Institution Of Electrical Engineers Research Articles

An Improved Whale Optimization Algorithm with Adaptive Fitness‐Distance Balance

AbstractWhale optimization algorithm (WOA) is a new bio‐meta‐heuristic algorithm presented to simulate the predatory humpback whales' behavior in the ocean. In previous studies, WOA has been observed to exhibit lower accuracy and slower convergence rates. In this paper, we propose an improved the WOA by innovatively incorporating an adaptive fitness‐distance balance strategy, namely AFWOA. AFWOA can continuously and efficiently identify the maximum potential candidate solutions from the population within the search process, thus improving the accuracy rate and convergence speed of the algorithm. Through various experiments in IEEE CEC2017 and an ill‐conditional problem, AFWOA is proven to be more competitive than the original WOA, several other state‐of‐the‐art WOA variants and other four classic meta‐heuristic algorithms. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Self‐Assembling Design Method of Stator Slots for AC Rotating Machines

To maximize the performance of rotating machines, it is essential to establish a design method with minimal arbitrariness. This paper proposes a self‐assembling method to design stator slots for alternating current (AC) rotating machines. First, we develop a line‐current approximation model of the stator winding using pulse‐width modulation technique and realize an ideal spatial magnetic flux density distribution. Subsequently, we design the realistic stator with an acceptable current density of the winding by forming unique slot shapes in a self‐assembling manner under two synthetic conditions using the developed line‐current approximation model as the starting model. The designed stator realizes a rotating magnetic field similar to the conventional design results. Additionally, we perform an analysis of motor rotation characteristics by combining the self‐assembled stator with a surface‐mounted permanent magnet rotor to determine the possibility of a higher output compared to that when using a conventional stator. The self‐assembled motor without relying on empirical rules shows a possibility of further improving the torque characteristics, thereby contributing to designing the AC rotating machines. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Motion Trajectory Based Position Control Scheme for Switched Reluctance Machines

Switched reluctance machines (SRMs) are widely used in electric drive areas. However, due to the machine structure, the large stride angle makes it hard to achieve accurate position control for SRMs. In this paper, a position control scheme based on motion trajectory is proposed for SRM position control. In the proposed position control scheme, the SRM control is divided into the accelerating process, the steady operating process and the regenerating process. Development of the proposed position control strategy contains two parts. First, a Fuzzy logic controller is designed for rotor speed control in the accelerating process and steady operating process. Then, an aim speed regulator is proposed for the regenerating process, in which the aim speed is regulated according to the difference between aim position and real‐time rotor position. In the end, both simulation and experimental results are given to show the effectiveness of proposed motion trajectory based position control scheme. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

A Comprehensive and Pedagogical Treatment of the Origin, Selection, and Manifestations of Proximal Coupling in Magnetic Circuits

Appreciation/understanding of the factors that affect proximal coupling in magnetic circuits and the manifestations of the effect of coupling due to magnetic proximity are of paramount importance in electrical engineering. In this paper and in another companion paper we endeavor dealing with each of the aspects critically and putting them in one place. Mainly three aspects—factors that affect the coefficient of coupling, expressing the coefficient of coupling in terms of the design parameters—thus showing the symmetry of the inductance matrix, and the factors that contribute to the deviation of the voltage/current ratios from turns‐ratio is considered, with the introduction of a new factor—a measure of (a)symmetry between the magnetic paths seen by the associated coils. It is also attempted to counter the wide‐spread myth that the proximal coupling imperfection is because of the air‐medium and to highlight the fact that it can also arise because of and be controlled by providing controlled magnetic shunting between the associated coils—a very useful insight. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

