Electrical Engineering in Japan最新文献

We have investigated the mechanism of coercivity evolution in magnets through multi-scale microstructural characterization. On the basis of these findings, our goal is to enhance the coercivity of heavy rare-earth-lean magnets by optimizing their microstructure. We have developed several new processes and demonstrated improved magnet properties. This review article describes (1) coercivity enhancement by the eutectic alloy diffusion process, (2) the development of light rare earth magnets, and (3) the development of high electrical resistance magnets. Recently, there has been an increasing demand for highly efficient magnet development that not only improves properties but also meets multiple requirements. Accordingly, (4) data-driven methods are being employed in ongoing magnet development.

When railway line sections are partially disrupted due to damage from large-scale natural disasters, it is necessary to prepare crew schedule plans for temporary timetables. In such a case, a crew schedule plan ensures that duties once assigned to crew members are not changed. As this task is time-consuming for schedule planners, an automatic crew scheduling method for supporting planners is desired. In this paper, we focus on crew scheduling following a large-scale natural disaster. The proposed algorithm is based on mathematical programming and column generation. In addition, we show the results of computational experiments based on a real disaster case, which indicate that the proposed algorithm can generate an efficient schedule plan in a short time.

When railway line sections are partially disrupted due to damage from large-scale natural disasters, it is necessary to prepare crew schedule plans for temporary timetables. In such a case, a crew schedule plan ensures that duties once assigned to crew members are not changed. As this task is time-consuming for schedule planners, an automatic crew scheduling method for supporting planners is desired. In this paper, we focus on crew scheduling following a large-scale natural disaster. The proposed algorithm is based on mathematical programming and column generation. In addition, we show the results of computational experiments based on a real disaster case, which indicate that the proposed algorithm can generate an efficient schedule plan in a short time.

In this study, we propose a new type of hybrid excited motor called “doubly-excited variable-flux consequent pole motor (DeVFCM).” The DeVFCM consists of a three-phase double Y-connected armature winding with two neutral points, O₁ and O₂, and field poles with alternating permanent magnet and iron poles. When a DC voltage for supplying the field current is applied across O₁ and O₂ in such a configuration, the armature winding also acts as a field winding, and the magnetic flux through the iron poles can be adjusted. To validate the principle of this DeVFCM, a prototype machine was fabricated, and the basic characteristics of the prototype machine were investigated through three-dimensional finite element analysis and experiments. The results show that both high torque generation and loss reduction at high speeds can be achieved by adjusting the field current.

In this study, we developed a double-sided offset ring-electrode nanopore device and a prototype hand-held measurement system, and evaluated the overall performance for the application of an AI-driven AC nanopore method to microbial phenotypic sensing. First, the particle measurement performance was evaluated and found that the sensitivity was approximately three times higher than that of conventional systems for particles up to 120 nm in diameter. In microbial identification using a single nanopore device, the accuracy of classification of 16 types of bacteria and 11 types of viruses using a convolutional neural network was approximately 83% and 78%, respectively. This suggests the possibility of classifying both bacteria and viruses with a single nanopore diameter. Furthermore, the method achieved 94.5% accuracy in three basic classification tasks of the conventional colony counting method, demonstrating that this method is upward compatible with colony counting methods.

In this study, we propose a new type of hybrid excited motor called “doubly-excited variable-flux consequent pole motor (DeVFCM).” The DeVFCM consists of a three-phase double Y-connected armature winding with two neutral points, O₁ and O₂, and field poles with alternating permanent magnet and iron poles. When a DC voltage for supplying the field current is applied across O₁ and O₂ in such a configuration, the armature winding also acts as a field winding, and the magnetic flux through the iron poles can be adjusted. To validate the principle of this DeVFCM, a prototype machine was fabricated, and the basic characteristics of the prototype machine were investigated through three-dimensional finite element analysis and experiments. The results show that both high torque generation and loss reduction at high speeds can be achieved by adjusting the field current.

In this study, we developed a double-sided offset ring-electrode nanopore device and a prototype hand-held measurement system, and evaluated the overall performance for the application of an AI-driven AC nanopore method to microbial phenotypic sensing. First, the particle measurement performance was evaluated and found that the sensitivity was approximately three times higher than that of conventional systems for particles up to 120 nm in diameter. In microbial identification using a single nanopore device, the accuracy of classification of 16 types of bacteria and 11 types of viruses using a convolutional neural network was approximately 83% and 78%, respectively. This suggests the possibility of classifying both bacteria and viruses with a single nanopore diameter. Furthermore, the method achieved 94.5% accuracy in three basic classification tasks of the conventional colony counting method, demonstrating that this method is upward compatible with colony counting methods.

In this paper, we propose a new evaluation method for the spatial distribution of sensitivity to force in a cantilever-type MEMS tactile sensor developed in our laboratory. By combining the responses of two strain gauges attached to the cantilever with different coefficients, it is shown that the spatial distribution of sensitivity can be controlled, and the force application position and the magnitude of force can be predicted with high accuracy.

To further promote renewable energy, the feed-in-premium (FIP) system was launched in April 2022 in Japan. Under the FIP scheme, renewable energy power producers and aggregators have faced new issues, which are imbalance risk due to the responsibility for planned amounts of power and market risk due to fluctuating power market prices. In order to address these issues, highly accurate renewable energy generation forecasting, imbalance reduction, and profitability enhancement technologies are required. However, comprehensive studies on the effectiveness of these necessary technologies through empirical evaluations using actual energy resources under real environments have not been sufficiently conducted. In this paper, we conduct a large-scale demonstration evaluation of the renewable energy generation forecasting technologies, the power market trading strategy technologies, and the battery control technologies using actual energy resources across the country, such as photovoltaic (PV) generators, wind power generators, and storage batteries. Through the empirical evaluations, it is clarified that the evaluated technologies contribute to reducing imbalance risk and improving profitability with respect to imbalance risk and market risk, respectively.

Orthogonal-core-type variable inductors with permanent magnets can increase or decrease the line voltage without power capacitors in electric power systems. In this paper, 3-kVA orthogonal-core-type variable inductors with permanent magnets are designed based on reluctance network analysis (RNA), and verification tests are carried out. The results demonstrate that the prototypes perform as intended. Furthermore, to improve the calculation accuracy, the RNA model combined with an assumed flux path (AFP) method is proposed. The proposed method achieves a significant improvement in calculation accuracy without increasing calculation time.