arXiv subjects

Yoshihiro Kawahara

Publications and source records attributed to Yoshihiro Kawahara.

23 records · Page 2Linked to original sources

arXiv2025

Passive Reactance Compensation for Shape-Reconfigurable Wireless Power Transfer Surfaces

The powering range of wireless power transfer (WPT) systems is typically confined to areas close to the transmitter. Shape-reconfigurable two-dimensional (2-D) relay resonator arrays have been developed to extend this range, offering greater deployment flexibility. However, these arrays encounter challenges due to cross-coupling among adjacent resonators, which detune system impedance and create power dead zones. This issue often necessitates active components such as receiver position tracking, increasing system overhead. This study introduces a passive reactance compensation mechanism that counteracts detuning effects, enabling the simultaneous activation of all resonators at a fixed operating frequency, regardless of the array's shape, thus providing a consistent charging area. The key innovation involves mechanically appending reactance elements to neutralize detuning caused by inductive coupling, facilitating hassle-free resonator reconfiguration without requiring prior knowledge. Our experiments demonstrate the elimination of dead zones with multiple configurations, boosting the minimum power transfer efficiency from 3.0% to 56.8%.

physics.app-ph↗

arXiv2025

Room-scale magnetoquasistatic wireless power transfer using a cavity-based multimode resonator

Magnetoquasistatic wireless power transfer can be used to charge and power electronic devices such as smartphones and small home appliances. However, existing coil-based transmitters, which are composed of wire conductors, have a limited range. Here we show that multimode quasistatic cavity resonance can provide room-scale wireless power transfer. The approach uses multidirectional, widely distributed currents on conductive surfaces that are placed around the target volume. It generates multiple, mutually unique, three-dimensional magnetic field patterns, where each pattern is attributed to different eigenmodes of a single room-scale resonator. Using these modes together, a power delivery efficiency exceeding 37.1% can be achieved throughout a 3 m * 3 m * 2 m test room. With this approach, power exceeding 50 W could potentially be delivered to mobile receivers in accordance with safety guidelines.

physics.app-ph↗

arXiv2025

Suppressing Leakage Magnetic Field in Wireless Power Transfer using Halbach Array-Based Resonators

Wireless power transfer has the potential to seamlessly power electronic systems, such as electric vehicles, industrial robots, and mobile devices. However, the leakage magnetic field is a critical bottleneck that limits the transferable power level, and heavy ferromagnetic shields are needed for transferring large amounts of power. In this paper, we propose a ferrite-less coil design that generates an asymmetric magnetic field pattern focused on one side of the resonator, which effectively reduces the leakage magnetic field. The key to enabling the asymmetric field pattern is a coil winding strategy inspired by the Halbach array, a permanent magnet arrangement, which is then tailored for wireless power using an evolutionary strategy algorithm. Numerical analyses and simulations demonstrated that the proposed coil structure delivers the same amount of power as spiral coils, while achieving an 86.6% reduction in magnetic field intensity at a plane located 75 mm away from the resonator pair and a power efficiency of 96.0%. We verified our approach by measuring the power efficiency and magnetic field intensity of a test wireless power system operating at 6.78 MHz. These findings indicate that our approach can efficiently deliver over 50 times more power without increasing magnetic field exposure, making it a promising solution for high-power wireless power transfer applications.

physics.app-ph↗

arXiv2024

Plug-n-play e-knit: prototyping large-area e-textiles using machine-knitted magnetically-repositionable sensor networks

Prototyping electronic textile (e-textile) involves embedding electronic components into fabrics to develop smart clothing with specific functionalities. However, this process is still challenging since the complicated wiring setup is required during experimental phases. This paper presents plug-n-play e-knit, a large-scale, repositionable e-textile for providing trial-and-error prototyping platforms across the textile. Plug-n-play e-knit leverages industrial digital knitting machines loaded with conductive thread to automatically embed a communication and power supply network into garments, in addition to using soft magnet connectors to rearrange electronic components while preserving the stretchability of the garment. These combinations enable users to quickly establish e-textile sensor networks, and moreover test the performance and optimal placement of the electric devices on the textile. We demonstrated that our textiles leveraging custom I2C protocols could achieve the motion-resilient motion-tracking sensor network over a 2700 $cm^2$ garment area.

cs.HC↗

arXiv2019

Building a Computer Mahjong Player via Deep Convolutional Neural Networks

The evaluation function for imperfect information games is always hard to define but owns a significant impact on the playing strength of a program. Deep learning has made great achievements these years, and already exceeded the top human players' level even in the game of Go. In this paper, we introduce a new data model to represent the available imperfect information on the game table, and construct a well-designed convolutional neural network for game record training. We choose the accuracy of tile discarding which is also called as the agreement rate as the benchmark for this study. Our accuracy on test data reaches 70.44%, while the state-of-art baseline is 62.1% reported by Mizukami and Tsuruoka (2015), and is significantly higher than previous trials using deep learning, which shows the promising potential of our new model. For the AI program building, besides the tile discarding strategy, we adopt similar predicting strategies for other actions such as stealing (pon, chi, and kan) and riichi. With the simple combination of these several predicting networks and without any knowledge about the concrete rules of the game, a strength evaluation is made for the resulting program on the largest Japanese Mahjong site `Tenhou'. The program has achieved a rating of around 1850, which is significantly higher than that of an average human player and of programs among past studies.

cs.AI↗