SOD‐YOLO: Small Object Detection Network for UAV Aerial Images

With the rapid development of the UAV industry, object detection using UAV has become a research hotspot. However, most current object detection models based on deep learning have large parameter counts and are difficult to deploy on embedded devices with limited memory and computational power. To address this problem, a Small Object Detection network for UAV aerial images SOD‐YOLO based on YOLOv8 is proposed, which can meet the application requirements of resource‐constrained devices while ensuring the detection accuracy. First, cross‐domain fusion attention (CDFA) mechanism is proposed to build the C2f‐Attention module in this paper, which is embedded in the backbone network in order to improve the extraction capability of key object features. Meanwhile, the AIFI_LSPE feature fusion module with improved RT‐DETR and the IoU‐aware query selection mechanism are added to the path aggregation network to improve the accuracy of multi‐scale object detection. In addition, in order to balance the sample size ratio and improve the robustness of the network model, we make a new UAV image dataset named VisDrone2019 Extended Edition (VDEE) using images from the VisDrone2019 and UAVDT public datasets. Finally, Shape‐IoU is used as a loss function to reduce the difference between the object GT frame and the detection frame. Experiments show that SOD‐YOLO has a mAP@0.5 of 42.8% in the VDEE dataset, which is increased by 5.1% over YOLOv8. In the VisDrone2019 dataset mAP@0.5 is 39.2%, an improvement of 5.8% over YOLOv8. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

A Multi‐Objective Evolutionary Algorithm Based on Bilayered Decomposition for Constrained Multi‐Objective Optimization

This paper proposes a multi‐objective evolutionary algorithm based on bilayered decomposition (MOEA/BLD) for solving constrained multi‐objective optimization problems. MOEA/D is an effective method for solving unconstrained multi‐objective optimization problems. It decomposes the objective space using weight vectors and simultaneously searches for solutions for the subproblems. However, real‐world applications impose many constraints, and these constraints must be handled appropriately when searching for good feasible solutions. The proposed MOEA/BLD treats such constraints as an additional objective function. Furthermore, in addition to the conventional weight vector, an augmented weight vector is introduced that decomposes the objective space and constraint violation space hierarchically. In the first stage, the objective space is decomposed by conventional weight vectors. In the next stage, the bi‐objective space consisting of the scalarizing function and constraint violation is decomposed by augmented weight vectors. The augmented weights are adjusted so that they decrease linearly in the search process as the search gradually moves from infeasible regions to feasible regions. The proposed algorithm is compared to several state‐of‐the‐art constrained MOEA/Ds using multi‐ and many‐objective problems. The results show that the proposed method outperforms existing methods, in terms of search performance, under various conditions. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Enhancing DC Microgrids Stability by Integrating DAB Converters with Consensus Algorithms for Bus Voltage Drop Mitigation

Renewable energy sources (RESs) have become a prominent trend in the development of energy systems in many countries. To manage RESs, microgrids have emerged as an efficient and optimal solution. However, a microgrid might occasionally have problems like overloading and voltage sag. To address this problem, this paper proposes a technique that combines a consensus algorithm and a dual active bridge (DAB) to transfer energy between two different microgrids. A detection technique for overloading and voltage sag is part of the proposed control approach. In these circumstances, the DAB facilitates consensus adjustments to the stable microgrid current to bring it into balance with the overloaded current. By applying this proposed control method, voltage drops are significantly reduced, leading to an improvement in the power system quality. The simulation results of the microgrid and a scaled‐down laboratory system are conducted to verify the feasibility of the proposed control scheme. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Wind Speed Modeling under Quasi‐Linear Autoregressive Neural Network Model for Prediction of Production Power

Accurate wind speed modeling is beneficial for the design of wind energy conversion systems. Models of wind speed are used to assess the adequacy and dependability of a power supply. However, precise wind speed modeling is challenging due to the sporadic availability of wind speed. In this note, we propose a wind speed model with an autoregressive (AR) structure. A hybrid model is developed under linear and nonlinear parts based on a quasi‐linear autoregressive exogenous neural network (Q‐ARX‐NN). The model's structure is composed of a regression vector and its coefficients. The coefficients are divided into linear and nonlinear coefficients. A set of linear coefficients is identified under the algorithm of least square error (LSE), and a set of nonlinear coefficients is modeled by using a neural network to refine the residual error of the nonlinear part. Some artificial neural network (ANN) models can be set as nonlinear part sub‐models to sharpen the model's accuracy. The proposed model is tested for wind speed modeling to estimate wind energy production. Various nonlinear parts of the sub‐model are tested, such as neural networks, radial basis function networks, and ANN networks. Moreover, we evaluate the effects of the order of the model by varying hidden and output nodes, which can be summarized as the number of coefficients of the regression vector. Using specific wind turbine performance data, prediction models estimate production power. © 2024 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Fast Terminal Synergetic Speed Control for SMPMSM Drive System

