2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS)最新文献_第9页

Horizontal Path-following Control Based on ESO for Parafoil Systems with Error Constraint 基于ESO的误差约束伞系统水平路径跟踪控制

2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS) Pub Date : 2023-05-08 DOI: 10.1109/ICPS58381.2023.10128023

Erlin Zhu, Youwu Du, Haitao Gao, W. Song

引用次数: 0

Integrating Worker Assistance Systems and Enterprise Resource Planning in Industry 4.0 工业4.0中工人辅助系统与企业资源规划的整合

2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS) Pub Date : 2023-05-08 DOI: 10.1109/ICPS58381.2023.10128096

Marc Brünninghaus, Magnus Redeker

引用次数: 0

Scene-insensitive Driving Style Recognition using CAN Signals based on Factor Analysis 基于因子分析的CAN信号场景不敏感驾驶风格识别

2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS) Pub Date : 2023-05-08 DOI: 10.1109/ICPS58381.2023.10128100

Chaopeng Zhang, Wenshuo Wang, Jian Zhang, Zhiyang Ju, Zhaokun Chen, Junqiang Xi

{"title":"Scene-insensitive Driving Style Recognition using CAN Signals based on Factor Analysis","authors":"Chaopeng Zhang, Wenshuo Wang, Jian Zhang, Zhiyang Ju, Zhaokun Chen, Junqiang Xi","doi":"10.1109/ICPS58381.2023.10128100","DOIUrl":"https://doi.org/10.1109/ICPS58381.2023.10128100","url":null,"abstract":"Driving style recognition plays a vital role in devel-oping human-centered intelligent vehicles that consider drivers' preferences. However, the feature selection of driving style recognition is diverse and inconsistent, which varies with driving scenarios. Therefore, the application of driving style is limited by the accuracy and the rapidity of the driving scene recognition algorithm, which is difficult for low-cost onboard chips. To solve the problem, this paper proposes a scene-insensitive method for driving style recognition. Factor analysis is employed to extract common factors in diverse driving scenes from high-dimensional driving data segmentation. The unified common factors reflect the differences in drivers' driving behaviors with different styles, verified in the publicly available dataset and 100-driver experimental data. Then, an efficient driving style recognition algorithm is developed based on K-means Clustering. Finally, natural driving data from 100 drivers in Changchun, China, is collected to evaluate the proposed method with the driving style questionnaire. Compared with six supervised learning methods, experimental results demonstrate that the proposed method provides an efficient and scene-insensitive way to recognize the driving style.","PeriodicalId":426122,"journal":{"name":"2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS)","volume":"42 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":"121313632","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

Online Learning-based Trust Prediction for Reliable and Energy-efficient Transmission 基于在线学习的可靠节能输电信任预测

2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS) Pub Date : 2023-05-08 DOI: 10.1109/ICPS58381.2023.10128092

Xiaolin Wang, Jinglong Zhang, Xuanzhao Lu, Xiaojing Wen, Fangfei Li

{"title":"Online Learning-based Trust Prediction for Reliable and Energy-efficient Transmission","authors":"Xiaolin Wang, Jinglong Zhang, Xuanzhao Lu, Xiaojing Wen, Fangfei Li","doi":"10.1109/ICPS58381.2023.10128092","DOIUrl":"https://doi.org/10.1109/ICPS58381.2023.10128092","url":null,"abstract":"Industrial wireless communication networks (IWCNs) have been widely applied in data interaction between field sensors and edge computing units. Nevertheless, harsh industrial environments and malicious attacks cause data loss and delay, which makes it challenging to satisfy the reliability and timeliness requirements of IWCNs. Considering the limited communication energy budget, computation capacity, and multiple unreliable factors, traditional reliable transmission policies become less efficient for IWCNs. To handle these issues, in this paper, we introduce an online learning-based trust model and present a trust-delay aware energy-efficient transmission scheme (TDEETs) to reduce communication energy consumption while satisfying data reliability and control stability constraints. Firstly, a novel trust prediction mechanism based on online extreme learning machine (ELM) with a forgetting factor is proposed. Then, with the aid of low-complexity trust prediction, the optimal path selection strategy and retransmission policy are designed by online solving the optimization problem. Finally, numerical examples demonstrate the effectiveness of the proposed trust prediction mechanism and the transmission performance improvement using TDEETs.","PeriodicalId":426122,"journal":{"name":"2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS)","volume":"86 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":"121967250","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

Improving the Traceability of Wood-based Sheet Leftovers using Computer Vision 利用计算机视觉提高木片废料的可追溯性

2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS) Pub Date : 2023-05-08 DOI: 10.1109/ICPS58381.2023.10127865

N. Guedes, I. Capitanio, H. Rosse, J. Coelho, José Barbosa, Nélio Pires, J. Magalhaes

引用次数: 0

Semantic Interoperability of Digital Twins: Ontology-based Capability Checking in AAS Modeling Framework 数字孪生的语义互操作性:AAS建模框架中基于本体的能力检查

