The time-domain statistical parameters of the dimensionless pressure pulsation signal 1.
\\n\\n
IntechOpen was founded by scientists, for scientists, in order to make book publishing accessible around the globe. Over the last two decades, this has driven Open Access (OA) book publishing whilst levelling the playing field for global academics. Through our innovative publishing model and the support of the research community, we have now published over 5,700 Open Access books and are visited online by over three million academics every month. These researchers are increasingly working in broad technology-based subjects, driving multidisciplinary academic endeavours into human health, environment, and technology.
\\n\\nBy listening to our community, and in order to serve these rapidly growing areas which lie at the core of IntechOpen's expertise, we are launching a portfolio of Open Science journals:
\\n\\nAll three journals will publish under an Open Access model and embrace Open Science policies to help support the changing needs of academics in these fast-moving research areas. There will be direct links to preprint servers and data repositories, allowing full reproducibility and rapid dissemination of published papers to help accelerate the pace of research. Each journal has renowned Editors in Chief who will work alongside a global Editorial Board, delivering robust single-blind peer review. Supported by our internal editorial teams, this will ensure our authors will receive a quick, user-friendly, and personalised publishing experience.
\\n\\n"By launching our journals portfolio we are introducing new, dedicated homes for interdisciplinary technology-focused researchers to publish their work, whilst embracing Open Science and creating a unique global home for academics to disseminate their work. We are taking a leap toward Open Science continuing and expanding our fundamental commitment to openly sharing scientific research across the world, making it available for the benefit of all." Dr. Sara Uhac, IntechOpen CEO
\\n\\n"Our aim is to promote and create better science for a better world by increasing access to information and the latest scientific developments to all scientists, innovators, entrepreneurs and students and give them the opportunity to learn, observe and contribute to knowledge creation. Open Science promotes a swifter path from research to innovation to produce new products and services." Alex Lazinica, IntechOpen founder
\\n\\nIn conclusion, Natalia Reinic Babic, Head of Journal Publishing and Open Science at IntechOpen adds:
\\n\\n“On behalf of the journal team I’d like to thank all our Editors in Chief, Editorial Boards, internal supporting teams, and our scientific community for their continuous support in making this portfolio a reality - we couldn’t have done it without you! With your support in place, we are confident these journals will become as impactful and successful as our book publishing program and bring us closer to a more open (science) future.”
\\n\\nWe invite you to visit the journals homepage and learn more about the journal’s Editorial Boards, scope and vision as all three journals are now open for submissions.
\\n\\nFeel free to share this news on social media and help us mark this memorable moment!
\\n\\n\\n"}]',published:!0,mainMedia:{caption:"",originalUrl:"/media/original/237"}},components:[{type:"htmlEditorComponent",content:'
After years of being acknowledged as the world's leading publisher of Open Access books, today, we are proud to announce we’ve successfully launched a portfolio of Open Science journals covering rapidly expanding areas of interdisciplinary research.
\n\n\n\nIntechOpen was founded by scientists, for scientists, in order to make book publishing accessible around the globe. Over the last two decades, this has driven Open Access (OA) book publishing whilst levelling the playing field for global academics. Through our innovative publishing model and the support of the research community, we have now published over 5,700 Open Access books and are visited online by over three million academics every month. These researchers are increasingly working in broad technology-based subjects, driving multidisciplinary academic endeavours into human health, environment, and technology.
\n\nBy listening to our community, and in order to serve these rapidly growing areas which lie at the core of IntechOpen's expertise, we are launching a portfolio of Open Science journals:
\n\nAll three journals will publish under an Open Access model and embrace Open Science policies to help support the changing needs of academics in these fast-moving research areas. There will be direct links to preprint servers and data repositories, allowing full reproducibility and rapid dissemination of published papers to help accelerate the pace of research. Each journal has renowned Editors in Chief who will work alongside a global Editorial Board, delivering robust single-blind peer review. Supported by our internal editorial teams, this will ensure our authors will receive a quick, user-friendly, and personalised publishing experience.
\n\n"By launching our journals portfolio we are introducing new, dedicated homes for interdisciplinary technology-focused researchers to publish their work, whilst embracing Open Science and creating a unique global home for academics to disseminate their work. We are taking a leap toward Open Science continuing and expanding our fundamental commitment to openly sharing scientific research across the world, making it available for the benefit of all." Dr. Sara Uhac, IntechOpen CEO
\n\n"Our aim is to promote and create better science for a better world by increasing access to information and the latest scientific developments to all scientists, innovators, entrepreneurs and students and give them the opportunity to learn, observe and contribute to knowledge creation. Open Science promotes a swifter path from research to innovation to produce new products and services." Alex Lazinica, IntechOpen founder
\n\nIn conclusion, Natalia Reinic Babic, Head of Journal Publishing and Open Science at IntechOpen adds:
\n\n“On behalf of the journal team I’d like to thank all our Editors in Chief, Editorial Boards, internal supporting teams, and our scientific community for their continuous support in making this portfolio a reality - we couldn’t have done it without you! With your support in place, we are confident these journals will become as impactful and successful as our book publishing program and bring us closer to a more open (science) future.”
\n\nWe invite you to visit the journals homepage and learn more about the journal’s Editorial Boards, scope and vision as all three journals are now open for submissions.
\n\nFeel free to share this news on social media and help us mark this memorable moment!
\n\n\n'}],latestNews:[{slug:"intechopen-supports-asapbio-s-new-initiative-publish-your-reviews-20220729",title:"IntechOpen Supports ASAPbio’s New Initiative Publish Your Reviews"},{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"}]},book:{item:{type:"book",id:"3412",leadTitle:null,fullTitle:"Theory and Practice of Cryptography and Network Security Protocols and Technologies",title:"Theory and Practice of Cryptography and Network Security Protocols and Technologies",subtitle:null,reviewType:"peer-reviewed",abstract:"In an age of explosive worldwide growth of electronic data storage and communications, effective protection of information has become a critical requirement. 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Balasubramaniam, Sathiyaraj Thambiayya, Kuru Ratnavelu and JinRong Wang",coverURL:"https://cdn.intechopen.com/books/images_new/10972.jpg",editedByType:"Edited by",editors:[{id:"252215",title:"Dr.",name:"P.",surname:"Balasubramaniam",slug:"p.-balasubramaniam",fullName:"P. Balasubramaniam"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}],publishedBooksByAuthor:[]},onlineFirst:{chapter:{type:"chapter",id:"80787",title:"A Review of Signal Analysis Methods and Their Applications in the Reversible Pump Turbine",doi:"10.5772/intechopen.102883",slug:"a-review-of-signal-analysis-methods-and-their-applications-in-the-reversible-pump-turbine",body:'There are a large number of rotating machineries being intensively employed in the modern industry, such as hydro-turbines, pumps, steam turbines, compressors, and generators. They are widely employed in the power generation, heating, refrigeration, and chemical industries. The rotating machineries often involve extreme conditions such as extremely low load, high temperature, high pressure, high speed, and overload. Hence, the malfunctions may occur after the long-term operation with possibly catastrophic consequences. Therefore, it is very important to understand and monitor the operational states of rotating machineries.
