Approach to Object Hardness Prediction by Rubber Ball Hardness Prediction Using Capsule Network

A hardness is often used as an index to compare similar objects such as fruits or wood. To measure an object’s hardness, a hardness meter is required, and certain conditions must be met. The conditions are that the hardness meter is compatible with the object and must be close at hand. This research shows the possibility of measuring hardness without a hardness meter using a neural network. The method employs machine learning using a capsule network (CapsNet) of a neural network model. This research experimented using CapsNet with routing-by-agreement, CapsNet with expectation-maximization routing (EM routing) and the EM routing method with the addition of Tasks-Constrained Deep Convolutional Network (TCDCN). The four-layer CapsNet with EM routing implemented has achieved the state-of-the-art.  Multi-layered CapsNet with EM routing was a very effective method for regression analysis as well. And, CapsNet has higher discriminative power using EM-routing than routing-by-agreement

Interactive Visualization System for Psychological Topology

Recently, there is increasing interest in mental support activities, including mental health care, counseling, and mental training in workplaces, schools, and sports teams. As a background to these things, various analysis methods have been developed to clarify and visualize the subject’s mental state based on these data. We tried to reveal and visualize the transition patterns of the subjects’ mental states by analyzing their utterances. Furthermore, we developed an interactive system of visualization of psychological state to support visual understanding of psychological topology. Features have been implemented to enable multidimensional data to visualize the movements shown on the SOM map. In this paper, we describe the system that can interactively visualize psychological states

グラフ描画の計算量とアルゴリズム

制度:新 ; 文部省報告番号:乙1054号 ; 学位の種類:博士(理学) ; 授与年月日:1994/10/13 ; 早大学位記番号:新2089 ; 理工学図書館請求番号:1795本文PDFは平成22年度国立国会図書館の学位論文(博士)のデジタル化実施により作成された画像ファイルをPDFに変換したものである