近年の情報通信技術の急速な進歩により，多くの障碍者にとって生活の質の向上がみられている．意思疎通のできない患者さんは何も考えていない植物状態と思われてきたが，コミュニケーション法を得ることで健常者と変わらないことが分かった場合もある．これらの技術は障碍者のために開発されたものよりは，他の技術の転用の場合が多く，またある障害のための支援機器が他の障害にも使えることもある．そのため，どのような技術が利用可能かの把握が重要となる．国内外における障碍者に利用可能な意思疎通支援技術についてまとめ，課題や展望に関して報告する．

Free vibration characteristics of the CFRTP rectangular plate printed by 3D-printer is studied in this paper. A CFRTP composite rectangular plate has been printed by a 3D-printer such as the Mark-Forged Mark Two printer. The vibration characteristics are measured by impulse vibration test, and then experimental modal analysis detects the natural frequencies, modal dumping ratios and natural vibration mode shapes. The numerical analysis is also carried out by using a commercial FEA software, ANSYS, to check the vibration characteristics of the plate obtained experimentally by impulsive vibration test.

　本研究では,Text-to-Speechサービスを利用し,PowerPoint内のテキストを音声ナレーションへ変換し,印加するコードの開発を試みた.結果, Google TTSを利用した音声ナレーションを印加できるようになった.ただし,それだけだと聞き取りづらいという問題点があった.しかし,タイトル前後に無音を印加する工夫をしたことで,タイトルと本文に区切りがついて,聞き取りやすくなったと感じられる.また,新たな問題点を発見することができた.使用したライブラリでは,スライドを開いた時に,添付されたオーディオファイルを自動で再生を開始するauto-play設定をONにする事ができていない.今後は,今回実現できなかったことを研究し,より完成度の高いものを実現しようと考えている.

Purpose of study: Stan is a new programming language used for Bayesian inference that implements the Hamiltonian Monte Carlo (HMC) algorithm. There is a high possibility that, going forward, this language will be widely adopted within the healthcare domain as well for research that makes use of Markov chain Monte Carlo (MCMC) methods. We have developed a new software, with the principle aim of helping healthcare researchers become acquainted with Bayesian inferences. This software is free and makes it simpler to run models written using Stan. Materials and methods: The current software “Fatsia” is an operation management tool or macro tool that controls data files, Stan files, Stan, and R. The core specifications for Fatsia are as follows: The operation windows were positioned in a manner that followed the procedure for running the MCMC modeling on Stan. Several MCMC models were prepared as templates, with the user able to directly run the template they selected, or to edit that template. Results and findings: The free software, Fatsia, that we developed met the above specifications. It could be operated via a graphical user interface (GUI), with the use of keyboard input outside of the necessary procedures, reduced to the smallest degree possible. When controlling Stan and R, Fatsia can export Stan files to R and commands R to execute MCMC methods on the R console. Conclusions: The usefulness of Fatsia was in streamlining the procedure for executing an MCMC using Stan. With Fatsia, we were able to run Stan MCMC models with the smallest possible amount of keyboard use. When compared with OpenBUGS or RStudio, the operation of Fatsia is easier, showing a high degree of utility. Hence, the implementation of more MCMC templates can be expected in the future.

Image analyses based on deep learning techniques have been expanding rapidly in various fields, and the development environment has become more accessible. In our previous study, numerous time-lapse images (approximately 9000 images) of juvenile Yesso scallops reared in lantern nets were acquired, but only a few have been used for data analysis owing to the difficulty associated with the automatic processing of such vast numbers of images. In this study, an algorithm was developed for the automatic detection of scallops from training and test images selected from the time-lapse images (195 images) based on the use of a deep learning technique referred to as “semantic segmentation.” The developed algorithm recognized the juvenile scallops with high accuracy in the test images. The algorithm was also applied to the other time-lapse images, and high accuracy was confirmed by visual inspection with the exception of a few cases. Data analysis was conducted within the automatically recognized areas of the scallops to explain their growth and behavioral changes owing to stormy weather. Thus, numerical analyses conducted based on semi-automatic processing of massive time-lapse image data may be useful for ecological studies of cage-cultured scallops.

