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The 1974 trade act substantially increased the executive branch's authority in trade negotiations through the granting of fast‐track and Section 301 authority. This paper evaluates the effect on U.S. voting behavior resulting from trade with Japan over 1976–1992 time period. To capture U.S. trade exposures to Japan, we develop the Bartik index from Autor et al. (2013) for import competition with Japan and show that local exposure to import competition had statistically significant negative impacts on Republican presidential candidates over the 1976–1984 period. Although the second Reagan administration used Section 301 to open Japan's markets and Japanese firms shifted production to the United States, job‐creation effects of exports and foreign direct investment did not have any influence on voting outcomes. [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]

Japanese farms currently occur on a small but expanding average physical scale and annual sales, in a decreasing and aging labor force, increasingly abandoned farmland and massive access of corporations powerful in capital and management. The government has proposed to accelerate agricultural growth through adopting high‐level smart technologies in large‐scale farms, with a series of favorite policies and measures. This paper reviews the connotations, characteristics and technologies of smart agriculture in the views of Japanese scholars, the practice of smart agriculture in Japan, followed by a case study on the smart rice production model 'NoshoNavi1000'. The results show that Japanese scholars have conducted in‐depth and massive studies in smart agriculture, with regional characteristics in the orientations on labor‐saving, precise management, disaster reduction and inheriting traditional farming skills. The government's ambitious objective is supported by its all‐round policy package, including project demonstration, supporting services, environmental improvement, education and training, and overseas outreaching. Closely combined with the planting structure of Japanese agriculture, rice production is an important sector for the R&D and application of smart farming in the form of either individual technologies or models. The policies and technologies have achieved benefits in saving labor and production costs, and improving the profit margin. Through years of on‐farm application, 'NoshoNavi1000' has been developed to involve the major technological components of smart agriculture. It has contributed to improving rice yield, production efficiency and profitability of large‐scale farms, through real‐time data collection, comprehensive and professional data mining, and specific and practical feedbacks. © 2022 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Japan's fine ceramics (advanced ceramics) industry, which reached US$30 billion of production in 2018 (an annual growth rate of 6.3%), accounts for 40% of the global market. The mission of the Japan Fine Ceramics Association (JFCA), which consists of 116 corporate members related to fine ceramics, is to further promote the development of this industry. In this paper, we describe the recent trends of fine ceramics industry and the latest JFCA's activities, including the publication of the FC Roadmap 2050 (2021 edition). This roadmap addresses fine ceramics technologies and products that will meet increasingly diverse needs of society, market, and industry in the year 2050. [ABSTRACT FROM AUTHOR]

Beech has long been the subject of ecological research in many fields. Recent studies using new methods are now being conducted. This special feature focuses on local beech forests and offers a collection of articles to readers of Ecological Research. [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]

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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]

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]

Data mining extracts useful knowledge from big data. The extracted knowledge in data mining is often represented by association rules, and association rules can be also used for classification. However, when association rules for classification (called class association rules) are extracted, a large number of data with class labels are necessary, which requires a lot of cost of manual annotation. Therefore, this paper proposes a semisupervised learning method for rule extraction, where a small number of labeled data and a large number of unlabeled data are used to efficiently extract class association rules. In detail, this paper proposes two types of classifiers using class association rules: one is a classifier based on semisupervised learning, and the other is that based on both supervised and semisupervised learning. The second method builds several classifiers using supervised learning and semisupervised learning and the classification results of these classifiers are integrated to make the final decision. As an association rule mining method, Genetic Network Programming (GNP), which is one of the graph‐based evolutionary algorithms is used. GNP has shown distinguished classification ability in some applications; therefore, one of the other objectives of this paper is to extend the applicability of GNP to data mining based on semisupervised learning. In the experiments, the classification accuracy is evaluated using some benchmark datasets by comparing with some conventional methods. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

This paper presents a new fault location technique based on the fundamental frequency positive sequence fault components, featuring robustness to the operating state of the prefault system and fault path resistance. Using the fundamental frequency positive sequence fault components, the technique described in this paper does not require the user to know the fault phases and fault type in advance or use a line zero sequence parameter, which is not only difficult to obtain but is also affected by soil resistivity and thus may be variable. In addition, based on the fault components protection principle, the method is inherently unaffected by the operating state of the prefault system. Accurate transmission lines' fault location is important for expediting their repair or reducing outage times, so the proposed technique could improve the quality and security of the electricity supply. This paper describes the characteristics of the proposed technique and assesses its performance by using the PSCAD/EMTDC simulation. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

