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Semantic segmentation has become one of the trending topics in the world of computer vision and deep learning. Recently, due to an increasing demand to solve a semantic segmentation task simultaneously with attribute recognition of objects, a new task named attribute‐aware semantic segmentation has been introduced. Since the task requires to handle pixel‐wise object class estimation with its attributes such as a pedestrian's body orientation, previous works had difficulties to handle ambiguous attributes such as body orientations in object‐level, especially when segmenting the pedestrians with their attributes correctly. This paper proposes the ColAtt‐Net that is an attribute‐aware semantic segmentation model augmented by a column‐wise mask branch to predict the pedestrians' orientations in the horizontal perspective of the input image. We firmly assume that the pedestrians captured by a car‐mounted camera are distributed horizontally so that for each column of the input image, the pedestrian pixels can be labeled with one orientation uniformly. In the proposed method, we split the output of the base semantic segmentation model into two branches; one branch for segmenting the object categories, while the other one, as the novel column‐wise attribute branch, is to map the recognition of pedestrian's orientations that are distributed horizontally. This method successfully enhances the performance of attribute‐aware semantic segmentation by reducing the ambiguity on segmenting the pedestrian's orientation. Improvements on the pedestrian orientation segmentation are confidently shown by the proposed method in the experimental results, both in quantitative and qualitative views. This paper also discusses how the improved performance becomes an advantage in the autonomous driving system. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Large‐volume pulsed discharge plasmas have been studied to gain insight on their phenomena and to realize their applications in various areas, such as sterilization, water treatment, and cancer treatment. Since 2000, when a review paper titled 'Streamer Discharges in Liquids and their Application' was published, a number of related papers have been reported in the literature; these reports are summarized in this review paper. Four types of pulsed power‐generated large‐volume discharges in contact with water are presented as representative examples. The discharges are reviewed from the perspectives of the physical and chemical phenomena, as follows: the initiation of filamentary and bush‐like streamers in water; the propagation and extinction of streamer discharge plasmas; the production, behavior, and collapse of bubbles; the measurement of temperature, density, electric field, shock wave, and radicals; the wide discharge on the water surface. Finally, the applications of these large‐volume discharge plasmas are described. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Most face detection and recognition tasks are based on the training of intact facial images and corresponding labels. Both the three‐dimensional (3D) structure and two‐dimensional (2D) appearance from the frontal view of human faces are bilaterally symmetrical in general. However, sometimes, illumination on the left and right halves of faces is uneven. In such cases, the symmetrical characteristic of human faces can facilitate expressing distinct identity information. This is because even if one side of the facial image is corrupted by noise, the opposite side can still be used for feature extraction. This paper proposes an automatic selection of the better half of the face using only a half‐face for identity recognition. Unlike the MegaFace challenge of recognizing millions of identities in the wild, this paper focuses on building recognition systems for a small number of people with fewer training images; the recognition system can, for example, build access control systems for research laboratory members or family members. This paper proposes an artificial face image construction method and a half‐face training strategy for transfer learning of pretrained conventional neural network models. Extensive experimental results show that the proposed method improves the performance of state‐of‐the‐art models by utilizing the symmetrical characteristics of human faces. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

This paper presents a deep learning‐based face recognition device. It aims to instantly remind users of the identities of those who they met before in parties or conferences. When greeted, one, who is correctly identified, may frequently feel more respected than another who is mistaken for someone else in the first place. The golden rule suggests one to treat others with respect, and then others will treat the one with respect in return. Empirically, making others feel respected can definitely facilitate communication among people for social and particularly business purposes. For this sake, this paper employed a pair of smart glasses as a hardware platform into which a deep learning‐based multi‐task cascaded convolutional networks and a FaceNet algorithm were integrated. This move provided a high face recognition accuracy and a strong robustness. In the end, this smart device as intended recognized captured images and displayed the identities of matched contacts in a face image database. Hopefully, this proposal can be applied to a wide diversity of fields. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Compared with multi‐phase single‐module permanent magnet motors, three‐phase multi‐modularized permanent magnet motors have stronger fault tolerance ability, widely used in aerospace, ship propulsion, and other special fields. Especially in ship direct‐drive propulsion, low speed and high torque modular combined stator fault‐tolerant permanent magnet motor (MCS‐FTPMSM) has high torque density, and fault‐tolerant operation conditions may appear in actual operation, so it is essential to analyze the temperature of the fault‐tolerant motor accurately. This paper analyzes the temperature of MCS‐FTPMSM under different operating conditions and determines the longest operating time of the motor under different fault‐tolerant operating conditions based on the 3D electromagnetic‐temperature bi‐directional coupling method. Among them, the loss of the motor is calculated by the 3D electromagnetic field, which is added to the 3D temperature field as an excitation, and then the material properties in the 3D electromagnetic field are updated according to the calculated temperature distribution. The cyclic iteration method is used to make the calculated temperature deviation less than the given error value. When analyzing the electromagnetic loss of the motor, the field‐circuit coupling method is adopted to consider the influence of the current time‐harmonics of the controller on the loss of the motor during actual operation. Finally, the 12kw100r/min 3 × 3 modular prototype was tested. By comparing the results of experiments, one‐way coupling method (OWCM) and bi‐directional coupling method (BDCM), it is found that the electromagnetic‐thermal bi‐directional coupling can more accurately predict the temperature of the fault‐tolerant motor. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

