHz) | \nCapacitor | \nT. line 100 mm | \nT. line 200 mm | \nT. line 300 mm | \nExpected value | \nStd. dev | \n
---|---|---|---|---|---|---|
1 | \n1 | \n1.1 | \n1.3 | \n1.2 | \n1.15 | \n0.13 | \n
2 | \n1.6 | \n2.0 | \n1.7 | \n1.7 | \n1.75 | \n0.17 | \n
3 | \n2.1 | \n2.6 | \n2.3 | \n2.2 | \n2.30 | \n0.22 | \n
5 | \n3.2 | \n3.1 | \n3.3 | \n3.1 | \n3.20 | \n0.096 | \n
7 | \n4.4 | \n5 | \n4.3 | \n4 | \n4.40 | \n0.42 | \n
10 | \n6.0 | \n6.5 | \n5.5 | \n5.1 | \n5.80 | \n0.6 | \n
20 | \n10 | \n11 | \n9.4 | \n8.4 | \n9.70 | \n1.08 | \n
30 | \n14.4 | \n14.6 | \n12.7 | \n11.1 | \n13.20 | \n1.60 | \n
50 | \n21.9 | \n21.5 | \n20.8 | \n16 | \n20.00 | \n2.7 | \n
Electric conductivity of dry sand \n
The values of the electrical conductivity and the electrical permittivity are very useful to evaluate the propagation of surface waves in real ground, where the attenuation depends mostly on the conductivity of the soil. Such is the case that AM transmitters include radials, which consist of metallic conductors, placed at the base of the monopole antenna to increase conductivity, and in this way the losses due to Joule effect on the earth’s surface are reduced. When the conductivity of the soil is perfect, the electric field vector that propagates will be perpendicular to the earth’s surface; however, in real soils the electric field vector tilts and partly spreads into the earth, which dissipates power and transforms into heat [2]. This constitutes losses on earth.
\nApparent soil electrical conductivity (ECa) to agriculture has its origin in the measurement of soil salinity, in arid-zone problem, which is associated with irrigated agricultural land. ECa is a quick, reliable, easy-to-take soil measurement that often relates to crop yield. For these reasons, the measurement of ECa is among the most frequently used tools in precision agriculture research for the spatiotemporal characterization of edaphic and anthropogenic properties that influence crop yield [37]. There are portable instruments for measuring the electrical conductivity of the soil by the method of electromagnetic induction and by the method of the four conductors, which are installed in the agricultural machinery to obtain a map of the soil, before carrying out the work of tilling the earth.
\nFor geophysics applications, the solar disturbances (flares, coronal mass ejections) create variations of the Earth’s magnetic field. These geomagnetic variations induce a geoelectric field at the Earth’s surface and interior. The geoelectric field in turn drives geomagnetically induced currents, also called telluric currents along electrically conductive technological networks, such as power transmission lines, railways, and pipelines [38]. This geomagnetically induced currents create conditions where enhanced corrosion may occur. Earth conductivity can create geomagnetically induced current variations, in particular where a pipeline crosses a highly resistive intrusive rock. It is important to make pipeline surveys once a year to measure the voltage at test posts to ensure that pipe-to-soil potential variations are within the safe range, impressed by cathodic protection systems [38].
\nAuthors are listed below with their open access chapters linked via author name:
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\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nJocelyn Chanussot (chapter to be published soon...)
\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nYuekun Lai
\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nPrevious years (alphabetically by surname)
\\n\\nAbdul Latif Ahmad 2016-18
\\n\\nKhalil Amine 2017, 2018
\\n\\nEwan Birney 2015-18
\\n\\nFrede Blaabjerg 2015-18
\\n\\nGang Chen 2016-18
\\n\\nJunhong Chen 2017, 2018
\\n\\nZhigang Chen 2016, 2018
\\n\\nMyung-Haing Cho 2016, 2018
\\n\\nMark Connors 2015-18
\\n\\nCyrus Cooper 2017, 2018
\\n\\nLiming Dai 2015-18
\\n\\nWeihua Deng 2017, 2018
\\n\\nVincenzo Fogliano 2017, 2018
\\n\\nRon de Graaf 2014-18
\\n\\nHarald Haas 2017, 2018
\\n\\nFrancisco Herrera 2017, 2018
\\n\\nJaakko Kangasjärvi 2015-18
\\n\\nHamid Reza Karimi 2016-18
\\n\\nJunji Kido 2014-18
\\n\\nJose Luiszamorano 2015-18
\\n\\nYiqi Luo 2016-18
\\n\\nJoachim Maier 2014-18
\\n\\nAndrea Natale 2017, 2018
\\n\\nAlberto Mantovani 2014-18
\\n\\nMarjan Mernik 2017, 2018
\\n\\nSandra Orchard 2014, 2016-18
\\n\\nMohamed Oukka 2016-18
\\n\\nBiswajeet Pradhan 2016-18
\\n\\nDirk Raes 2017, 2018
\\n\\nUlrike Ravens-Sieberer 2016-18
\\n\\nYexiang Tong 2017, 2018
\\n\\nJim Van Os 2015-18
\\n\\nLong Wang 2017, 2018
\\n\\nFei Wei 2016-18
\\n\\nIoannis Xenarios 2017, 2018
\\n\\nQi Xie 2016-18
\\n\\nXin-She Yang 2017, 2018
\\n\\nYulong Yin 2015, 2017, 2018
\\n"}]'},components:[{type:"htmlEditorComponent",content:'New for 2018 (alphabetically by surname).
\n\n\n\n\n\n\n\n\n\nJocelyn Chanussot (chapter to be published soon...)
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\n\nAbdul Latif Ahmad 2016-18
\n\nKhalil Amine 2017, 2018
\n\nEwan Birney 2015-18
\n\nFrede Blaabjerg 2015-18
\n\nGang Chen 2016-18
\n\nJunhong Chen 2017, 2018
\n\nZhigang Chen 2016, 2018
\n\nMyung-Haing Cho 2016, 2018
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\n\nCyrus Cooper 2017, 2018
\n\nLiming Dai 2015-18
\n\nWeihua Deng 2017, 2018
\n\nVincenzo Fogliano 2017, 2018
\n\nRon de Graaf 2014-18
\n\nHarald Haas 2017, 2018
\n\nFrancisco Herrera 2017, 2018
\n\nJaakko Kangasjärvi 2015-18
\n\nHamid Reza Karimi 2016-18
\n\nJunji Kido 2014-18
\n\nJose Luiszamorano 2015-18
\n\nYiqi Luo 2016-18
\n\nJoachim Maier 2014-18
\n\nAndrea Natale 2017, 2018
\n\nAlberto Mantovani 2014-18
\n\nMarjan Mernik 2017, 2018
\n\nSandra Orchard 2014, 2016-18
\n\nMohamed Oukka 2016-18
\n\nBiswajeet Pradhan 2016-18
\n\nDirk Raes 2017, 2018
\n\nUlrike Ravens-Sieberer 2016-18
\n\nYexiang Tong 2017, 2018
\n\nJim Van Os 2015-18
\n\nLong Wang 2017, 2018
\n\nFei Wei 2016-18
\n\nIoannis Xenarios 2017, 2018
\n\nQi Xie 2016-18
\n\nXin-She Yang 2017, 2018
\n\nYulong Yin 2015, 2017, 2018
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