This paper presents a hardware/software (HW/SW) design of a rational filter to remove mixed noise detected in medical color images. The used filter is the Vector Directional Distance Rational Hybrid Filter (VDDRHF). It is an effective filter for mixed noise reduction and fine color image details preservation, which is a very important topic for medical images. Our proposed design incorporates a sequence of optimizations for performance improvement, including: pipeline optimization to maximize throughput and minimize latency, parallel processing of the VDDRHF algorithm to process multiple pixels concurrently, some nonlinear function approximations to reduce hardware complexity and power consumption. Extensive validation based on SoPC (System on Programmable Chip) using FPGA board has demonstrated the effectiveness of our proposed architecture in preserving color image quality by removing mixed noise and significantly reducing filtering execution time, setting a new benchmark for real‐time medical color image processing. © 2026 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.
During the manufacturing of metal materials, various complex surface defects are inevitably generated, and complex background textures easily cause defect misclassification and missed detection. To boost the detection robustness and real‐time performance of lightweight detection networks, this paper designs a metal surface defect detection model named YOLO‐CTU based on YOLOv11n. Three targeted structural optimizations are proposed: a channel‐enhanced ADown (CED) module to mitigate fine‐grained feature loss during downsampling, a C3k2_UIB unit to strengthen extraction of tiny defect textures, and Triplet Attention embedded on the newly added P2 shallow feature fusion branch to excavate low‐level small defect features and suppress background noise. Experiments are carried out on GC10‐DET and NEU‐DET datasets. Compared with the baseline YOLOv11n, the proposed YOLO‐CTU achieves a 2.8% mAP@0.5 improvement on GC10‐DET and maintains competitive detection accuracy on NEU‐DET with fewer parameters and lower computational overhead, which satisfies the real‐time inspection demands of industrial production lines. © 2026 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.
Robots operating in harsh environments require self‐repair capabilities to maintain their functionality. While various self‐healing methodologies have been proposed, most rely on the intrinsic self‐healing properties of the materials themselves. Consequently, it remains challenging to address irregular or complex damage involving material loss or to perform repairs when materials are unavailable within the robot's internal structure or surrounding environment. This paper proposes a self‐repairing robot that acquires fragments from conspecific robots as repair resources, processes them into materials, and repairs missing parts inspired by necrophagy observed in termites and other insects. As a proof‐of‐concept for outer‐shell restoration using external resources, this paper focuses on the outer shell and the transport mechanism. To evaluate the suitability of paraffin wax as the repair material, we conducted experiments measuring changes in hardness during melting‐solidification cycles and confirmed that no systematic degradation in hardness occurred. Furthermore, to assess repair performance against simplified damage models involving material loss, we measured the filling rate of damaged holes in the outer shell. The results demonstrated stable repair of defects of 4 mm in diameter and a certain degree of restoration performance for larger circular defects, supporting the feasibility of the proposed outer‐shell repair process. © 2026 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.
When online monitoring devices for transmission corridors detect typical hazards (e.g., construction machinery, wildfires), traditional object detection adopts a full‐image indiscriminate scanning paradigm, resulting in an excessively high proportion of non‐genuine and low‐risk alarms that severely constrain the efficiency of hazard identification and disposal. To address this issue, this paper proposes a novel twin‐subnetwork collaborative method for transmission corridor risk target detection (CoTwin‐YOLOv8). The Semantic Filtering Sub‐Network (SFSN) achieves pixel‐level scene parsing to distinguish scene elements from hazards and generate semantic maps, then filters non‐genuine alarms via semantic logic rules. The Low‐risk Suppression Sub‐Network (LSSN) dynamically generates electronic fences using historical alarm data to specifically suppress long‐standing low‐risk alarms. Experiments on a self‐constructed dataset show that our CoTwin‐YOLOv8 model improves mAP@0.5 by 2.1% over the baseline, reduces the false alarm rate to 9.0%, and achieves a 71.7% low‐risk alarm suppression rate, a capability that other models do not possess. This proves the model effectively cuts false alarms, suppresses low‐risk alerts and eases manual review workload while maintaining detection accuracy. © 2026 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.
