Institution Of Electrical Engineers Research Articles

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.

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.

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.

The Influence of Relative Position on Ground Potential and Electric Field Based on Interfacial Charge Accumulation in HVDC

AbstractWith the development of high‐voltage direct current (HVDC) engineering around the world, there has been increasing attention to the issue of selecting site for direct current (DC) grounding electrodes since the pollution coming from ground potential and electric field. As previous study result, the distance is closer to the grounding electrode, the influence is greater for ground potential and electric field, as well as vice versa. Through proposing the analysis approach of equivalent interfacial charge accumulating, the article goes beyond this previous general‐conclusion and extracts the relative position conception from the mono‐distance for further studying on weakening their impacts on the environment. In comparison with the previous results, the research finding shows that the movement of relative position is able to achieve the best distribution of ground potential or electric field corresponding to different soil‐layered structure, furthermore, the specific mathematic formula about the best relative position has been derived by the accumulated charge model proposed. Finally, the results have been demonstrated by simulation calculation of the CDEGS software for a practical HVDC project in China, moreover, regarding soil resistivity variation related to the different seasons, the optimized relative position about 13 ~ 24 km has been adopted in the real HVDC grounding project as a suggested site. It reveals that the underlying relative position behind the mono‐distance could be applied to selecting the site of grounding electrode as reference in the future. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Loss Minimization Model Predictive Current Control for Linear Induction Motors

To further improve the operating efficiency of linear induction motor, this paper proposes a loss minimization model predictive current control method based on governable loss online calculation. First, the method derives the equivalent circuit of linear induction motor containing independent iron loss branches, and establishes the loss model containing iron loss. Second, the expressions of stator d‐axis current with governable losses and secondary flux amplitude are derived from theoretical analysis, and then a given value of stator d‐axis current is derived when the governable losses are minimal. Then, a chain observer is designed to observe the secondary flux and calculate the governable loss and stator d‐axis current under the current operation. Finally, a method is proposed to introduce the stator d‐axis current observation results into the model predictive current control, which ensures the loss suppression effect and improves the dynamic performance at the same time. In addition, this article also compares the loss minimization controllers with or without iron loss. The simulation results are consistent with the theory, and the optimal control of the secondary flux of the linear induction motor can be successfully realized, thus effectively reducing the energy loss of the motor in the dynamic operation process. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Interactive Skin Lesion Segmentation Considering Behavioral Preference in Clicking

Interactive Medical Image Segmentation (IMIS) aims to improve the accuracy of image segmentation by incorporating human guidance, primarily through click‐based interactions. IMIS for skin lesion segmentation is a challenging task because the edges of lesion regions on the skin are often ambiguous, and training IMIS models requires the generation of pseudo‐clicks to simulate human clicks. Most previous methods generate pseudo‐clicks by sampling from the entire mis‐segmented region. However, such clicks are inconsistent with human behavior, resulting in performance degradation, particularly for skin lesion segmentation. In this study, we address this issue by integrating human preference into the process of generating pseudo clicks to train the segmentation model, which is simple yet effective. Specifically, through a user study, we find that people are more inclined to click on larger mis‐segmented regions during interactive segmentation. Inspired by this, a roulette selection strategy is used to generate the pseudo‐clicks based on the area of the mis‐segmented subregions. Our proposed method, BehaviorClick, can be easily integrated with existing interactive segmentation models to improve the performance. The accuracy improvement on four dermoscopic datasets under six state‐of‐the‐art interactive segmentation methods is confirmed, which demonstrates the generalizability and effectiveness of our approach. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Noise Enhances Excitability of a Neuronal Population with Heterogeneous Excitatory Neurons

AbstractCortical excitability is sensitive to noise perturbations despite homeostatic regulation. However, modeling the effect of noise in a heterogeneous neuronal population is a mathematical challenge. Using a mean‐field model of a randomly connected excitatory population, we investigated how noise affects excitability at the resting state. Stable equilibrium analysis revealed that increasing noise intensity enhances excitability, indexed by synaptic conductance. Further, this enhancement disappeared when excitability is sufficiently high due to strong mean input current, but not influenced by the connection probability of the population. Finally, the mechanism of noise‐induced enhancement was explained by eigenvalues moving toward the imaginary axis. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Research on Transformer Fault Diagnosis by WOA‐SVM Based on Feature Selection and Data Balancing

