（都産技研）
著者に関連した記事は2件です。
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第16回
広角映像の歪みに頑健な注目点検出手法の開発と人物動作解析への応用
著者:
三木 大輔,（東京大,都産技研）,阿部 真也,（都産技研）,陳 実,出町 和之,（東京大）
発刊日:
公開日:
キーワードタグ:
Convolutional neural network Human motion recognition Video surveillance Wide-angle image Worker Safety
三木 大輔,（東京大,都産技研）,阿部 真也,（都産技研）,陳 実,出町 和之,（東京大）
発刊日:
公開日:
キーワードタグ:
Convolutional neural network Human motion recognition Video surveillance Wide-angle image Worker Safety
Tracking human motion from video sequences is a notable technique that is used to detect anomalies in individual human behavior. Several commercially available motion capture devices are based on the use of depth cameras. However, there are a couple of problems with the use of a depth camera. Firstly, a complicated camera system is required, and secondly, the optical field of view is limited. To overcome these problems, we need a technique that can recognize human motion from wide-angle images. In this stud...
英字タイトル:
Development of Robust Keypoint Detector for Distorted Wide-Angle Images and Application to Human Motion Analysis
英字タイトル:
Development of Robust Keypoint Detector for Distorted Wide-Angle Images and Application to Human Motion Analysis
第17回
振動データ解析を目的とした深層ニューラルネットワークおよび その軸受故障識別への適用
著者:
三木 大輔,（都産技研）,出町 和之,（東京大）
発刊日:
公開日:
キーワードタグ:
Bearings Deep learning Fault diagnosis Neural network Time-series analysis
三木 大輔,（都産技研）,出町 和之,（東京大）
発刊日:
公開日:
キーワードタグ:
Bearings Deep learning Fault diagnosis Neural network Time-series analysis
Condition-based maintenance (CBM) technology is required to perform maintenance on rotating machinery at the optimal time before deterioration and failure occurs. In this study, we describe a deep neural network (DNN) model and corresponding training method for analyzing vibration data to realize CBM for rotating machinery. To apply DNN models in order to analyze time-series data such as vibration data, we first need to optimize the parameters of the models by training them on a dataset consisting of data a...
英字タイトル:
Deep Neural Networks for Vibration Signal Analysis and Its Application for Bearing Fault Classification
英字タイトル:
Deep Neural Networks for Vibration Signal Analysis and Its Application for Bearing Fault Classification