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

A StyleGAN3-Based Data Augmentation Method for Ceramic Defect Detection 基于stylegan3的陶瓷缺陷检测数据增强方法

2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS) Pub Date : 2023-05-08 DOI: 10.1109/ICPS58381.2023.10128067

Huimin Ou, Jianqi An, Xingjun Wang, Jianru Xiong, Xin Chen, Qingyi Wang

引用次数: 0

Boundary Protection Control of a Hybrid Vertical Take-Off and Landing UAV 混合垂直起降无人机的边界保护控制

2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS) Pub Date : 2023-05-08 DOI: 10.1109/ICPS58381.2023.10128057

Tongqing Chen, Dawei Wu, She Xv, Bolin Wang, Xiaoqi Huang

引用次数: 0

A Model-Predictive-Enabled Equivalent-Input-Disturbance Approach for Disturbance Rejection 一种基于模型预测的干扰抑制等效输入干扰方法

2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS) Pub Date : 2023-05-08 DOI: 10.1109/ICPS58381.2023.10128063

Yujian Zhou, Jinhua She, Feng Wang, M. Iwasaki

引用次数: 0

Research on Dynamic Labels in Network Pruning 网络剪枝中动态标签的研究

2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS) Pub Date : 2023-05-08 DOI: 10.1109/ICPS58381.2023.10128021

Lijun Zhang, Yaomin Luo, Shiqi Xie, Xiucheng Wu

{"title":"Research on Dynamic Labels in Network Pruning","authors":"Lijun Zhang, Yaomin Luo, Shiqi Xie, Xiucheng Wu","doi":"10.1109/ICPS58381.2023.10128021","DOIUrl":"https://doi.org/10.1109/ICPS58381.2023.10128021","url":null,"abstract":"Convolutional neural network compression technology plays an extremely important role in model transplantation and deployment, especially in mobile and embedded hardware platforms with small memory and low computing power, compression technology is even more critical. Convolutional neural network channel pruning technology has developed rapidly in recent years, and a number of excellent pruning algorithms have emerged. The channel pruning technology has gradually developed from the earliest static pruning to dynamic pruning, which adopts different pruning schemes for different inputs. However, the current dynamic pruning scheme needs to introduce multiple modules to predict the mask to prune the feature maps, and some schemes also introduce multiple hyperparameters in the loss function to balance the model accuracy and pruning rate, which leads to The model has difficulty converging during training. We propose a dynamic pruning method, each convolution structure configures a simple prediction module, and generating dynamic labels through the input's norm and similarity to guide the prediction module training, which will not bring new parameters to the loss function. We conducted related experiments on multiple models on the Cifar10 datasets. The experiments on ResNet56 show that our scheme is 1.3% higher than the most advanced scheme in terms of compression rate under the premise of the same accuracy.","PeriodicalId":426122,"journal":{"name":"2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS)","volume":"401 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":"115919568","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

Interaction-awareness based Intention Inference of Lag Vehicle in Lane Changing Decision-Making Process for Autonomous Driving 基于交互感知的自动驾驶变道决策滞后车辆意图推理

2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS) Pub Date : 2023-05-08 DOI: 10.1109/ICPS58381.2023.10154160

Guofu Yan, Huilong Yu, Chaopeng Zhang, Junqiang Xi

{"title":"Interaction-awareness based Intention Inference of Lag Vehicle in Lane Changing Decision-Making Process for Autonomous Driving","authors":"Guofu Yan, Huilong Yu, Chaopeng Zhang, Junqiang Xi","doi":"10.1109/ICPS58381.2023.10154160","DOIUrl":"https://doi.org/10.1109/ICPS58381.2023.10154160","url":null,"abstract":"Cooperative driving behavior during lane-changing decision-making processes is expected to improve the safety of autonomous vehicles. However, since the intention of the lag vehicle driver is changeable and cannot be directly observed, the challenge of achieving cooperative driving behavior still remains. In this paper, an intention inference method that combines interaction-awareness information is proposed to infer the uncertain intention of the lag vehicle driver from multiple time-series driving data. The method is based on the Hidden Markov Model with the Gaussian Mixture Model (GMM-HMM) structure, which could enhance the inference performance by catching the feature that human drivers’ intentions cannot change instantaneously. Furthermore, variables are selected to train the proposed model based on the decision-making mechanism of both drivers with the interaction during lane-changing processes. High dimensional training data is alleviated by using the virtual collision point which could convert numerous training variables into an associative variable. Experimental results demonstrate that the proposed method can improve the inference performance and the collision avoidance performance in lane-change scenes compared with previous methods.","PeriodicalId":426122,"journal":{"name":"2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS)","volume":"44 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":"127460087","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

Intelligent Vehicle Trajectory Tracking Control Based on $H_{infty}$ Proportional-differential Controller 基于$H_{infty}$比例差分控制器的智能车辆轨迹跟踪控制

2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS) Pub Date : 2023-05-08 DOI: 10.1109/ICPS58381.2023.10128098

Gongwei Pan, Zhiyong Feng, Huiru Guo

引用次数: 0

2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS) Pub Date : 2023-05-08 DOI: 10.1109/ICPS58381.2023.10128059

