Journal of Modeling and Simulation in Electrical and Electronics Engineering
Volume:2 Issue: 1, Winter 2022

Due to the non-linearity and large dimensions of permanent magnet motor optimization, the use of metaheuristic methods such as GA, PSO, etc. would not be the most appropriate method especially if the fitness assessment is done by a time-consuming solver such as finite element analysis (FEA). The FEA, which is widely used by most researchers, requires a lot of time and space and leads to huge computational costs. On the other hand, the accuracy of approximate analytical models is not sufficient for high-dimensional optimization tasks. To overcome these problems, a new space reduction optimization method is developed and presented in this paper. The proposed method gradually shrinks the search space and approaches an interesting subspace so that the wide variable space becomes smaller. As a result, FEA modeling accuracy is achieved as well as computational cost reductions. To validate the method, the design optimization is performed on a 2004 Toyota Prius Interior Permanent Magnet (IPM) motor. The results are compared with other optimization algorithms in terms of accuracy and number of performance evaluations. The comparison results show the superiority of the proposed algorithm, which can be a desirable alternative to industrial optimization tasks that necessarily require the least number of function evaluations.

Energy-only electricity markets and energy+capacity markets have both been experienced in the real world, and each has advantages and shortcomings. Still, there are lots of arguments supporting each of the two designs. Furthermore, the energy+capacity market has different known forms itself, and several capacity mechanisms have been introduced in the literature and most of them have been tried in the real world. It is evident that the electricity market design in each country is customized to fit its socio-economic aspects. The main purpose of this paper is to look closely at Iran Electricity Market (IREMA) and assess the pros and cons of its main design in using the capacity mechanism. To this end, the history of IREMA evolution and the specific design of the capacity mechanism in this market is meticulously surveyed. The theoretical and practical principles that led IREMA founders to choose the EnCa mechanism are discussed. Also, positive/negative impacts of the capacity mechanism are analyzed.

Human action retrieval as a challenging research area has wide-spreading applications in surveillance, search engines, and human-computer interactions. Current methods seek to represent actions and create a model with global and local features. These methods do not consider the semantics of actions to create the model, so they do not have proper final retrieval results. Each action is not considered a sequence of sub-actions, and their model is created using scattered local or global features. Furthermore, current action retrieval methods ignore incorporating Convolutional Neural Networks (CNN) in the representation procedure due to a lack of training data for training them. At the same time, CNNs can help them improve the final representation. In the present paper, we propose a CNN-based human action representation method for retrieval applications. In this method, the video is initially segmented into sub-actions to represent each action based on their sequence using keyframes extracted from the segments. Then, the sequence of keyframes is given to a pre-trained CNN to extract deep spatial features of the action. Next, a 1D average pooling is designed to combine the sequence of spatial features and represent the temporal changes by a lower-dimensional vector. Finally, the Dynamic Time Wrapping technique is used to find the best match between the representation vectors of two videos. Experiments on real video datasets for both retrieval and recognition applications indicate how created models for the actions can outperform other representation methods.

Personal identification based on vein pattern is one of the latest biometric approaches that have attracted lots of attention. Besides, Convolutional Sparse Coding (CSC) is a popular model in the signal and image processing communities, resolving some limitations of the traditional patch-based sparse representations. As most existing CSC algorithms are suited for image restoration, we present a novel discriminative model based on CSC for dorsal hand vein recognition in this paper. The proposed method, discriminative local block coordinate descent (D-LoBCoD), is based on extending the LoBCoD algorithm by incorporating the classification error into the objective function that considers the performance of a linear classifier and the representational power of the filters simultaneously. Thus, for training, in each iteration, after updating the sparse coefficients and convolutional filters, we minimize the classification error by updating the classifier’s parameters according to the label information. Finally, after training, the label of the query image will be determined by the trained classifier. One thousand two hundred dorsal hand vein images taken from 100 individuals are used to verify the validity of the proposed methods. The experimental results show that our method outperforms other competing methods. Further, we demonstrate that our proposed method is less dependent on the number of training samples because of capturing more representative information from the corresponding images.

In this article, an X-band low phase noise dielectric resonator oscillator is investigated. For this purpose, a dielectric resonator as a frequency stabilization section at the almost center frequency of 12 GHz is designed. The active device is a packaged GaAs FET (an ATF-36077 pHEMT). Firstly, the ATF36077 microwave transistor has been biased. The substrate of this nonplanar oscillator is Rogers RT/Duroid 5880. Finally, the dielectric resonator oscillator has been introduced as a series feedback structure. This presented X-band dielectric resonator oscillator, operating at nearly 12 GHz, exhibits a phase noise of -71 dBc/Hz and -133 dBc/Hz at 1-kHz and 1-MHz frequency offset, respectively. Also, the output power level of nearly 7 dBm is achieved. The second and third harmonic power levels are more than 50 dB and 30 dB lower than the main harmonic power level.

This paper reviews the current state of the double-stator permanent magnet vernier (DS-PMV) machine technology. At first, the recent advances in PMV machines are presented. In the following, the classification of various PMV machines' configuration is elucidated. In general, this review covers the principle of better understanding of the novel PMV machines' topologies using different windings configurations, flux modulation poles and stator/rotor structures.

This research presents a new sliding-mode adaptive technique for stabilization of the output voltage of a single-phase Switched Inductor Z-source Inverter (SIZSI) as an interface in renewable energy sources. The proposed method is based on a sliding mode controller modified by an online adaptation of uncertain inverter parameters. The sliding mode controller improves the system's robustness in the face of external disturbances and preserves the system's output for any load, such as linear, nonlinear, and even changing loads. The proposed approach has been simulated in MATLAB/Simulink software package to show the controller's performance. The comparison results with the traditional sliding mode control have been conducted to validate the proposed method's superiority in resolving problems such as adaptive and robust against instantaneous deviations of input voltage and output current. The presented sliding-mode adaptive control technique shows a more efficient dynamic response to the system and less Total Harmonic Distortion (THD) than traditional controllers.