Unlike a limited number of success stories of industrial clusters such as Silicon Valley and the Third Italy, many local industrial clusters in advanced economies are shrinking due to severe international competition. Some firms, however, continue to be resilient even in struggling clusters. This paper argues that managing a balanced web of trust‐based "pipelines" and "buzz" in the glocal relational space – balancing detachment and embeddedness – plays a crucial part in the success of these firms. In recent decades, economic geography has had a significant focus on intra‐ and inter‐firm pipelines and buzz inside and outside the cluster. Theoretical debates on the topic have flourished. However, empirical case studies, particularly from Japan, are limited. This study attempts to fill the gap through a longitudinal case study of leading firms in a local industry – the glove‐related industry of Shikoku, Japan. The paper suggests that bringing together the flying geese theory with the pipeline and buzz concept furthers our understanding of the processes at work. The study's results suggest that successful firms – "the lead geese" – manage long‐held pipelines as well as newly established ones in the glocal relational space. The paper concludes that it is forward‐looking, international‐oriented leading firms which have the potential to make the cluster resilient in sales and employment rather than the cluster taking the dominant role itself. [ABSTRACT FROM AUTHOR]

In recent years, the power quality problem has become more complicated in power grids because of the extensive usage of power electronics and multisource multitransformation features. The method, based on physical characteristics such as time domain, frequency domain and transform domain, is facing challenges in terms of adaptability, algorithm efficiency and accuracy for the recognition of complex disturbance recognition. The bidirectional long short‐term memory network is an algorithm in deep learning. It is based on data for characterization learning, which can effectively overcome the problem of information loss and generalization ability of physical methods. Moreover, it has the characteristics of memory, which can simultaneously consider historical information and future information and can better learn data features with time series characteristics. Aiming at the transient voltage sag time series data, this paper proposes a recognition method of the voltage sag causes based on the bidirectional long short‐term memory network's extraction eigenvalue, the full‐connection layer's high‐dimensional feature extraction and the Softmax network layer's classification. The experiment uses simulation data and measured data to prove that the model has good recognition ability and good antinoise performance in the recognition of voltage sag causes and can be reliably applied in practical engineering. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

Very‐short‐term load forecasting (VSTLF) predicts the load from minutes to 1‐hour timescale. Effective forecasting is important for in‐day scheduling of the power systems. In this paper, a VSTLF method based on empirical mode decomposition and deep neural network is proposed. The extreme point span is used to determine a proper empirical modal number, so as to successfully decompose the load data into different timescales, based on which the deep‐neural‐network‐based forecasting model is established. The accuracy of the proposed method is verified by the testing results in this paper. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

This paper proposes a novel model for the analysis and design of a virtual synchronous generator (VSGs). To enhance the stability in power systems with distributed generators, the concept of VSG has been proposed. The VSG is designed based on the swing equation of a synchronous generator ignoring the dynamics of current flowing through an interconnection reactor. However, responses obtained with actual system are different from those calculated from the model. It implies that the VSG cannot be correctly analyzed and designed by using the conventional model. This paper constructs an accurate VSG model considering the dynamics of current. Moreover, we address the stability analysis of the VSG and reveal a relation between the parameters of VSG controller and the rated power/energy capacity of energy storage systems equipped with the VSG based on the proposed model. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

This paper deals with load extrapolation methods and algorithms for the estimation of the energy efficiency of induction motors, starting from the electrical data generally available after parameter identification. The main aim of this paper is to help the readers move from the theoretical analysis to the experimental verification, which is suitable to be included in its own testing. For this reason, step‐by‐step algorithms are included in this paper. The first part deals with the determination of energy efficiency based on an original load extrapolation. Finally, experimental verification of the high‐power and high‐voltage induction motors is provided. Comparison between the values of the in–out method and load extrapolation method confirms the validity of the proposed algorithms. Compared to the direct in–out method, the error of the load extrapolation proposed in this paper is less than 1%.The load extrapolation method is mainly used for the high‐voltage and high‐power asynchronous motor. The high‐voltage and high‐power motor can be used to drive a low‐power motor to achieve coaxial twin trawling in the absence of a suitable power supply and matched load. The energy efficiency of a high‐power motor under different loads are measured and estimated using a mathematical fitting method. The estimated energy efficiency curves in the whole load range can be used to evaluate the high‐power and high‐voltage induction motor. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