AbstractOil‐immersed transformers as one of the most important equipment in the power system, the fault prediction of it in advance can effectively reduce the subsequent harm. Aiming at the selection of input features and data sample imbalance in the transformer fault diagnosis model, this paper adopts the recursive feature elimination (RFE) method combined with SMOTETomek comprehensive sampling method to optimize the above problems. First, RFE is used to traverse all the features and filter the optimal combination of them as input features, then SMOTETomek is used to perform balancing operation on the samples of the train set, and finally, whale optimization algorithm (WOA) is used to find the best hyperparameters for support vector machine (SVM), and the results are compared with the diagnostic models operated without processing and after single processing operation, respectively. After several sets of experiments, it is proved that the optimized comprehensive fault diagnosis model performs better on the test set than both the untreated and the singly processed models, which proves the effectiveness of the methodology used in this paper. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Partial Discharge Behavior Prior to Breakdown in Epoxy Resin

To evaluate the deterioration degree of insulation material by monitoring partial discharges (PDs), it is necessary to fully understand the PD behavior prior to breakdown. We performed measurements of temporal variations in the number and magnitude of PDs in a void of epoxy resin. It was found that the number of PDs with larger magnitude is less before breakdown and that there is a decreased tendency of the number and magnitude of PDs with the elapsed time. The tendency is remarkable as the applied electric field is small. Additionally, the time to breakdown becomes short when the scattering of the number of PDs per unit time decreases. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Adaptive Optimal Resonance Fault Feature Enhancement for Wind Turbine Based on Frequency Search and Uncertainty Analysis

The wind turbine fault feature has strong uncertainty due to various factors such as wind speed and equipment parameters. Based on the analysis of the vibration characteristics of wind turbines with unknown prior conditions, this paper conducts research on the enhancement method of fault features. First, analyzing the change in fault features caused by the change in operating conditions to lay the foundation for subsequent research; Second, for the uncertainty of wind turbine fault feature frequency, searching for fault features by constructing the energy in each spectral interval, then use signal noise optimal resonance to transfer noise energy to low frequency fault features to achieve the purpose of adaptive optimal resonance enhancement of fault feature; Finally, validation of the proposed method by simulation experiments and fault signals of actual wind turbine rolling bearings. The case shows that the proposed algorithm can effectively realize the uncertain fault feature of wind turbines search and enhancement. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Road Target Detection in Different Weather Conditions Based on Deep Learning

AbstractAddressing the challenge of ensuring robustness in vision‐based target recognition algorithms under adverse weather conditions, such as rain, snow, and fog, is crucial. In this paper, we introduce a novel approach for road target detection that can effectively operate under various weather conditions. Our method is based on the cascade task framework of target detection, complemented by image restoration techniques. Specifically, we have developed a denoising network tailored to meet the demands of de‐raining and snow removal tasks. This network leverages prior knowledge about the mask, enhancing its effectiveness. In real‐world scenarios featuring fog, wet conditions, and snow‐covered roads, our proposed method demonstrates a significant improvement in both recall rate and accuracy compared to conventional single‐object detection algorithms. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

AENet: Image Retrieval of Kazakh Handwritten Documents Based on Attention Mechanism and Feature Aggregation

Kazakh is one of the multilingual languages of China and is widely spoken in some areas of Xinjiang, China. However, due to the fact that Kazakh is a language in which several characters are glued together to form a continuous word with a unique shape and complex structural combinations of relationships. This paper explores a solution for offline image retrieval of handwritten Kazakh words, which is a challenging task because, due to the lack of relevant datasets and the special writing morphology of the Kazakh language, traditional text image retrieval algorithms often struggle to achieve satisfactory results when dealing with writing styles that are varied and adherent to the language. Therefore, a dataset of offline Kazakh handwritten document images was created in this paper. The dataset contains 300 pages of document images with 20 500 words. Then, a new model called the ‘AENet’ is proposed. The model utilizes an attention mechanism to focus more finely on focal regions such as centers, inflection points, and contours of handwritten word images and to capture important local features from different scales. Fusion space pyramid pooling, feature aggregation, encoding operations, and feature downscaling and reconstruction are used to extract and reconstruct more representative feature representations from local to global to capture the overall information in the word images. Through experimental evaluation on Kazak‐80, Zilla‐64, and HWDB1.1‐375 datasets, it is verified that the method significantly improves the mAP for image retrieval of handwritten words, which is especially applicable to adhesive languages like Kazakh. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Attention Similarity Outlier Suppression with Hierarchical Feature Integration for Defect Segmentation

Defect segmentation holds significant importance in industrial applications. However, most existing algorithms are tailored to specific scenarios, lacking a unified defect segmentation algorithm for defect segmentation. Based on the analysis of the characteristics of defects, we aim to propose a unified transformer‐based defect segmentation algorithm. On the one hand, the defect foreground exhibits significant differences from the normal background. Nevertheless, the attention mechanism in the transformer involves all feature nodes in the feature map to the similarity score of one patch with all other patches. This approach may compromise the features of potential defect areas where the defect foreground significantly contrasts with the background. We introduce the Attention Similarity Outlier Suppression (ASOS) strategy to address this challenge. Our method is designed to select valid attention scores and filter out outlier scores for appropriate attention fusion, in which the feature of potential defect areas is not easily affected by background. On the other hand, the defect regions often have relatively small areas. We incorporate a Hierarchical Feature Integration (HFI) module in our framework to facilitate defect recognition at different scales. By progressively up‐sampling, features can achieve better integration to generate the final segmentation result. Extensive experiments have verified the effectiveness of our proposed method in defect segmentation tasks. Moreover, thorough ablation studies have demonstrated the efficacy of our algorithm, reinforcing its robust performance. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Identification of PV Parameters Based on Bézier Curve and AWSO

