IEEJ Transactions on Electrical and Electronic Engineering最新文献

The modulated model predictive control (MMPC) method utilizes multiple voltage vectors in sequence within one switching cycle, achieving a fixed switching frequency. However, the control performance of using the MMPC technique still suffers from imprecise vector duration derivation and high computational burden. To address this, this paper proposes an improved MMPC technique aided by the Lagrange multiplier method to accurately calculate the duration of each vector, and then efficiently select the optimal voltage vector. Compared to the existing MMPC techniques, the proposed one exhibits a lower computational burden and superior steady-state performance. Those benefits have been proven by the experimental results of a 1.5 k rpm permanent magnet synchronous motor drive. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Home appliances such as residential air conditioners are based on the variable speed drives for AC motors such as IPMSM and IM. These variable speed drives always require the simple and low-cost system configurations without the large electrolytic capacitors, reactors, and power devices that consist of PFC converters. This paper surveys and analyzes the proposed and advanced control techniques to achieve high input power factor and sinusoidal source current control in electrolytic capacitor-less inverters. The investigated control methods consist of control algorithms based on the energy flow between input and output of the electrolytic capacitor-less inverter, and all of them are useful as high-power factor control methods for compressor drive motors such as air conditioners. Moreover, these techniques realize the variable speed drives for AC motors to meet power supply harmonics regulations (IEC61000-3-2). © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

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

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

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

This work presents a construction of a novel system for autonomous inspection of power transmission lines using a quadrotor helicopter. The aircraft follows the power lines at a constant distance and acquires high-resolution images of the conductors, dampers, and towers. Monitoring of the vegetation under the power lines is also performed. The traditional inspection is carried out by helicopters equipped with high-resolution cameras or by direct visual examination by personnel climbing over de-energized power lines. However, visual inspection is time-consuming, labor-intensive, and dangerous, and often, helicopter cameras cannot capture the acute details needed for accurate and comprehensive examinations. This work successfully evaluates a mathematical model for the quadrotor helicopter used in autonomous inspection through simulations and flight experiments. Two control strategies for power line tracking are compared: a classical PID and a fuzzy PD control. Several simulations and experimental results confirm the efficiency of both control laws, but the fuzzy controller performs better under strong wind disturbances. The results of this work will be implemented by an electric power company to inspect electrical transmission facilities regularly. © 2024 The Author(s). IEEJ Transactions on Electrical and Electronic Engineering published by Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Myoelectric is a biological signal produced from physiological variations in muscle fibers when they contract and relax. The study of muscle activity through the recording and analysis of myoelectric signals is called electromyography. Electromyography can provide a comprehensive view of the operation and performance of internal muscle groups and cells. The objective of this study is an investigation into hand movement classification using surface electromyography (sEMG) signals. For training and evaluating the proposed model, two data sets from the Ninapro project, a publicly available database for prosthetic hand control, were used: DB5 with low-cost 16 channels and 200 Hz sampling rate setting and DB10 with 12 channels and 1926 kHz sampling rate setup. First, we divided the EMG data into segments using the windowing technique. These segments were then used to extract a set of time features. Finally, the retrieved feature information was loaded into a simple pattern recognition model: Artificial Neural Network. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

We proposed a current-biased kinetic inductance detector (CB-KID) as a novel superconducting detector to construct a neutron transmission imager. The characteristics of a superconducting neutron detector have been systematically studied to improve a spatial resolution down to 10$��\mathrm{\mu m}�$ in transmission imaging. In this study, we report the application of the energy-resolving neutron imaging to investigate major components of materials by analyzing neutron transmission spectra from 1 meV to 500 keV. We succeeded in identifying that copper (Cu) and iron (Fe) are major components respectively in commercial nuts and screws as test samples with the aid of Rietveld imaging of transmission spectra (RITS) program in analyzing transmission spectra in longer wavelengths. The Ti screw was also confirmed by comparing the nuclear resonance absorption measurements and simulations in high-energy regions. We demonstrated that our superconducting neutron detector is applicable to reveal the transmission spectra in the wide range from cold-neutron energies $��\sim �\mathrm{meV}�$ to higher neutron energies even up to 500 keV. By selecting distinctive energy regions of pulsed neutrons, we succeeded in mapping the distribution of SmSn₃ compound using the strong neutron absorption in samarium (Sm) and the selective nuclear-resonance dips in Sm. By taking advantage of using CB-KID in conducting neutron imaging, the CB-KID method is extensively useful for various purposes in material sciences through energy-selective neutron spectroscopy from 1 meV to 500 keV. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

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

In order to improve the intuitiveness and automation of demand-side resource selection, a demand-side resource selection method based on load mapping matching is proposed in view of the increasing challenges of supply–demand balance in power networks and the rapid development of power demand-side management technologies. First, a two-dimensional load mapping of demand-side resources is drawn, and the load mapping is processed by Gaussian convolutional difference method. Then, feature points are extracted and located for the target resources and the loads of other resources in the demand-side resource pool, and similar feature key point pairs of demand-side resources are obtained. Finally, the demand-side resources with similar load characteristics to the target resources in the resource pool are screened according to the number of similar feature key point pairs, and the load resources similar to the target resources are finally identified by dividing the resource selection into priority levels. The experimental results show that the method can effectively extract feature key points, clearly and intuitively represent the features of demand-side resource load mapping, and can match to load resources with similar characteristics, which greatly transforms the demand-side resource selection mode. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.