\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\\n"}]',published:!0,mainMedia:{caption:"Highly Cited",originalUrl:"/media/original/117"}},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 191 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 261 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
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His main interest is cardiac surgery, and he is a member of the Society of Thoracic Surgery, Eastern Cardiothoracic Surgical Society, and Asian Society for Cardiovascular and Thoracic Surgery. He has published fifty-three papers in peer-reviewed journals and ten book chapters. 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The problem is that as we age, brain structures mediating the experience of negative affect become more easily activated [7, 8]. Such heightened sensitivity leads to a vicious cycle of increased manifestation of negative affect under stress, which in turn is related to additional aversive emotional and physical health outcomes [9]. Luckily, this mostly automatic process of enhanced emotional sensitivity [10, 11] can be influenced through training, such as meditation. For example, expert meditators showed less activation in the amygdala during focused attention meditation compared to novice meditators [12]. Decreased activation in the amygdala during a computerized emotion visualization task was also reported following an eight-week (2 h of class time per week) mindful attention training [13].
\nAlong with decreased activation of the amygdala, meditation is known to enhance alpha synchronization, which has been suggested to support emotional regulation [14–18]. In fact, different practices, such as breathing meditation (BM), improve emotional and physical well-being as well as cognitive functions (e.g., information processing and attention) [17–22]. For example, intensive meditation/emotion regulation interventions have been found to reduce negative affect, rumination, depression, and anxiety and to increase positive affect and
Similar to sitting meditation techniques, a novel movement meditation, Quadrato Motor Training (QMT), has also been found to improve different cognitive functions, such as creativity and spatial cognition (for review see [18]). QMT was further found to increase alpha synchronization [30, 31], mindfulness, and emotional well-being [17]. In a recent pilot study, we further found improved emotional well-being following a month of daily QMT in contrast to Simple Motor Training (SMT) in university students [32], as assessed using the Affect Balance Scale (ABS) [33].
\nBradburn’s [33] classic work on the structure of psychological well-being provided the initial distinction between positive and negative affect. Thus, Bradburn’s Affect Balance Scale [33] is based on the definition of “happiness” as a preponderance of positive over negative affect (PA and NA, respectively). Affect balance can be defined as the difference between positive affect and negative affect, with a high score meaning that positive feelings outbalance negative feelings [33], which can, in turn, promote resilience and coping with stress [34–36].
\nHigh General Self-Efficacy (GSE) is also considered a resource that buffers against stressful experiences, as high self-efficacious individuals perceive demands as challenging, not as threatening [37]. Physical exercise, as well as different meditation practices, such as Tai Chi, has been found to enhance self-efficacy (for reviews, see Refs. [38, 39]). Nevertheless, to the best of our knowledge, no studies have compared the effects of sitting versus movement meditation on self-efficacy.
\nConsequently, in the current study, we chose to examine the effects of QMT and of breathing meditation (BM) on emotional well-being using the Affect Balance Scale (ABS) and on General Self-Efficacy (GSE) [40]. To this aim, we compared two one-week intense training programs: (1) breathing meditation retreat with QMT training (QMT) and (2) breathing meditation retreat without QMT (BM). Since both physical exercise and meditation have previously been linked to increased synchronization and emotional well-being [15, 16, 18] and based on our previous results demonstrating QMT-induced enhanced local alpha synchronization in the frontal and parietal lobes as well as alpha parietal-limbic and occipito-limbic connectivity and emotional well-being, we hypothesized that the unique combination of mindfulness and specifically structured movement will have a greater effect on emotional well-being, resulting in greater improvement in affect balance and self-efficacy.
\nA total of 84 participants volunteered in the study, which took place in the Research Institute for Neuroscience, Education and Didactics of the Patrizio Paoletti Foundation. The participants signed an informed consent, and the study was approved by the ethics committee of Bar-Ilan University. At each time point, participants also reported their emotional and psychological condition using two questionnaires, the Affect Balance Scale (ABS) [33] and General Self-Efficacy (GSE) Scale [40]. The ABS and GSE were completed at the beginning of the week and at the end of the retreat.
\nVolunteers were recruited from participants who were registered for two different one-week intense retreats organized by the Ideas—Knowledge of Excellence, International School of Self-Awareness: (1) breathing meditation retreat with QMT training (QMT,
The QMT group included additional two sessions of daily QMT sessions, which were conducted once in the morning and the afternoon of days 2–5 in group setting. During QMT, each participant stood at one corner of a 0.5 m × 0.5 m square and was asked to make a step in one of three directions in response to verbal instructions given by the instructor. Participants were instructed to keep the eyes focused straight ahead and their hands loose at the side of the body. They were also told to immediately continue with the next instruction and not to stop due to mistakes. At each corner, there are three possible directions to move. The training thus consists of 12 possible movements (3 directions × 4 corners): 2 forward, 2 backward, 2 left, 2 right, and 4 diagonals. For example, if the sequence required is 1, 2, 1, 2, 1, 2, 3, 2, 4, 3, 1…. this means moving to the first corner, then to the second, then back to the first, and so on. See Figure 1 for a graphical illustration.
\nGraphical illustration of the Quadrato Motor Training.
The purpose of the Affect Balance Scale is to assess positive and negative affect as indicators of life satisfaction or well-being [33]. It comprises a 10-item scale, with five items that measure positive affect and five that measure negative affect, asking the participants to answer if they have felt certain emotions in the past week on a four-point scale ranging from never, rarely, sometimes, and frequently. Final scoring was conducted by subtracting the average of the negative affect (NA) items from the average of the positive affect (PA) items [33–35].
\nThe ABS has been extensively used in empirical research with a broad range of populations since its development. In a study of 2735 adults, Bradburn [33] reported the reliability coefficients for each component (PA, NA) of the ABS score. These were 0.83 for PA and 0.81 for NA, reflecting the consistency of the subscales [33]. Reliabilities reported in other studies in the literature have ranged from 0.55 to 0.73 for PA and from 0.61 to 0.73 for NA [42]. In the current study, we found the reliability coefficients for pre- and posttraining to be 0.66 and 0.68 for PA and 0.55 and 0.65 for NA.
\nThe General Self-Efficacy Scale has the aim to assess a general sense of perceived self-efficacy, through a 14-item questionnaire that reflects the respondent’s beliefs regarding his or her capacities [40]. The participant was asked to state the degree to which he or she agrees with each of the statements in the questionnaire on a scale ranging from “1” (strongly disagree) to “5” (strongly agree). The total scores on the questionnaire range from 14 to 70, with the highest score reflecting higher self-efficacy. The reliability of the GSE is generally high (
To answer the question regarding the effects of QMT on well-being, we ran a Group (QMT, BM) × Training (pre-, post-) analysis of variance (ANOVA) on the ABS score, and on the GSE score, separately, adopting the Greenhouse-Geisser criterion. Whenever needed, we added
There were no baseline (pre-) difference between groups for the ABS [
Change in Affect Balance Scale score as a function of Group and Training. Change in Affect Balance Scale score was calculated by subtracting ABS score before the training (ABSpre) from the ABS score following the training (ABSpost).
No base line differences nor a Group × Training interaction were found for GSE [
In addition, while there was no correlation between ABS and GSE before training (
Correlation between Affect Balance Score (PA-NA) and General Self-Efficacy (GSE) following training.
In the current study, we have found that 1 week of intense training combining breathing meditation and QMT can significantly increase the affect balance. The current results are consistent with previous findings that mindfulness-based interventions were associated with lowered intensity and frequency of negative affect [20, 43]. They are further in line with previous findings regarding QMT-induced affect balance in contrast to simple motor training (SMT) in university students [32], emphasizing the specific effects of QMT, as opposed to simple motor training without the mindfulness aspect. Here we further demonstrate that the combination of BM and QMT has an added value to affect balance compared to intense BM training.
\nWe build on the previous QMT-induced electrophysiological and cognitive changes and the meditation literature to posit the possible underlying mechanisms of QMT-induced changes in affect. We suggest three interrelated possible underlying mechanisms that might mediate this change: (1) neuronal synchronization and connectivity with limbic areas, especially within the alpha band (8–12 Hz); (2) activity of the default mode network (DMN); and (3) improved allocation of attention following training.
\nFirst, based on our previous results and model of an interrelationship between alpha synchronization, improved cognitive, emotional, and bodily function [18, 44–46], we hypothesize that the effects of QMT on affect balance may have resulted from increased alpha activity. Alpha activity has consistently been found to increase following QMT, especially within frontal and parietal areas, as well as between occipito-limbic areas [30, 31, 46, 47]. The occipito-limbic circuit links the occipital cortex and amygdala and is usually activated in response to emotional processing [48–50]. Interestingly, this network is dysfunctional in affective disorders and impulsivity [51].
\nIn fact, alpha activity has been linked to emotional well-being [52, 53]. Supportive evidence of this hypothesis comes also from the fact that both physical exercise and meditation increase frontal alpha activity [53, 54], and that this effect is thought to be independent of meditation technique and degree of experience [14].
\nSecond, neuronal synchronization in the alpha range is negatively correlated with default mode network (DMN) activity [55]. The DMN, which includes regions such as the amygdala, hippocampus, and ventromedial prefrontal cortex, is thought to mediate negative emotions. While DMN activity is related to negative emotion, meditation is known to decrease DMN activity [56, 57].
