This paper focuses on voltage-integral-based reference tracking modulation (VIRTM) and direct torque control (DTC), proposing the control strategy that minimizes switching frequency while achieving excellent dynamic performance. In the proposed scheme, the optimal voltage integral trajectory calculated by exhaustively searching all switching patterns in an offline process is applied to the DTC. One of the advantages of the proposed scheme is its superior versatility and robustness, as it does not rely on motor parameters. Secondly, it significantly reduces the switching frequency at all operating points while preserving the dynamic characteristics of DTC. The effectiveness of the proposed scheme has been validated through experiments, demonstrating up to a 28% reduction in switching frequency compared to conventional standard DTC.

This paper presents a modulation method that minimizes the impact on common-mode noise and switching loss. The proposed method introduces a priority factor into the evaluation function to satisfy both requirements and adjust these characteristics. The effectiveness of the proposed method is tested through simulations and experiments, which confirm the switching and common-mode voltage frequencies.

This paper proposes a transposition point optimization method for voltage unbalance mitigation in electric power distribution systems by integer form of population-based incremental learning. In power distribution systems, since voltage unbalance has become a problem due to the recent penetration of large amounts of low-voltage photovoltaic, a method for voltage unbalance mitigation is required. Transposition is one of the methods for voltage unbalance mitigation. It is necessary to determine how and at which points transposition should be performed in order to apply transposition. The problem can be formulated as a combinatorial optimization problem. Since the number of combinations in the combinatorial optimization problem increases exponentially as the system size expands, it is difficult to obtain an optimal solution in practical time. However, no research has been conducted on the problem until now. Evolutionary computation can be applied to the problem. The proposed integer form of population-based incremental learning based method is applied to a real-scale distribution model system based on a JST-CREST126 distribution line model. Effectiveness of the proposed method is verified by comparing it with the tabu search based method and the Mann–;Whitney U test.

We propose a method of using a focal stack, i.e., a set of differently focused images, as the input for a novel light field display called a "tensor display." Although this display consists of only a few light attenuating layers located in front of a backlight, it can be viewed from many directions (angles) simultaneously without the resolution of each viewing direction being sacrificed. Conventionally, a transmittance pattern is calculated for each layer from a light field, namely, a set of dense multi-view images (typically dozens) that are to be observed from different directions. However, preparing such a massive amount of images is often cumbersome for real objects. We developed a method that does not require a complete light field as the input; instead, a focal stack composed of only a few differently focused images is directly transformed into layer patterns. Our method greatly reduces the cost of acquiring data while also maintaining the quality of the output light field. We validated the method with experiments using synthetic light field datasets and a focal stack acquired by an ordinary camera.

A new type of light field display called a tensor display was investigated. Although this display consists of only a few light attenuating layers located in front of a backlight, many views can be emitted in different directions simultaneously without sacrificing the resolution of each view. The transmittance pattern of each layer is calculated from a light field, namely, a set of dense multi-view images (typically dozens) that are to be observed from different directions. However, preparing such images is often cumbersome for real objects. We propose a method that does not require multi-view images as the input; instead, a focal stack composed of only a few differently focused images is directly transformed into the layer patterns. Our method greatly reduces the data acquisition cost while also maintaining the quality of the output light field. We validated the method with experiments using synthetic light field datasets and a focal stack acquired by an ordinary camera.