In this paper, we present a methodology to synthesize bone‐conduced (BC) speech. Without relying on commonly used techniques for speech synthesis, we consider to transform air‐conducted (AC) speech into BC speech. An infinite impulse response (IIR) filter is explored for the transformation. From the concept of system identification, the least squares (LS) method is employed and the IIR filter is designed through the recorded AC and BC speech data. By experiments, it is shown that the BC speech synthesis method is satisfactory. Filter model, order selection and stability in the methodology are discussed and noise‐robustness gained by the BC speech synthesis is also investigated. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Considering the difficulty of extracting bearing fault signals of pumping unit and the complexity of diagnosis, a bearing fault diagnosis method based on refined composite multi‐scale dispersion entropy (RCMDE) and Extenics is proposed. Firstly, RCMDE is used to extract the features of bearing data. Using Fisher Ratio to reduce the dimensionality of RCMDE features and select them. The selected RCMDE features are used as the feature parameters of the matter element models to construct the classical domains and node domain of the four matter element models, and build the matter element models of the four healthy states of the bearing. Finally, the sample sets to be tested are input into the four health state models to calculate the comprehensive correlation degree value. The fault diagnosis is realized according to the principle of maximum comprehensive correlation degree. The results show that the detection rate of the proposed method for the four health states of the bearing is 98.8%. In order to verify the superiority of the proposed method, the method proposed in this paper is compared with the fault diagnosis method based on the combination of MDE and extenics, and the results verify the superiority of the proposed method in this paper. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

The oil chromatography online monitoring system can monitor the insulation status of power transformers continuously and real‐time. The accuracy of its detection data directly affects the stable operation of the transformers. Aiming at the problem of abnormal data in the current transformer online monitoring data set, this paper proposes an online oil chromatographic data processing method based on association rules and support vector regression (SVR) analysis. First, using piecewise linearization to convert numerical variables into Boolean variables, and then exploring gas indicators with strong correlations in oil chromatogram. Then, using the improved box plot method to detect the abnormal value points in it, and distinguish the data of different abnormal patterns based on the association rules. Finally, the advanced particle swarm optimization support vector regression (APSO‐SVR) algorithm is used to repair the data to improve the credibility of the data set. Using actual transformer oil chromatographic monitoring data for testing, the results show that the proposed data processing method can effectively improve the accuracy of oil chromatographic online monitoring data. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

As an important part in the power systems, the status of the distribution network affects the safety, reliability, and economy of the whole power supply. When a fault occurs in the distribution network, it is of great significance to accurately and quickly locate the fault. At present, a cost‐effective synchronous phase measurement unit (PMU) device has been developed for the monitoring of the distribution network, which has opened up a new path for fault location in the distribution network. In this paper, the fuzzy clustering algorithm is used to judge the fault section, and the multiple fault information is extracted for fusion, so that the fault characteristic index can highlight the fault information. Then a new method based on multi‐terminal PMU data to realize the fault location of the distribution network is proposed. This method realizes fault location through the relationship between multiple fault indexes, and the simulation verification of PSCAD/EMTDC shows the accuracy and effectiveness of the method. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

For the calculation of corona loss of extra‐/ultra‐high voltage (EHV/UHV) transmission lines, especially the estimation of real‐time corona loss, there exists no accurate method. In this paper, a calculation method of corona loss in the EHV/UHV system based on a distributed parameter characteristic is proposed. Based on the π‐shaped equivalent circuit of the long‐line distributed parameter characteristic, and considering the voltage distribution along the line, a formula for calculating the corona loss under a variety of weather and annual average conditions is derived and the power equation for the corona loss of EHV/UHV system is established by the method. The example results show that the method is quite accurate. The calculation method of this paper can consider different weather conditions of the line sections to calculate the corona loss. It is more in line with the actual weather conditions on the surface of the EHV/UHV transmission lines. And the method can analyze the corona loss and its proportion to the total loss of the system under the annual average and different weather conditions. It is of great significance to the design and operation of an EHV/UHV system. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

Accurate fault classification is the premise of fault location and management study in a power distribution network. In most of the traditional fault classification methods used in power distribution network, the characteristic quantities are selected by experience, which will increase the uncertainty of fault classification results. A novel fault classification method based on deep belief networks (DBN) is proposed in this paper. Samples of fault current and voltage are preprocessed by min–max standardization and waveform splicing firstly, then they are used to train the DBN together with fault type label. Characteristic quantities of the current and voltage will be automatically extracted by the well‐trained DBN model, and the reliable fault type classification of distribution network can be realized. Simulation and experimental results show that the fault classification method is suitable for distribution network, and it has not only characteristics of obvious fault feature extraction and high fault classification accuracy, but also has good adaptability while the neutral grounding modes changing or used in power distribution network with distributed generator. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

This paper presents a novel frequency compensation scheme for the stability of low dropout regulator (LDO). The proposed compensation scheme introduces a zero in the voltage regulation loop to enlarge gain and phase margin at high‐frequencies. As a result, high open‐loop‐gain and improved load regulation performances are achieved, and a particular resistor is introduced in the compensation structure to cancel the voltage drop originating from cable resistances at the output to further improve the load regulation performance. The proposed LDO with wide input/output range and high‐power supply rejection is designed and implemented in a 180 nm CMOS process. The LDO operates with an input voltage range of 4–16 V, an output voltage range of 1.5–5 V and an output current range of 0–1000 mA. High open loop gain and high bandwidth are achieved, and the simulated performances of load regulation and line regulation are less than 32 μV/mA and less than 3 mV/V over the whole operation range, respectively. The on‐chip compensation capacitor required by the compensation module is only 1.8 pF. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Point‐of‐interest (POI) recommendation has always faced serious data sparsity problem. Most of the existing POI recommendation methods do not consider the bidirectional relationship between the user‐preference for locations and the location‐ attractiveness to users in the original dataset when solving the sparsity. For this problem, a point‐of‐interest recommendation method based on bidirectional matrix and deep belief network (POI_DBNBM) is proposed in this paper. Specially, from the perspectives of users and locations, we first construct two different matrices with bidirectional relationship by mapping the original dataset, and we use deep belief network (DBN) with strong learning ability to predict the two matrices. Then, a local–global weight optimization method is developed to weight and fuse the two result matrices obtained by DBN, so as to achieve the more precise user's rating prediction matrix for locations. The abundant experimental studies and evaluations are conducted on FourSquare dataset. Results are encouraging and better than those previously reported on the dataset. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Dynamic neural networks (DNNs) are widely used in data‐driven modeling of nonlinear control systems. Due to the complexity of the actual operating nonlinear power systems, rigorous dynamic models are always unknown. DNNs can focus on methods that only use input and output information to establish accurate dynamic models and reduce noise in measured data, which is called data‐driven modeling. The core of the DNN is the feedback with memory function. This paper analyzes the traditional Elman neural network (ENN) and nonlinear auto‐regression with exogenous input (NARX) neural network with different structure feedback structures, and proposes a full feedback dynamic neural network (FF‐DNN) with exogenous input. Eight different kinds of neural networks (including ENN, NARX, etc.) are compared and analyzed. The eight kinds of neural networks are applied on the experimental data of the DC‐AC inverter and the power system of Zhejiang Juchuang Smart Technology Company Park in Wenzhou. The experimental results are used to compare the performance of the data‐driven models established by eight different kinds of neural networks under different noise conditions, verify the robustness and generalization performance of dynamic data‐driven modeling based on FF‐DNN, and demonstrate the feasibility and effectiveness of FF‐DNN in actual power systems. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