This paper reports an easy‐to‐fabricate micro battery that can be directly printed on a bio‐sensing device such as an immunoreaction‐based disposable complementary metal oxide semiconductor bio‐sensing chip. The printed Mg and AgCl films were used as both cathode and anode materials, while the inspection solution for the bio‐sensing was utilized as an electrolyte, which simplified the structure of the battery. A micro battery fabricated on the printed circuit board, in which each electrode size was 3 × 3 mm2, could generate an open circuit voltage as high as 1.58 V, a maximum power as high as 1.4 mW and a total stored energy as high as 400 mJ. Wireless sensor test platforms self‐powered by the developed on‐chip battery successfully worked in a 0.14 M NaCl solution, and digital data could be successfully transferred using both optical and capacitive coupling methods. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

The proliferation of drilling expeditions focused on characterizing natural gas hydrate as a potential energy resource has spawned widespread interest in gas hydrate reservoir properties and associated porous media phenomena. Between 2017 and 2019, a Special Section of this journal compiled contributed papers elucidating interactions between gas hydrate and sediment based on laboratory, numerical modeling, and field studies. Motivated mostly by field observations in the northern Gulf of Mexico and offshore Japan, several papers focus on the mechanisms for gas hydrate formation and accumulation, particularly with vapor phase gas, not dissolved gas, as the precursor to hydrate. These studies rely on numerical modeling or laboratory experiments using sediment packs or benchtop micromodels. A second focus of the Special Section is the role of fines in inhibiting production of gas from methane hydrate, controlling the distribution of hydrate at a pore scale, and influencing the bulk behavior of seafloor sediments. Other papers fill knowledge gaps related to the physical properties of hydrate‐bearing sediments and advance new approaches in coupled thermal‐mechanical modeling of these sediments during hydrate dissociation. Finally, one study addresses the long‐standing question about the fate of methane hydrate at the molecular level when CO2 is injected into natural reservoirs under hydrate‐forming conditions. Key Points: The arc of laboratory and modeling research focused on hydrate‐bearing sediments has been based on the findings of major field programsThe Special Section highlights hydrate formation from vapor phase methane in porous media and the role of fines in hydrate reservoirsThe Special Section underscores the importance of coupled mechanical and thermodynamic models for tracking gas hydrate reservoir evolution [ABSTRACT FROM AUTHOR]

Cryptomeria japonica (sugi) and Chamaecyparis obtusa (hinoki) are major Japanese timber species whose plantation area accounts for 44 and 25%, respectively, of the plantation forests in Japan. Physiology, anatomy and ecology of the species have been intensively studied for this half century, which now forms a huge stock of information. These data, however, were scattered in diverse sources, including papers, bulletins of research institutes, reports of other kinds and books, and were presented in nonstandardized, diverse styles in each source. This paper provides a database (SugiHinoki DB) that compiles 177 plant traits of sugi and hinoki from 364 primary sources published since 1950. The compiled traits include physiological, morphological, anatomical and biochemical features that are recognized as relevant to life history strategies, vegetation modeling and global change responses. Collected data have been obtained under different environmental conditions, for plants with different ages and for organs with a different age or different position, which provide information of within‐species variation for a given trait. Each data entry is accompanied by detailed ancillary information describing the site of measurement, stand of measurement and detailed measurement conditions, which help users account for data variations as a consequence of phenotypic plasticity and genetic variations and to filter data. To provide data in a consistent format, the data and the ancillary information were standardized, and units were converted. After data compilation, outliers were detected by calculating interquartile range for each trait per each species. As of August 2019, SugiHinokiDB contains 24,683 data entries (16,410 for sugi, 8,273 for hinoki). As sugi and hinoki are major plantation species in Japan, the data mostly came from study sites in Japan (from Hokkaido in the north to Yakushima island in Kyushu to the south) but were also obtained from arboretums or plantation forests in Taiwan, Korea and China. Data were largely obtained from plants in plantation forests but also from plants in natural forests and under experimental conditions. The improved availability of trait data offered by SugiHinokiDB provides new research opportunities, such as the intensive parameterization of vegetation models for a more accurate prediction of climate change impacts. [ABSTRACT FROM AUTHOR]