近年，コネクトミクスと呼ばれる，脳神経の網羅的な解剖が注目を浴びている．特に，電子顕微鏡（EM）3次元画像に基づく「EMコネクトミクス」は神経回路の配線図を具体的に記述することのできる唯一の方法であり，世界で積極的に推進されている．EMコネクトミクスは，画像撮影の技術的困難もさることながら，EM画像より神経回路を再構築する情報処理が技術的なポイントとなる．特に，EM画像から神経細胞（ニューロン）の形状を抽出するセグメンテーションと呼ばれる工程は，近年発展が著しい深層学習が直接に貢献しており，現在の神経大規模配線図～コネクトーム～を完成させるに至っている．本稿では，セグメンテーションを行う深層学習（Convolutional Neural Network, CNN）とともに，関連する情報技術，著者らが開発を行っている統合ソフトウェアUNI-EM，大規模データを扱うデータベース技術までを概観したい．

Pupil response plays an important role in expression of talker’s affect in an embodied interaction and communication. Focusing on the pupil response in human voice communication, we analyzed the pupil response during utterance, and demonstrated that the pupil enlarges and contracts in synchronization with the burst-pause (ON-OFF) of the utterance. In addition, it was confirmed that the pupil response is effective for enhancing affective conveyance by using the developed system in which an interactive CG character generates the pupil response based on the synchronization with the burst-pause of utterance. In this study, we developed a video communication system with a virtual pupil CG superimposed on the partner’s pupil for enhancing affective conveyance. This system generates a virtual pupil response in synchronization with the talker’s burst-pause of utterance. We performed a communication experiment under the condition that the virtual pupil CG is generated by being synchronized with the talker’s burst-pause of utterance. The effectiveness of the system was demonstrated by means of sensory evaluations of 12 pairs of participants in the video communication.

Medium-sized molecules have attracted significant attention as new chemical modalities. In this study, we compared the performances of three methods of 3D structure generation for medium-sized molecules using free and commercial software. The benchmark dataset consisted of 2131 protein-binding ligands with molecular weights greater than 600, which were selected from the Protein Data Bank (PDB). When selecting the smallest root mean square deviation between the generated 3D conformers and the PDB ligand structures, 43% of the conformations determined with the software CORINA were within 1 Å, followed by 10% from OMEGA and 5% from RDKit. According to our results, comparing the polar solvent-accessible surface area (PSA) and normalized principal moment of inertia ratio (NPR) among the three methods, 83% of the conformers generated with CORINA were within 20 in PSA, and 53% of the conformers from CORINA were within 0.05 in the NPR1 and NPR2 spaces. Thus, we concluded that CORINA has the highest performance in terms of efficient conformer generation. We also examined 3D descriptor calculation using Mordred, which is a free descriptor computation tool. The results showed that OMEGA-generated conformers exhibited the highest success rate, indicating that OMEGA is a suitable conformer generation tool for various 3D descriptors. Our results could contribute to the selection of conformation generators for the rapid construction of various predictive models for medium-sized molecules and can be shared with the research community for further validation.

This paper presents the numerical stiffness evaluations of CFRTP composite laminates made by prepreg patch, which is the CFRTP prepreg sheet cut into small pieces from an usual prepreg sheet. For this purpose, the numerical model is constructed by using the ACP function of commercial finite element analysis software, ANSYS. In calculation the 3-point bending problem is picked up, and the deformations and the bending stresses are obtained numerically. The stiffness of the laminates is thus discussed from the numerical results.

Julia is a new programming language that was released in 2012. Similar to Python, in the future, Julia has the potential for use in numerical analysis applications. In this study, we developed a free software named J-Assist that automatically executes statistical analysis operations in Julia version 1.4, which could introduce and popularize Julia among researchers and clinicians in the field of health and medical welfare. J-Assist provides a simple graphical user interface and is easy to install. Through this study, we demonstrate that it is possible to configure a system that automatically executes calculations and scripts in Julia. We compare J-Assist with EZR and Jupyter Notebook. J-Assist can be considered a simpler version of EZR. However, the execution languages used in J-Assist and EZR are different. J-Assist is intended for users who are new to Julia and programming and want to use Julia for quick statistical analysis; by contrast, Jupyter Notebook is suitable for users who want to develop complex programs and perform other complex analyses in Julia. This study also discusses the limitations of the current J-Assist software and functions that might be added to it in the future to expand its applicability for statistical analysis.

Recently, with the rapid increase in electric power supply, ground faults such as electric shocks and fires frequently occur. Hence, leakage protection has become increasingly important. Meanwhile, with the development of power electronic technology, distortions in power systems owing to power supply switching have increased considerably. In this paper, we present the leakage current caused by ground capacitance when the system voltage includes harmonics, and a large error is noted in the measurement results of the ground fault detection method Ior, which is currently mainstream.