For the initial attachment parts of the human body discharge under live working conditions of transmission lines, this paper proposes a calculation method suitable for any working posture and tower type based on the leader discharge theory. By simulating the formation of the leader channel, the minimum background field for the initiation of the stable leader in various parts of the human body is calculated, and the probability distribution of the initial discharge attachment position on the body surface of the worker is obtained. The results show that the stable transformation of the secondary corona to the leader channel at the tip of the human body is a precondition for this part to become an attachment point of the discharge; In the process of the worker entering the equipotential, affected by the entering direction and posture, the parts with a large curvature such as the hands and feet are likely to become the initial discharge attachment position; Due to the shielding effect from the limbs, the rear part of the worker has a low probability of becoming the initial discharge attachment position. The purpose of this research is to explore an effective method for identifying the discharge location of a live line worker, and to strengthen the protection of workers from the electric field. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

In this work, we investigated strength variations of the ventriloquism effect when the loudspeaker's position was indicated to the subject. The ventriloquism effect is an audiovisual cross‐modality in which a sound is heard from the position of an image, even though the sound and image are presented at different locations. Previously, this effect has been studied under conditions where the sound location is not revealed. In contrast, this paper proposes measuring the ventriloquism effect using a single loudspeaker displayed as a 3D scanned image in the background of a virtual reality (VR) image. The angular discrimination threshold (the perceived angle of disparity between the sound and the image shifted horizontally) was employed to evaluate the ventriloquism effect. The resulting ventriloquism effect was compared to the effect noted in a previous study, where measurements were conducted without revealing the loudspeaker's position to the subjects. The results indicate that revealing the loudspeaker's position emphasizes the ventriloquism effect. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

This paper proposes a controller for single‐phase synchronous inverters (SSIs) that was designed to stabilize the performance of a grid while providing overcurrent protection during the occurrence of a fault. The proposed controller for an SSI eliminates the multiple feedback control loops consisting of voltage and current controllers under normal conditions to avoid the instability attributed to them. To output the designed voltage precisely calculated using the core circuit, including a swing equation and speed governor, we employ the latched limit strategy for the overcurrent protection, which is combined with a proportional resonance current controller. Numerical simulations and experiments demonstrated that the proposed controller provides the designed performance while ensuring overcurrent protection during normal operation. © 2023 The Authors. IEEJ Transactions on Electrical and Electronic Engineering published by Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