\nThird, the DMN is further related to mind-wandering and autobiographic self-processing (for review see [58]). An antagonistic network to the DMN is the
In addition, we found that self-efficacy significantly increased following a week of intense training. The construct of self-efficacy reflects an optimistic self-belief that one can cope with adversity and difficulty in various domains of human functioning [37]. Self-efficacy facilitates goal-setting and can be regarded as a source of resilience and, therefore, is relevant for behavior change. The current result is especially important as reactivity to stress increases as we grow older due to a lifetime of repeated activations of the neural systems that mediate negative affect, including the limbic system and the amygdala [44, 63].
\nAutomatic responses to affective stimuli and stress can disrupt performance during demanding goal-directed behavior as compared to neutral stimuli [10]. Noteworthy in this context is the fact that DMN activity decreases during goal-directed tasks compared to baseline rest conditions; while the sensorimotor and attention-related cortices become activated during goal-directed tasks [64, 65]. These important observations have practical implications for health and well-being, through the orientation of attention and can be modulated by training mindfulness. For example, neuroimaging studies have shown that even simply verbally labeling affective stimuli activates right ventrolateral prefrontal cortex and attenuates responses in the amygdala through the ventromedial prefrontal cortex [66–68]. DMN activation can be reduced with attention focused on breath-related sensations [69].
\nThis goal-directed behavior occurs both in breathing meditation, in which the participant is instructed to focus on his breath, as well as during the QMT, emphasizing the importance of training. In addition, we found that while there was no correlation between ABS and GSE scores prior to training, there was a significant correlation between them following training. The relationship between affect balance and self-efficacy in the literature is not strong [70] and can perhaps be strengthened by training.
\nIn conclusion, the current results suggest that QMT can be advantageous for improving affect, and that training can improve perceived self-efficacy. The current findings are in line with recent results demonstrating QMT-induced increased connectivity in the limbic system and strengthen previous claims that mindfulness practices and sensorimotor training may improve well-being. The examination of emotional changes resulting from mindful movement trainings should be conducted in parallel to the examination of changes in neuronal synchronization and functional and structural connectivity. Although we have not conducted this in the current study, future studies should be conducted in order to verify whether local alpha oscillations and limbic connectivity as well as the DMN comprise the underlying electrophysiological mechanism mediating the change in affect. We are currently working in this direction.
\nAt this historic moment, which is characterized by exponential external technological development, which increasingly influences our lives, we need tools to help us cope better with induced stress. What may be required in order to keep pace with this rapid development is a work on the
With the rapid development of nanoscience and technology, precise positioning platforms and related technologies with micron motion range and nanoscale positioning accuracy have been widely applied in various fields, such as micromechanical systems, atomic force microscopy, and biomedical science [1, 2, 3]. In nanopositioning technology, system processing and measurement accuracy are increasingly demanding, so the resolution of nanopositioning needs to be achieved for the positioning system. Piezoelectric actuators are widely used in devices to achieve precise positioning because of their small sizes, simple structure, and high resolution. However, it cannot achieve long-distance motion owing to its limited motion range. Therefore, infinite stroke motion can be achieved by combining the piezoelectric actuator principle and the frictional inertia principle. The piezoelectric stick-slip actuator with high resolution and long stroke has advantages that other types of actuators do not have. The introduction and review of other types of actuators can be discovered in the literature [4]. The development and research of piezoelectric rod-slip actuators have been a very active field for several decades. A large number of studies on precision positioning stages based on rod-slip actuators have been carried out at home and abroad.
In the process of the stick-slip actuator, it is often affected by a variety of factors, such as the electrical and hysteresis characteristics of the piezoelectric element, dynamic characteristics of the piezoelectric stack, the mechanical structure, and friction characteristics between slider and drive rod. By analyzing the motion law of the main influencing factors, some non-significant nonlinear factors can be appropriately simplified in the process of establishing the model. Peng et al. proposed an end-effector model considering the linear dynamics and hysteresis characteristics of the piezoelectric stick-slip actuator, as well as the friction of the end-effector. However, the model is suitable for piezoelectric stick-slip actuators with horizontal movement of the end-effector. The effect of gravity should be taken into account in subsequent model studies [5]. The structure of the piezoelectric stick-slip actuator is simple, but since the system relies on the frictional drive for long-distance positioning, there are uncertainties and disturbances. To achieve great motion characteristics, the control requirements of the system are extremely important. The control method is mainly divided into open-loop control and closed-loop control. The open-loop control is often used to compensate for the influence of hysteresis characteristics, and the adjustment and control method is used to realize positioning and tracking. Song et al. proposed feedforward control based on the Preisach model to eliminate the hysteresis characteristics [6]. Li et al. proposed a parasitic-type piezoelectric actuator, and this control strategy was used in scanning mode (high precision motion) to improve the positioning accuracy simply and effectively [7].
In the past few decades, a lot of research has been conducted on the modeling and control of hysteresis nonlinearity of piezoelectric actuators. In this paper, we plan to focus on the piezoelectric stick-slip actuator. Section 2 introduces various models and modeling methods under different characteristics of piezoelectric stick-slip actuators and summarizes the overall comprehensive model including each sub-model. Section 3 reviews various control schemes of the piezoelectric stick-slip actuator, including open-loop control and closed-loop control, and presents its limitations and related challenges. Finally, summarizes the problems in modeling and control of the piezoelectric stick-slip actuator and the future development direction in Section 4.
The piezoelectric stick-slip actuator is a type of actuator that uses the inverse piezoelectric effect and the friction principle to realize the stepping displacement. Its motion process is more complex and influenced by more factors. Therefore, the establishment of a piezoelectric stick-slip actuator model needs to reflect as many motion laws as possible. By analyzing the principle of stick-slip drive and establishing a comprehensive representative model of piezoelectric stick-slip drive based on a simplified model. On the one hand, the influence of each factor on the system can be analyzed by simulation. On the other hand, for the control method containing the system model, the establishment of the model is the theoretical basis for control, and the accuracy of its model directly affects the performance of the control system.
A typical piezoelectric stack-frictional rod type consists of four principal parts—the fixed part, the piezoelectric stack, the frictional rod, and the slider. The piezoelectric element elongates and contracts under the action of a sawtooth wave drive signal driving the friction rod into a corresponding reciprocating motion. The slider is displaced forward by the frictional force with the friction rod and its own inertia. The basic drive principle of a piezoelectric stick-slip actuator is illustrated in Figure 1. The drive process is divided into a stick phase and a slip phase within one cycle, and its force analysis is shown in Figure 1b. In the sticky stage, when a slowly increasing voltage is added to the piezoelectric element, the piezoelectric element slowly extends to drive the friction rod to the right. At this time, the friction force between the slider and the friction rod is greater than the inertia force. The slider and the friction rod remain relatively stationary and move together with the right
Operation principle. (a) Driving principle of the stick-slip actuator. (b) Force analysis of the stick-slip actuator. (c) The actual object of the stick-slip actuator.
Research shows that the modeling accuracy of piezoelectric stick-slip actuators is mainly determined by the following aspects—the hysteresis effect of the piezoelectric stack, the relationship between the driving voltage and the driving force, the model of the mechanical structure of the piezoelectric stick-slip actuator, and the friction model between the slider and the friction rod. Therefore, a representative integrated model of piezoelectric stick-slip actuators is discussed based on these factors in this paper.
The piezoelectric actuator part is an electromechanical coupling system. When a certain drive voltage is applied, the piezoelectric actuator generates a certain displacement and output force because of the inverse piezoelectric effect. The modeling of the piezoelectric actuator needs to reflect the relationship between the driving voltage and the deformation and output force generated by the piezoelectric actuator at the same time, which are coupled with each other.
To quantitatively analyze a system, it is necessary to describe the dynamics of the system through a mathematical model. This enables more information about the system to be described, resulting in better system control. Adriaens et al. pointed out that if the piezoelectric positioning system is properly designed, a second-order approximate modeling approach can be used to represent the dynamics of the system very well [8]. Typically, it can be viewed as a simplified spring-damped mass second-order system with a friction bar. Its linear dynamics is represented as
where
There are both hysteresis and creep effects in the process of applying a voltage to the ends of the piezoelectric stack. When the nonlinear characteristics of the system are not considered, the piezoelectric actuator input voltage and output force can be expressed as
where
In the ideal case, the output displacement of the piezoelectric stick-slip actuators is linearly related to the input control voltage curve. Due to the inherent characteristics of this material, there are certain nonlinear characteristics such as the hysteresis effect and creep phenomenon in the actual driving process. However, because the creep effect is so small that it is mostly ignored and the hysteresis effect is mainly considered in the existing literature. The hysteresis phenomenon refers to the non-coincidence between the boost displacement curve and the bulk displacement curve when the drive voltage is applied to the piezoelectric driver. The hysteresis model is used to represent the force generated by the piezoelectric driver with a new equation as
Currently, they can be broadly classified into physical and phenomenological models based on the modeling principles. Physical models are based on the physical properties of materials, among which the Jiles-Atherton model and Ikuta-K model are more common [9, 10]. However, physical modeling is based on the physical properties of the material, so the model implementation is difficult and affects the generality of the physical hysteresis model. Phenomenological models are based on input-output relations of hysteresis systems and are described using similar mathematical models. There are three broad categories based on the modeling approach, operator hysteresis models, differential equation hysteresis models, and intelligent hysteresis models.
The common operator hysteresis models are the Preisach model, Prandtl-Ishlinskii (PI) model, and Krasnosel\'skii-Pokrovskii (KP) model.