For the surface‐mounted permanent magnet synchronous motor (SMPMSM) drive system, a fast terminal synergetic speed control (FTSSC) is proposed to improve its speed dynamic performance and robustness. In the proposed control scheme, the ultra‐local model of SMPMSM drive system, which considers motor parameter uncertainties and unknown disturbances, is set up based on the algebraic parameter identification method. Then, the nonlinear terminal manifold is designed to achieve speed rapid convergence in finite time, moreover, the fast synergetic speed control law that satisfies the voltage and current constraints of SMPMSM drive system is derived. Meanwhile, the Lyapunov function is designed to prove the stability of the proposed fast terminal synergetic speed‐controlled SMPMSM drive system. Finally, the effectiveness and feasibility of proposed control is verified through the experimental research of SMPMSM drive system. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Core Loss Analysis of Axial Modular Flux Switching Permanent Magnet Machine Considering the Impact of No‐Magnetic Ring

This paper investigates the relationship between the distributions of the core loss and the flux density harmonic amplitude along the axial direction in an axial‐modular flux‐switching permanent‐magnet (AM‐FSPM) machine. The flux leakage in the no‐magnetic ring leads to an uneven distribution of flux density in the core due to the modular cooperation structure. Subsequently, variations in the fundamental flux density amplitudes in the PM field and the armature reaction field are obtained with changes in the thickness of the no‐magnetic ring. In addition, the impact of no‐magnetic ring thickness on machine torque and core loss is analyzed to determine the optimal thickness for the no‐magnetic ring. Finally, the validity of the theoretical analysis is confirmed by experimental validation. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

String‐Like Self‐Capacitance‐Based Proximity and Tactile Sensor that Can be Wrapped Around Robotic Arms

AbstractCooperative robots that perform safety functions as part of cooperative work with humans have been recently drawing attention. Proximity and tactile sensors that can be retrofitted to a robot's surface are important safety measures for existing robots that are not manufactured as cooperative robots to be used as cooperative robots. In this paper, a string‐like self‐capacitance‐based proximity and tactile sensor that can be easily wrapped around various types of existing robots is proposed. The novel and useful point in this paper is that it can be easily mounted on various robots by a flexible string‐like proximity and tactile sensor. The sensor module can be adjusted to the required length by connecting different numbers of modules depending on the mounting surface. As a demonstration, six prototype sensor modules were connected by each connector and wrapped around a robotic arm. The sensor modules on the robot's surface could detect an object within its proximity range and detect the rough pressure after contact. In addition, the robotic arm with the sensor modules could be controlled in real time using the data obtained from the sensor that are object detection data at proximity range. These results confirm that the proposed sensor module can be used in the proximity and tactile sensors of existing robots. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Analysis of Electromagnetic Buffer Characteristics of DC Fast Vacuum Switch

In order to solve the contradiction between the rapid isolation of low voltage DC faults and the low bounce of small stroke vacuum switches, taking the fast vacuum switch for 400 V DC power distribution system as the research object, the electromagnetic buffer simulation and experimental research in the opening process are carried out. The influencing factors of the dynamic and reaction mechanism and buffer output characteristics are analyzed, and the electromagnetic buffering effect under typical working conditions is simulated, which proves that electromagnetic buffer is a necessary part of 400 V fast vacuum switch and the motion characteristics of fast switching are optimal when applying 220 V electromagnetic buffer. According to the simulation calculation results, the engineering prototype was made, the experimental research on the electromagnetic buffer under typical working conditions was carried out, and the design criteria of the small stroke electromagnetic buffer were proposed, which solved the problem of fast switching of small stroke and reliable control and low bounce. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

An Adaptive Eccentricity Correction Method for Arrayed Single‐Axis TMR Current Sensors

Current sensors based on the tunneling magnetoresistive effect (TMR) are widely used for current measurement due to their high sensitivity, small size, and low power consumption. This paper proposes an effective error correction model to rectify the eccentricity of the transmission line, which can cause a significant measurement error in the ring‐array single‐axis TMR sensor. The model employs a convolutional neural network (CNN) to identify the relationship between the conductor eccentricity and the output of three sensors. The resulting correction factor is then fed back to eliminate the error associated with wire eccentricity. Concurrently, the Sparrow search algorithm (SSA) is employed to optimize the hyperparameters of the convolutional neural network (CNN) in order to enhance the model's performance. The experimental results demonstrate that the maximum error of the ring‐array single‐axis TMR current sensor, corrected by SSA‐CNN, is less than 0.42%, which markedly enhances the precision of the measurement. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