With the developments of data analysis technology, signal analysis methods become gradually mature and have been widely applied in the field of condition monitoring and fault diagnosis of various kinds of rotating machineries. The signal analysis could extract useful information from the original signal, and judge the operational states of the equipment based on the obtained information. Signal analysis is of great significance to the rotating machineries both for condition monitoring and fault diagnosis.
A reversible pump turbine is the core part of the pumped hydro energy storage power station. It can switch between the pumping and the generating mode according to the actual demand. When the reversible pump turbine deviates from the design working condition, it is easy to produce serious pressure pulsation and vibration due to the influence of rotor-stator interaction in the vaneless space and the vortex rope in the draft tube, which will affect the normal operation of the pump turbine and the safety of the whole power station [1, 2, 3]. Therefore, to monitor the operational states of reversible pump turbine and ensure its safety and efficient operation, it is very necessary to employ the signal analysis methods extensively.
This chapter will review several typical signal analysis methods and introduce their applications in the field of the reversible pump turbine in detail through specific cases. This chapter will be divided into the following four parts. The first part will introduce the time-domain analysis methods of the signals. The second part will introduce the time-frequency analysis methods of the signals. The third part will introduce the signal decomposition and the signal de-noising. The fourth part will demonstrate the applications of the introduced signal analysis methods in the field of the reversible pump turbines with the aid of on-site measured signals.
Signals can convey information by expressing the relationship between time and other physical quantities. The time-domain analysis methods of signals refer to a series of processing such as amplification, filtering, statistical feature calculation and correlation analysis in the time domain. Through time-domain analysis methods, the characteristic parameters reflecting the operational state of the mechanical equipment can be extracted from the signal, which could be further employed for the purpose of the evaluation of the operational state and fault diagnosis of the equipment. Time-domain analysis methods could also analyze the auto-correlation (AC) and cross-correlation characteristics of the signal together with the degree of chaos. This section will be divided into three parts including the time domain statistical parameters, the correlation analysis and the chaos evaluation.
The classical time-domain statistical parameters include the peak value, peak-to-peak value, mean value, mean square value, root mean square, variance and standard deviation. The meanings of these statistical parameters are explained in detail below. Here, the
Peak value refers to the maximum amplitude of a signal.
Peak-to-peak value refers to the difference between the maximum value and the minimum value of a signal within a given period. In the practical application, to eliminate the influences of distortion and noise in the signal, the peak-to-peak value is usually calculated by the confidence interval method with employing the confidence coefficient (e.g., 97 or 95%). Then, the peak-to-peak value could be calculated by using the upper and the lower limits of the obtained signal.
The mean value is the averaged value of the whole signal.
Mean square value refers to the mean of the square of the signal, indicating the strength and average power of the signal.
Root mean square refers to the arithmetic square root of the mean square value.
Variance is the average square value of the difference between the amplitude of the original signal and the mean value. It describes the fluctuation range of the signal and represents the strength of the fluctuating component in the signal.
Standard deviation is the arithmetic square root of variance.
When there are multiple signals, sometimes it is necessary to study the relationship between them. Correlation analysis (including auto-correlation function and cross-correlation analysis) could show the similarity or dependence between signals.
The auto-correlation (AC) function describes the correlation of a signal between its values at a certain time and after a certain time delay
The cross-correlation function describes the degree of similarity of the time-domain waveforms of two signals
The higher the
In the practical application of signal correlation analysis, to compare the magnitude of the correlation, we often use the normalized form of the correlation function. The normalized form of the auto-correlation function is defined as follows:
The normalized form of the cross-correlation function is defined as follows:
where
Chaos is an inherent characteristic of a nonlinear dynamic system. When a signal is generated by a nonlinear system, it may show the characteristic of chaos. At this time, the signal waveform is very irregular and is very similar to the characteristic of random noise. Entropy can be adopted to measure the chaos of a nonlinear dynamic system. The more chaotic the system, the higher its entropy. Therefore, we can use entropy to evaluate the degree of chaos in a signal. As a typical method, the permutation entropy for evaluating the degree of chaos in a signal is introduced below [4].
Permutation entropy is an index to measure the complexity of the time series [4]. The more regular the time series is, the smaller the permutation entropy is. The corresponding calculation process is given as follows [4].
Considering a time series
Here, the “
Now, each
Here, the “
There are up to “
When
The variation range of the normalized permutation entropy is between 0 and 1. In the practical application of calculating the permutation entropy of a signal, the whole data points within the signal can be intercepted by a window with a certain length. And the permutation entropy of the data points in each window is calculated by moving the window one data at a time. In the present chapter, the average permutation entropy of all the windows will be taken as the permutation entropy of the whole signal.
In this section, the pressure pulsation signal (the pressure pulsation signal 1) in the vaneless space of a prototype pump turbine under the dimensionless load condition (the ratio of the actual operational load to the rated load)
The time-domain diagram of the dimensionless pressure pulsation signal 1.
The statistical parameters of the dimensionless pressure pulsation signal 1 in the time domain are shown in the Table 1 as follows:
Statistical parameter | Value |
---|---|
Peak value | 0.9492 |
Peak-to-peak value (confidence coefficient 97%) | 0.0911 |
Peak-to-peak value (confidence coefficient 95%) | 0.0883 |
Mean | 0.9081 |
Mean square value | 0.8252 |
Root mean square | 0.9084 |
Variance | 0.0006 |
Standard deviation | 0.0244 |
The time-domain statistical parameters of the dimensionless pressure pulsation signal 1.
Figures 2 and 3 show the normalized AC functions of the dimensionless pressure pulsation signal 1 and a random noise signal. When
The normalized AC function of the dimensionless pressure pulsation signal 1.
The normalized AC function of the random noise.
In the experiment, another dimensionless pressure pulsation signal (the dimensionless pressure pulsation signal 2) was measured in the vaneless space at the same operational condition (
The time-domain diagram of the dimensionless pressure pulsation signal 2.