A novel duty‐cycle‐sensing overload detector for DC‐DC converter is proposed in this paper. Firstly, the theoretical analysis is conducted to reveal the susceptibility of the conventional overload detectors to temperature variation and the input/output‐voltage change of the DC‐DC converter. Then, the schematic of the novel duty‐cycle‐sensing overload detector is presented and its operation principle is analyzed. Thanks to the time register, the proposed detector generates a theoretically voltage‐and‐temperature‐insensitive threshold duty cycle, compares it with the real‐time duty cycle of the converter's PWM signal, and announces an overload event if the real‐time duty cycle exceeds the threshold. Finally, a prototype of a BUCK DC‐DC converter including the proposed detector and a conventional current‐sensing overload detector is constructed and measured. As the measurement results show, the threshold duty cycle only varies by about 2.4% as the temperature changes from 0 to 90 °C while the threshold voltage of the conventional current‐sensing detector changes by about 4.6%. The proposed detector can keep effective even the temperature rises to 90 °C while the conventional current‐sensing detector can work at a temperature no higher than 85 °C. Without circuitry modification, the proposed detector correctly senses the overload event as the DC‐DC converter's output voltage changes in a range of 2.1–2.9 V while the conventional one loses its effectiveness if the output voltage is set higher than 2.54 V. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Aiming at the problems existing in the application of traditional power workers' work clothes, an early warning system for power transmission and distribution workers based on ZigBee is designed. The system completes the real‐time collection and monitoring of human body temperature information, heart rate information, blood pressure information and location information of power workers. It can give early warning according to human body information, and automatically dial 120 alarm telephone according to human heart rate signal to transmit location information, so that workers can be rescued as soon as possible. In order to increase the accuracy and reliability of transmission information, the system adopts distributed fusion algorithm for signal transmission, which makes the life safety of power workers better guaranteed. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Future labor forces are anticipated to include collaborative robots that work alongside humans in the same environment. Proximity and tactile sensors are among the most important devices for collaborative robots to operate safely. In this paper, we propose a proximity and tactile sensor for a collaborative robot that combines multiple time‐of‐flight (ToF) sensors and a self‐capacitating proximity and tactile sensor. ToF sensors can detect the distance to an object and have a long measurement range. However, at close distances, their detection accuracy deteriorates, and blind spots may likely not be detected. Conversely, the self‐capacitance proximity and tactile sensor detect objects within the blind spots of ToF sensors and contact conditions because the sensor can detect an object before and after contact, being highly sensitive in the proximity range. In the proposed sensor, the electrodes of the self‐capacitance are enlarged, and multiple ToF sensors are used to expand the measurement range and reduce blind spots. A prototype sensor was seamlessly able to measure objects from approximately 400 mm to contact conditions. In addition, the robot arm with the prototype sensor could be controlled in real time using the measured data of the sensor. Therefore, we believe that the proposed sensor can be potentially used as a proximity and tactile sensor for collaborative robots. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Stimulus reconstruction decodes the listener's auditory attention through the greater neural tracking of the attended speech over the unattended stream. While acoustic features of speech are vital to the listening task and comprehension, very few studies have analyzed the effects of acoustic features of speech on stimulus reconstruction. This paper investigates approaches of stimulus reconstruction, where correlations between the neurally decoded and actual speech envelopes are calculated from specific speech segments, varying in transient levels as measured by spectral transition measures. Additionally, two methods of calculating correlations were adopted enabling analysis of the effects of relatively lower and higher frequency components of the speech envelope. Correlation after concatenation analysis showed that STM level of only the attended speech affected decoding performance, hinting at a top‐down attentional effect. A bottom‐up effect of salient aspects of speech momentarily dominating neural entrainment was also inferred from the weighted mean of multiple correlations. Future studies on the link between acoustic features of speech and its corresponding neural tracking behavior are suggested. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Vehicle routing problem (VRP) is one of the classic combinatorial optimization problems where an optimal tour to visit customers is required with a minimum total cost in the presence of some constraints. Recently, VRP is being solved with the use of deep reinforcement learning (DRL), with node sets considered (represented) as a graph structure. Existing Transformer based DRL solutions for VRP rely only on node information ignoring the role of information on the edges between nodes in the graph structure. In this paper, we proposed an attention‐based end‐to‐end DRL model to solve VRP which embeds edge information between nodes for rich graph representation learning. We used Transformer based encoder‐decoder framework with an edge information embedded multi‐head attention (EEMHA) layer in the encoder. The EEMHA‐based encoder learns underlying structure of the graph and generates an expressive graph topology representation by merging node and edge information. We trained our model using proximal policy optimization (PPO) with some code‐level optimization techniques. We conducted an experiment on randomly generated instances and on a real‐world data generated from road networks to verify the performance of our proposed model. The result from all experiments show that our model performs better than the existing DRL methods and most of the conventional heuristics with good generalizability from random instance training to real‐world instance testing of different problem size. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

When processing ultrasonic non‐destructive testing signals, the ensemble empirical mode decomposition (EEMD) method can eliminate the phenomenon of modal aliasing, and the processing effect is better than the empirical mode decomposition (EMD) method. However, in signal reconstruction, the intrinsic mode function (IMF) will be eliminated directly, resulting in the loss of some signals and errors. Therefore, a signal processing method of ultrasonic non‐destructive testing based on EEMD combined with wavelet packet is proposed in this paper. In this method, the signal is preprocessed by wavelet packet, and then decomposed by EEMD. The correlation coefficient method is used in reconstruction. This method not only retains the advantages of EEMD, but also uses the characteristics of wavelet packet decomposition of high frequency and low frequency to ensure the integrity of the signal. Finally, the simulation and actual measurement signals are used for verification. Compared with the EEMD algorithm, the signal‐to‐noise ratio is increased by 31.60% and the root mean square error is reduced by 31.63%, and the cross‐correlation coefficient is increased by 22.09%. This method is more suitable for ultrasonic non‐destructive testing signal processing. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

The current‐limiting reactor is currently the most used fault current limiting technique to ensure the fault ride‐through of the multi‐terminal dc grid. However, its configuration will significantly prolong the fault current clearance time and increase the dissipated energy of dc circuit breakers. Therefore, this paper proposes a cost‐effective multiline fault current limiter for multiport application in multi‐terminal dc grids, which can effectively limit the rising speed of fault current through the integrated current‐limiting reactors without time delay and significantly reduce their negative impacts on the performance of dc circuit breakers by bypassing the current‐limiting reactors through the solid‐state switch units during the fault current interruption process. The topology and operation process of the multiline fault current limiter are introduced at first. Then, the economic comparison of the proposed device with the existing solutions is presented. Finally, the feasibility and superiorities of the proposed device are verified by extensive simulations and scaled‐down experiments. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

In this paper, we investigate power adjustment for multi‐agent systems in a wireless network where communication range is limited. The approach in which communication range is dynamically changed is utilized so as to save power consumption. However, since drawback of this approach is that agents cannot expand their communication range, it causes that communication between agents tends to be disconnected. To overcome this problem, we propose two power adjustment methods. In the first method, communication range is temporarily expanded in accordance with the number of neighboring agents. In the second method, agents change their communication range by using information on the total number of agents and relative distance between an agent and the farthest neighboring agent. Through numerical simulation, we show that the proposed power adjustment methods reduce power consumption and improve success rate for consensus. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