2024 HRS perspective on advancing workflows for CIED remote monitoring

Cardiac implantable electronic devices (CIEDs) generate substantial data, often stored in image or PDF formats. Remote monitoring, now an integral component of patient care, places considerable administrative burdens on clinicians and staff, in large part due to the challenge of integrating these data seamlessly into electronic health records (EHRs). Since 2006, the Heart Rhythm Society, in collaboration with the CIED industry, has led an initiative to establish a unified standard nomenclature. This effort has harmonized terminology, aligning diverse terms with single terms approved by the Institute of Electrical and Electronics Engineers. With this foundational work complete, attention now turns to developing technical standards for interoperability, which would enable the smooth communication of CIED data between information technology systems used in clinical practice. In this manuscript, by leveraging Health Level 7 Fast Healthcare Interoperability Resources, we present a roadmap for the technical committee to guide this endeavor. We identify critical data exchange points between remote transceivers, EHRs, and third-party platforms commonly used for CIED patient data management. Our objective is to establish bidirectional communication among these resources, ensuring the accuracy, timeliness, and accessibility of clinical data for clinicians. We also anticipate substantial benefits for both clinical research and administrative efficiency through the implementation of this interoperability framework.

Establishment of DC Electric Field Measurements by Linear Dipole Type Sensor System

Nowadays, electric fields above the ground are measured mainly by so‐called ‘field mill type’ sensor systems. However, their measurements are limited to vertical field component only due to its technical constraint. Out of necessity of measurements of electric field components in arbitrary directions in any spaces, a linear dipole type DC electric field sensor system, which can be immersed within a fully uniform electric field, was developed. However, it had been remained unknown whether its sensor output is parameter‐dependent or not, and unknown whether it can provide reliable intensity value of measured electric fields or not. In order to clarify these points, test observations of natural electric fields have been conducted by means of plural sensor systems having different values of parameter. Obtained electric field intensity derived from the observed data has shown a parameter‐dependence despite that it should be constant for any parameter values. After a detailed analysis of the effect of the parameter along the equivalent electric circuit of the sensor system, a field‐calibrating formula, which can make the sensor system to be parameter‐independent, has been found. Finally, the linear dipole type DC electric field sensor system for obtaining reliable electric field intensities has been established. © 2024 The Author(s). IEEJ Transactions on Electrical and Electronic Engineering published by Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

EA‐YOLO: An Efficient and Accurate UAV Image Object Detection Algorithm

An improved EA‐YOLO object detection algorithm based on YOLOv5 is proposed to address the issues of drastic changes in target scale, low detection accuracy, and high miss rate in unmanned aerial vehicle aerial photography scenarios. Firstly, a DFE module was proposed to improve the effectiveness of feature extraction and enhance the whole model's ability to learn residual features. Secondly, a CWFF architecture was introduced to enable deeper feature fusion and improve the effectiveness of feature fusion. Finally, in order to solve the traditional algorithm's shortcomings it is difficult to detect small targets. We have designed a novel SDS structure and adopted a strategy of reusing low‐level feature maps to enhance the network's ability to detect small targets, making it more suitable for detecting some small objects in drone images. Experiments in the VisDrone2019 dataset demonstrated that the proposed EA‐YOLOs achieved an average accuracy mAP@0.5 of 39.9%, which is an 8% improvement over YOLOv5s, and mAP@0.5:0.95 of 22.2%, which is 5.2% improvement over the original algorithm. Compared with YOLOv3, YOLOv5l, and YOLOv8s, the mAP@0.5 of EA‐YOLOs improved by 0.9%, 1.8%, and 0.6%, while the GFLOPs decreased by 86.4%, 80.6%, and 26.7%. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Optimal Scheduling of Island Microgrids with Seawater Pumped Storage Plants for Multi‐Energy Complementarity

