2017 IEEE International Systems Engineering Symposium (ISSE)最新文献_第8页

Integrated multivariate degradation prediction by RVM 基于RVM的综合多元退化预测

2017 IEEE International Systems Engineering Symposium (ISSE) Pub Date : 2017-10-01 DOI: 10.1109/SYSENG.2017.8088323

P. Jiang, B. Guo, Shiqi Liu, Y. Xing

引用次数: 1

Approach for model-based change impact analysis in factory systems 工厂系统中基于模型的变更影响分析方法

2017 IEEE International Systems Engineering Symposium (ISSE) Pub Date : 2017-10-01 DOI: 10.1109/SYSENG.2017.8088301

H. Bauer, Alexander Schoonmann, G. Reinhart

引用次数: 12

Face detection and posture recognition in a real time tracking system 人脸检测与姿态识别的实时跟踪系统

2017 IEEE International Systems Engineering Symposium (ISSE) Pub Date : 2017-10-01 DOI: 10.1109/SYSENG.2017.8088265

Hung-Yuan Chung, C. Hou, Shou-Jyun Liang

引用次数: 5

Design an ultrasonic imaging system for characterization of two-phase flow in metal pipe 设计一种用于表征金属管道两相流的超声成像系统

2017 IEEE International Systems Engineering Symposium (ISSE) Pub Date : 2017-10-01 DOI: 10.1109/SYSENG.2017.8088325

J. Abbaszadeh, Sahar Sarafi

引用次数: 0

Engineering framework for the future: Cynefin for engineers 未来的工程框架:工程师的Cynefin

2017 IEEE International Systems Engineering Symposium (ISSE) Pub Date : 2017-10-01 DOI: 10.1109/SYSENG.2017.8088286

J. Vollmar, M. Gepp, Herbert Palm, Ambra Calá

引用次数: 7

Reliability importance assessment using cost-based credible improvement potential 使用基于成本的可靠改进潜力进行可靠性重要性评估

2017 IEEE International Systems Engineering Symposium (ISSE) Pub Date : 2017-10-01 DOI: 10.1109/SYSENG.2017.8088259

M. Catelani, L. Ciani, M. Venzi

引用次数: 0

New real-time methods for operator situational awareness retrieval and higher process safety in the control room 操作员态势感知实时检索新方法，提高控制室过程安全性

2017 IEEE International Systems Engineering Symposium (ISSE) Pub Date : 1900-01-01 DOI: 10.1109/SYSENG.2017.8088300

Nikodem Rybak, M. Hassall, K. Parsa, Daniel Angus

{"title":"New real-time methods for operator situational awareness retrieval and higher process safety in the control room","authors":"Nikodem Rybak, M. Hassall, K. Parsa, Daniel Angus","doi":"10.1109/SYSENG.2017.8088300","DOIUrl":"https://doi.org/10.1109/SYSENG.2017.8088300","url":null,"abstract":"Objective: To evaluate the application of a Deep Learning based emotion recognition system for detecting operator stress, where operator stress is a proxy for Situation Awareness (SA) changes during abnormal/contingency situation management and decision making. Background: When operators are overwhelmed by stress, their perceptions, thinking, and judgments are impaired, increasing the chance of misinterpretation of events and increasing the potential for human error. The \"intelligent control room\" has been proposed as a possible solution for helping operators to deal with such stress. The control room comprises a variety of components used to monitor the operator and infrastructure under his/her control in an effort to optimize the performance of the humantechnical system as a whole. A critical component of this control room solution is the provision of human monitoring and assessment data in order to determine the operator's situation awareness. Methods: An emotion recognition system is designed based on two Deep Learning models, the Bidirectional Long Short Term Memory network (BiD-LSTM) and the Deep Convolutional Neural Network (DCNN), in order to process audio and facial data respectively. The system is first validated against a standard corpus of expert-coded emotion data. Post-validation, a dataset of expert-coded user stress data is coded by the system for emotional valence, and these system-generated emotional readings are compared to the expert-coded stress markers to determine any significant correlations. Contribution: This research contributes to developing the idea of intelligent and automated decision-making support in situational awareness measurement systems. Such systems support users by real-time collecting and processing data, and assist decision-making based on operator behavioral patterns.","PeriodicalId":354846,"journal":{"name":"2017 IEEE International Systems Engineering Symposium (ISSE)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129795790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A kinect-based workplace postural analysis system using deep residual networks 基于动作的工作场所姿势分析系统

2017 IEEE International Systems Engineering Symposium (ISSE) Pub Date : 1900-01-01 DOI: 10.1109/SYSENG.2017.8088272

A. Abobakr, D. Nahavandi, Julie Iskander, M. Hossny, S. Nahavandi, M. Smets

{"title":"A kinect-based workplace postural analysis system using deep residual networks","authors":"A. Abobakr, D. Nahavandi, Julie Iskander, M. Hossny, S. Nahavandi, M. Smets","doi":"10.1109/SYSENG.2017.8088272","DOIUrl":"https://doi.org/10.1109/SYSENG.2017.8088272","url":null,"abstract":"Human behavior understanding is a well-known area of interest for computer vision researchers. This discipline aims at evaluating several aspects of interactions among humans and system components to ensure long term human well-being. The robust human posture analysis is a crucial step towards achieving this target. In this paper, the deep representation learning paradigm is used to analyze the articulated human posture and assess the risk of having work-related musculoskeletal discomfort in manufacturing industries. Particularly, we train a deep residual convolutional neural network model to predict body joint angles from a single depth image. Estimated joint angles are essential for ergonomists to evaluate ergonomic assessment metrics. The proposed method applies the deep residual learning framework that has demonstrated impressive convergence speed and generalization capabilities in addressing different vision tasks such as object recognition, localization and detection. Moreover, we extend the state-of-the-art data generation pipeline to synthesize a dataset that features simulations of manual tasks performed by different workers. An inverse kinematics stage is proposed to generate the corresponding ground truth joint angles. Experimental results demonstrate the generalization performance of the proposed method.","PeriodicalId":354846,"journal":{"name":"2017 IEEE International Systems Engineering Symposium (ISSE)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122344629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 23

A skeleton-free kinect system for body mass index assessment using deep neural networks 一种使用深度神经网络评估身体质量指数的无骨骼kinect系统

2017 IEEE International Systems Engineering Symposium (ISSE) Pub Date : 1900-01-01 DOI: 10.1109/SYSENG.2017.8088252

D. Nahavandi, A. Abobakr, H. Haggag, M. Hossny, S. Nahavandi, D. Filippidis

引用次数: 16