The auxiliary resonant snubber‐based converter (ARSC) has the advantages of low dead‐time effect and low switching loss. However, the auxiliary switches suffer from high reverse recovery loss and high reverse recovery current in high‐switching frequency application. Thus, the ARSC with saturable inductor is proposed to solve the problem above. In this paper, the operating principle of ARSC with saturable inductor is analyzed, and the duty cycle operating range that can realize ZVS in the full load range is deduced. The reverse recovery model of the auxiliary switching device body diode is established to analyze the circuit parameters' influence. Then, the influence of the saturable inductor on the reverse recovery peak current and reverse recovery loss is analyzed. Based on the analysis results, the saturable inductance parameters are designed. Finally, based on the experimental platform of the ARSC, the effectiveness of the proposed method is verified. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Driven by deep learning, great breakthroughs had been made in the field of target detection. Small target detection algorithms were widely used in industry, agriculture and other fields. But the small target had few available features and the loss of small target detail information in feature extraction. So it led to the low accuracy of the small target detection algorithms. In this paper, we proposed DBF‐YOLO algorithm based on the classical YOLOV5. The classical YOLOV5 algorithm with high speed. The detection speed of the minimum model could reach 24 ms. However, the deep network structure led to the low detection accuracy of small targets. Our proposed DBF‐YOLO algorithm was an improvement on the problem of small target information being lost. The main contributions of this article were mainly: First, a shallow feature extraction network was introduced in P1 layer, more details of small targets could be well retained. Second, by adding the feature fusion network of shallow feature map and the detection output part in the FPN + PAN layers, the algorithm's accuracy and generalization ability were significantly enhanced. Compared to YOLOV5, the performance of the DBF‐YOLO algorithm was significantly improved. On the validation set, mAP@0.5 and mAP@0.5:0.95 were increased by 8.80 and 5.90%, respectively. Recall was increased from the initial 34.50–41.80%. Precision was increased from initial 44.20 to 50.70%. On the test set, mAP@0.5 and mAP@0.5:0.95 were increased by 6.40 and 3.90%, respectively. Recall was increased 5.10%. Precision was increased 6.60%. Experiments had shown that the improved algorithm achieved good results in accuracy. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

In this paper, a novel doubly salient PM motor based on parallel path magnetic technology (PPMT) is presented. PPMT is an advanced magnetic control technology, which can be applied to linear and rotary actuators. First, the fundamental principle of the PPMT prototype is elaborated, and then a comprehensive research and analysis of PPMT motor is carried out, with particular emphasis on machine topology, operation principle and electromagnetic performance. The equivalent no‐load PM magnetic circuit model is established and equations for calculating the main PM flux are derived. Besides, a dual‐stator 6‐pole PPMT motor prototype has been developed to valid the analysis. According to the similarity of topology and operation principle, comparison between the FSPM machine and PPMT machine is conducted, which leads to the conclusion that the PPMT motor has lower cogging torque, lower torque ripple and better sine back EMF waveform, the PPMT motor is more suitable for servo applications compared to FSPM motor. Finally, to improve the power density of PPMT motor, FE parametric modeling method for designing the three coupled stator parameters, which are the PM width, the stator yoke width and the second airgap length is given out. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

To further improve the control accuracy of the magnetic field generated by particle accelerator electromagnets, it is necessary to model not only the magnetic hysteresis behavior but also the magnetic after effect, which has never been studied in the previous literature. In this paper, the gap magnetic field of an accelerator magnet was measured under various excitation conditions to acquire the experimental data required for modeling. Consequently, it was clarified that the magnetic after effect may influence the magnetic hysteresis behavior. Moreover, it was accordingly indicated that a novel modeling technique is required in addition to the conventional hysteresis models developed in the electric machine field. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

With the continuous increase of solar penetration rate, the volatility and randomness of solar irradiance have brought difficulties to grid management. Therefore, accurate characterization and maximum power point prediction of photovoltaic (PV) modules is important for maximum power point tracking and ensuring grid stability. The single‐diode model is that traditional characterization method commonly used but is inconvenient and complex because it employs an implicit equation. This paper proposes a novel model, based on an artificial neural network (ANN) with 4‐input and an explicit analytical model, for estimating the maximum power point and current–voltage (I–V) characteristics of PV modules under different operating conditions. Based on irradiance and temperature, the model considers the solar zenith angle and the solar azimuth angle, reducing the loss of accuracy of the solar spectrum and the solar incident angle. The input layer of the neural network has four neurons, which are solar radiation, module temperature, zenith and azimuth. The outputs are the four shape parameters in the explicit analytical model. Once the neural network is trained using historical measured data, it can build a shape model based on the temperature, solar radiation, zenith angle and azimuth angle at a certain moment, and estimate the maximum power point and I–V characteristics without solving any nonlinearity formula equation. Through experimental verification, it can be concluded that the 4‐input neural network after increasing the zenith angle and the azimuth angle is more accurate than the 2‐input neural network. Finally, the experimental data of four types of photovoltaic modules are used to verify the reliability and accuracy of the proposed model. Since the zenith angle and azimuth angle are considered, the accuracy of the model is improved and the calculation time is reduced. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