The Preisach model was first used to describe the hysteresis phenomenon in ferromagnetic materials. It was then gradually extended to describe the hysteresis behavior of smart materials, such as piezoelectric ceramics and magnetically controlled shape memory alloys. It is one of the most frequently studied nonlinear models of hysteresis [11]. The model consists of an integral accumulation of relay operators in the Preisach plane. The relay operator is shown in Figure 2a. Its mathematical expression is given by
Operators. (a) Relay operator. (b) Play operator. (c) Stop operator.
where
Based on the previous work, Li et al. proposed that a multilayer neural network can be used to approximate the Preisach model. It can use any algorithm trained for neural networks to identify the model. The model is more flexible to adapt to different working conditions than the conventional model [12]. Later, Li et al. proposed a transformation operator for neural networks that can transform the multi-valued mapping of Lagrange into a one-to-one mapping. By adjusting its weights, the neural network model is made applicable to different operating conditions. The drawback that the Preisach model cannot be updated online is solved [13].
Both the PI model and the KP model evolved from the Preisach model. The PI model has a single threshold and two continuum hysteresis operators, the reciprocal inverse play operator and the stop operator [14], as shown in Figure 2b and 2c. Therefore, the PI model can be derived from the PI inverse model by the stop operator, which can be easily used to design feedforward control compensators by the inverse model. The KP model uses a modified and improved Play operator with its corresponding density function superimposed for hysteresis modeling. While the traditional play operator has only one threshold that determines its width and symmetry, the KP operator has two different thresholds, enabling it to describe more complex hysteresis nonlinear behavior [15].
The common differential equation hysteresis models include the Duhem hysteresis model, the Bouc-Wen hysteresis model, and the Backlash-like hysteresis model. P. Duhem et al. proposed the Duhem model, which is a differential equation. The model was later improved by Coleman and Hodgdon and applied to describe the hysteresis behavior of piezoelectric ceramics [16]. Its common mathematical expression is given by
where
Although the Duhem model is also applied to describe the piezoelectric ceramic hysteresis problem, its application in engineering is greatly limited due to the difficulty of solving the model inverse model. Su et al. proposed a simplified dynamic hysteresis Backlash-like model based on the Duhem model [17]. This model has fewer parameters compared to the Duhem model and is a first-order differential equation with the mathematical expression
where
The Bouc-Wen model was originally proposed as a differential equation by Bouc [18] and was later refined by Wen [19] to form the current Bouc-Wen model. The classical Bouc-Wen model can describe a large class of hysteresis phenomena and has a concise expression [20], which is given as follows
where
The Bouc-Wen model is simple in form and has few identification parameters, which is convenient for controller design. However, it cannot completely describe the hysteresis characteristics of piezoelectric ceramics, its accuracy is low and it is only applicable to single frequency signals. The Bouc-Wen model is difficult to accurately describe the hysteresis phenomenon under the effect of frequency signals [21]. Therefore, the application of this model in practical engineering is greatly limited.
In addition to the above hysteresis models, there are some other classes of hysteresis models used in hysteresis modeling of smart materials, such as neural network models polynomial models, and other nonlinear models. Gan et al. proposed a polynomial model for the hysteretic nonlinearity of piezoelectric actuators. Experimental results show that the proposed model has higher modeling accuracy than the conventional PI model [22]. Cheng et al. proposed a method for nonlinear model prediction. First, a multilayer neuron network is used to identify the nonlinear autoregressive sliding average model of piezoelectric ceramics. Then, the tracking control problem is transformed into an optimization problem for model prediction. Finally, the Levenberg-Marquardt method is used to solve the numerical solution of the nonlinear minimization [23].
There are some other models, for example, Zhang et al. proposed a proposed Rayleigh model to describe the hysteresis characteristics of the piezoelectric drive system. The parameters of the rate-dependent Rayleigh model were obtained and validated based on the functional and experimental data [24]. Li et al. proposed a simplified interval type 2 (IT2) fuzzy system for hysteresis modeling of piezoelectric drives. In the experiments, gradient resolution and inverse resolution are used to identify the IT2 fuzzy hysteresis model [25]. Although these models are not as widely applied as the three major classes of models, they can often achieve good results in some cases when dealing with the hysteresis characteristics in some specific situations.
The output of the drive system is ultimately transferred to the slider in the form of friction, so the choice of friction model will directly affect the accuracy of the stick-slip drive platform model. At present, with the in-depth research of many international scholars on friction models, a variety of friction models have been established, which can be broadly divided into two categories, static friction models and dynamic friction models. Static friction models describe the friction force as a function of relative velocity. The dynamic friction model describes the friction force as a function of relative velocity and displacement. In contrast to static friction, which only considers the case where the relative velocity is not zero, the dynamic friction model uses differential equations to refer to the case where the relative velocity speed is zero. Therefore, in terms of accuracy, the dynamic friction model is more comprehensive and realistic than the static model. However, in addition to the accuracy of the friction model, it is also necessary to consider the complexity of the model, not all cases need to use the dynamic friction model.
The most widely used models in static friction modeling can be broadly classified into a series of coulomb and the stribeck model. Leonardo da Vinci took the lead in discovering that friction is related to the mass of an object and constructed a model. The model considers that the frictional force is proportional to the mass of the object and opposite to the direction of motion. Later this model was improved and called the coulomb model with the expression for friction
where
In some studies, classical friction models have been used to represent the friction between the slider and the friction bar. The four static friction models commonly used in the early days are shown in Figure 3 [26]. However, the slider step is only a few tens of nanometers to a few microns. The friction at this point is determined by the pre-slip displacement, which is the motion of the object before it is about to slide formally. When the friction surface is rough, the Coulomb friction model cannot accurately predict the friction force at pre-slip. Experiments have shown that in the pre-slip domain, the friction force depends on the micro-displacement between the two contacting surfaces [27]. However, the Coulomb friction model does not accurately predict this effect and will result in a relatively large error in the system. Therefore, a more accurate friction model is needed to describe the friction between the drive block and the terminal output.
Four classical static friction models. (a) Classical Coulomb friction model. (b) Coulomb and stick friction models. (c) Static friction, coulomb, and stick friction models. (d) Stribeck model.
These models need to exhibit some important static and dynamic properties of friction, such as the stribeck effect, Coulomb friction, stick friction, and pre-slip displacement. Li et al. proposed the stribeck model, which was the first model to describe dynamic and static frictional transition processes [28]. Its mathematical expression is as follows
where
It not only reflects the linear relationship between dynamic friction and velocity but also expresses the change of friction during the transition between dynamic and static friction. It lays the groundwork for future research and the establishment of a dynamic friction model.
Dahl et al. proposed the Dahl model, which is a dynamic friction model [29]. It describes for the first time the pre-slip displacement in a friction model, represented by the partial differential equation
where
However, the model does not capture the variation of the static friction phase and also does not explain the stribeck phenomenon. It is far from an adequate description of the stick-slip-driven friction interface. However, due to its simple and accurate expressiveness, it provides a solid foundation for the subsequent dynamic modeling.
Based on the study of the Dahl model, the French scholar Canudas proposed the LuGre model [30]. Its principle formula is
where
The LuGre model introduced the stribeck effect in addition to combining the idea of pre-slip displacement in the Dahl model. The idea of the bristle effect was designed to address the changing situation of the static friction phase. Swevers et al. proposed a new structure of the dynamic friction model [31]. The non-local memory hysteresis function and the modeling of arbitrary transition curves were added on the basis of the LuGre model. This allows the model to accurately describe the experimentally obtained friction characteristics, stribeck friction during slip, hysteresis behavior during slip, and stick-slip behavior. Since the structure of the obtained model is flexible, it can be further extended and generalized.
By combining the above models and considering other influencing factors, a comprehensive dynamics model considering the electrical model of the piezoelectric stick-slip actuator, the hysteresis effect, the linear dynamics performance, and the frictional characteristics of the system can be obtained, as shown in Figure 4.
Flowchart of a comprehensive model of a piezoelectric stick-slip actuator.
In the stick-slip drive system, the output force of the piezoelectric stack is first obtained by the change of voltage at both ends of the piezoelectric element. Then, the electromechanical conversion model of the drive transmission system composed of the piezoelectric stack and the flexible transmission mechanism is transformed into the displacement and force output of the transmission system. Finally, the displacement is transferred to the slider by the kinematic friction conversion, and the final displacement output is obtained. The mathematical equation of its integrated model is as follows
where
Wang et al. developed a kinetic friction model of the actuator and investigated the effect of the input drive voltage on the viscous slip motion of the actuator through simulation [32]. Nguyen et al. used the method of dimensionality reduction to describe the frictional contact behavior of the stick-slip microactuator. The model accurately predicts the frictional contact behavior of the actuator on different geometric scales without using any empirical parameters [33]. Piezoelectric stick-slip actuators also have more complex dynamic characteristics. The ability to simulate a wide range of dynamic characteristics is the direction of increasing research. Shao et al. found that the contact behavior of the piezoelectric feed element produced an inconsistent displacement response. So the Hunt-Crossley kinetic model, LuGre model, distributed parameter method combined with Bouc-Wen hysteresis model were used to model the viscous slip actuator. This model can effectively model the step inconsistency in the front-to-back direction of the actuator [34]. Wang et al. proposed a stick-slip piezoelectric actuator dynamics model considering the overall system deformation. The model introduced stiffness coefficients and damping coefficients for the whole system and successfully simulated three single-step characteristics, namely, backward motion, smooth motion, and a sudden jump for the first time [35]. Due to a large number of parameters in the dynamic model, the accurate identification of each parameter will be quite difficult. Therefore, more accurate identification of simulation parameters needs further research, which may be our future work.
