2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS)最新文献_第3页

Emotion Recognition via Environmental Context and Human Body 基于环境背景和人体的情绪识别

2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS) Pub Date : 2023-05-08 DOI: 10.1109/ICPS58381.2023.10128084

Cheng-Shan Jiang, Z. Liu

{"title":"Emotion Recognition via Environmental Context and Human Body","authors":"Cheng-Shan Jiang, Z. Liu","doi":"10.1109/ICPS58381.2023.10128084","DOIUrl":"https://doi.org/10.1109/ICPS58381.2023.10128084","url":null,"abstract":"To promote the humanized interactive experience of the intelligent device and system, emotional intelligence has become a popular research field in the human-machine interaction. The previous research on emotion recognition based on computer vision has mostly been carried out by analysing facial expression or body posture, and psychological studies show that scene context also contributes some important information on emotion recognition. In addition, most context-aware emotion recognition studies focus on exploring the relevance analysis of environmental semantics, but the influence of feature encoder on semantic information embedding has not been fully discussed. In this paper, we proposed a Global Semantic Feature Enhancement-Dual Stream Densely Connected Network (GSFE-DSDCN) to enhance global semantic information learning from the perspectives of dimension and spatial. Densely connected pattern is introduced to concatenate the shallow and deep layers output, which fuses the semantic information of low-dimensional geometric features and high-dimensional abstract context features together. The Global Multi-Scale Feature Recalibration (GMSFR) module expands the receptive field in spatial, which effectively improves the global semantic features extraction capability of feature encoder. We evaluate the proposed method on the EMOTIC data set, and experimental results are shown to be competitive with the state-of-the-art algorithms.","PeriodicalId":426122,"journal":{"name":"2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134095397","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: 0

Quasi-synchronization of chaotic systems with parameters mismatch via intermittent sampled-data control 基于间歇采样数据控制的参数不匹配混沌系统的准同步

2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS) Pub Date : 2023-05-08 DOI: 10.1109/ICPS58381.2023.10128038

Ying-Xu Yang, Ya-Li Zhi, Zhi-Ming Zhang, Qing-Zhi Wang

引用次数: 0

Ensemble Common Features Technique for Lightweight Intrusion Detection in Industrial Control System 工业控制系统中轻量级入侵检测的集成公共特征技术

2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS) Pub Date : 2023-05-08 DOI: 10.1109/ICPS58381.2023.10128040

Uneneibotejit Otokwala, Andrei V. Petrovski

引用次数: 0

Short-term load forecasting for holidays based on similar days selecting and XGBoost model 基于相似日选择和XGBoost模型的假日短期负荷预测

2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS) Pub Date : 2023-05-08 DOI: 10.1109/ICPS58381.2023.10128055

A. Huang, Juan Zhou, Tao Cheng, Xiangzhen He, Ji Lv, Min Ding

引用次数: 0

Encoding Techniques on Multivariate Time Series Signals for Failure Prevention of Industrial Assets with Unsupervised Deep Anomaly Detection 基于无监督深度异常检测的工业资产故障预防多变量时间序列信号编码技术

2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS) Pub Date : 2023-05-08 DOI: 10.1109/ICPS58381.2023.10128068

F. Bayram, Md Nur Amin, Aleksandra Melke, Roland Schneider, R. Radtke, Alexander Jesser

引用次数: 1

A Practical Vision-Aided Multi-Robot Autonomous Navigation using Convolutional Neural Network 基于卷积神经网络的视觉辅助多机器人自主导航

2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS) Pub Date : 2023-05-08 DOI: 10.1109/ICPS58381.2023.10128041