By virtue of a great progress of semiconductor fabrication technologies, micro electro‐mechanical systems (MEMS) have been developed for a wide variety of industries. For reliability of MEMS, micromaterials in MEMS need to be examined experimentally, and the obtained results need to be reflected in the design of MEMS. In addition, with the benefit of such the MEMS technology, nowadays mechanical characteristics of nanomaterials have been evaluated precisely. Nanoscale mechanical testing provides an opportunity to find new unique physical phenomena, such as plastic deformation in Si at intermediate temperatures. In this review paper, mechanical testing technologies developed for investigating micro and nanoscale materials are reviewed. Then, mechanical characteristics of Si and other materials evaluated using the micro and nano mechanical testing technologies are introduced. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Energy‐efficient train operation (EETO) in high‐speed railways (HSRs) is an extra cost‐effective and flexible means to promote energy‐saving. This paper first examines the energy consumption sources and energy‐saving measures of high‐speed trains (HSTs). Then presents the EETO in HSRs, including three categories: energy‐efficient train control, energy‐efficient train timetabling, and EETO considering train timetabling and driving strategy. Next, the current research status and progress on three aspects of EETO in HSRs, namely optimization algorithm, constraint condition, and oriented scenario, are sorted out. Finally, the research on EETO in HSRs has prospected and proposed directions that can study in depth in the future. By summarizing and reviewing the research on EETO in HSRs, it is hoped to furnish a reference for the railway sector to enhance quality, efficiency, and green, low‐carbon development. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Ice accretion on transmission lines seriously threaten the stable operation of power grid. The current research focuses on the discharge development and flashover characteristics of ice‐covered insulator. Due to the irregular structure of the insulator, very few studies concentrate on the mechanism of ice accumulation. In the present study, the local collision coefficient was determined, and then a three‐dimensional numerical model for rime ice deposit accumulation on polymeric insulator was established based on computational fluid dynamics and verified by experiments. Results show that the local collision efficiency decreases along the rod and the edge of the shed from the front stagnation point to both sides, whose maximum values are 0.73 and 0.74 in the research scope. Rime ice amount on the insulator increases with the increment of environmental parameters including wind velocity, liquid water content, and median volume diameter of water droplet. It was proved by numerical simulation and artificial tests that the position with a high collision efficiency is heavily covered with rime ice. Through comparison and analysis, the numerical model can well simulate the process of rime ice accretion on polymeric insulator, and the relative error of ice amount between calculated by the model and that obtained by the test is less than 18.8%. The research in this paper realizes the visualization of rime ice‐coating process of polymeric insulator. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Aiming at the problem of calculation speed for distribution transformer on‐line fault diagnosis, and the shortage of on‐line monitoring type and routine test data, an on‐line fault diagnosis model of distribution transformer based on parallel big data stream and transfer learning is established. First, the fault identification feasibility based on the present on‐line monitoring data of distribution transformer is analyzed, and the main indicators of on‐line fault identification are proposed. Second, the on‐line fault identification method based on ARIMA is proposed, on this basis, the on‐line fault identification model of distribution transformer based on big data stream is established, and the distribution transformers that possess hidden fault are selected, which would be diagnosed further. In order to improve the efficiency, the model is completed on Storm platform in parallel form. Then, the distribution transformer fault diagnosis indicator system is construct. In order to achieve fault diagnosis of the above selected distribution transformers, the effective fault information from other distribution transformers is extracted using the transfer learning algorithm TrAdaBoost, which is used as auxiliary data for the fault diagnosis tool training of the distribution transformer to be diagnosed. At the same time, the model is completed on Storm platform to improve the efficiency. Finally, based on the distribution transformer fault data, the distribution transformer fault diagnosis is simulated, and results show that the fault diagnosis is accurate and efficient. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

This paper proposes a simple and efficient method to weaken the cogging torque of the direct‐drive permanent magnet motor with inner enhance force (IEF‐DDPMM). It is characterized by adjusting the relative position of the inner and outer permanent magnets so that the cogging torque generated by the inner and outer air‐gaps is opposite in phase, thus eliminating or weakening a certain order of cogging torque. For IEF‐DDPMM, the expression of cogging torque and the variation law of cogging torque with the staggered‐pole angle are derived by the analytical method. The relationship between optimal staggered‐pole angle and the pole‐slot combination is analyzed. The influence of staggered‐pole on average torque and torque ripple is analyzed. Finally, the commercial finite element software ANASYS‐Maxwell is used for simulation, and the simulation results are consistent with the analytical results. The results show that the cogging torque of IEF‐DDPMM is the linear superposition of cogging torque from inner and outer parts; choosing a suitable staggered‐pole angle can effectively weaken the cogging torque and reduce the torque ripple. Besides, the average torque reduction caused by the staggered‐pole method can be compensated by adopting the internal power factor angle compensation strategy. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

The rapid growth of distributed energy resources integrated in distribution systems leads to an increasing need of continuously and automatically changing the system topology to realize the economic operation of distribution networks. This paper proposes an optimization model of dynamic reconfiguration for distribution networks based on a new method of time series analysis. Equivalent daily curve considering time‐varying nature of distributed generator and load demands is divided by an improved fuzzy C‐means clustering algorithm, where the indicator of section function is set to find the optimal reconfiguration time intervals. The uncertainty of distributed generator outputs and load demands is described by the interval algorithm. Then the affine Taylor expansion is adopted to solve the interval power flow equations. The reconfiguration optimization model is solved with decimal particle swarm optimization algorithm based on loop search. The optimal dynamic reconfiguration of a modified 70‐bus test system with distributed generators is carried out and the simulation results demonstrate the effectiveness and superiority of the proposed method. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Brushless doubly‐fed generator (BDFG) is a most promising candidate for the wind turbine due to its simple structure, high reliability, and low converter capacity. To improve space utilization and achieve higher power density, this paper proposes a new dual‐stator BDFG (DS‐BDFG), which provides two separated windings, namely, the power winding (PW) placed in the outer stator and the control winding (CW) placed in the inner stator. A cup‐shaped rotor sandwiched between the inner and outer stators consists of the short‐circuited coil and the salient‐pole core. The coupling between the PW and the CW is realized via the rotor based on the general airgap magnetic field modulation theory for electrical machines. A prototype of the DS‐BDFG with rated PW power of 7.2 kW is designed and optimized. The keys of this paper are the power equation derivation, the determination of pole/slot combinations, and the rotor optimization. The electromagnetic characteristics and unbalanced magnetic force of the DS‐BDFG are analyzed by 2D finite element analysis to verify the reasonableness of the proposed model. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