Chuanfang Zhang, Kai-xiang Peng, Jie Dong, Liang Ma, Yangfan Wang, Dongjie Hua

{"title":"Exergy-related Operating Performance Assessment for Hot Rolling Process Based on Multiple imputation and Multi-class Support Vector Data Description","authors":"Chuanfang Zhang, Kai-xiang Peng, Jie Dong, Liang Ma, Yangfan Wang, Dongjie Hua","doi":"10.1109/ICPS58381.2023.10128059","DOIUrl":"https://doi.org/10.1109/ICPS58381.2023.10128059","url":null,"abstract":"In process industry, operating performance assessment (OPA) is important for ensuring production efficiency. With the development of modern information technology, the collection, storage and transmission of information in the process industry has been gaining popularity. However, the massive streaming industrial data obtained in real time has some non-ideal characteristics, such as missing values, which greatly increases the difficulty of OPA. Besides, traditional data-driven methods pay more attention to the utilization of process data and ignore the process mechanism. It is necessary to consider the energy flow of the process. As the unity of quality and quantity of energy, exergy contains the performance change information of the process and can be used as another way of achieving the required dimensionality reduction. To handle above issues, a novel exergy-related OPA based on multiple imputation (MI) and multi-class support vector data description (SVDD) is proposed in this paper. First, the initial incomplete process data are imputed by MI. Second, exergy efficiency are calculated and exergy-related process variables are obtained by the minimal redundancy maximal relevance (mRMR). Then, the exergy-related assessment model are developed. Finally, case study on a real hot rolling process (HRP) is given to illustrate the effectiveness of the proposed method.","PeriodicalId":426122,"journal":{"name":"2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS)","volume":"96 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":"126491640","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

Short - Term Prediction of Coke Pushing Current Peak Based on Improved ARIMA Model 基于改进ARIMA模型的焦炭推流峰值短期预测

2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS) Pub Date : 2023-05-08 DOI: 10.1109/ICPS58381.2023.10128081

Haiyang Wei, Luefeng Chen, Jie Hu, Yi Ren, Min Wu, W. Pedrycz, Kaoru Hirota

{"title":"Short - Term Prediction of Coke Pushing Current Peak Based on Improved ARIMA Model","authors":"Haiyang Wei, Luefeng Chen, Jie Hu, Yi Ren, Min Wu, W. Pedrycz, Kaoru Hirota","doi":"10.1109/ICPS58381.2023.10128081","DOIUrl":"https://doi.org/10.1109/ICPS58381.2023.10128081","url":null,"abstract":"Coke pushing current is an indicator to evaluate the difficulty of coke pushing operation. The higher coke pushing current is, the greater coke pushing resistance is, and the more difficult coke is to push out. Accurate prediction of current peak during future coke pushing operation can provide more time for production personnel to adjust production status and avoid difficult coke pushing in carbonization chamber. In this paper, a combination prediction model based on VMD (Variational Mode Decomposition) and improved ARIMA (Autoregressive Integrated Moving Average) models is proposed. Firstly, VMD algorithm is used to decompose time series of coke pushing current peak, de-noising data, and extracting main information of time series. Then, ARIMA model is used to predict mean change of linear elasticity and GARCH (Generalized Autore-gressive Conditional Heteroskedasticity) model is introduced to predict ARIMA model residual and improve heteroscedasticity of nonlinear part of time series, and then ARIMA-GARCH model is established. Finally, predicted value is obtained by the sum of each component prediction. The experimental results show that the proposed prediction model has a high prediction accuracy in the short-term prediction of coke pushing current peak. The scheme is applied to actual coking production to guide production of coke.","PeriodicalId":426122,"journal":{"name":"2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS)","volume":"51 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":"121551869","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

Simulation and Prediction of Bubble Size and Motion Characteristics of Underwater Horizontal Flow 水下水平流气泡尺寸及运动特性的模拟与预测

2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS) Pub Date : 2023-05-08 DOI: 10.1109/ICPS58381.2023.10127991

Zhong Yu, Zhi-yong Feng, X. Deng, Sanbao Hu

引用次数: 0

Deep Reinforcement Learning based Demand Response for Domestic Variable Volume Water Heater 基于深度强化学习的家用变容量热水器需求响应

2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS) Pub Date : 2023-05-08 DOI: 10.1109/ICPS58381.2023.10128065

Leihua Chen, Yongxin Su, Tao Zhang

{"title":"Deep Reinforcement Learning based Demand Response for Domestic Variable Volume Water Heater","authors":"Leihua Chen, Yongxin Su, Tao Zhang","doi":"10.1109/ICPS58381.2023.10128065","DOIUrl":"https://doi.org/10.1109/ICPS58381.2023.10128065","url":null,"abstract":"Domestic water heater is an important demand response resource in the home energy system. The variable volume water heater can dynamically adjust the water storage volume and participate in demand response for better energy efficiency. The system uncertainty caused by stochastic operating environments is an unavoidable challenge of the scheduling problem. Under this background, a deep reinforcement learning based optimization method is proposed to deal with the scheduling problem of the variable volume water heater, and the proposed method considers the comfort, safety and water hygiene of occupants. To achieve online automatic optimization, an optimization framework based on deep reinforcement learning method is established, and proposed an optimization algorithm to achieve cost-minimizing online scheduling. The simulation results show that the intelligent control method of variable volume water heater proposed in this paper can deal with the uncertainties of dynamic conditions, and the scheduled variable volume water heater can reduce energy cost by 22.7% than fixed volume water heater.","PeriodicalId":426122,"journal":{"name":"2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS)","volume":"20 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":"115454931","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