AbstractTo achieve accurate PV cell parameter identification, a method combining the second‐order Bézier function with the adaptive war strategy optimization (AWSO) algorithm is proposed, the Bézier curve for fitting the PV output characteristic curve is given by using the linear connection between the filling factor and the control points of the second‐order Bézier function, and then, the Bézier‐AWSO model is constructed, and an adaptive weight‐based updating and allocation method is given to improve the global search capability of the AWSO algorithm in the problem of recognizing five important parameters of photovoltaic, and avoid falling into the local optimum in the process of recognition. Finally, the optimal solution for the unknown parameters in the single diode topology of silicon‐based photovoltaic cells is obtained by using the data points of the fitted curves and the AWSO algorithm, and the accuracy of the AWSO algorithm for photovoltaic cell parameter identification is verified through the analysis of the arithmetic examples and real experiments. The results demonstrate that the parameter identification of the proposed improved model reduces the average relative error by 0.5%–1% compared with that before the improvement under standard and non‐standard test conditions, which improves the accuracy of the 5‐parameter identification results of the silicon‐based PV cell and provides a reference for the subsequent fault analysis. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Cooperative Path Planning for Persistent Surveillance in Large‐Scale Environment with UAV‐UGV System

AbstractIn this paper, we consider the path planning problem of the Unmanned Air‐Ground Vehicle (UAV‐UGV) system for large‐scale environmental persistent surveillance. The goal is to acquire a set of periodically visited surveillance nodes while minimizing the traveling distance. Some expected key problems are the limitations of UAV speed, UGV speed, UAV endurance, and UAV field of view. To address this issue, the path planning of UAV‐UGV surveillance system is modeled as a TSP optimization problem based on multiple constraints, aiming to minimize the total path length of the system to perform the task. UAV is responsible for visiting the surveillance node and UGV serves as a mobile charging station. And a two‐layer chaotic aptenodytes forsteri optimization algorithm (Two‐CAFO) is proposed to solve this problem. Our solution has been tested in several simulated and real‐world environments. The results demonstrate that the proposed Two‐CAFO has superior performance compared to other state‐of‐the‐art algorithms in solving the path planning problem for large‐scale environmental persistent surveillance tasks of UAV and UGV. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

A Cascaded Controller Design for Switched Reluctance Motor Based on Dung Beetle Optimizer

Due to the strong nonlinearity and large torque ripple of switched reluctance motor, a cascaded fractional order PID sliding mode control system is designed. Considering the difficulty of parameter adjustment caused by the complexity of the system, the optimization technology using dung beetle optimizer is introduced to optimize the designed cascaded controller and control the speed and torque ripple. To prove the effectiveness of the proposed cascaded controller, a control model is built in MATLAB/Simulink, and compared with whale optimization algorithm, particle swarm optimization, sparrow search algorithm to verify the response effect of the cascaded controller. A cascaded double PID controller is also built for comparison to verify the effectiveness of fractional order PID sliding mode controller. The results show that the cascaded fractional order PID sliding mode controller optimized by dung beetle optimizer is better than another cascaded double PID controller, produces the minimum torque ripple, and has better speed response and stability. The torque ripple of motor is suppressed. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Transformer Connections: Improving Segmentation in Blurred Near‐Infrared Blood Vessel Image in Different Depth

AbstractHigh‐fidelity segmentation of blood vessels plays a pivotal role in numerous biomedical applications, such as injection assistance, cancer detection, various surgeries, and vein authentication. Near‐infrared (NIR) transillumination imaging is an effective and safe method to visualize the subcutaneous blood vessel network. However, such images are severely blurred because of the light scattering in body tissues. Inspired by the Vision Transformer model, this paper proposes a novel deep learning network known as transformer connection (TRC)‐Unet to capture global blurred and local clear correlations while using multi‐layer attention. Our method mainly consists of two blocks, thereby aiming to remap skip connection information flow and fuse different domain features. Specifically, the TRC extracts global blurred information from multiple layers and suppresses scattering to increase the clarity of vessel features. Transformer feature fusion eliminates the domain gap between the highly semantic feature maps of the convolutional neural network backbone and the adaptive self‐attention maps of TRCs. Benefiting from the long‐range dependencies of transformers, we achieved competitive results in relation to various competing methods on different data sets, including retinal vessel segmentation, simulated blur image segmentation, and real NIR blood vessel image segmentation. Moreover, our method remarkably improved the segmentation results of simulated blur image data sets and a real NIR vessel image data set. The quantitative results of ablation studies and visualizations are also reported to demonstrate the superiority of the TRC‐Unet design. © 2024 The Author(s). IEEJ Transactions on Electrical and Electronic Engineering published by Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Image Analysis Method of Substation Equipment Status Based on Cross‐Modal Learning