Alexandre Rocchi, Zike Wang, Yajun Pan

{"title":"A Practical Vision-Aided Multi-Robot Autonomous Navigation using Convolutional Neural Network","authors":"Alexandre Rocchi, Zike Wang, Yajun Pan","doi":"10.1109/ICPS58381.2023.10128041","DOIUrl":"https://doi.org/10.1109/ICPS58381.2023.10128041","url":null,"abstract":"In this paper, a low-cost practical approach for the collision avoidance of a multi-robot system by using a single camera. A convolutional neural network (CNN) is applied to obtain an estimation of the depth of the image at the output of a monocular camera, assisting the team of mobile robots to detect obstacles in an unknown environment, determining navigation strategies, overcoming the limitation of the onboard LiDAR sensor. An avoidance controller was designed over a modified artificial potential field (APF) method, leading robots to avoid obstacles to reach the goal point. This paper provides an alternative solution for range measuring and environment sensing, replacing common distance sensors such as LiDAR sensors and ultrasonic sensors. The camera captures more data about the environment while being relatively cheaper than most sensors. An open-source CNN machine learning model called MiDaS is applied to help estimate the depth of detected obstacles from the input image. Simulations and experiments with three TurtleBot3 mobile robots were conducted to validate the proposed algorithms. Experimental studies have been carried out to test the effectiveness of the proposed approach in the paper.","PeriodicalId":426122,"journal":{"name":"2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130249974","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: 0

Quantized Finite-Time $mathcal{H}_{infty}$ Control of Fuzzy Distributed Parameter CPSs 模糊分布参数cps的量化有限时间$mathcal{H}_{infty}$控制

2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS) Pub Date : 2023-05-08 DOI: 10.1109/ICPS58381.2023.10128039

Teng-fei Li, Ju H. Park

引用次数: 0

Grid Voltage Feedforward control of GFI for Improving Dynamical Performance 改进GFI动态性能的电网电压前馈控制

2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS) Pub Date : 2023-05-08 DOI: 10.1109/ICPS58381.2023.10128050

Zaixun Ling, Li You, Bing Zhou, Yibo Cui, Zhiqiang Zhou, Dingbang Huang

引用次数: 0

An efficient condition monitoring and fault diagnosis method for bearings under multiple working conditions 一种高效的多工况轴承状态监测与故障诊断方法

2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS) Pub Date : 2023-05-08 DOI: 10.1109/ICPS58381.2023.10128051

Qiong Zeng, Qing Zhu, Yun Feng, Yaonan Wang

{"title":"An efficient condition monitoring and fault diagnosis method for bearings under multiple working conditions","authors":"Qiong Zeng, Qing Zhu, Yun Feng, Yaonan Wang","doi":"10.1109/ICPS58381.2023.10128051","DOIUrl":"https://doi.org/10.1109/ICPS58381.2023.10128051","url":null,"abstract":"Traditional model-based fault diagnosis of rotating machinery established upon the well-known observer design and analysis are limited to the choices of parameters. Meanwhile, signal processing-based methods heavily rely on expert experiences to extract features, hence the usability is heavily limited. Besides, existing researches usually carried out in a single working condition while bearings often work in multiple working conditions in applications. To cope with these issues, a novel fault diagnosis method which combines both signal processing and data-driven techniques is proposed. First, empirical mode decomposition and principal component analysis are employed to separate useful signals from the original non-stationary and nonlinear vibration signals. Then the high dimension and nonlinear features are extracted and kernel principal component analysis and linear discriminant analysis are used to reduce the dimension of features. Finally, the optimized support vector machine is adopted for fault classification. To deal with the few fault sample problem, overlapping sampling is utilized to enhance the data. The proposed methodology is able to conduct fault diagnosis efficiently and precisely in multiple working conditions. Experimental results showed that the prediction accuracy is satisfied even in the case of relatively few faulty samples.","PeriodicalId":426122,"journal":{"name":"2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122478786","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: 0

Reachable Set Control for Cyber-Physical Systems with False Data Injection Attacks 虚假数据注入攻击下网络物理系统的可达集控制

2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS) Pub Date : 2023-05-08 DOI: 10.1109/ICPS58381.2023.10127989

Xiu-Yang Fan, Wen-Juan Lin

引用次数: 0