When the delay time
In the present chapter, the embedded dimension, delay time and window length required in the analysis procedure of the permutation entropy are 6, 3 and 128, respectively. The analysis results of the permutation entropy of the dimensionless pressure pulsation signal 1, the dimensionless pressure pulsation signal 2 and the random noise signal are as follows:
It can be seen from the Table 2 that the analysis results of the permutation entropy of the dimensionless pressure pulsation signals 1 and 2 are smaller than that of the random noise, because the dimensionless pressure pulsation signals contain certain characteristic frequencies with regularity. The permutation entropy of the random noise is large, which indicates its high degree of chaos.
Signal | Permutation entropy |
---|---|
Dimensionless pressure pulsation signal 1 | 0.4254 |
Dimensionless pressure pulsation signal 2 | 0.4271 |
Random noise | 0.5666 |
Analysis results of permutation entropy of different types of signals.
The aim of the time-frequency analysis is to study the time-varying signals, which can reflect the relationship of the variations of the frequency and amplitude with time within the signal. Generally speaking, the typical time-frequency analysis methods include the short-time Fourier transform [5], the Hilbert-Huang transform [6] and the VMD-Hilbert transform with a detailed introduction in this section.
Before introducing the time-frequency analysis methods, the traditional Fourier transform is:
where
The formula of the short-time Fourier Transform (STFT) is presented above [5]. Compared with the traditional Fourier transform, the window function is added to the formula. STFT needs to select a window function, which can be moved continuously to identify the frequencies at different times. The window function will affect the resolution of the spectrum. The smaller the window size, the higher the time resolution of the spectrum. Normally, the low frequency band requires high frequency resolution and low time resolution. And, the high frequency band requires low frequency resolution and high time resolution. However, after the window function is selected, the resolution of the entire spectrum is fixed and cannot be adjusted. Therefore, STFT is suitable for analyzing the segmented stationary signals or approximately stationary signals.
Hilbert-Huang transform (HHT) consists of two parts including the empirical mode decomposition (EMD) and the Hilbert spectrum analysis (HSA). These two parts will be introduced in detail below.
The EMD method can decompose the original signal into several intrinsic mode functions (IMFs) and the residual component
Firstly, calculating all the extreme points of the signal
Secondly, calculating the mean curve
Thirdly, the screening stop condition is given as [6]:
Here, the “
Fourthly, repeating the above steps to obtain all the remaining IMF until the residual component
Although, the EMD can realize signal decomposition, it has the problems of endpoint effect and mode mixing. Endpoint effect means that analysis results at the signal endpoints will produce errors, which will affect the internal data. Mode mixing means that the EMD method cannot effectively separate different mode components based on the time characteristic scales. These problems can lead to serious performance degradation of EMD methods.
HSA performs a Hilbert transform on each IMF obtained by EMD. The specific steps are given as follows:
Firstly, performing the Hilbert transform on each IMF
Here,
Secondly, using the above formula to construct the analytical signal
Here [6],
Thirdly, the instantaneous frequency of each IMF can be obtained from the following formula [6]:
After expressing the results on the time-frequency plane, the Hilbert amplitude spectrum of each IMF
Fourthly, by presenting the amplitude spectrum of all IMFs in a spectrum, the Hilbert spectrum of the original signal can be obtained as follows [6]:
The advantage of HHT is that it is not restricted by the linear and stationary characteristics of the signal and can analyze non-linear and non-stationary signals. At the same time, the HHT is self-adaptive through using the EMD to eliminate the choice of basis function. In addition, HHT is not restricted by Heisenberg’s uncertainty principle.
The principle of VMD-Hilbert transform is similar to HHT. It also uses the signal decomposition method to obtain the IMF component of the signal, and then obtains the Hilbert spectrum of the signal through HAS. The difference between these two is that the variational mode decomposition (VMD) [7] is adopted as the signal decomposition method.
The core idea of the VMD method is to construct and solve the variational optimization problem in the frequency domain. The specific steps include the following ones.
Firstly, decomposing the original signal into
Here,
Secondly, the constraints are added to ensure that the sum of the estimated bandwidths of each IMF is minimum and the sum of
Here,
Thirdly, the Lagrange multiplication operator is introduced to transform the constrained variational problem into an unconstrained variational problem. And an augmented Lagrange expression is obtained as follows [7]:
Here,
Fourthly, the alternating direction multiplier method is used to iteratively solve IMF
Here, “^” refers to the Fourier transform operation. The “
When the calculation result meets the given solution accuracy
Fifthly, the
Compared with the EMD, the VMD overcomes the problem of mode mixing and has a more solid mathematical theoretical foundation.
In the present chapter, the “
This section uses the vibration signal at the top cover of a prototype reversible pump turbine during the start-up transient process of generating mode as the analysis object to demonstrate the applications of the time-frequency analysis methods introduced in Sections 3.1 and 3.2. The length of the signal is 2 s and the sampling frequency is 1000 Hz. Figure 5 shows the time-domain diagram of the vibration signal at the top cover.
The time-domain diagram of the vibration signal at the top cover.
Figure 6 shows the result of the fast Fourier transform (FFT) of the vibration signal. It can be seen from the Figure 6 that the most obvious frequency component in the vibration signal is 40 Hz with an amplitude of about 0.46 mm. In addition, there is also a relatively obvious peak near 80 Hz with an amplitude of about 0.17 mm. However, Figure 6 cannot provide information about the amplitude variation of different frequency components with time, which is a shortcoming of the traditional Fourier transform.
The FFT spectrum of the vibration signal.
Figures 7 and 8 are the STFT results of the vibration signal with the window length of 64 and 256 respectively. From these two figures, not only the amplitudes of different frequency components in the vibration signals but also the variations of the amplitude of each frequency varying with the time can be seen. However, the STFT results are significantly affected by the window size. By comparing Figures 7 and 8, it can be concluded that there exist obvious amplitudes at and around 40 Hz in the Figure 7, while it almost has obvious amplitudes only at 40 Hz in the Figure 8, which is caused by the difference in the window size between them. In the actual application of the STFT, it is difficult to choose the appropriate window size, which is the main problem of this method.
The STFT result of the vibration signal with the window length of 64 and the window function of Blackman.
The STFT result of the vibration signal with the window length of 256 and the window function of Blackman.