The piezoelectric stick-slip actuator introduces mechanical structures, such as friction rods and linear cross roller guides on the basis of piezoelectric stacks. A sawtooth wave signal is applied to the piezoelectric stack to achieve a stepping large stroke and high precision motion. In the actual application process, the traditional mechanical structure can no longer meet the demand of real positioning accuracy due to the complex nonlinear effects in the system and the influence of external disturbances. Therefore, intelligent control algorithms combined with computer hardware devices are usually introduced to eliminate or reduce the impact of the above problems on motion accuracy. This section mainly discusses the existing controller design methods from two parts—open-loop control and closed-loop control.
In this paper, control schemes and hardware facilities for piezoelectric stick-slip actuators are briefly described in most of the literature. Depending on whether a closed-loop is formed, the main categories are feedforward control, feedback control, and composite control with a combination of feedforward and feedback. The inverse model-based control is mostly feedforward control, which is usually used to compensate for the hysteresis characteristics of piezoelectric stick-slip actuators. The control system is shown in Figure 5,
Feedforward control system principle based on inverse model.
Feedback control is the real-time feedback of the actual measured displacement through data measurement equipment, such as sensors, and the feedback value is one of the inputs of the controller. Feedback control can effectively improve the robustness of the control system, and its control mode is shown in Figure 6. Where,
Feedback control system principle.
In actual control, simple feedforward control and feedback control cannot meet the control demand. Therefore, in most cases, researchers combined the advantages of feedforward control and feedback control, usually the feedforward control and feedback control of the compound control scheme are applied to the piezoelectric stick-slip actuator position tracking control. The control system is shown in Figure 7.
Feedforward and feedback compound control system.
Feedforward control is essentially an open-loop control. However, in the overall control of piezoelectric stick-slip actuators, the advantages of this control method are very limited. This has been found by many scholars—since the voltage changes for each step displacement are very fast, there is almost no hysteresis, creep effect, and the use of feed-forward control in this motion mode is not necessary.
Feedforward control is often used to improve the quality of the end motion output of piezoelectric stick-slip actuators. Holub et al. compensated for the hysteresis of piezoelectricity by varying the amplitude of the input voltage for position errors and hysteresis modeling [36]. Chang et al. compensate for hysteresis by changing the phase lag of the actuator [37].
Feedforward control of end-effectors is challenging as the control accuracy of feedforward control is heavily dependent on the accuracy of the above model. The main source of difficulty comes from the many problems in the real system, including the hysteresis of the piezoelectric, the creeping nature, and the non-linearity of the frictional motion, the vibration between the stick and sliding points, the wear of the material between the movers and the stator, and other uncertainties [38].
Another feed-forward control by some scholars-charge control to compensate for hysteresis in the actuator. Špiller et al. developed a hybrid charge-controlled driver that slides by generating a high-voltage asymmetric sawtooth wave and feeding it into a capacitive load to compensate for the piezoelectric hysteresis as well as to achieve fast back-off. A simplified circuit diagram of the piezoelectric driver is shown in Figure 8a. This control method combines a charge control scheme with a switch is an effective solution, and the proposed hybrid amplifier has better motion linearity as shown in Figure 8b [39].
(a) A simplified circuit diagram. (b) Experiment effect.
The control of piezoelectric stick-slip actuators is usually divided into one-step control stage and sub-step control stage, also known as step mode and scan mode. Step mode refers to the piezoelectric stick-slip actuator moving forward at a fixed step size and a fixed frequency when it is far from the desired position. Until the error between the actual position and the desired position is less than the single-step displacement of the piezoelectric stick-slip actuator, the precise positioning is realized by controlling the elongation of the piezoelectric stack, which is called the scanning control stage. In the stepping mode, the voltage changes rapidly and there is almost no creep. At the same time, in the stepping mode, the control accuracy is not necessary. Therefore, in the control process of stick-slip motion, there are few overall feedforward control cases. And feedforward control is usually implemented in the scanning control stage. The core component of the piezoelectric stick-slip actuator is the piezoelectric stack, which moves through the inverse piezoelectric effect of the piezoelectric stack. Compared with the motion control of the piezoelectric stick-slip actuator, feedforward motion control of the piezoelectric stack actuator is more mature. Chen of Harbin Institute of Technology defined a new function named mirror function, which connected the dynamic hysteresis model with the classical Preisach model and established a new dynamic hysteresis model to describe the input-output relationship of the piezoelectric actuator under different conditions. On this basis, a feedforward control scheme based on the dynamic hysteresis inverse model is designed [40].
In addition, Ha et al. experimentally identified the hysteretic parameters of the Bouc-Wen model, and on this basis designed a feedforward compensator to compensate for the influence caused by the nonlinearity of the hysteretic effect. Finally, the simulation results of the compensator and the designed voltage waveform are given to realize the feedforward control of the piezoelectric stack [41]. Wei et al. proposed a feedforward controller based on an improved rate-dependent PI hysteresis inverse model, which achieved the expected effect [42]. In recent years, Zhang et al. also proposed a third-order rate-dependent Rayleigh model to describe the hysteresis nonlinearity of piezoelectric stacks. And proposed a feedforward control scheme based on the inverse third-order rate-dependent Rayleigh model, which also verified the effectiveness of the method through experiments [43]. Feedforward control often plays an obvious role in hysteresis compensation. In the future piezoelectric stick-slip drive controller design, the existing piezoelectric stack feedforward control methods can be used for reference to realize the feedforward control in the scanning stage. Simple feedforward control has poor robustness in the application, so most researchers use compound control to improve the control accuracy.
Piezoelectric stick-slip actuators are affected in practice by factors such as environmental vibration and their own nonlinear characteristics, and their controllability becomes poor. Therefore, appropriate control methods are needed for closed-loop control to meet the actual working requirements. Zhong B et al. found that differences in object surface roughness and wear can cause inconsistent velocities during the movement of a piezoelectric stick-slip actuator. Therefore, a dual closed-loop controller for velocity and position was designed to achieve high accuracy positioning. Its principle of double closed-loop control is shown in Figure 9. The experimental results show that the standard deviation of the speed of the controller is less than 0.1 mm/s, and the repeated positioning accuracy reaches 80 nm, both of which achieve a good control effect [44]. The design of the controller considers a more accurate speed control scheme, which has a high reference value for realizing the fast positioning of piezoelectric stick-slip actuators. Rong et al. introduced strain gauge as positioning sensor of the precision manipulator with piezoelectric stick-slip actuators and developed a displacement prediction method based on this. The feedforward PID control method is used throughout the system to improve the dynamic performance of the system. As shown in Figure 10a, it is the simulation of displacement-prediction control positioning performance (target displacement of 5.5 μm). Figure 10b shows the comparison of displacement response under the open-loop control and displacement response under the displacement-prediction control (target displacement of 20 μm). It can be seen from the experimental results that the 200 nm steady-state error of the proposed control method is much lower than that of the open-loop control [45]. The commonly used classical control methods are difficult to achieve high control accuracy in practical applications due to the limitations of parameters, weak automatic regulation and poor robustness.
Double closed-loop control principle.
(a) Simulation of displacement-prediction control positioning performance (target displacement of 20 μm). (b) Comparison between displacement response under the open-loop control and displacement response under the displacement-prediction control (target displacement of 20 μm).
Rakotondrabe et al. designed a micro-positioning device based on a stick-slip actuator. The control process is divided into a step mode and a scan mode, where the scan mode is precisely controlled by a PI controller [46]. Theik et al. used an inertial piezoelectric actuator to suppress the vibration of the hanging handle and designed three controllers—PID manual setting, PID self-setting, and PID-AFC. The best damping effect was achieved by experimentally comparing the PID-AFC controller. When the mass of the inertia block is larger, the vibration damping effect is more obvious [47]. These control methods are developed by the classical control theory in the actual control process of piezoelectric stick-slip actuators.
By introducing intelligent control algorithms, such as sliding mode control algorithm and neural network algorithm, the self-adjusting control of piezoelectric stick-slip actuator is realized. Closed-loop control with feedback is a common control mode of piezoelectric stick-slip actuators, which can effectively compensate for the effects of hysteresis nonlinearity, complex friction relations and external interference on the positioning accuracy, and improve the robustness of the controller. The closed-loop control is mainly divided into two kinds of closed-loop control. The first closed-loop control is the voltage amplitude of the driving signal, which adjusts the single-step size of the piezoelectric stick-slip actuator by controlling the voltage amplitude. The other is the control of the driving signal frequency. By adjusting the frequency of the piezoelectric stick-slip actuator, the speed of the piezoelectric stick-slip actuator can be controlled. Cao et al. proposed a sliding mode control method based on linear autoregressive proportional integral-differential. It can solve the problem that the hysteretic characteristics of piezoelectric stacks in piezoelectric stick-slip actuators and the nonlinear friction relationship between end-effector and workbench affect the control effect. Firstly, an ARX model of the system is designed, and its state space description is obtained. Then, the sliding mode control is introduced, and PID control is introduced as the frequency switching controller in the sliding mode control, so that the error tends to zero, to achieve better speed control [48].
In addition to introducing the inherent mathematical model into the controller, the controller can also be designed by introducing the neural network algorithm to online model identification. Cheng et al. proposed a neural network-based controller to reduce the effect of complex nonlinearities between the end-effector and the driving object. The structure block diagram of the overall controller is shown in Figure 11. The control paradigm of piezoelectric stick-slip actuators is usually divided into two phases—the one-step control phase and the sub-step control phase. In the one-step control phase, when the error between the desired position and the actual position is less than the maximum single-step displacement length by continuous sawtooth wave excitation, the controller switches to the sub-step control phase. In the experiment, the steady-state tracking error is kept within 50 nm, realizing ultra-precise motion control at the nanometer level [49].