In response to increasing power supply needs, maintaining stable substations is vital for reliable electricity. Traditional manual equipment inspections in these substations are inefficient and risky, often leading to hazards and delayed detection of faults. Therefore, there's a growing shift towards using intelligent image recognition technology in video surveillance systems for safer and more efficient inspections. This paper focuses on enhancing the level of intelligent inspection in substations using artificial intelligence‐based visual recognition technology. It introduces a novel small‐sample classification algorithm based on the CLIP architecture. This method uses cross‐modal equipment status information as additional training samples, optimizing the loss function together with image samples, and devises hand‐crafted strategies for text sample inputs to distinguish between equipment and states. The experimental results show that with only 16 training samples per category for 21 types of electrical equipment states, our method achieved a maximum accuracy of 93.38%. This represents a 2.98% higher accuracy than the PPLCNet trained on the full dataset and an 8.63% higher accuracy than the PPLCNet trained with an equal number of samples, with significantly reduced training time. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

EMC Research Efforts in the IEEJ Technical Committee and State‐of‐the‐Art Case Studies on ESD Found in its Technical Papers

Electrostatic discharge (ESD) has been known to cause electromagnetic noise against electronic equipment and devices even before the technical concept of electromagnetic compatibility (EMC) was established 68 years ago in the United States and 48 years ago in Japan, while the ESD still continues to be studied as one of the most significant issues that should be solved in EMC areas. This is largely because the underlying nature of ESD has yet to be essentially elucidated from an EMC perspective. On the other hand, as an academic research organization on EMC in Japan, a Technical Committee named ‘EMCJ’ on ‘Environmental Electromagnetic Engineering’ was founded in 1976 in both the Institute of Electrical Engineers of Japan (IEEJ) and the Institute of Electronics and Communication Engineers or the present IEICE (Institute of Electronics, Information and Communication Engineers) to address EMC concerns. Since then, the EMCJ has been jointly conducting EMC research activities under the IEICE as the nominating body and the IEEJ as the associative body, whereas the EMCJ of IEEJ was dissolved in 1999 to form a new Technical Committee on EMC (TC‐EMC) in order to deal with EMC issues appropriate to the IEEJ. In this review, to mark the 25th anniversary of the IEEJ Technical Committee on EMC (TC‐EMC) in 2024, the EMC research efforts and activities that the TC‐EMC has conducted over the past 25 years from January 2000 to March 2024 are surveyed from 1272 technical papers presented at the technical meetings. Furthermore, among the ESD issues identified in these technical papers, the latest case studies utilizing ‘spark resistance laws’ that allow the analysis of the initial behavior of sparks, in which a spark is the essence of ESD and is the key to open the door to elucidate the phenomenon, particularly from an EMC perspective, are reviewed. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Improvement of AC Conditioning Effect in Vacuum by High Frequency Voltage

To improve the dielectric strength in vacuum, spark conditioning through repetitive breakdowns by AC or impulse voltage is an effective method. AC conditioning at commercial power frequencies has been widely used due to its cost‐effectiveness, but the breakdown voltage after AC conditioning is generally lower compared to impulse conditioning. We have confirmed that multiple breakdowns occur successively in a half cycle of AC voltage application. It is considered that the multiple breakdowns are harmful for conditioning and triggered due to the previous breakdown in the same half cycle. In this paper, we discuss dielectric strength improvement by high frequency AC conditioning and how the multiple breakdowns are induced. We experimentally confirmed that the AC conditioning effect is improved by increasing the frequency of AC voltage for different applied AC conditioning voltages in a sphere‐plane electrode system. We focused on the breakdown position from the light emission images during AC conditioning to investigate whether the multiple breakdowns were induced or affected by the previous breakdown. We found that the multiple breakdowns occurred close to the previous breakdown position. The multiple breakdowns would be induced by the collision of metal particles due to the previous breakdown with the counter electrode. These breakdowns do not contribute to removing protrusion and would lead to damage to the electrode surface. The increasing frequency of AC conditioning would suppress the multiple breakdowns and improve the dielectric strength in vacuum. © 2024 The Author(s). IEEJ Transactions on Electrical and Electronic Engineering published by Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.