Different with the fixed basis functions of traditional Fourier transform and STFT, HHT and VMD-Hilbert transform can obtain basis functions through adaptive mode decomposition. In addition, HHT and VMD-Hilbert transform do not need to select the window functions and they can achieve high resolutions both in time and frequency domains. Figures 9 and 10 are the results of the HHT and VMD-Hilbert transform of the signal, respectively. It can be found that the analysis result in the Figure 9 is unclear because the dominant frequencies of the signal cannot be clearly observed, while the analysis result in the Figure 10 is very clear. This can be attributed to that the VMD overcomes the mode mixing problem in the EMD adopted in the HHT.
The analysis result of HHT of the vibration signal.
The analysis result of VMD-Hilbert transform of the vibration signal.
Mode refers to the natural vibration characteristics of the mechanical structure, which is composed of natural frequency, damping ratio and mode shape. In practical engineering, the vibration of rotating machinery is often composed of different complex modes. The process of decomposing the vibration signal into sub-signals with different frequency bands on the basis of different modes is called the mode decomposition of the signal. Through the mode decomposition, the modes containing noise can be found and will be further deleted through the signal de-noising. In our previous research [8], similar methods with those shown in this section have been adopted to analyze the vibration signal from the rotating machinery of nuclear power. However, the specific frequency components between the signals between the rotating machinery of nuclear power and the reversible pump turbine are a little different.
Typical signal analysis methods, such as EMD and VMD, have been fully described in Section 3.2. In this section, a simulated swing signal at the turbine guide bearing of a prototype pump turbine will be used to demonstrate the signal de-noising effects based on these two methods. The simulated rotational speed of the unit is 500 rpm. The characteristic frequencies contained in the simulated signal are 1 time, 2 times and 3 times, the rotational frequency of the impeller with the corresponding amplitudes of 10, 2 and 1 μm, respectively. The signal is dimensionless by dividing the corresponding amplitude of the rotational frequency of the impeller (10 μm). The length of the signal is 2 s and the sampling frequency is 1000 Hz. The white noise with the signal-noise ratio (
The time-domain diagrams of simulated swing signals. (a) The simulated swing signal without noise. (b) The added noise with the
The EMD analysis results of the simulated swing signal.
The VMD analysis results of the simulated swing signal.
Through mode decomposition, several IMFs are obtained to further constitute the given signal with low noise. The IMFs contain different frequency bands. By removing the IMFs dominated by the random noises and retaining the IMFs dominated by the useful signals, the signal de-noising will be proceeded. Generally speaking, the cross-correlation coefficient between the noise-dominated IMF and the original signal is small, while the cross-correlation coefficient between the IMF component dominated by the useful signal and the original signal is relatively large. Based on this principle, the noise-dominated IMFs can be identified according to the cross-correlation coefficients.
Table 3 shows the cross-correlation coefficients of the IMFs obtained by EMD and VMD and the simulated swing signal. Here, the threshold of the cross-correlation coefficient is set to 0.12 [8]. And the IMFs with the cross-correlation coefficients larger than this threshold are retained.
Mode component | EMD | VMD |
---|---|---|
IMF1 | 0.0757 | 0.9732 |
IMF2 | 0.0537 | 0.2378 |
IMF3 | 0.7777 | 0.0453 |
IMF4 | 0.3762 | 0.0430 |
IMF5 | 0.0067 | 0.0407 |
IMF6 | 0.0242 | 0.0412 |
IMF7 | 0.0349 | 0.0400 |
residual | 0.0103 | — |
Cross-correlation coefficients of IMFs based on EMD and VMD and the simulated swing signal.
Based on the analysis results in Table 3, the IMF3 and IMF4 in the EMD analysis results and the IMF1 and IMF2 in the VMD analysis results are selected to reconstruct the de-noised swing signals, respectively. Figure 14 shows the de-noising results of the simulated swing signal based on EMD and VMD, respectively.
The de-noising results of the simulated swing signal based on EMD and VMD. (a) The simulated swing signal without noise. (b) The de-noised signal is based on EMD. (c) The de-noised signal is based on VMD.
Figure 14 shows that both the de-noised signals based on EMD and VMD are relatively smooth oscillation curves. Compared with the simulated swing signal without noise, the de-noised signal based on EMD has some abnormal spikes or irregularities as indicated by the red circles in the Figure 14b, while the time-domain waveform of the de-noised signal based on VMD is much closer to the simulated swing signal without noise, which indicates that the signal de-noising based on VMD has a better effect than that based on EMD.
The evaluation indexes of signal de-noising can help us quantitatively analyze the de-noising effects. Some evaluation indexes will be introduced below. Here
The larger the SNR, the better the de-noising effect.
The larger the
The smaller the RMSE, the better the de-noising effect.
The evaluation indexes of signal de-noising effects based on EMD and VMD are calculated respectively and the results are shown in Table 4.
Evaluation index | EMD | VMD |
---|---|---|
12.1328 | 29.5778 | |
0.969 | 0.9995 | |
0.1856 | 0.024 |
The evaluation indexes of de-noising for the simulated swing signal based on EMD and VMD.
It can be seen from Table 4 that compared with the results of signal de-noising based on EMD, the results of signal de-noising based on VMD have a larger
In Sections 4.1–4.3, the simulated swing signal is adopted to demonstrate the signal decomposition and the signal de-noising processes. In this section, the analysis results of mode decomposition and de-noising of measured swing signal of a prototype pump turbine are presented. The swing signal at the turbine guide bearing of a pump turbine was measured at
The time-domain diagram of the measured swing signal.
The EMD and VMD analysis results of the measured swing signal are shown in Figures 16 and 17.
The EMD analysis results of the measured swing signal.
The VMD analysis results of the measured swing signal.
The cross-correlation coefficients of the IMFs obtained by EMD and VMD and the measured swing signal are shown in Table 5.
Mode component | EMD | VMD |
---|---|---|
IMF1 | 0.0635 | 0.9471 |
IMF2 | 0.0797 | 0.4739 |
IMF3 | 0.7677 | 0.1096 |
IMF4 | 0.2904 | 0.0323 |
IMF5 | 0.0500 | 0.0321 |
IMF6 | 0.0715 | 0.0200 |
IMF7 | 0.1455 | 0.0154 |
residual | 0.0444 | — |
The cross-correlation coefficients of the IMFs based on EMD and VMD and the measured swing signal.
The threshold of the cross-correlation coefficient is also set to 0.12 [8]. Based on the analysis results in Table 5, the IMF3, IMF4, and IMF7 in the EMD analysis results and the IMF1 and IMF2 in the VMD analysis result are selected to reconstruct the de-noised swing signals, respectively. Figure 18 shows the de-noising results of the measured swing signal based on EMD and VMD, respectively. As shown in Figure 18, the VMD shows a better de-noising effect by comparing the smoothness of the curves.