In this paper, we address the task of segmenting small organs (i.e., the pancreas) from abdominal CT scans. As the target often occupies a relatively small region in the input image, deep neural networks can be easily confused by complex and variable backgrounds. We propose a method that uses a parallel multi‐scale network with an attention mechanism for pancreas segmentation, which can better grasp the balance between the semantic segmentation, classification, and localization tasks. We use a parallel network to connect the feature maps between different bottleneck layers, which contain rich semantic information and complete spatial information. We apply an attention module to enhance the key features of semantic information. Then, we fuse the two modules and apply the fused module as attention information on the feature map to ensure the full fusion between contextual semantic information and spatial information, thereby improving segmentation accuracy. We conduct extensive experiments on the NIH pancreas segmentation data set. In particular, our model achieves a mean coefficient Dice of 86.6. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive generalized dynamic inversion (AGDI) control methodology to the problem of angular position tracking of the rotary flexible joint system while suppressing joint vibrations. The AGDI control is composed of an equivalent control, which is responsible to enforce the prescribed constraint dynamics that encompasses the control objectives and control expression is obtained by inverting the constraint dynamics using Moore–Penrose generalized inversion. Furthermore, robustness is achieved by integrating a switching (discontinuous) term based on the sliding mode control method. The sliding mode gain of the switching control law is made adaptive for chattering reduction and improved tracking capability. The performance of AGDI control is evaluated through both numerical simulation and experimental investigations executed on Quanser's rotary flexible joint system. Simulation and experimental results reveal that the AGDI control is quiet capable to achieve the desired response curves against set‐point tracking and disturbance rejection scenarios. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

To solve the problem of battery capacity degradation caused by high current magnitudes and frequent current variations in electric vehicles (EVs), a hybrid energy storage system (HESS) incorporating high energy density storage (battery) and high power density storage (ultracapacitor) is proposed. The HESS contains a multi‐port DC‐DC converter, which controls the energy flow among the battery pack, the ultracapacitor (UC) pack, and the port of output. Considering the state of charge (SOC) of UC, the speed, and demand power of EV, an energy management strategy (EMS) based on fuzzy logic control (FLC) for the HESS is proposed in this paper. According to the strategy, the battery pack serves as a base power source while the UC pack serves as a transient power source so as to smooth the battery pack current and prolong the battery life. Simulation and experiment results illustrate the effectiveness of the proposed strategy for UC/battery HESS in EVs. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Dusty weather is a most important disturbance for photovoltaic (PV) systems, that may result in PV panels exposure to partial shading conditions where the PV panel characteristics show several maximum power points. Moreover, dusty weather conditions may lead to rapid changes in solar radiations. Thus, conventional power system tracking algorithms are not being capable to track the global maximum power point (GMPP). This paper provides an advanced maximum power point tracker for partial shading and dusty weather conditions. In the presented technique, scanning and power lock conditions are added to the conventional perturbations and observation technique in photovoltaic system under partial shading or dusty weather conditions. This tracking method scans the PV characteristic in steps and stores all the maximum power points in order to get the GMPP. The proposed tracker is capable to identify the uniform weather conditions to save it from having unnecessary scanning. In addition, this tracker permits dynamic tracking of the GMPP under dusty weather conditions with less fluctuations and stable operation compared to the conventional methods. Accurate and fast conversion to the GMPP during various weather conditions is accomplished using the proposed technique. The proposed modified perturb and observe technique is validated using simulation and practical results. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Intrinsic localized mode (ILM) can be generated in multiple mass‐spring systems using nonlinear springs, and ILM has attracted a lot of attention due to its special vibration modes that are not found in linear systems. However, the difficulty of creating nonlinearity remains a barrier to ILM research. In this paper, we propose a rotating magnetic spring based on magnetic nonlinearity. The nonlinear restoring properties of the proposed magnetic spring are confirmed by finite element method (FEA). Finally, using the restoring properties obtained from FEA, it is shown that ILM can be excited by MATLAB numerical analysis. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Adding a Battery Energy Storage System (BESS) in the vicinity of renewable energy sources is a feasible solution to overcome the inherent negative impact caused by their random power fluctuating problems. Power conversion system performs as an interface between the battery packs and grid. The control method of power conversion system is essential to BESS. Finite Control Set Model Predictive Control (FCS‐MPC) is favorable to be chosen as the core control method for grid‐injected current regulation in such a system due to its outstanding benefactions, including fast dynamics, multiobjective optimization and simple implementations. Hence, this paper presents a FCS‐MPC with active damping feature for an Inductor(L) Capacitor(C) Inductor(L) (LCL)‐filter‐based power conversion system first. However, it is also well known that the practical effect of model predictive control significantly relies on the accuracy of mathematical model embedded within the controller. Parametric mismatch of the model tends to generate a prediction error, leading to a deterioration of the power quality of power conversion system or even instability issues furthermore. Therefore, this paper proposes an additional model correction strategy based on a comprehensive on‐line analysis on the difference between predictive and real value of respective voltage or current signals during the ever‐lasting several grid cycles to eliminate the influence of parametric mismatch. Simulation of an LCL‐filter‐based power conversion system is carried out to verify the validity of the theoretical analysis and control method in this paper. Finally, experimental results obtained from a down‐scaled prototype are provided to confirm the feasibility of the overall control strategy. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

This paper presents a parameter estimation based method to diagnose winding deformation and turn‐to‐turn fault of power transformers. First, an estimation model of transformer parameters is built, in which five equations are taken in account including voltage loop equation, active loss equation, input impedance equation, no‐load current equation and no‐load loss equation. Then, particle swarm optimization (PSO) is used to solve the model, and the characteristics of estimation data under winding faults are analyzed. At last, based on the estimation data, random forest (RF) algorithm is employed to classify transformer states and realize fault diagnostic. The simulation results show that the proposed parameter estimation method has high precision and are not affected by the factors including load power factors and the type and degree of winding deformation, and the impact of load rates can be avoided; the fault diagnostic scheme based on RF is quite sensitive and effective. The proposed method eliminates the need of transformer outage, and compared with other methods based on parameter estimation, it can distinguish winding deformation and turn‐to‐turn fault. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