Process planning is well known as the key toward achieving highly efficient machining. However, it is difficult to standardize machining skills for process planning, which depend heavily on skilled operators. Hence, in a previous study, a computer aided process planning (CAPP) system using machine learning is developed to determine the operation parameters for finish machining of dies and molds. On the other hand, in rough machining, it is assumed that some machining operations are conducted sequentially using a respective tool according to the workpiece shape, which induces a much higher complexity in process planning. Therefore, in this study, machine learning is adopted to determine operation parameters for rough machining. The developed CAPP system converts the removal volume into a voxel model and infers a machining operation for each voxel. The inferred machining operation is visualized using different colors and identified corresponding to the voxel. Finally, the removal volume is classified using three different machining operations. However, machine learning is said to have a critical problem in that it is difficult to understand the reasons for the inferred results. Hence, it is necessary for the CAPP system to demonstrate the certainty level of the determined operation parameters. Thus, this study proposes a method for calculating the degree of certainty. If an artificial neural network is trained sufficiently, similar inferred results would always be obtained. Consequently, by using the Monte Carlo dropout to delete weights at random, the certainty level is defined as the variance of the inferred results. To verify the usefulness of the CAPP system, a case study is conducted by assuming rough machining of dies and molds. The results confirm that the machining operations are inferred with high accuracy, and the proposed method is effective for evaluating the certainty of the inferred results.

This paper presents the numerical structure analysis of CFRTP laminated stay rod made by prepreg patch, which is the CFRTP prepreg sheet cut into small pieces from an usual prepreg sheet. For this purpose, the numerical model is constructed by using the ACP function of commercial finite element analysis software, ANSYS. In numerical calculation the 3-point bending problem is picked up for the laminates, and the deformations, the bending stresses and the bending strains are obtained numerically. The stiffness of the laminates is thus discussed from the numerical results.

We propose a visual analysis approach for exploring the compositional process of composers in the Spectral School, with a particular focus on its sub-processes of sound analysis and synthesis. Spectral music has been one of the significant trends in contemporary music since the 1970s. Composers in the Spectral School use the acoustic properties of sounds as the basis of their compositional materials. One of the representative software systems they use is AudioSculpt, developed by IRCAM. We develop an accompanying system with AudioSculpt to manage the history of creating sounds with spectral analysis of sound materials. The salient feature of our system lies in its tessellated spectrogram space whose axes represent the elapsed time of the sound and the progress of composition. On the pixel-oriented, spatial substrate, the system makes it possible for users to analyze the compositional processes by using dedicated interactive manipulations. The present system can also be regarded as an initial attempt at managing the provenance of time-series events in the music visualization field. Our approach is intended to pave the way for composers and musicologists to analyze, develop, and share the compositional methodologies.

The number of small satellites in Low Earth Orbit (LEO) is increasing. In LEO, the dominant non-gravitational force arises from aerodynamic effects. These effects must, therefore, be taken into account when determining, predicting, and designing the orbit. This paper presents a detailed analysis of the aerodynamic effects on a small satellite in LEO, using numerical simulation. A sensitivity analysis of the orbital environment and satellite specifications was performed, and the critical parameters were identified. The results were compared with those from analytical models reported in previous studies. Approaches to improving the analytical model were considered. Finally, realistic simulations were performed, to investigate the significance of detailed aerodynamic analysis in predicting the orbital lifetime.

In emergency medicine, imaging diagnosis by un-enhanced computed tomography (CT) is frequently used due to the remarkable progress and spread of CT. In the case of acute diseases such as trauma, accurate diagnosis of the upper abdominal region may be difficult depending on the experience of the doctor who interprets the images and the display conditions. In this study, we propose a method using deep convolutional neural network (DCNN) to automatically classify the presence or absence of traumatic hematoma in coronal images reconstructed from plain CT images. Coronal images are useful for observing a wide area of the upper abdomen and are often used. 337 images with traumatic hematoma and 492 images without were divided into 8 data sets. Finally, 17 types of DCNN were used, and the images were output into two categories, with and without traumatic hematoma, by 8-fold cross-validation. Receiver operatorating characteristic (ROC) analysis was performed to calculate accuracy and area under the curve (AUC). The highest accuracy was 0.841 and AUC was 0.909 when DenceNet-201 was used.

In this paper, in order to avoid the cascading failure by increasing the number of links in the physical network in D2D-based SNS, we propose an autonomous device placement algorithm. In this method, some relay devices are placed so as to increase the number of links in the physical network. Here, relay devices can be used only for relaying data and those are not SNS users. For example, unmanned aerial vehicles (UAV) with D2D communication capability and base stations with D2D communication capability are used as the relay devices. In the proposed method, at first, an optimization problem for minimizing node resilience which is a performance metric in order to place relay devices. Then, we investigate how relay devices should be placed based on some approximate optimal solutions. From this investigation, we propose an autonomous relay device placement in the physical network. In our proposed algorithm, relay devices can be placed without the complete information on network topology. We evaluate the performance of the proposed method with simulation, and investigate the effectiveness of the proposed method. From numerical examples, we show the effectiveness of our proposed algorithm.