The de-noising results of the measured swing signal is based on EMD and VMD. (a) The original measured swing signal. (b) The de-noised signal is based on the EMD. (c) The de-noised signal based on VMD.
In summary, signal analysis and processing can help us better understand the operational states of the reversible pump turbines. Through the time-domain analysis, the time-domain characteristic parameters such as peak value, peak-to-peak value and average value can be obtained. Through the time-frequency analysis, the time-frequency variation characteristics of signals can be grasped. Through the mode decomposition of the signal, various components within the signal can be distinguished and the signal de-noising can be further performed. Therefore, signal analysis plays a very important role for the condition monitoring and fault diagnosis of the reversible pump turbines.
This work was financially supported by the National Natural Science Foundation of China (Project Nos.: U1965106, 51976056 and 52076215).
Edited by Jan Oxholm Gordeladze, ISBN 978-953-51-3020-8, Print ISBN 978-953-51-3019-2, 336 pages,
\nPublisher: IntechOpen
\nChapters published March 22, 2017 under CC BY 3.0 license
\nDOI: 10.5772/61430
\nEdited Volume
This book serves as a comprehensive survey of the impact of vitamin K2 on cellular functions and organ systems, indicating that vitamin K2 plays an important role in the differentiation/preservation of various cell phenotypes and as a stimulator and/or mediator of interorgan cross talk. Vitamin K2 binds to the transcription factor SXR/PXR, thus acting like a hormone (very much in the same manner as vitamin A and vitamin D). Therefore, vitamin K2 affects a multitude of organ systems, and it is reckoned to be one positive factor in bringing about "longevity" to the human body, e.g., supporting the functions/health of different organ systems, as well as correcting the functioning or even "curing" ailments striking several organs in our body.
\\n\\nChapter 1 Introductory Chapter: Vitamin K2 by Jan Oxholm Gordeladze
\\n\\nChapter 2 Vitamin K, SXR, and GGCX by Kotaro Azuma and Satoshi Inoue
\\n\\nChapter 3 Vitamin K2 Rich Food Products by Muhammad Yasin, Masood Sadiq Butt and Aurang Zeb
\\n\\nChapter 4 Menaquinones, Bacteria, and Foods: Vitamin K2 in the Diet by Barbara Walther and Magali Chollet
\\n\\nChapter 5 The Impact of Vitamin K2 on Energy Metabolism by Mona Møller, Serena Tonstad, Tone Bathen and Jan Oxholm Gordeladze
\\n\\nChapter 6 Vitamin K2 and Bone Health by Niels Erik Frandsen and Jan Oxholm Gordeladze
\\n\\nChapter 7 Vitamin K2 and its Impact on Tooth Epigenetics by Jan Oxholm Gordeladze, Maria A. Landin, Gaute Floer Johnsen, Håvard Jostein Haugen and Harald Osmundsen
\\n\\nChapter 8 Anti-Inflammatory Actions of Vitamin K by Stephen J. Hodges, Andrew A. Pitsillides, Lars M. Ytrebø and Robin Soper
\\n\\nChapter 9 Vitamin K2: Implications for Cardiovascular Health in the Context of Plant-Based Diets, with Applications for Prostate Health by Michael S. Donaldson
\\n\\nChapter 11 Vitamin K2 Facilitating Inter-Organ Cross-Talk by Jan O. Gordeladze, Håvard J. Haugen, Gaute Floer Johnsen and Mona Møller
\\n\\nChapter 13 Medicinal Chemistry of Vitamin K Derivatives and Metabolites by Shinya Fujii and Hiroyuki Kagechika
\\n"}]'},components:[{type:"htmlEditorComponent",content:'This book serves as a comprehensive survey of the impact of vitamin K2 on cellular functions and organ systems, indicating that vitamin K2 plays an important role in the differentiation/preservation of various cell phenotypes and as a stimulator and/or mediator of interorgan cross talk. Vitamin K2 binds to the transcription factor SXR/PXR, thus acting like a hormone (very much in the same manner as vitamin A and vitamin D). Therefore, vitamin K2 affects a multitude of organ systems, and it is reckoned to be one positive factor in bringing about "longevity" to the human body, e.g., supporting the functions/health of different organ systems, as well as correcting the functioning or even "curing" ailments striking several organs in our body.
\n\nChapter 1 Introductory Chapter: Vitamin K2 by Jan Oxholm Gordeladze
\n\nChapter 2 Vitamin K, SXR, and GGCX by Kotaro Azuma and Satoshi Inoue
\n\nChapter 3 Vitamin K2 Rich Food Products by Muhammad Yasin, Masood Sadiq Butt and Aurang Zeb
\n\nChapter 4 Menaquinones, Bacteria, and Foods: Vitamin K2 in the Diet by Barbara Walther and Magali Chollet
\n\nChapter 5 The Impact of Vitamin K2 on Energy Metabolism by Mona Møller, Serena Tonstad, Tone Bathen and Jan Oxholm Gordeladze
\n\nChapter 6 Vitamin K2 and Bone Health by Niels Erik Frandsen and Jan Oxholm Gordeladze
\n\nChapter 7 Vitamin K2 and its Impact on Tooth Epigenetics by Jan Oxholm Gordeladze, Maria A. Landin, Gaute Floer Johnsen, Håvard Jostein Haugen and Harald Osmundsen
\n\nChapter 8 Anti-Inflammatory Actions of Vitamin K by Stephen J. Hodges, Andrew A. Pitsillides, Lars M. Ytrebø and Robin Soper
\n\nChapter 9 Vitamin K2: Implications for Cardiovascular Health in the Context of Plant-Based Diets, with Applications for Prostate Health by Michael S. Donaldson
\n\nChapter 11 Vitamin K2 Facilitating Inter-Organ Cross-Talk by Jan O. Gordeladze, Håvard J. Haugen, Gaute Floer Johnsen and Mona Møller
\n\nChapter 13 Medicinal Chemistry of Vitamin K Derivatives and Metabolites by Shinya Fujii and Hiroyuki Kagechika
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Hatch",authors:[{id:"130802",title:"Dr.",name:"Grant",middleName:null,surname:"Hatch",slug:"grant-hatch",fullName:"Grant Hatch"},{id:"170126",title:"Prof.",name:"Donald",middleName:null,surname:"Miller",slug:"donald-miller",fullName:"Donald Miller"},{id:"170127",title:"Mr.",name:"Michael",middleName:null,surname:"Pogorzelec",slug:"michael-pogorzelec",fullName:"Michael Pogorzelec"},{id:"170128",title:"Ms.",name:"Hieu",middleName:null,surname:"Nguyen",slug:"hieu-nguyen",fullName:"Hieu Nguyen"},{id:"170129",title:"Dr.",name:"Ngoc",middleName:null,surname:"On",slug:"ngoc-on",fullName:"Ngoc On"},{id:"170130",title:"Dr.",name:"Siddhartha",middleName:null,surname:"Dalvi",slug:"siddhartha-dalvi",fullName:"Siddhartha Dalvi"}]},{id:"62431",doi:"10.5772/intechopen.79266",title:"The United Chemicals of Cannabis: Beneficial Effects of Cannabis Phytochemicals on the Brain and Cognition",slug:"the-united-chemicals-of-cannabis-beneficial-effects-of-cannabis-phytochemicals-on-the-brain-and-cogn",totalDownloads:1841,totalCrossrefCites:4,totalDimensionsCites:12,abstract:"‘Medicinal cannabis’ can be defined as pharmaceutical