In this paper, a novel control method based on model predictive control (MPC) and torque sharing function (TSF) is addressed to minimize the torque ripple of switched reluctance motor (SRM). Firstly, an accurate SRM model is established based on flux‐linkage characteristic curves obtained from the locked rotor test, which can predict the future operation state of the SRM drive system. Secondly, an improved TSF curve is proposed, and genetic algorithm is used to optimize TSF parameters to reduce torque ripple in commutation region. Besides, the MPC is integrated into the TSF control framework to replace the traditional hysteresis controller, and establishes a new TSF‐based model predicted torque control (MPTC) method, which avoids the frequency conversion problem caused by torque or current hysteresis controller. Further, a sector division scheme is addressed to decrease the number of candidate voltage states, whereby the computation of the controller in each sampling period is effectively reduced. Finally, in order to verify the correctness of the MPTC method, it is compared with the traditional direct instantaneous torque control (DITC). Both simulations and experiments on a three‐phase 12/8 pole SRM have confirmed that MPTC scheme can not only decreases torque ripple and stator copper losses, but also has higher efficiency than DITC. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Machine learning (ML) has become a rising sophisticated technological application trend in the electrical industry in recent years. Such innovation provides optional methodologies for many existing applications, such as power and load profile forecasting, reliability evaluation, substation behavior detection and state observation of electrical equipment, and so on. This paper presents a review of various supervised and unsupervised ML techniques and applications for electrical power systems, including generation, transmission, distribution and micro‐grid. The algorithms and applications are mainly summarized from IEEE journals and the interest of this paper shows the roles and developments of most used algorithms and its corresponding extensions and performance in different applications. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

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This paper intends to derive a detailed representation and attain a suitable computation method for the traction network. The transient analysis of the lossy multiconductor transmission lines (MTLs) is performed considering the electromagnetic characteristics of the rail track, which accounts for the frequency‐dependent and distributed nature of the line parameters over the entire frequency range for the traction network. Due to the practical difficulty in wideband parameter determination and the complex layout for the traction power supply system, the model implemented has been validated by comparison to several field tests that are carried out on a real high speed railway line for the purpose of identifying possible power quality problem. The good high representation of the frequency‐dependent parameter and the numerical solution will provide a reliable method for the transient analysis of the electrified railway. Afterwards, the transient calculation using vector fitting and modal decomposition is presented to evaluate how the emission is propagating along the MTLs of the electrified railways. Performing detailed and suitable analysis is necessary to determine the overvoltage conditions during the transients. The outcome of the analysis could be beneficial for the transient protection study in the electrified railway system. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

To comprehensively investigate how regional household contribute to national carbon emission, this paper evaluates both the direct and indirect Japanese household carbon‐based emissions by using an environmentally extended input‐output table as well as the regional household consumption inventory. The results indicate that the household sector is the key driving force behind Japan's emissions, and it accounts for approximately 80% of the country's total emissions when indirect emissions are included. Moreover, significant regional differences exist in terms of the carbon footprint, which is in part caused by household and fuel‐type differences found across the country. Finally, this paper suggests that the impact of regional household and fuel‐type differences on Japan's overall carbon footprint should be a primary focus when practitioners design customized strategies to decarbonize Japan at the subnational scale. Plain Language Summary: As the major emitters of Greenhouse Gases, households release emissions both directly through combusting fossil fuels, and indirectly through by consuming goods or service. Carbon footprint represents how a household can impact the environment in both ways. Here, household lifestyles have major ramifications on the environment, and lifestyle choices may impact on the carbon footprint gap among different population groups. Our results show that households are responsible for approximately 80% of the country's total emissions. An increment of 4.19 t‐CO2 has been found at per capita level from direct to total accounting. Moreover, significant regional differences exist in terms of the carbon footprint by fuel‐type differences. We suggest that the impact of regional household and fuel‐type differences on Japan's carbon overall footprint should be a primary focus when practitioners design customized strategies to decarbonize Japan at the sub‐national scale. Key Points: This paper evaluates both the direct and indirect Japanese household carbon‐based emissions by fuel typesIndirect emission accounts for approximately 80% of the changes in terms of the country's total emissionsSignificant regional differences exist on household carbon footprint, which caused by fuel‐type consumption differences across the country [ABSTRACT FROM AUTHOR]