The schematic of the overall controller in the sub-step control phase—the desired reference
Oubellil et al. applied proportional control to the macro motion control of nanorobots based on the piezoelectric stick-slip motion principle. Under macro motion control, the amplitude and frequency of the sawtooth wave voltage signal are adjusted by proportional control. When switching to scan control mode, Hammerstein dynamic model based on the PI hysteresis model is established, and then H∞ robust control scheme based on the model is designed. The hybrid stepper/scan controller can effectively meet the stability, robustness, hysteresis, and accuracy of multi-target nanorobots [50]. Oubellil et al. also applied piezoelectric stick-slip actuators to the nanorobot system of fast scanning probe microscope. To meet the requirements of fast scanning in closed-loop bandwidth and vibration reduction, the uncertain model of the piezoelectric actuator was defined by the multi-linear approximation method. A 2-DOF H∞ control scheme is designed to provide robust performance for the positioning of the nanorobot system. The fast and accurate positioning of the piezoelectric stick-slip actuator is realized [51].
In addition to its characteristics, the model of piezoelectric stick-slip actuators can also absorb the modeling mode of the piezoelectric stack. In a sense, due to the coupling relationship of the structure, the model can be regarded as an inclusion relation. The control mode of piezoelectric stick-slip actuators can also be the control mode of the piezoelectric stack, such as model-based feedforward control, inversion of control, sliding mode control method, active disturbance rejection control and some intelligent control methods can be applied to the precise control of piezoelectric stick-slip actuators. The research on piezoelectric stack also has reference significance in the precise control of piezoelectric stick-slip actuators.
Sliding mode controller often appears in the control of piezoelectric stack actuators nonlinear system [52, 53]. It is an effective and simple method to deal with the defects and uncertainties of a nonlinear system model. Sliding mode control is not dependent on an accurate mathematical model, which makes it popular in nonlinear system control of piezoelectric actuators. Li et al. proposed a sliding mode controller with disturbance estimation is designed for piezoelectric actuators. The Bouc-Wen model is chosen to describe the input and output relations of the piezoelectric actuator, and a particle swarm optimization algorithm is used for real-time identification of the model parameters. Considering the external and own uncertain disturbances, adaptive control rules are introduced to change the controller parameters. Experimental results show that the proposed controller can significantly improve the transient response speed of the system [54]. Mishra et al. designed a new continuous third-order sliding mode robust control scheme for the hinged piezoelectric actuator. To ensure the overall stability of the closed-loop system, a disturbance estimator was designed to counteract the effects of external disturbances and nonlinearities [55]. Xu Q et al. proposed an enhanced model predictive discrete sliding mode control (MPDSMC) with proportional-integral (PI) sliding mode function and a novel continuous third-order integral terminal sliding mode control (3-ITSMC) strategy [56, 57].
Because of the hysteresis nonlinearity of the piezoelectric actuator and the existence of system vibration and external disturbance, the robustness of the controller is usually required. Wei et al. proposed a variable bandwidth active disturbance rejection control method for piezoelectric actuators. The control method of the nanopositioning system is based on a cascade model of the hysteresis model and the system structure. Information about all uncertainties and disturbances excluded items in the model is estimated by a time-varying extended state observer (TESO). Afterwards, a variable bandwidth controller based on the control error is designed. Its control system is shown in Figure 12.
The variable bandwidth active disturbance rejection control.
Neural networks are widely used in the design of adaptive controllers for nonlinear systems because of their strong self-learning ability. In view of the system uncertainty and hysteresis nonlinearity of piezoelectric actuator, Li et al. proposed a neural network self-tuning control method. Two nonlinear function variables about hysteresis output are established and two neural networks are introduced to identify the two hysteresis function variables on line, respectively. Experiments verify that the neural network self-tuning controller has a good track tracking effect [59]. Napole et al. proposed a new method combining super torsion algorithm (STA) and artificial neural network (ANN) to improve the tracking accuracy of high voltage stack actuator [60]. Lin et al. proposed a dynamic Petri fuzzy cerebellar (DPFC) model joint controller for magnetic levitation system (MLS) and two-axis piezoelectric ceramic motor (LPCM) drive system, which is used to control the position of MLS metal ball and track tracking of the two-axis LPCM drive system. The experimental results also show that this method can obtain a high-precision trajectory tracking response [61].
The neural network has a strong self-learning ability and can approach complex nonlinear functions. It plays an important role in the design of the piezoelectric stick-slip actuators controller. In addition to neural network and sliding mode control, data-driven model-free adaptive control is also suitable for systems with model uncertainty. Model-free adaptive control (MFAC) as a typical data-driven control method, this method was proposed in Mr. Hou Z\'s doctoral thesis in 1994 [62]. In the past two decades, both the continuous development and improvement of theoretical achievements, and the successful practical application in the fields of motor control, chemical industry, machinery and so on, have made MFAC become a new control theory with a systematic and rigorous framework. As for the application of model-free control in the piezoelectric stack, Muhammad designed a data-driven feedforward controller and feedback controller. To avoid chattering caused by noise and affect the convergence of the learning process, several rules about parameters are also proposed. The experimental results show that the controller can realize high-precision position tracking at low frequency [63].
Piezoelectric stick-slip actuators have great potential in the field of precision operation. However, whether at the experimental level or in the application, the hysteresis nonlinearity of the piezoelectric stick-slip actuator, the complex friction motion relationship in the driving mechanism, its vibration and external disturbance will have a great impact on its motion control accuracy, so that the piezoelectric stick-slip actuator cannot achieve the ideal output performance. In this paper, the modeling and control of piezoelectric stick-slip actuators are summarized and studied.
In the aspect of modeling, the existing mathematical models describing the hysteresis characteristics of piezoelectric stick-slip actuators and the mathematical models of complex friction relationships in the structure are introduced. Hysteresis models mainly include Prandtl-Ishlinskii (PI) model, Krasnosel\'skii-Pokrovskii (KP) model, Preisach model, Bouc-Wen model, and Rayleigh model. In terms of the friction model, the existing dynamic friction and static friction models are mainly introduced. The model of piezoelectric stick-slip actuators usually includes the hysteresis model and friction model. In the modeling part, the mathematical model of piezoelectric stick-slip actuators proposed by people is summarized and studied, which provides a reference for the control and model analysis of piezoelectric stick-slip actuators.
In terms of control, according to open-loop control and closed-loop control, this paper summarizes and studies the efforts made by people to make up for control accuracy, and summarizes many control cases, such as feedforward control, sliding mode control, PID control, neural network control, and so on. In the future development of piezoelectric stick-slip actuators, opportunities and difficulties coexist. The control mode can effectively make up for the output performance of piezoelectric stick-slip actuators and make them meet the actual need in various complex environments.
Based on this paper, a more comprehensive dynamics model can be developed in the future by analyzing the characteristics of piezoelectric viscous-slip actuators in-depth to extend to actuators of different mechanical structures. By combining various control methods to eliminate system nonlinearity, higher accuracy and precision motion can be achieved. With the combination of intelligent control field and piezoelectric actuators, piezoelectric stick-slip actuators will be applied to more fields in the future.
This work was financially supported by the Science and Technology Development Plan of Jilin Province (Nos. 20200201057JC and 20190201108JC), and the Technology Research Planning Project of Education Department of Jilin Province (No. JJKH20220690KJ).