Wirelessly‐operated microdevice technology for informatization of non‐electric objects will be a key breakthrough for advanced IoT framework. In this paper, we identify the requirements for microdevice technology in such applications and explain why an optical power transfer/energy harvesting platform is a promising primary powering option. The architecture and circuits suitable for versatile optical powering/energy harvesting are described and demonstrated. © 2026 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.
The proliferation of internet usage has made people increasingly dependent on it. Some fraudulent content makers make the content in an intriguing way that engenders users' curiosity. However, after clicking, they are deceived, which wastes users' time and degrades the credibility of the content. The majority of the existing clickbait detectors are title‐based and article‐based, but billions of people use video platforms daily. A few studies have been conducted on low‐resource languages, such as Bangla, due to the scarcity of resources. Therefore, to address this issue, we proposed a Bangla multimodal clickbait video detector that integrates video title and thumbnail and attained 94% precision. The model is trained and tested on a curated Bangla multimodal clickbait video dataset consisting of 253 570 samples. In this research, we used BanglaBERT followed by a BiLSTM model to maintain the rich contextual semantics for title encoding and ResNet50 with CBAM to capture the video thumbnail's prominent feature. Furthermore, we use cross‐modal attention to align the title and thumbnail data and an ablation study to ensure the contribution of each module. The results underscore the efficacy of each module and validate the effectiveness of our approach in detecting clickbait content across modalities. © 2026 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.
Switched flux machines (SFM) provide a robust rotor structure and effective thermal management because their excitation windings are entirely located on the stator. These machines are capable of producing high torque density. Permanent magnet (PM) designs offer high electromagnetic performance. However, the increasing cost of rare earth magnets, their temperature sensitivity, and the risk of irreversible demagnetization constitute significant limitations. For this reason, wound field switched flux machines (WFFSM) have emerged as an alternative solution. These structures do not contain permanent magnets, and the field current is controllable, enabling flexible operation over a wide speed range. In this review study, wound field switched flux machines are examined systematically. Applications in renewable energy systems, electric and hybrid vehicle drives, as well as industrial and marine systems are evaluated. In addition, performance enhancement methods, efficiency improvement approaches, cost oriented design strategies, modeling techniques, and control methods are analyzed. The study synthesizes the existing literature comprehensively and provides a guiding framework for future research. © 2026 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.
The existence of counterfeit certificates presents threats to deserving graduates as well as reduces public trust in institutions. The existing methods, which are mainly paper‐based, and a very few methods that are digitized and inefficient since certificates can be easily tampered with, lack transparency, and are also very time‐consuming. To tackle these challenges mainly, this paper suggests an integrated strategy based on a hybrid blockchain for issuance, revocation, and verification of educational certificates, which will prevent certificates from being tampered. Hash function mapping has been implemented into our proposed solution, and efficiency has been enhanced through the Bloom filter and Cuckoo filter , and the scan score of the smart contract is 96.83 . The performance of the proposed solution is measured through gas consumption in terms of execution and transaction costs. A comparative analysis has been shown between similar types of existing solutions. The cost regarding the deployment of smart contracts is drastically minimal for the proposed framework. In addition, the revocation of certificates is improved using the cuckoo filter. The evaluation section demonstrates the immense reduction of search time for unavailable certificates. © 2026 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.