grade cannabis-based products used for the treatment of illness. Beneficial treatment effects of cannabidiol (CBD), a major non-intoxicating compound isolated from the cannabis plant, have been shown in multiple states of cognitive impairment, including neurodegenerative (Alzheimer’s, Huntington’s and Parkinson’s disease), neuroinflammatory (sepsis-induced encephalopathy) and neurological disorders (ischemic brain injury). CBD can also treat some of the symptoms of schizophrenia, including cognitive deficits (impairments in learning and memory), which is a major symptom domain of the illness that is largely resistant to existing antipsychotic medications. However, empirical evidence suggests the presence of an ‘entourage effect’ in cannabis; that is, observations that medicinal cannabis seems to work better in some instances when administered as a whole-plant extract. While scientific evidence highlights isolated CBD as a strong candidate for treating cognitive impairment, the entourage effect suggests that the co-operation of other plant molecules could provide further benefits. This chapter explores the scientific evidence surrounding the benefits of CBD and other specific key phytochemicals in cannabis: linalool, α-pinene, β-caryophyllene, flavonoids and anthocyanin, on brain health and cognition.",book:{id:"7040",slug:"recent-advances-in-cannabinoid-research",title:"Recent Advances in Cannabinoid Research",fullTitle:"Recent Advances in Cannabinoid Research"},signatures:"Katrina Weston-Green",authors:null},{id:"64031",doi:"10.5772/intechopen.81224",title:"Trends of Protein Aggregation in Neurodegenerative Diseases",slug:"trends-of-protein-aggregation-in-neurodegenerative-diseases",totalDownloads:1622,totalCrossrefCites:6,totalDimensionsCites:11,abstract:"Protein aggregation trends in neurodegenerative diseases are largely unmapped due to the complex nature of protein-protein interactions and their regulatory machineries such as protein proteolytic systems. Since the protein aggregation process in humans is a slow process, early determination of the patients that will develop neurodegenerative diseases later in life is critical in terms of starting effective treatment, which will reduce the expensive health care. In this chapter, I will discuss the nature of protein aggregation of signature proteins and the status of protein proteolytic systems such as proteasome and autophagosome in Alzheimer’s disease, Parkinson’s disease, amyotrophic lateral sclerosis, frontotemporal lobar degeneration, Huntington’s disease, and prion disease under the light of recent studies including our new findings.",book:{id:"7480",slug:"neurochemical-basis-of-brain-function-and-dysfunction",title:"Neurochemical Basis of Brain Function and Dysfunction",fullTitle:"Neurochemical Basis of Brain Function and Dysfunction"},signatures:"Abdulbaki Agbas",authors:[{id:"250609",title:"Prof.",name:"Abdulbaki",middleName:null,surname:"Agbas",slug:"abdulbaki-agbas",fullName:"Abdulbaki Agbas"}]}],mostDownloadedChaptersLast30Days:[{id:"57103",title:"GABA and Glutamate: Their Transmitter Role in the CNS and Pancreatic Islets",slug:"gaba-and-glutamate-their-transmitter-role-in-the-cns-and-pancreatic-islets",totalDownloads:3565,totalCrossrefCites:4,totalDimensionsCites:10,abstract:"Glutamate and gamma-aminobutyric acid (GABA) are the major neurotransmitters in the mammalian brain. Inhibitory GABA and excitatory glutamate work together to control many processes, including the brain’s overall level of excitation. The contributions of GABA and glutamate in extra-neuronal signaling are by far less widely recognized. In this chapter, we first discuss the role of both neurotransmitters during development, emphasizing the importance of the shift from excitatory to inhibitory GABAergic neurotransmission. The second part summarizes the biosynthesis and role of GABA and glutamate in neurotransmission in the mature brain, and major neurological disorders associated with glutamate and GABA receptors and GABA release mechanisms. The final part focuses on extra-neuronal glutamatergic and GABAergic signaling in pancreatic islets of Langerhans, and possible associations with type 1 diabetes mellitus.",book:{id:"6237",slug:"gaba-and-glutamate-new-developments-in-neurotransmission-research",title:"GABA And Glutamate",fullTitle:"GABA And Glutamate - New Developments In Neurotransmission Research"},signatures:"Christiane S. Hampe, Hiroshi Mitoma and Mario Manto",authors:[{id:"210220",title:"Prof.",name:"Christiane",middleName:null,surname:"Hampe",slug:"christiane-hampe",fullName:"Christiane Hampe"},{id:"210485",title:"Prof.",name:"Mario",middleName:null,surname:"Manto",slug:"mario-manto",fullName:"Mario Manto"},{id:"210486",title:"Prof.",name:"Hiroshi",middleName:null,surname:"Mitoma",slug:"hiroshi-mitoma",fullName:"Hiroshi Mitoma"}]},{id:"58817",title:"Clinical Application of MR Spectroscopy in Identifying Biochemical Composition of the Intracranial Pathologies",slug:"clinical-application-of-mr-spectroscopy-in-identifying-biochemical-composition-of-the-intracranial-p",totalDownloads:2097,totalCrossrefCites:0,totalDimensionsCites:5,abstract:"Magnetic resonance spectroscopy (MRS) provides useful information regarding metabolic composition in the tissues, and advanced spectroscopic methods are used to quantify markers of tumor membrane turnover and proliferation (e.g., choline (Cho)), energy homoeostasis (e.g., creatine (Cr)), intact glioneuronal structures (e.g., N-acetylaspartate (NAA)), and necrosis (e.g., lactate (Lac) or lipids). Results are usually expressed as metabolite ratios rather than absolute metabolite concentrations. Because glial tumors have some specific metabolic characteristics that differ according to the grade of tumor, there is a potential for MR spectroscopy to increase the sensitivity of routinely used diagnostic imaging. MRS also has many diagnostic applications in neurosciences to support the diagnosis in conditions like demyelination, infections, and dementia and in postradiotherapy cases. Biochemical changes in the metabolism of tumor cells related to malignant transformation are reflected in changes of particular metabolite concentration in the tumor tissue. Our prospective study aimed to analyze the usefulness of proton MR spectroscopy in grading of glioma and to correlate various metabolite ratios like choline/creatine, choline/N-acetylaspartate, N-acetylaspartate/creatine, and lactate/creatine with the histopathological grades of