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
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MRI is commonly used once treating brain, prostate cancers, ankle and foot. The Magnetic Resonance Imaging (MRI) images are usually liable to suffer from noises such as Gaussian noise, salt and pepper noise and speckle noise. So getting of brain image with accuracy is very extremely task. An accurate brain image is very necessary for further diagnosis process. During this chapter, a median filter algorithm will be modified. Gaussian noise and Salt and pepper noise will be added to MRI image. A proposed Median filter (MF), Adaptive Median filter (AMF) and Adaptive Wiener filter (AWF) will be implemented. The filters will be used to remove the additive noises present in the MRI images. The noise density will be added gradually to MRI image to compare performance of the filters evaluation. The performance of these filters will be compared exploitation the applied mathematics parameter Peak Signal-to-Noise Ratio (PSNR).",book:{id:"6144",slug:"high-resolution-neuroimaging-basic-physical-principles-and-clinical-applications",title:"High-Resolution Neuroimaging",fullTitle:"High-Resolution Neuroimaging - Basic Physical Principles and Clinical Applications"},signatures:"Hanafy M. Ali",authors:[{id:"213318",title:"Dr.",name:"Hanafy",middleName:"M.",surname:"Ali",slug:"hanafy-ali",fullName:"Hanafy Ali"}]},{id:"41589",doi:"10.5772/50323",title:"The Role of the Amygdala in Anxiety Disorders",slug:"the-role-of-the-amygdala-in-anxiety-disorders",totalDownloads:9671,totalCrossrefCites:4,totalDimensionsCites:28,abstract:null,book:{id:"2599",slug:"the-amygdala-a-discrete-multitasking-manager",title:"The Amygdala",fullTitle:"The Amygdala - A Discrete Multitasking Manager"},signatures:"Gina L. Forster, Andrew M. Novick, Jamie L. Scholl and Michael J. Watt",authors:[{id:"145620",title:"Dr.",name:"Gina",middleName:null,surname:"Forster",slug:"gina-forster",fullName:"Gina Forster"},{id:"146553",title:"BSc.",name:"Andrew",middleName:null,surname:"Novick",slug:"andrew-novick",fullName:"Andrew Novick"},{id:"146554",title:"MSc.",name:"Jamie",middleName:null,surname:"Scholl",slug:"jamie-scholl",fullName:"Jamie Scholl"},{id:"146555",title:"Dr.",name:"Michael",middleName:null,surname:"Watt",slug:"michael-watt",fullName:"Michael Watt"}]},{id:"26258",doi:"10.5772/28300",title:"Excitotoxicity and Oxidative Stress in Acute Ischemic Stroke",slug:"excitotoxicity-and-oxidative-stress-in-acute-ischemic-stroke",totalDownloads:7157,totalCrossrefCites:6,totalDimensionsCites:25,abstract:null,book:{id:"931",slug:"acute-ischemic-stroke",title:"Acute Ischemic Stroke",fullTitle:"Acute Ischemic Stroke"},signatures:"Ramón Rama Bretón and Julio César García Rodríguez",authors:[{id:"73430",title:"Prof.",name:"Ramon",middleName:null,surname:"Rama",slug:"ramon-rama",fullName:"Ramon Rama"},{id:"124643",title:"Prof.",name:"Julio Cesar",middleName:null,surname:"García",slug:"julio-cesar-garcia",fullName:"Julio Cesar García"}]},{id:"62072",doi:"10.5772/intechopen.78695",title:"Brain-Computer Interface and Motor Imagery Training: The Role of Visual Feedback and Embodiment",slug:"brain-computer-interface-and-motor-imagery-training-the-role-of-visual-feedback-and-embodiment",totalDownloads:1439,totalCrossrefCites:13,totalDimensionsCites:23,abstract:"Controlling a brain-computer interface (BCI) is a difficult task that requires extensive training. Particularly in the case of motor imagery BCIs, users may need several training sessions before they learn how to generate desired brain activity and reach an acceptable performance. A typical training protocol for such BCIs includes execution of a motor imagery task by the user, followed by presentation of an extending bar or a moving object on a computer screen. In this chapter, we discuss the importance of a visual feedback that resembles human actions, the effect of human factors such as confidence and motivation, and the role of embodiment in the learning process of a motor imagery task. Our results from a series of experiments in which users BCI-operated a humanlike android robot confirm that realistic visual feedback can induce a sense of embodiment, which promotes a significant learning of the motor imagery task in a short amount of time. We review the impact of humanlike visual feedback in optimized modulation of brain activity by the BCI users.",book:{id:"6610",slug:"evolving-bci-therapy-engaging-brain-state-dynamics",title:"Evolving BCI Therapy",fullTitle:"Evolving BCI Therapy - Engaging Brain State Dynamics"},signatures:"Maryam Alimardani, Shuichi Nishio and Hiroshi Ishiguro",authors:[{id:"11981",title:"Prof.",name:"Hiroshi",middleName:null,surname:"Ishiguro",slug:"hiroshi-ishiguro",fullName:"Hiroshi Ishiguro"},{id:"231131",title:"Dr.",name:"Maryam",middleName:null,surname:"Alimardani",slug:"maryam-alimardani",fullName:"Maryam Alimardani"},{id:"231134",title:"Dr.",name:"Shuichi",middleName:null,surname:"Nishio",slug:"shuichi-nishio",fullName:"Shuichi Nishio"}]}],mostDownloadedChaptersLast30Days:[{id:"29764",title:"Underlying Causes of Paresthesia",slug:"underlying-causes-of-paresthesia",totalDownloads:192666,totalCrossrefCites:3,totalDimensionsCites:7,abstract:null,book:{id:"1069",slug:"paresthesia",title:"Paresthesia",fullTitle:"Paresthesia"},signatures:"Mahdi Sharif-Alhoseini, Vafa Rahimi-Movaghar and Alexander R. Vaccaro",authors:[{id:"91165",title:"Prof.",name:"Vafa",middleName:null,surname:"Rahimi-Movaghar",slug:"vafa-rahimi-movaghar",fullName:"Vafa Rahimi-Movaghar"}]},{id:"63258",title:"Anatomy and Function of the Hypothalamus",slug:"anatomy-and-function-of-the-hypothalamus",totalDownloads:4558,totalCrossrefCites:6,totalDimensionsCites:12,abstract:"The hypothalamus is a small but important area of the brain formed by various nucleus and nervous fibers. Through its neuronal connections, it is involved in many complex functions of the organism such as vegetative system control, homeostasis of the organism, thermoregulation, and also in adjusting the emotional behavior. The hypothalamus is involved in different daily activities like eating or drinking, in the control of the body’s temperature and energy maintenance, and in the process of memorizing. It also modulates the endocrine system through its connections with the pituitary gland. Precise anatomical description along with a correct characterization of the component structures is essential for understanding its functions.",book:{id:"6331",slug:"hypothalamus-in-health-and-diseases",title:"Hypothalamus in Health and Diseases",fullTitle:"Hypothalamus in Health and Diseases"},signatures:"Miana Gabriela Pop, Carmen Crivii and Iulian Opincariu",authors:null},{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:3478,totalCrossrefCites:3,totalDimensionsCites:9,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:"35802",title:"Cross-Cultural/Linguistic Differences in the Prevalence of Developmental Dyslexia and the Hypothesis of Granularity and Transparency",slug:"cross-cultural-linguistic-differences-in-the-prevalence-of-developmental-dyslexia-and-the-hypothesis",totalDownloads:3601,totalCrossrefCites:2,totalDimensionsCites:7,abstract:null,book:{id:"673",slug:"dyslexia-a-comprehensive-and-international-approach",title:"Dyslexia",fullTitle:"Dyslexia - A Comprehensive and International Approach"},signatures:"Taeko N. Wydell",authors:[{id:"87489",title:"Prof.",name:"Taeko",middleName:"N.",surname:"Wydell",slug:"taeko-wydell",fullName:"Taeko Wydell"}]},{id:"58597",title:"Testosterone and Erectile Function: A Review of Evidence from Basic Research",slug:"testosterone-and-erectile-function-a-review-of-evidence-from-basic-research",totalDownloads:1331,totalCrossrefCites:2,totalDimensionsCites:2,abstract:"Androgens are essential for male physical activity and normal erectile function. Hence, age-related testosterone deficiency, known as late-onset hypogonadism (LOH), is considered a risk factor for erectile dysfunction (ED). This chapter summarizes relevant basic research reports examining the effects of testosterone on erectile function. Testosterone affects several organs and is especially active on the erectile tissue. The mechanism of testosterone deficiency effects on erectile function and the results of testosterone replacement therapy (TRT) have been well studied. Testosterone affects nitric oxide (NO) production and phosphodiesterase type 5 (PDE-5) expression in the corpus cavernosum through molecular pathways, preserves smooth muscle contractility by regulating both contraction and relaxation, and maintains the structure of the corpus cavernosum. Interestingly, testosterone deficiency has relationship to neurological diseases, which leads to ED. Testosterone replacement therapy is widely used to treat patients with testosterone deficiency; however, this treatment might also induce some problems. Basic research suggests that PDE-5 inhibitors, L-citrulline, and/or resveratrol therapy might be effective therapeutic options for testosterone deficiency-induced ED. Future research should confirm these findings through more specific experiments using molecular tools and may shed more light on endocrine-related ED and its possible treatments.",book:{id:"5994",slug:"sex-hormones-in-neurodegenerative-processes-and-diseases",title:"Sex Hormones in Neurodegenerative Processes and Diseases",fullTitle:"Sex Hormones in Neurodegenerative Processes and Diseases"},signatures:"Tomoya Kataoka and Kazunori Kimura",authors:[{id:"219042",title:"Ph.D.",name:"Tomoya",middleName:null,surname:"Kataoka",slug:"tomoya-kataoka",fullName:"Tomoya Kataoka"},{id:"229066",title:"Prof.",name:"Kazunori",middleName:null,surname:"Kimura",slug:"kazunori-kimura",fullName:"Kazunori Kimura"}]}],onlineFirstChaptersFilter:{topicId:"18",limit:6,offset:0},onlineFirstChaptersCollection:[{id:"81646",title:"Cortical Plasticity under Ketamine: From Synapse to Map",slug:"cortical-plasticity-under-ketamine-from-synapse-to-map",totalDownloads:14,totalDimensionsCites:0,doi:"10.5772/intechopen.104787",abstract:"Sensory systems need to process signals in a highly dynamic way to efficiently respond to variations in the animal’s environment. For instance, several studies showed that the visual system is subject to neuroplasticity since the neurons’ firing changes according to stimulus properties. This dynamic information processing might be supported by a network reorganization. Since antidepressants influence neurotransmission, they can be used to explore synaptic plasticity sustaining cortical map reorganization. To this goal, we investigated in the primary visual cortex (V1 of mouse and cat), the impact of ketamine on neuroplasticity through changes in neuronal orientation selectivity and the functional connectivity between V1 cells, using cross correlation analyses. We found that ketamine affects cortical orientation selectivity and alters the functional connectivity within an assembly. These data clearly highlight the role of the antidepressant drugs in inducing or modeling short-term plasticity in V1 which suggests that cortical processing is optimized and adapted to the properties of the stimulus.",book:{id:"11374",title:"Sensory Nervous System - Computational Neuroimaging Investigations of Topographical Organization in Human Sensory