2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS) Pub Date : 2023-05-08 DOI: 10.1109/ICPS58381.2023.10128003

Yining Huang, S. Dhouib, Luis Palacios Medinacelli, J. Malenfant

{"title":"Semantic Interoperability of Digital Twins: Ontology-based Capability Checking in AAS Modeling Framework","authors":"Yining Huang, S. Dhouib, Luis Palacios Medinacelli, J. Malenfant","doi":"10.1109/ICPS58381.2023.10128003","DOIUrl":"https://doi.org/10.1109/ICPS58381.2023.10128003","url":null,"abstract":"Industry 4.0 currently prepares a major shift towards extreme flexibility into production lines management. Digital Twins are one of the key enabling technologies for Industry 4.0. However, the interoperability gap among digital representation of Industry 4.0 assets is still one of the obstacles to the development and adoption of digital twins. If the Asset Administration Shell (AAS), the standard proposed to represent the I4.0 components, caters for syntactic interoperability, a more semantic kind of interoperability is deeply needed to develop flexible and adaptable production lines. In our work, we overcome the limitation of current syntactic-only resource matching algorithms by implementing semantic interoperability based on ontologies i.e., by transforming AAS-based plant models into MaRCO (Manufacturing Resource Capability Ontology) instances and then query the expanded ontology to find the needed resources. This article presents this ontology-based approach as the first step towards the design and implementation of an automated I4.0 flexible plant supervision and control system based on model-driven engineering (MDE) within the “Papyrus for Manufacturing” toolset. We show how an MDE approach can aggregate around digital twin modeling tools from the Papyrus platform both I4.0 technologies and AI (Knowledge Representation and Reasoning) tools. Our platform aligns modeling and ontological elements to get the best of both worlds. This method has two main advantages: (1) to provide semantic descriptions for digital twin models, (2) to complement model-driven engineering tools with automated reasoning. This paper showcases this approach through a robotic cell use case.","PeriodicalId":426122,"journal":{"name":"2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS)","volume":"97 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":"123437136","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: 2

Interpretable analysis of feature importance and implicit correlation based on sEMG grayscale. images 基于 sEMG 灰度图像的特征重要性和隐含相关性的可解释性分析。

2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS) Pub Date : 2023-05-08 DOI: 10.1109/ICPS58381.2023.10128002

AO Xiaohu, Feng Wang, Juan Zhao, Jinhua She

引用次数: 0

Electric Vehicle Charging Load Time-Series Prediction Based on Broad Learning System 基于广义学习系统的电动汽车充电负荷时序预测

2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS) Pub Date : 2023-05-08 DOI: 10.1109/ICPS58381.2023.10128054

Wang Sike, Liansong Yu, Pang Bo, Xiaohu Zhu, Cao Peng, Shen Yang

引用次数: 0

Trajectory prediction method using deep learning for intelligent and connected vehicles 基于深度学习的智能网联车辆轨迹预测方法

2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS) Pub Date : 2023-05-08 DOI: 10.1109/ICPS58381.2023.10128049

Tianqi Qie, Weida Wang, Chaowei Yang, Ying Li, Yuhang Zhang, Wenjie Liu

引用次数: 0

A Decomposition-based Encoder-Decoder Framework for Multi-step Prediction of Burn-Through Point in Sintering Process 烧结过程烧透点多步预测的基于分解的编码器-解码器框架

2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS) Pub Date : 2023-05-08 DOI: 10.1109/ICPS58381.2023.10128029

Yuanfeng Xie, Bocun He, Xinmin Zhang, Zhihuan Song

{"title":"A Decomposition-based Encoder-Decoder Framework for Multi-step Prediction of Burn-Through Point in Sintering Process","authors":"Yuanfeng Xie, Bocun He, Xinmin Zhang, Zhihuan Song","doi":"10.1109/ICPS58381.2023.10128029","DOIUrl":"https://doi.org/10.1109/ICPS58381.2023.10128029","url":null,"abstract":"Sintering process is a critical step in the ironmaking process. Burn-through point (BTP), as a key performance index of sintering ore, has a great influence on the quality of the sintering product. The existing prediction methods attempt to use a single model to establish the relationship between variables. However, due to the strong volatility, uncertainty, and multivariable coupling of sintering process, the traditional prediction model cannot produce reliable predictions. In order to deal with the complex characteristics of sintering process, this paper proposes a decomposition-based encoder-decoder modeling framework, in which a sequence decomposition module is designed to decompose the input time series into different sub-sequences. Then, these sub-sequences are constructed by the encoder-decoder models separately. The effectiveness of the proposed multi-step ahead prediction modeling framework was evaluated in a real-world sintering process. Compared with the traditional prediction modeling framework, the proposed modeling framework has more accurate results in multi-step ahead prediction.","PeriodicalId":426122,"journal":{"name":"2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS)","volume":"8 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":"132281153","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