矢入 郁子

The design ICT systems that can provide application services that quickly and flexibly integrate network and software is currently the key method in DX based on ICT. Non-etheless, the problem of the huge amount of time required for the automatic design of ICT systems has been paid more and more attention. Considering the rapidly changing demand for ICT systems in various industries, the need to build and monitor systems frequently, and the difficulty in securing engineers due to the declining birthrate and aging population, the huge design time problem cannot be ignored, especially if AI is expected to reduce design time. To this end, we study the problem of reducing the design time of ICT systems with deep reinforcement learning algorithms , Weaver. First of all, we set the graph neural network as the learning model, so that deep reinforcement learning algorithm is used to solve this problem. Secondly, we have designed an algorithm that combines the representative reinforcement learning algorithm Double DQN and Noisy Network. Then, we attempted to shorten the design time by changing the normal distribution to a truncated normal distribution at the optimal value among them. Finally, sufficient trials are conducted to verify our proposed method. The results show that, the proposed method reduces the learning time required to complete learning of the design.

In pursuing an intelligent society, the deployment of Beyond 5G/6G is anticipated. A crucial aspect of realizing this vision lies in establishing a robust non-terrestrial network encompassing satellite-based communication systems. However, space-based communication faces challenges from atmospheric disturbances. For instance, Ka-band, a crucial frequency range for satellite communication, is attenuated by rain. Similarly, optical satellite communication links are disrupted by clouds. To ensure reliable and high-quality communication, it is imperative to accurately predict the impact of weather on signal propagation, enabling the selection of ground stations and modulation methods. This research focuses on developing a predictive model for radio wave attenuation during space-to-ground communication, leveraging data from meteorological satellites. The model's core is a deep learning architecture that integrates CNNs, renowned for their proficiency in image feature extraction. The rain attenuation prediction with this model achieved a high coefficient of determination. In addition, to improve the prediction accuracy, we analyzed the complex relationship between the radio wave reception strength and Himawari standard data, a comprehensive dataset acquired from the Himawari meteorological satellite.

Objective sleep deprivation detection can enhance workplace safety and productivity in professions that require long working hours. To address this, we proposed deep learning models for classifying sleep-deprived individuals using EEG data. In this study, we utilized resting-state EEG data collected from both sleep-deprived and well-rested participants and generated five datasets (EyesClosed, EyesOpen-Raw, EyesOpen-AR, EyesClosed+EyesOpen-Raw, and EyesClosed+EyesOpen-AR), then applied them to 1D CNN and 1D CNN-LSTM models. Both models achieved their peak performance with EyesOpen-AR, which slightly outperformed EyesOpen-Raw, while demonstrating comparable performance across all datasets. Applying feature extraction using differential entropy within delta, theta, alpha, and beta bands to the five datasets resulted in decreased performance. The results suggest that artifact-removal from EyesOpen-Raw is not essential for sleep deprivation detection using deep learning models. Additionally, they suggest that 1D CNN may be a more suitable choice for sleep deprivation detection, and non-feature-extracted data is more suitable than feature-extracted data.