Cortex",coverURL:"https://cdn.intechopen.com/books/images_new/11374.jpg"},signatures:"Ouelhazi Afef, Rudy Lussiez and Molotchnikoff Stephane"},{id:"81582",title:"The Role of Cognitive Reserve in Executive Functioning and Its Relationship to Cognitive Decline and Dementia",slug:"the-role-of-cognitive-reserve-in-executive-functioning-and-its-relationship-to-cognitive-decline-and",totalDownloads:22,totalDimensionsCites:0,doi:"10.5772/intechopen.104646",abstract:"In this chapter, we explore how cognitive reserve is implicated in coping with the negative consequences of brain pathology and age-related cognitive decline. Individual differences in cognitive performance are based on different brain mechanisms (neural reserve and neural compensation), and reflect, among others, the effect of education, occupational attainment, leisure activities, and social involvement. These cognitive reserve proxies have been extensively associated with efficient executive functioning. We discuss and focus particularly on the compensation mechanisms related to the frontal lobe and its protective role, in maintaining cognitive performance in old age or even mitigating the clinical expression of dementia.",book:{id:"11742",title:"Neurophysiology",coverURL:"https://cdn.intechopen.com/books/images_new/11742.jpg"},signatures:"Gabriela Álvares-Pereira, Carolina Maruta and Maria Vânia Silva-Nunes"},{id:"81488",title:"Aggression and Sexual Behavior: Overlapping or Distinct Roles of 5-HT1A and 5-HT1B Receptors",slug:"aggression-and-sexual-behavior-overlapping-or-distinct-roles-of-5-ht1a-and-5-ht1b-receptors",totalDownloads:19,totalDimensionsCites:0,doi:"10.5772/intechopen.104872",abstract:"Distinct brain mechanisms for male aggressive and sexual behavior are present in mammalian species, including man. However, recent evidence suggests a strong connection and even overlap in the central nervous system (CNS) circuitry involved in aggressive and sexual behavior. The serotonergic system in the CNS is strongly involved in male aggressive and sexual behavior. In particular, 5-HT1A and 5-HT1B receptors seem to play a critical role in the modulation of these behaviors. The present chapter focuses on the effects of 5-HT1A- and 5-HT1B-receptor ligands in male rodent aggression and sexual behavior. Results indicate that 5-HT1B-heteroreceptors play a critical role in the modulation of male offensive behavior, although a definite role of 5-HT1A-auto- or heteroreceptors cannot be ruled out. 5-HT1A receptors are clearly involved in male sexual behavior, although it has to be yet unraveled whether 5-HT1A-auto- or heteroreceptors are important. Although several key nodes in the complex circuitry of aggression and sexual behavior are known, in particular in the medial hypothalamus, a clear link or connection to these critical structures and the serotonergic key receptors is yet to be determined. This information is urgently needed to detect and develop new selective anti-aggressive (serenic) and pro-sexual drugs for human applications.",book:{id:"10195",title:"Serotonin and the CNS - New Developments in Pharmacology and Therapeutics",coverURL:"https://cdn.intechopen.com/books/images_new/10195.jpg"},signatures:"Berend Olivier and Jocelien D.A. Olivier"},{id:"81093",title:"Prehospital and Emergency Room Airway Management in Traumatic Brain Injury",slug:"prehospital-and-emergency-room-airway-management-in-traumatic-brain-injury",totalDownloads:49,totalDimensionsCites:0,doi:"10.5772/intechopen.104173",abstract:"Airway management in trauma is critical and may impact patient outcomes. Particularly in traumatic brain injury (TBI), depressed level of consciousness may be associated with compromised protective airway reflexes or apnea, which can increase the risk of aspiration or result in hypoxemia and worsen the secondary brain damage. Therefore, patients with TBI and Glasgow Coma Scale (GCS) ≤ 8 have been traditionally managed by prehospital or emergency room (ER) endotracheal intubation. However, recent evidence challenged this practice and even suggested that routine intubation may be harmful. This chapter will address the indications and optimal method of securing the airway, prehospital and in the ER, in patients with traumatic brain injury.",book:{id:"11367",title:"Traumatic Brain Injury",coverURL:"https://cdn.intechopen.com/books/images_new/11367.jpg"},signatures:"Dominik A. Jakob, Jean-Cyrille Pitteloud and Demetrios Demetriades"},{id:"81011",title:"Amino Acids as Neurotransmitters. The Balance between Excitation and Inhibition as a Background for Future Clinical Applications",slug:"amino-acids-as-neurotransmitters-the-balance-between-excitation-and-inhibition-as-a-background-for-f",totalDownloads:19,totalDimensionsCites:0,doi:"10.5772/intechopen.103760",abstract:"For more than 30 years, amino acids have been well-known (and essential) participants in neurotransmission. They act as both neuromediators and metabolites in nervous tissue. Glycine and glutamic acid (glutamate) are prominent examples. These amino acids are agonists of inhibitory and excitatory membrane receptors, respectively. Moreover, they play essential roles in metabolic pathways and energy transformation in neurons and astrocytes. Despite their obvious effects on the brain, their potential role in therapeutic methods remains uncertain in clinical practice. In the current chapter, a comparison of the crosstalk between these two systems, which are responsible for excitation and inhibition in neurons, is presented. The interactions are discussed at the metabolic, receptor, and transport levels. Reaction-diffusion and a convectional flow into the interstitial fluid create a balanced distribution of glycine and glutamate. Indeed, the neurons’ final physiological state is a result of a balance between the excitatory and inhibitory influences. However, changes to the glycine and/or glutamate pools under pathological conditions can alter the state of nervous tissue. Thus, new therapies for various diseases may be developed on the basis of amino acid medication.",book:{id:"10890",title:"Recent Advances in Neurochemistry",coverURL:"https://cdn.intechopen.com/books/images_new/10890.jpg"},signatures:"Yaroslav R. Nartsissov"},{id:"80821",title:"Neuroimmunology and Neurological Manifestations of COVID-19",slug:"neuroimmunology-and-neurological-manifestations-of-covid-19",totalDownloads:41,totalDimensionsCites:0,doi:"10.5772/intechopen.103026",abstract:"Infection with SARS-CoV-2 is causing coronavirus disease in 2019 (COVID-19). Besides respiratory symptoms due to an attack on the broncho-alveolar system, COVID-19, among others, can be accompanied by neurological symptoms because of the affection of the nervous system. These can be caused by intrusion by SARS-CoV-2 of the central nervous system (CNS) and peripheral nervous system (PNS) and direct infection of local cells. In addition, neurological deterioration mediated by molecular mimicry to virus antigens or bystander activation in the context of immunological anti-virus defense can lead to tissue damage in the CNS and PNS. In addition, cytokine storm caused by SARS-CoV-2 infection in COVID-19 can lead to nervous system related symptoms. Endotheliitis of CNS vessels can lead to vessel occlusion and stroke. COVID-19 can also result in cerebral hemorrhage and sinus thrombosis possibly related to changes in clotting behavior. Vaccination is most important to prevent COVID-19 in the nervous system. There are symptomatic or/and curative therapeutic approaches to combat COVID-19 related nervous system damage that are partly still under study.",book:{id:"10890",title:"Recent Advances in Neurochemistry",coverURL:"https://cdn.intechopen.com/books/images_new/10890.jpg"},signatures:"Robert Weissert"}],onlineFirstChaptersTotal:17},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:87,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:98,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:27,numberOfPublishedChapters:288,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:9,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:139,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:129,numberOfOpenTopics:0,numberOfUpcomingTopics:2,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!1},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:107,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:10,numberOfPublishedChapters:103,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:12,numberOfOpenTopics:2,numberOfUpcomingTopics:1,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:0,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!1},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:11,numberOfOpenTopics:4,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}},{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}}]},series:{item:{id:"13",title:"Veterinary Medicine and Science",doi:"10.5772/intechopen.73681",issn:"2632-0517",scope:"Paralleling similar advances in the medical field, astounding advances occurred in Veterinary Medicine and Science in recent decades. These advances have helped foster better support for animal health, more humane animal production, and a better understanding of the physiology of endangered species to improve the assisted reproductive technologies or the pathogenesis of certain diseases, where animals can be used as models for human diseases (like cancer, degenerative diseases or fertility), and even as a guarantee of public health. Bridging Human, Animal, and Environmental health, the holistic and integrative “One Health” concept intimately associates the developments within those fields, projecting its advancements into practice. This book series aims to tackle various animal-related medicine and sciences fields, providing thematic volumes consisting of high-quality significant research directed to researchers and postgraduates. It aims to give us a glimpse into the new accomplishments in the Veterinary Medicine and Science field. By addressing hot topics in veterinary sciences, we aim to gather authoritative texts within each issue of this series, providing in-depth overviews and analysis for graduates, academics, and practitioners and foreseeing a deeper understanding of the subject. Forthcoming texts, written and edited by experienced researchers from both industry and academia, will also discuss scientific challenges faced today in Veterinary Medicine and Science. 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After almost 32 years of teaching at the University of Trás-os-Montes and Alto Douro, she recently moved to the University of Évora, Department of Veterinary Medicine, where she teaches in the field of Animal Reproduction and Clinics. Her primary research areas include the molecular markers of the endometrial cycle and the embryo–maternal interaction, including oxidative stress and the reproductive physiology and disorders of sexual development, besides the molecular determinants of male and female fertility. She often supervises students preparing their master's or doctoral theses. 