glioma.",book:{id:"6237",slug:"gaba-and-glutamate-new-developments-in-neurotransmission-research",title:"GABA And Glutamate",fullTitle:"GABA And Glutamate - New Developments In Neurotransmission Research"},signatures:"B C Hamsini, Bhavana Nagabhushana Reddy, Sankar Neelakantan\nand Sunitha Palasamudram Kumaran",authors:[{id:"211054",title:"Dr.",name:"Sunitha",middleName:null,surname:"P Kumaran",slug:"sunitha-p-kumaran",fullName:"Sunitha P Kumaran"},{id:"221485",title:"Dr.",name:"Sankar",middleName:null,surname:"Neelakantan",slug:"sankar-neelakantan",fullName:"Sankar Neelakantan"},{id:"398223",title:"Dr.",name:"B C",middleName:null,surname:"Hamsini",slug:"b-c-hamsini",fullName:"B C Hamsini"},{id:"398224",title:"Dr.",name:"Bhavana",middleName:null,surname:"Nagabhushana Reddy",slug:"bhavana-nagabhushana-reddy",fullName:"Bhavana Nagabhushana Reddy"}]},{id:"62431",title:"The United Chemicals of Cannabis: Beneficial Effects of Cannabis Phytochemicals on the Brain and Cognition",slug:"the-united-chemicals-of-cannabis-beneficial-effects-of-cannabis-phytochemicals-on-the-brain-and-cogn",totalDownloads:1845,totalCrossrefCites:4,totalDimensionsCites:12,abstract:"‘Medicinal cannabis’ can be defined as pharmaceutical grade cannabis-based products used for the treatment of illness. Beneficial treatment effects of cannabidiol (CBD), a major non-intoxicating compound isolated from the cannabis plant, have been shown in multiple states of cognitive impairment, including neurodegenerative (Alzheimer’s, Huntington’s and Parkinson’s disease), neuroinflammatory (sepsis-induced encephalopathy) and neurological disorders (ischemic brain injury). CBD can also treat some of the symptoms of schizophrenia, including cognitive deficits (impairments in learning and memory), which is a major symptom domain of the illness that is largely resistant to existing antipsychotic medications. However, empirical evidence suggests the presence of an ‘entourage effect’ in cannabis; that is, observations that medicinal cannabis seems to work better in some instances when administered as a whole-plant extract. While scientific evidence highlights isolated CBD as a strong candidate for treating cognitive impairment, the entourage effect suggests that the co-operation of other plant molecules could provide further benefits. This chapter explores the scientific evidence surrounding the benefits of CBD and other specific key phytochemicals in cannabis: linalool, α-pinene, β-caryophyllene, flavonoids and anthocyanin, on brain health and cognition.",book:{id:"7040",slug:"recent-advances-in-cannabinoid-research",title:"Recent Advances in Cannabinoid Research",fullTitle:"Recent Advances in Cannabinoid Research"},signatures:"Katrina Weston-Green",authors:null},{id:"68776",title:"Introductory Chapter: The Chemical Basis of Neural Function and Dysfunction",slug:"introductory-chapter-the-chemical-basis-of-neural-function-and-dysfunction",totalDownloads:1133,totalCrossrefCites:1,totalDimensionsCites:2,abstract:null,book:{id:"7480",slug:"neurochemical-basis-of-brain-function-and-dysfunction",title:"Neurochemical Basis of Brain Function and Dysfunction",fullTitle:"Neurochemical Basis of Brain Function and Dysfunction"},signatures:"Thomas Heinbockel and Antonei B. Csoka",authors:[{id:"70569",title:"Dr.",name:"Thomas",middleName:null,surname:"Heinbockel",slug:"thomas-heinbockel",fullName:"Thomas Heinbockel"},{id:"245650",title:"Dr.",name:"Antonei B.",middleName:null,surname:"Csoka",slug:"antonei-b.-csoka",fullName:"Antonei B. Csoka"}]},{id:"68712",title:"Synaptic Transmission and Amino Acid Neurotransmitters",slug:"synaptic-transmission-and-amino-acid-neurotransmitters",totalDownloads:1383,totalCrossrefCites:6,totalDimensionsCites:7,abstract:"Amino acids are the most abundant neurotransmitters in the brain. Neurotransmitters are synthesized and stored in presynaptic terminals, released from terminals upon stimulation with specific receptors on the postsynaptic cells. Chemical and electrical synapses are specialized biological structures found in the nervous system; they connect neurons together and transmit signals across the neurons. The process of synaptic transmission generates or inhibits electrical impulses in a network of neurons for the processing of information. Glutamate is the primary excitatory neurotransmitter in the brain, while GABA is the principal inhibitory neurotransmitter. The balance of glutamatergic and GABAergic tone is crucial to normal neurologic function. Through synaptic transmission, this information is communicated from the presynaptic cell to the postsynaptic cell. Amino acid neurotransmitters primarily glutamic acid, GABA, aspartic acid, and glycine are single amino acid residues released from presynaptic nerve terminals in response to an action potential and cross the synaptic cleft to bind with specific receptor on the postsynaptic membrane. The integral role of amino acid neurotransmitters is important on the normal functioning of the brain. The presynaptic and postsynaptic events in chemical synapses are subject to use dependent and highly regulated as per the changes in synaptic neurotransmitter release and function.",book:{id:"7480",slug:"neurochemical-basis-of-brain-function-and-dysfunction",title:"Neurochemical Basis of Brain Function and Dysfunction",fullTitle:"Neurochemical Basis of Brain Function and Dysfunction"},signatures:"Manorama Patri",authors:[{id:"196763",title:"Dr.",name:"Manorama",middleName:null,surname:"Patri",slug:"manorama-patri",fullName:"Manorama Patri"}]}],onlineFirstChaptersFilter:{topicId:"212",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:90,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:107,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:33,numberOfPublishedChapters:330,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:14,numberOfPublishedChapters:145,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:9,numberOfPublishedChapters:139,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:122,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:112,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:21,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:10,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:"2753-6580",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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",coverUrl:"https://cdn.intechopen.com/series/covers/3.jpg",latestPublicationDate:"August 4th, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:9,editor:{id:"419588",title:"Ph.D.",name:"Sergio",middleName:"Alexandre",surname:"Gehrke",slug:"sergio-gehrke",fullName:"Sergio