This paper proposes a new method to compute the mean time to failure (MTTF) of a system that can be expressed with a combinatorial model whose components fail with i. i. d. The key idea is to use a matrix approach that replaces every component reliability with a specific matrix and replaces every operation (addition, subtraction, multiplication, and division) with its corresponding matrix operation in the system reliability computation. We can determine all coefficients of the polynomial expressing the system reliability by replacing each component reliability with a special matrix and thereby easily compute the MTTF by taking the integral of this polynomial. We assume two assumptions, where the first is i. i. d. component failure and the second is that the computational complexity of integral of the j ‐th power of reliability of each component is polynomial order for any natural number j not more than the number of components (like when component fails with exponential distribution). We can compute the MTTF of the system with polynomial order complexity when we can compute the system reliability with polynomial order complexity, under the above two conditions. © 2026 The Author(s). IEEJ Transactions on Electrical and Electronic Engineering published by Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.
Traditional scheduling methods fail to accurately capture the dynamic carbon emission characteristics of thermal power units across different operating stages, leading to inefficient unit selection, increased emissions, and higher operating costs. To address these challenges under low‐carbon constraints, this paper proposes a low‐carbon optimal scheduling strategy that integrates a dynamic carbon emission model into the decision‐making process. First, a nonlinear model of the dynamic carbon emission factor of thermal power units is constructed by combining spatiotemporal attention mechanisms and absolute position encoding. This model captures the complex relationships between unit type, fuel characteristics, and load levels, achieving high‐precision estimation of the carbon emission factor. Second, the obtained dynamic carbon emission factor is embedded into a power‐carbon coupled scheduling framework for heterogeneous generating units, thereby achieving coordinated operation of units under low‐carbon conditions and improving system‐level decision‐making. Finally, simulation results based on a provincial power grid in China show that, compared with traditional scheduling methods using static emission factors, the proposed strategy can reduce total operating costs by 2.69%, increase renewable energy utilization by 0.18%, and reduce carbon emissions by 3.35%, providing strong technical support for low‐carbon scheduling and carbon neutrality goals in power systems with high renewable energy penetration. © 2026 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.
This study proposes a novel attention‐based spatial–temporal model for indoor temperature estimation, which integrates positional encoding and an attention mechanism with a fully connected neural network. By jointly capturing temporal dynamics and spatial correlations, the model enables high‐resolution estimation of temperature fields across the entire indoor space. Using temperature data collected from 136 distributed sensors in an indoor space, along with time and coordinate information, the model effectively captures both short‐term and long‐term dependencies in temperature variations influenced by external environmental conditions. Ablation studies demonstrated that incorporating an encoder for the temperature series significantly improves prediction accuracy compared to models using encoders solely for time or coordinate data. The model was evaluated using multiple metrics, and results showed that it achieved a mean absolute error of 0.15 °C. It also exhibited robust performance across various sequence lengths and sensor configurations. © 2026 The Author(s). IEEJ Transactions on Electrical and Electronic Engineering published by Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.
This letter proposes an underwater visible light communication system using low‐cost devices for real‐time fish farm monitoring. The system transmits data using four‐color shift keying with blue, red, green, and white LED lights. To decode the signals from captured video, in this letter, a two‐stage method is proposed: YOLOv8 detects LED positions, and ResNet18 classifies their colors. Experimental evaluations conducted across four distinct underwater environments demonstrate high detection and color estimation accuracy under various turbidities and distances. Furthermore, the processing speed reaches 69 fps, confirming its capability for real‐time aquaculture applications. © 2026 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.
In this study, the estimation accuracy of multifractal detrended fluctuation analysis (MFDFA) and multifractal detrending moving average (MFDMA) was compared using finite‐length cascade data generated by log‐normal and log‐gamma cascades. For finite‐length data, not all regions of the theoretical spectrum derived under infinite‐length assumptions can be stably observed. Therefore, using the histogram method, the existence probability at each fractal dimension value under finite‐length conditions was quantified, and the comparable part of the spectrum was identified. Furthermore, the accuracy of MFDFA and MFDMA was compared using the error in the Hölder exponent at each fractal dimension value and the maximum distance error from the theoretical values. In addition, samples showing non‐arc‐shaped spectra were excluded, and the comparison used the same valid sample set. The results demonstrated that for finite‐length cascade data, the multifractal spectrum range that can be meaningfully compared depends on the time‐series length, whereas the existence probability decreases toward smaller fractal dimension values. MFDMA is more stable in preserving a properly arc‐shaped spectrum, whereas neither method exhibits uniform superiority in terms of estimation accuracy. The relative performance of MFDFA and MFDMA varies with the cascade model, degree of multifractality, and time‐series length. © 2026 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.