The edge effect due to the special structure of two degree‐of‐freedom direct induction motor (2DoFDDIM) results in three‐phase asymmetric current, high torque ripple, more loss and low efficiency. In this paper, to weaken edge effect of 2DoFDDIM rotary part, poles optimization is carried out considering the constraint of rotary arc‐armature size. In order to obtain optimum parameters and high efficiency of rotary part, The Taguchi method of multi‐objective optimization design is carried out. The 3D finite element models of final design and initial design of rotary part and initial design prototype are established, respectively. Through the comparison between the final design simulation, initial design simulation and test results, the results indicate that the performances of 2DoFDDIM rotary part have a great improvement after optimizations and verify the feasibility of Taguchi method used for 2DoFDDIM multi‐objective optimization design. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Halbach array has been more frequently used in the field of electromagnetism in recent days because of some unique advantages such as the self‐shielding and sinusoidal field distribution. In order to reduce the impact of the high order harmonic content of the air‐gap flux density of the interior permanent magnet synchronous motor (IPMSM) on the performance of the machine, a new non‐uniform Halbach array structure is proposed in this paper. Firstly, an equivalent analytical model is established to analyze the influence on the air‐gap flux density. At the same time, the interior permanent magnet synchronous motor model is built by using the finite element analysis. Then, the relationship between the air‐gap flux density and the four design parameters (the thickness of the permanent magnet, the magnetization angle, the angle of the permanent magnet, and the proportion of auxiliary magnetic poles) is analyzed by using the Taguchi method orthogonal experiment. The total harmonic distortion (THD) of air‐gap magnetic fields and its fundamental harmonic amplitude are employed as the evaluation criteria. Finally, the effectiveness of this optimization method is verified by comparing the harmonic content of the air‐gap flux density before and after the optimization. © 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]

Convolutional neural networks possess the capability of feature learning and nonlinear mapping, which has significant advantages in classifying and recognizing optical fiber vibration signals. In order to further enhance the recognition rate of vibration signals, this paper combines wavelet transform with convolutional neural networks and designs a convolutional layer based on parameterized wavelets. In this layer, the initial signal is convolved with parameterized Laplace wavelet dictionaries to complete the wavelet transform. Such customized filters make more sense than filters with randomly initialized parameters for traditional CNNs. Simultaneously, we introduce the channel attention mechanism to enhance the features of the filtered signals. Subsequently, standard CNNs are employed to extract and process signal features, ultimately utilizing a multi‐layer perceptron for recognition and classification. The experimental results show that the network model possesses better recognition refinement. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Accurate prediction of employment rate of graduated students can greatly help education authorities to make informed decisions as well as for universities to adjust their teaching plans. Unfortunately, prediction of the employment rate of graduated students is still a difficult problem because the historical employment rate data exhibits fluctuations. In this paper, a new method is proposed for employment rate prediction using fractional gray GM(1,1|$$ \mid $$sin) model, which aims to alleviate the effect of data fluctuation on prediction accuracy and increase the contribution of new data in the prediction procedure. To further decrease prediction error, a Fourier series is adopted to model the residual series. The proposed model, called Fourier Fractional GM(1,1|$$ \mid $$sin) Model (FFGMsin), is used to predict the employment rate of graduated students of Yanshan University from 2010 to 2019. Results show that the proposed method can obtain more accurate prediction results than GM(1,1) model and its variants. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

The architecture of software‐defined network (SDN)enhances the openness of the network by separating the control and forwarding functions, but the centralized SDN control form is susceptible to distributed denial of service (DDoS) attacks. In this paper, entropy value and back‐propagation neural network (BPNN) were applied to the DDoS attack detection of SDN, and then the two detection algorithms were simulated in MATLAB software and compared with the K‐means algorithm. The results showed that in the face of four DDoS attacks, SYN Flood, ACK Flood, UDP Flood and ICMP Flood, the BPNN‐based DDoS detection had higher accuracy and less detection time; the switch that adopted the BPNN‐based DDoS detection algorithm adjusted the traffic ratio back to normal level faster when facing DDoS attacks, reducing the impact on other switches and maintaining the traffic stability of the network. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]