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Science",value:19,count:5}],publicationYearFilters:[{group:"publicationYear",caption:"2022",value:2022,count:2},{group:"publicationYear",caption:"2021",value:2021,count:3},{group:"publicationYear",caption:"2020",value:2020,count:3},{group:"publicationYear",caption:"2019",value:2019,count:1},{group:"publicationYear",caption:"2018",value:2018,count:1}],authors:{paginationCount:148,paginationItems:[{id:"165328",title:"Dr.",name:"Vahid",middleName:null,surname:"Asadpour",slug:"vahid-asadpour",fullName:"Vahid Asadpour",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/165328/images/system/165328.jpg",biography:"Vahid Asadpour, MS, Ph.D., is currently with the Department of Research and Evaluation, Kaiser Permanente Southern California. He has both an MS and Ph.D. in Biomedical Engineering. He was previously a research scientist at the University of California Los Angeles (UCLA) and visiting professor and researcher at the University of North Dakota. He is currently working in artificial intelligence and its applications in medical signal processing. In addition, he is using digital signal processing in medical imaging and speech processing. Dr. Asadpour has developed brain-computer interfacing algorithms and has published books, book chapters, and several journal and conference papers in this field and other areas of intelligent signal processing. He has also designed medical devices, including a laser Doppler monitoring system.",institutionString:"Kaiser Permanente Southern California",institution:null},{id:"169608",title:"Prof.",name:"Marian",middleName:null,surname:"Găiceanu",slug:"marian-gaiceanu",fullName:"Marian Găiceanu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/169608/images/system/169608.png",biography:"Prof. Dr. Marian Gaiceanu graduated from the Naval and Electrical Engineering Faculty, Dunarea de Jos University of Galati, Romania, in 1997. He received a Ph.D. (Magna Cum Laude) in Electrical Engineering in 2002. Since 2017, Dr. Gaiceanu has been a Ph.D. supervisor for students in Electrical Engineering. He has been employed at Dunarea de Jos University of Galati since 1996, where he is currently a professor. Dr. Gaiceanu is a member of the National Council for Attesting Titles, Diplomas and Certificates, an expert of the Executive Agency for Higher Education, Research Funding, and a member of the Senate of the Dunarea de Jos University of Galati. He has been the head of the Integrated Energy Conversion Systems and Advanced Control of Complex Processes Research Center, Romania, since 2016. He has conducted several projects in power converter systems for electrical drives, power quality, PEM and SOFC fuel cell power converters for utilities, electric vehicles, and marine applications with the Department of Regulation and Control, SIEI S.pA. (2002–2004) and the Polytechnic University of Turin, Italy (2002–2004, 2006–2007). He is a member of the Institute of Electrical and Electronics Engineers (IEEE) and cofounder-member of the IEEE Power Electronics Romanian Chapter. He is a guest editor at Energies and an academic book editor for IntechOpen. He is also a member of the editorial boards of the Journal of Electrical Engineering, Electronics, Control and Computer Science and Sustainability. Dr. Gaiceanu has been General Chairman of the IEEE International Symposium on Electrical and Electronics Engineering in the last six editions.",institutionString:'"Dunarea de Jos" University of Galati',institution:{name:'"Dunarea de Jos" University of Galati',country:{name:"Romania"}}},{id:"4519",title:"Prof.",name:"Jaydip",middleName:null,surname:"Sen",slug:"jaydip-sen",fullName:"Jaydip Sen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/4519/images/system/4519.jpeg",biography:"Jaydip Sen is associated with Praxis Business School, Kolkata, India, as a professor in the Department of Data Science. His research areas include security and privacy issues in computing and communication, intrusion detection systems, machine learning, deep learning, and artificial intelligence in the financial domain. He has more than 200 publications in reputed international journals, refereed conference proceedings, and 20 book chapters in books published by internationally renowned publishing houses, such as Springer, CRC press, IGI Global, etc. Currently, he is serving on the editorial board of the prestigious journal Frontiers in Communications and Networks and in the technical program committees of a number of high-ranked international conferences organized by the IEEE, USA, and the ACM, USA. He has been listed among the top 2% of scientists in the world for the last three consecutive years, 2019 to 2021 as per studies conducted by the Stanford University, USA.",institutionString:"Praxis Business School",institution:null},{id:"320071",title:"Dr.",name:"Sidra",middleName:null,surname:"Mehtab",slug:"sidra-mehtab",fullName:"Sidra Mehtab",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00002v6KHoQAM/Profile_Picture_1584512086360",biography:"Sidra Mehtab has completed her BS with honors in Physics from Calcutta University, India in 2018. She has done MS in Data Science and Analytics from Maulana Abul Kalam Azad University of Technology (MAKAUT), Kolkata, India in 2020. Her research areas include Econometrics, Time Series Analysis, Machine Learning, Deep Learning, Artificial Intelligence, and Computer and Network Security with a particular focus on Cyber Security Analytics. Ms. Mehtab has published seven papers in international conferences and one of her papers has been accepted for publication in a reputable international journal. She has won the best paper awards in two prestigious international conferences – BAICONF 2019, and ICADCML 2021, organized in the Indian Institute of Management, Bangalore, India in December 2019, and SOA University, Bhubaneswar, India in January 2021. Besides, Ms. Mehtab has also published two book chapters in two books. Seven of her book chapters will be published in a volume shortly in 2021 by Cambridge Scholars’ Press, UK. Currently, she is working as the joint editor of two edited volumes on Time Series Analysis and Forecasting to be published in the first half of 2021 by an international house. Currently, she is working as a Data Scientist with an MNC in Delhi, India.",institutionString:"NSHM College of Management and Technology",institution:null},{id:"226240",title:"Dr.",name:"Andri Irfan",middleName:null,surname:"Rifai",slug:"andri-irfan-rifai",fullName:"Andri Irfan Rifai",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/226240/images/7412_n.jpg",biography:"Andri IRFAN is a Senior Lecturer of Civil Engineering and Planning. He completed the PhD at the Universitas Indonesia & Universidade do Minho with Sandwich Program Scholarship from the Directorate General of Higher Education and LPDP scholarship. He has been teaching for more than 19 years and much active to applied his knowledge in the project construction in Indonesia. His research interest ranges from pavement management system to advanced data mining techniques for transportation engineering. He has published more than 50 papers in journals and 2 books.",institutionString:null,institution:{name:"Universitas Internasional Batam",country:{name:"Indonesia"}}},{id:"314576",title:"Dr.",name:"Ibai",middleName:null,surname:"Laña",slug:"ibai-lana",fullName:"Ibai Laña",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314576/images/system/314576.jpg",biography:"Dr. Ibai Laña works at TECNALIA as a data analyst. He received his Ph.D. in Artificial Intelligence from the University of the Basque Country (UPV/EHU), Spain, in 2018. He is currently a senior researcher at TECNALIA. His research interests fall within the intersection of intelligent transportation systems, machine learning, traffic data analysis, and data science. He has dealt with urban traffic forecasting problems, applying machine learning models and evolutionary algorithms. He has experience in origin-destination matrix estimation or point of interest and trajectory detection. Working with large volumes of data has given him a good command of big data processing tools and NoSQL databases. He has also been a visiting scholar at the Knowledge Engineering and Discovery Research Institute, Auckland University of Technology.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"314575",title:"Dr.",name:"Jesus",middleName:null,surname:"L. Lobo",slug:"jesus-l.-lobo",fullName:"Jesus L. Lobo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314575/images/system/314575.png",biography:"Dr. Jesús López is currently based in Bilbao (Spain) working at TECNALIA as Artificial Intelligence Research Scientist. In most cases, a project idea or a new research line needs to be investigated to see if it is good enough to take into production or to focus on it. That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:"Polytechnic University of Timişoara",institution:{name:"Polytechnic University of Timişoara",country:{name:"Romania"}}},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:null},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. Science CV available at: https://www.cienciavitae.pt//pt/8E1C-A8B3-78C5 and ORCID: https://orcid.org/0000-0002-0298-3974',institutionString:"University of Lisbon",institution:{name:"Universidade Lusófona",country:{name:"Portugal"}}},{id:"241400",title:"Prof.",name:"Mohammed",middleName:null,surname:"Bsiss",slug:"mohammed-bsiss",fullName:"Mohammed Bsiss",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241400/images/8062_n.jpg",biography:null,institutionString:null,institution:null},{id:"276128",title:"Dr.",name:"Hira",middleName:null,surname:"Fatima",slug:"hira-fatima",fullName:"Hira Fatima",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/276128/images/14420_n.jpg",biography:"Dr. Hira Fatima\nAssistant Professor\nDepartment of Mathematics\nInstitute of Applied Science\nMangalayatan University, Aligarh\nMobile: no : 8532041179\nhirafatima2014@gmal.com\n\nDr. Hira Fatima has received his Ph.D. degree in pure Mathematics from Aligarh Muslim University, Aligarh India. Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. She is a member of Indian Mathematical Society.",institutionString:null,institution:null},{id:"414880",title:"Dr.",name:"Maryam",middleName:null,surname:"Vatankhah",slug:"maryam-vatankhah",fullName:"Maryam Vatankhah",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Borough of Manhattan Community College",country:{name:"United States of America"}}},{id:"414879",title:"Prof.",name:"Mohammad-Reza",middleName:null,surname:"Akbarzadeh-Totonchi",slug:"mohammad-reza-akbarzadeh-totonchi",fullName:"Mohammad-Reza Akbarzadeh-Totonchi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Ferdowsi University of Mashhad",country:{name:"Iran"}}},{id:"414878",title:"Prof.",name:"Reza",middleName:null,surname:"Fazel-Rezai",slug:"reza-fazel-rezai",fullName:"Reza Fazel-Rezai",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"American Public University System",country:{name:"United States of America"}}},{id:"302698",title:"Dr.",name:"Yao",middleName:null,surname:"Shan",slug:"yao-shan",fullName:"Yao Shan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Dalian University of Technology",country:{name:"China"}}},{id:"125911",title:"Prof.",name:"Jia-Ching",middleName:null,surname:"Wang",slug:"jia-ching-wang",fullName:"Jia-Ching Wang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"National Central University",country:{name:"Taiwan"}}},{id:"357085",title:"Mr.",name:"P. 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