Asymmetric cascaded H-bridge multilevel inverter is well known for generating high-resolution output voltage levels using a limited number of switches. This paper presents a quaternary asymmetric inverter topology developed for STATCOM applications. Depending on the modulation index, the auxiliary bridges of the converter may exchange power in the opposite direction to the overall converter, posing challenges in maintaining the voltage balance of the floating capacitors. A closed-loop control strategy is employed to ensure capacitor voltage regulation to compensate for the device losses of the auxiliary H-bridges. Power required by all the floating capacitors of the auxiliary H-bridges is supplied from the main bridge dc-link through a high-frequency link of negligible rating sharing a common magnetic core and primary winding. The semiconductor losses under STATCOM operation are analyzed and found to be greater than the power absorbed by auxiliary sources across all operating conditions. This enables the adaptation of a unidirectional isolated dc-dc converter of negligible rating to have power flow from the main bridge to auxiliary bridges, which compensates for voltage sag in auxiliary capacitors because of the system non-idealities. The proposed inverter topology has been modeled in MATLAB/Simulink and experimentally validated, demonstrating effective capacitor voltage balancing and efficient performance in STATCOM operation.
Controlling the anomalous Hall effect (AHE) is crucial for advancing spintronic devices. In this work, we investigate how constant-volume biaxial strain affects the intrinsic anomalous Hall conductivity (AHC) of hexagonal close-packed (hcp) cobalt. The calculations combine first-principles density functional theory (DFT) with a Wannier-based tight-binding model. Our results reveal a non-monotonic AHC response to applied strain. The conductivity reaches a maximum under small compressive strain and is suppressed more rapidly by larger compressive strain than by equivalent tensile strain. We examine microscopically how strain shifts the energy position of anti-crossing band features near the Fermi level. This shift drives a redistribution of the Berry curvature hotspots. These results confirm constant-volume biaxial strain as a viable approach to tuning the intrinsic AHE in hcp cobalt.
This study introduces a comprehensive optimization framework for determining the optimal sizing and operation of variable renewable energy sources (VREs) and hybrid energy storage systems to facilitate cost-effective microgrid operations. The framework concurrently determines the optimal capacities and operational schedules for photovoltaic systems, wind generators, battery energy storage systems, and component-level hydrogen energy storage systems, including an electrolyzer, hydrogen tank, and fuel cell. Acknowledging the interdependence between capacity sizing and operational planning, the problem is formulated as a bilevel optimization model aimed at minimizing total costs, encompassing both investment and operational expenses. By employing the Karush-Kuhn-Tucker conditions, the bilevel problem is transformed into a single-level mixed-integer quadratic programming (MIQP) model, which is solved efficiently using the Gurobi Optimizer. Numerical simulations conducted for a grid-connected microgrid over a 1-month period compared two scenarios: perfect power balance and permissible VRE curtailment. The results indicate that allowing limited curtailment reduces storage capacity requirements and decreases total system costs compared with the no-curtailment scenario, while maintaining stable operation. The proposed approach offers a robust and computationally efficient framework for the integrated planning of renewable generation and hybrid storage in next generation microgrids.