The axial flux permanent magnet (AFPM) machines with the parallel magnetic circuit (PMC) rotor can effectively improve its torque density. The PMC rotor consists of two sub‐rotors, radial Halbach array permanent magnet (PM) and tangential PM. The magnetic fields generated by two sub‐rotors are not in the same 2‐D analytical planes. In this paper, an equivalent analytical model of PMC‐AFPM machines is proposed to solve this problem. The radial Halbach array PM rotor is equated to the axial Halbach array PM rotor based on the equivalent transformation principle. The exact subdomain models of equivalent axial Halbach array PM rotor and tangential PM rotor are built respectively and then superimposed to obtain the magnetic field distribution. The electromagnetic characteristics of the PMC‐AFPM machine under no‐load and load conditions are calculated by the proposed analytical model and compared with calculations of the finite element model (FEM). The results verify the accuracy of the equivalent analytical model. The comparative analysis of the AFPM machines with PMC rotor and tangential PM rotor verifies the advantage of PMC rotor configuration to improve the torque density. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

A nonlinear multivariable highly coupled system that is susceptible to both internal and external perturbations is the permanent magnet synchronous motor. A new reaching law‐based control approach that combines a sliding‐mode disturbance observer and an enhanced two‐vector model predictive current control is suggested in order to enhance the dynamic performance of the permanent magnet synchronous motor and its robustness to system disturbances. First, a new reaching law is developed in this paper to resolve the conflict between the traditional reaching law's chattering of the system and the reaching time of the sliding‐mode surface. This new reaching law not only makes the system's response more rapid but also reduces chattering; second, taking into account the possibility of systems with internal parameter uptake and external load disturbance, a sliding‐mode disturbance observer based on t is developed. Finally, the q‐axis current obtained from the speed sliding‐mode control rate is added to the model predictive current control, and an optimal duty cycle model predictive current control based on space vector modulation is designed because the traditional two‐vector model predictive current control is to optimize the duty cycle and voltage vectors separately, and the switching frequency is not fixed. The method suggested in this research may successfully suppress the sliding‐mode control system chattering and increase the resilience and dynamic response performance of the speed control system, according to simulation findings compared to the traditional reaching law and traditional duty cycle. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Federated Learning (FL) is a distributed machine learning paradigm that enables multiple data holders to collaborate on building machine learning models while preserving the privacy of their data. FL can be categorized as horizontal or vertical, depending on the distribution characteristics of the data. Specifically, horizontal FL uses data partitioned in the sample space, whereas vertical FL uses data partitioned in the feature space. Traditional vertical FL methods aim to facilitate collaboration among clients to infer a single global target. However, these methods may be impractical because each client often has a unique target to be inferred. In this paper, we propose a novel vertical FL method, called personalized vertical federated learning (pvFed), which addresses this limitation by allowing each client to perform inferences specific to their individual task. To the best of our knowledge, no existing method currently addresses this limitation. The objective of pvFed is to construct a global model that generates a representation vector to support client inference. The global model, constructed using distillation and dimensionality reduction, takes a sample ID common to all clients as input and outputs a sample‐specific representation vector. Clients utilize the intermediate representation of their own model and the representation vectors output by the global model for inference. Because these vectors are not dependent on client‐specific tasks, clients can repurpose them for any additional tasks. Our experiments, conducted on two distinct data types—image and tabular data sets, under a vertical partitioning where each client had its own specific task, demonstrated the efficacy of vectors generated by the global model in pvFed for client inference. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Incipient faults in interface converters can potentially lead to catastrophic failures. Detection of incipient faults contributes to proactive fault management and predictive maintenance, which effectively improves system reliability. In this paper, a detection method in correlation space is proposed to address this problem, which is based on the inherent feature of correlation changes when a fault occurs. First, a convolution process is used to weaken noise and highlight the correlation feature. Second, the current signals are transmitted to correlation space by using the Pearson correlation coefficient. Third, an accumulation and a reference compensation method are designed for enhancing the features and equalizing influence of reference adjustment. Finally, a fault detection rule is designed based on correlation features and fault excitation. Experiments on a hardware‐in‐loop(HIL) semi‐physical platform indicate that the proposed method outperforms three commonly used correlation analysis algorithms. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

The cascaded H‐bridge (CHB) converter can achieve rich functions, such as active rectification, active power filtering, distributed energy storage, etc. The control of the CHB converter is a multiobjective optimization problem. Finite control set model predictive control (FCS‐MPC) is an effective method developed in recent years to solve this problem. However, because of the large amount of calculation, traditional FCS‐MPC cannot be directly used to control the multilevel CHB converter. It is also difficult to tune the weighting factors in traditional FCS‐MPC. To solve these problems, this paper proposes an improved sequential MPC (IS‐MPC) method for the CHB converter. The proposed method divides the control objectives of the CHB converter into different layers according to their priorities and the control degrees of freedom of each layer can be adjusted according to the system state. Simulation and hardware‐in‐the‐loop (HIL) implementation on a three‐phase 7‐level CHB converter shows that the proposed method can effectively control the ac current and submodule voltage of the CHB converter and reduce the switching loss. Moreover, it significantly reduced the calculation burden of traditional FCS‐MPC, and the use of the weighting factors is also avoided. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

This paper proposes a new eye‐tracking data correction method for the development of an automatic detection system for difficult‐to‐read characters in Japanese text. The proposed method, which adjusts the x‐axis of the eye, was compared with existing methods that adjust only the y‐axis. Data correction was performed for each method, and the detection accuracy of difficult‐to‐read Japanese characters was calculated and compared. The experimental results indicate that adjusting the x‐axis and y‐axis can improve the detection accuracy of difficult‐to‐read characters in Japanese text. This research advances easy correction techniques for difficult‐to‐read character detection in Japanese text and broadens the use of eye trackers, including low‐cost eye trackers. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]