The rapid development of new energy sources, such as offshore wind power and photovoltaic power, has provided a new solution to the problem of power supply for islands far from the mainland. Wave energy is a kind of renewable energy originated from the ocean, but the existing island power supply programs seldom consider this favorable natural condition. In addition, seawater variable‐speed pumped storage is a new idea to consume offshore wind power and improve the reliability of coastal and island power systems. In view of the stochastic and intermittent nature of new energy sources, this paper adopts seawater variable‐speed pumped storage power plants as energy storage equipment, and put forward an island power supply scheme with wind power, photovoltaic power generation, wave power generation, pumped storage power plants and diesel generator sets as a multifunctional complementary isolated grid. Firstly, wave energy generators, wind farms, photovoltaic farms, pumped storage power stations and diesel generator sets are modeled separately. Then, considering their respective operating conditions, constraints and load requirements, the optimal scheduling of island microgrids with multi‐energy complementarity is constructed. Finally, based on the improved particle swarm optimization algorithm, the model is solved. According to the wind power photovoltaic and wave power output curves of several typical scenarios in an island far away from the mainland, the cost and benefit of different schemes are compared to verify the effectiveness of the optimal scheduling in this paper. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Document‐to‐Document Retrieval Using Self‐Retrieval Learning and Automatic Keyword Extraction

In this study, we propose self‐retrieval learning, a self‐supervised learning method that does not require an annotated dataset. In self‐retrieval learning, keywords extracted from documents are used as queries to construct training data that imitate the relationship between query and corpus, such that the documents themselves are retrieved. In the usual supervised learning for information retrieval, a pair of query and corpus document is required as training data, but self‐retrieval learning does not require such data. In addition, it does not use information such as reference lists or other documents connected to the query, but only the text of the documents in the target domain. In our experiments, self‐retrieval learning was performed on the EU and UK legal document retrieval task using a retrieval model called DRMM. We found that self‐retrieval learning not only does not require supervised datasets, but also outperforms supervised learning with the same model in terms of retrieval accuracy. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Torque Performance Analysis of Asymmetric Magnetic Pole Permanent Magnet Synchronous Motor for New Energy Vehicles

In view of the problem of large torque ripple and poor output performance of permanent magnet synchronous motor, a new topology of asymmetric magnetic pole permanent magnet synchronous motor is proposed, and the torque ripple of this motor structure and the suppression method are analyzed. First, the equivalent magnetic circuit method and Lorentz force law are used to derive the analytical formula of torque ripple of the motor. Then, based on the analytical formula, the parameters of permanent magnet of the asymmetric magnetic pole permanent magnet synchronous motor are optimized and verified by finite element software simulation; on this basis, the method of rotor eccentricity is used to further weaken the torque ripple of the motor, and the optimal eccentricity distance is analyzed. The research shows that when the parameters of the magnetic pole of the asymmetric magnetic pole permanent magnet synchronous motor is appropriate, the harmonic content of the air gap magnetic density of the optimized motor is greatly reduced, and the motor has good output characteristics, and the torque ripple is reduced to 5.4%. Finally, the prototype is trial‐manufactured and tested. The results show that after optimization, the cogging torque of the motor is reduced, the torque output performance is improved, and the overall motor performance is improved. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Template‐Refine Network for Siamese Object Tracking

Various mainstream target tracking algorithms based on Siamese networks are gradually becoming a trend in the field of deep learning tracking due to their concurrent advantages of accuracy and speed. Most Siamese network‐based trackers describe the tracking of a target object as a similar matching problem, and these trackers have achieved more advanced performance in several public tests. Most trackers often suffer from tracking drift or performance degradation owing to the non‐updating of the template in the first frame and the target appearance encounters disturbing environments such as occlusion and drastic deformation. Therefore, to address this problem, this paper introduces a template updating mechanism and proposes a refine structure network based on the template updating of Siamese networks as well as the greater similarity of target features in two adjacent frames, which improves the tracking accuracy while limiting the amount of computation using an anchor‐free method in order not to lose the tracking speed, and only needs to be trained by selecting the most suitable pre‐training network, thus greatly reducing the amount of network computation. Meanwhile, in the application of the refine structure, with the aim of making the weight design of the target localisation module more reasonable, we propose a new Refine Head section and analyze and design the update threshold to optimize the overall network. This method is practiced in SiamFC++ algorithm, which firstly designs the template refine module, inputs the image that needs to be improved, and then outputs it to the Refine Head to complete the template update and applies it to the tracking of the subsequent frames, thereby constituting the SiamTRN (Template‐Refine Network). According to the experiments, the improved structure of the method can effectively implement the refine module function and enhance the performance of the tracker on public datasets, such as OTB100, VOT2016, UAV123 and GOT‐10 k. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.