The high penetration of distributed photovoltaic is easy to cause voltage over-limit, and the centralized control method of traditional linear model is easy to fail in this scenario. To this end, this paper proposes a model-free voltage control method based on multi-agent reinforcement learning. Firstly, the influence mechanism of photovoltaic access on the voltage distribution of low-voltage distribution network is analyzed by theoretical modeling, and the coupling relationship between active power fluctuation and voltage deviation at the end of feeder is revealed. On this basis, the objective function is constructed to drive the agent to learn the global optimal strategy autonomously. Then, aiming at the problem of collaborative optimization of heterogeneous device control instructions, a multi-agent deep reinforcement learning collaborative framework is designed based on Markov decision process, and a multi-agent deep deterministic strategy gradient algorithm is adopted. Finally, through the centralized training-decentralized execution mechanism, the photovoltaic reactive power output and energy storage charge and discharge continuous instructions are jointly optimized to break through the bottleneck of discrete-continuous hybrid action coordination. The simulation results show that the voltage qualification rate of the proposed method can be increased to 98.7% compared with the traditional method.
Substantial efforts have been made on the research, development, and application of flagship models of industrial power electronics products, which are required to deliver high performance, prior to home electrical appliances. This paper clarifies recent trends of power electronics, focusing mainly on typical product applications, by studying many references on related elemental technology and dividing application examples into three fields (motor drive, PV, and ESS battery). Regarding motor drives, we will introduce three‐level products developed as high‐power motor drive utilized in steel mill operations. As elemental technologies, we will present research on sensor less vector control of induction motor speed using an adaptive magnetic flux observer, multi‐objective robust optimization design, and statistical neural networks for fault diagnosis and feature processing. Regarding solar inverters, we will introduce products without cooling fans, natural convection cooling for outdoor installation, and large capacity products exceeding 3 MW. As elemental technologies, we will introduce research on various power predictions, including artificial neural networks. Regarding ESS batteries, we will introduce 500 and 100 kW PCS products. As elemental technologies, we will introduce research on balanced market optimization, scheduling control for transactions with public power networks, and power flow restriction between power transmission systems. © 2026 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.
To improve dynamic current imbalance in multi-chip parallel SiC MOSFET power modules, this paper investigates the impact of inductive coupling on parallel chips' dynamic current under high-frequency operation. A self-inductance-mutual inductance coupling model was established based on a commercial 8-parallel module. By applying Kirchhoff's Voltage Law (KVL) equations to analyze the module's power and drive circuits during switching, mutual inductance coupling between adjacent lower-bridge-arm branches was quantified, identifying source parasitic inductance coupling differences as the core imbalance cause. Using Amp & egrave;re's Circuital Law, the "reverse inclined bonding wires + chip orientation adjustment" strategy was proposed, reducing inductive coupling without modifying the original layout. This method achieves the effect of reducing inductive coupling while keeping the original module layout unchanged. Joint Ansys Q3D-Simplorer simulations showed the lower-bridge-arm maximum current difference decreased from 17.475 A to 4.21 A. Trial-manufactured module tests yielded consistent results: 5.71 A maximum current difference and 9.47% imbalance ratio. This study provides theoretical and engineering references for dynamic current-sharing design in high-frequency, high-power-density SiC multi-chip parallel modules.
Dependence of the electron heating on the configuration of magnetic fields applied to an inductively coupled plasma is investigated by Monte Carlo simulations to seek for desirable conditions for sustaining processing plasmas at low pressures. The separation D between two DC coils to induce the confronting divergent magnetic fields, whose separatrix may work as a magnetic shutter applicable to plasma confinement and modulation, and the driving frequency f are taken as control parameters. D determines the volume of the resonant region, where the electron heating by the partial resonance is promoted, and its distance from the RF antenna. The electron heating is evaluated at f(1) = 13.56 MHz, f(2) = 27.12 MHz, and f(3) = 40.68 MHz by changing D. The expansion of the resonant region at f1 enhances the electron heating via increase of electrons in the resonant region, but the electron confinement weakens. On the other hand, the high-frequency driving at f2 and f3 enhances the electron heating, keeping the electron confinement operative. It is demonstrated that the high-frequency driving has an advantage that the resonant region formed in regions of stronger magnetic fields is wider and closer to the RF antenna.