Gehrke",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000038WgMKQA0/Profile_Picture_2022-06-02T11:44:20.jpg",biography:"Dr. Sergio Alexandre Gehrke is a doctorate holder in two fields. The first is a Ph.D. in Cellular and Molecular Biology from the Pontificia Catholic University, Porto Alegre, Brazil, in 2010 and the other is an International Ph.D. in Bioengineering from the Universidad Miguel Hernandez, Elche/Alicante, Spain, obtained in 2020. In 2018, he completed a postdoctoral fellowship in Materials Engineering in the NUCLEMAT of the Pontificia Catholic University, Porto Alegre, Brazil. He is currently the Director of the Postgraduate Program in Implantology of the Bioface/UCAM/PgO (Montevideo, Uruguay), Director of the Cathedra of Biotechnology of the Catholic University of Murcia (Murcia, Spain), an Extraordinary Full Professor of the Catholic University of Murcia (Murcia, Spain) as well as the Director of the private center of research Biotecnos – Technology and Science (Montevideo, Uruguay). Applied biomaterials, cellular and molecular biology, and dental implants are among his research interests. He has published several original papers in renowned journals. In addition, he is also a Collaborating Professor in several Postgraduate programs at different universities all over the world.",institutionString:null,institution:{name:"Universidad Católica San Antonio de Murcia",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},subseries:{paginationCount:2,paginationItems:[{id:"1",title:"Oral Health",coverUrl:"https://cdn.intechopen.com/series_topics/covers/1.jpg",isOpenForSubmission:!0,editor:{id:"173955",title:"Prof.",name:"Sandra",middleName:null,surname:"Marinho",slug:"sandra-marinho",fullName:"Sandra Marinho",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRGYMQA4/Profile_Picture_2022-06-01T13:22:41.png",biography:"Dr. Sandra A. Marinho is an Associate Professor and Brazilian researcher at the State University of Paraíba (Universidade Estadual da Paraíba- UEPB), Campus VIII, located in Araruna, state of Paraíba since 2011. She holds a degree in Dentistry from the Federal University of Alfenas (UNIFAL), while her specialization and professional improvement in Stomatology took place at Hospital Heliopolis (São Paulo, SP). Her qualifications are: a specialist in Dental Imaging and Radiology, Master in Dentistry (Periodontics) from the University of São Paulo (FORP-USP, Ribeirão Preto, SP), and Doctor (Ph.D.) in Dentistry (Stomatology Clinic) from Hospital São Lucas of the Pontifical Catholic University of Rio Grande do Sul (HSL-PUCRS, Porto Alegre, RS). She held a postdoctoral internship at the Federal University from Jequitinhonha and Mucuri Valleys (UFVJM, Diamantina, MG). She is currently a member of the Brazilian Society for Dental Research (SBPqO) and the Brazilian Society of Stomatology and Pathology (SOBEP). Dr. Marinho's experience in Dentistry mainly covers the following subjects: oral diagnosis, oral radiology; oral medicine; lesions and oral infections; oral pathology, laser therapy and epidemiological studies.",institutionString:null,institution:{name:"State University of Paraíba",institutionURL:null,country:{name:"Brazil"}}},editorTwo:null,editorThree:null},{id:"2",title:"Prosthodontics and Implant Dentistry",coverUrl:"https://cdn.intechopen.com/series_topics/covers/2.jpg",isOpenForSubmission:!0,editor:{id:"179568",title:"Associate Prof.",name:"Wen Lin",middleName:null,surname:"Chai",slug:"wen-lin-chai",fullName:"Wen Lin Chai",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRHGAQA4/Profile_Picture_2022-05-23T14:31:12.png",biography:"Professor Dr. Chai Wen Lin is currently a lecturer at the Department of Restorative Dentistry, Faculty of Dentistry of the University of Malaya. She obtained a Master of Dental Science in 2006 and a Ph.D. in 2011. Her Ph.D. research work on the soft tissue-implant interface at the University of Sheffield has yielded several important publications in the key implant journals. She was awarded an Excellent Exchange Award by the University of Sheffield which gave her the opportunity to work at the famous Faculty of Dentistry of the University of Gothenburg, Sweden, under the tutelage of Prof. Peter Thomsen. In 2016, she was appointed as a visiting scholar at UCLA, USA, with attachment in Hospital Dentistry, and involvement in research work related to zirconia implant. In 2016, her contribution to dentistry was recognized by the Royal College of Surgeon of Edinburgh with her being awarded a Fellowship in Dental Surgery. She has authored numerous papers published both in local and international journals. She was the Editor of the Malaysian Dental Journal for several years. Her main research interests are implant-soft tissue interface, zirconia implant, photofunctionalization, 3D-oral mucosal model and pulpal regeneration.",institutionString:null,institution:{name:"University of Malaya",institutionURL:null,country:{name:"Malaysia"}}},editorTwo:{id:"479686",title:"Dr.",name:"Ghee Seong",middleName:null,surname:"Lim",slug:"ghee-seong-lim",fullName:"Ghee Seong Lim",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003ScjLZQAZ/Profile_Picture_2022-06-08T14:17:06.png",biography:"Assoc. Prof Dr. Lim Ghee Seong graduated with a Bachelor of Dental Surgery from University of Malaya, Kuala Lumpur in 2008. He then pursued his Master in Clinical Dentistry, specializing in Restorative Dentistry at Newcastle University, Newcastle, UK, where he graduated with distinction. He has also been awarded the International Training Fellowship (Restorative Dentistry) from the Royal College of Surgeons. His passion for teaching then led him to join the faculty of dentistry at University Malaya and he has since became a valuable lecturer and clinical specialist in the Department of Restorative Dentistry. He is currently the removable prosthodontic undergraduate year 3 coordinator, head of the undergraduate module on occlusion and a member of the multidisciplinary team for the TMD clinic. He has previous membership in the British Society for Restorative Dentistry, the Malaysian Association of Aesthetic Dentistry and he is currently a lifetime member of the Malaysian Association for Prosthodontics. Currently, he is also the examiner for the Restorative Specialty Membership Examinations, Royal College of Surgeons, England. He has authored and co-authored handful of both local and international journal articles. 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