In this study, a brain network was created using graph theoretical analysis based on electroencephalography (EEG) data. The purpose of the study was to investigate the functional connectivity of the brain in different states of anxiety. Seventeen adults with anxiety (A-G), and 13 adults without anxiety (AF-G) were examined. They were given three different stimulations: resting, pleasant, and unpleasant. EEG was measured immediately after the stimulation. The EEG was analyzed by Fast Fourier Transform (FFT), coherence analysis, and graph theory. The results of FFT and coherence analysis showed that the anxiety group (A-G) had higher power spectra and coherence values than those for the anxiety-free group (AF-G) in all sessions. The results of graph theory analysis showed that the clustering coefficient and small-worldness in A-G were lower than those in AF-G, although the characteristic path length in A-G was higher than that in AF-G. This study shows that the brain of A-G has smaller clusters and longer paths to compare with those of AF-G. These events suggest that the brain of A-G would have an inefficient network structure to transmit emotional information.

In recent years, research on data science that automates the analysis of various data has been conducted. Although it is crucial to collect enough data to obtain valuable data analysis results, various issues that arise here are often overlooked and often hinder actual system construction difficulties. Especially in industrial applications, it is necessary to consider how to achieve stable communication in factories with low radio wave environment. How to select sensors in an environment unsuitable for computer operation is an issue. Data communication between the sensor and the analysis computer has to be designed considering the cost of installing related equipment. The issues for constructing a data collection system include the following: (1) Complete system design, (2) Selection and installation of sensors and IoT-GW, (3) Selection and stability of communication, and (4) communication that ensures reliability performance. In this paper, The monitoring system of a plant facility is explained as an example of how to resolve these issues.

Our purpose is to extract a target signal with binaural microphones in an environment where multiple speech sources exist. A previous system has been established to separate the observed signals and identify which segregation speech is desired. The identification is based on SPDV (Spectral Phase Difference Variance). In this paper, we introduce an auto-correlation-based phase unwrapping to improve the estimation accuracy of SPDV, and an upper limit of the number of analysis frames to reduce the computational cost. Simulations were carried out to confirm the effectiveness of the proposed system. Results show that it works in real-time and gives SDR (Signal-to-Distortion Ratio) slightly superior to conventional source separation techniques which are batch processors.

In this paper, consecutive meals planning is formulated as a multiset iteration permutation problem that determines the optimal meals plan on a period consisting of consecutive days. In this problem, a meal is characterized by some characteristics such as food style, ingredient, cooking method, and so on. The evaluation function is defined by use of information entropy for measuring the appearance order of meal's characteristics on the meals plan. For optimizing this problem, Genetic Algorithms (GAs) with escape from stagnation of search are proposed. It is empirically shown that the proposed GAs with escape operation work better than the conventional permutation GA without escape operation for small and large size optimization problems.

Traceability management service utilizing blockchain guarantees the tamper resistance of data. However, it is a problem to balance the performance of writing to blockchain and the data confidence of other company’s information when data of multiple vendors are mixed at the time of data disclosure. In the proposed method, we obtain split root hashes which are divided by manufacturer from multiple traceability data generated at a certain site, and multiple split root hashes are put into one transaction in blockchain. It is confirmed that write performance of approximately 3,000 transaction/sec could be achieved by reducing the number of writing to blockchain, and that the information of other companies at the time of data disclosure could be concealed.

The purpose of this study is to propose a quantitative evaluation method for clarifying intentions toward taste expressions that cannot be clarified, from word-of-mouth data of cooking recipe websites using text mining.

This study aims to clarify the use of the word “KOKU” as an example to verify the method. As a result of applying the prediction results of BERT to LIME with certain changes, it was found that the characteristic ingredients contributing to the KOKU were identified, and that sweetness and umami were related to the KOKU as taste elements. As a result of the comparison of the terms used to describe the ingredients characteristic of KOKU and SAPPARI, there was a significant difference between the terms used to describe the oil and fat. Through the analysis mentioned above, the features of KOKU were defined. In the past, there has been no attempt to clarify the features of ambiguous taste expressions using word-of-mouth data from cooking recipe websites. The series of analysis methods presented in this paper enabled quantitative evaluation for clarifying the intentions of taste expressions that have been used by ordinary people without clear definitions.

We investigated wireless communication with 920 MHz and 2.45 GHz bands around the human body to ensure the user’s mobility in a head-mounted display system. The electric field distribution and path loss characteristics were calculated based on electromagnetic field analysis, focusing on the positional relationship between the antennas and the body. As a result, it was shown that the path loss characteristics are frequency-dependent in the LOS and NLOS environment with the body. Therefore appropriate band selection provides stable communication.

Smart Parts (SP) proposed in this study is a novel hardware tool for electrical circuit experiments at primary, middle and high schools. SP can be "transformed" into any electrical part, instrument, or DC/AC power supply, and allows students to easily and safely create circuits. In this study, we implement a prototype, and conduct simple experiments.