2022 IEEE 18th International Conference on Automation Science and Engineering (CASE)最新文献

Neural network predictive schemes for building temperature control: a comparative study 建筑温度控制的神经网络预测方案比较研究

2022 IEEE 18th International Conference on Automation Science and Engineering (CASE) Pub Date : 2022-08-20 DOI: 10.1109/CASE49997.2022.9926557

L. Ferrarini, S. Rastegarpour

{"title":"Neural network predictive schemes for building temperature control: a comparative study","authors":"L. Ferrarini, S. Rastegarpour","doi":"10.1109/CASE49997.2022.9926557","DOIUrl":"https://doi.org/10.1109/CASE49997.2022.9926557","url":null,"abstract":"Starting from an application of a real medium-size university building, the present paper focuses on the comparison among different ways to synthesize a predictive control scheme to improve the energy performance for heating, ventilation and air conditioning system of the building. The main motivation is the comparison among a nonlinear predictive control structure previously developed (based on first principle equations) with a predictive control whose prediction model is an artificial neural network. Particular emphasis is given on how to tune the neural network to gain good closed-loop performance. Twenty-one different networks are designed and tuned in order to correlate their closed-loop performance with the type and length of training data set, for building energy efficiency applications. Finally, a linear time-variant predictive control is given, obtained as analytical linearization along the future system trajectory, of the nonlinear equations of the neural network model. The goal is to add to the comparison a low computational burden (linear controller) still derived from nonlinear data-driven methods.","PeriodicalId":325778,"journal":{"name":"2022 IEEE 18th International Conference on Automation Science and Engineering (CASE)","volume":"382 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115212664","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

Analysis and Improvement of Batch-Batch Production Systems 批量生产系统的分析与改进

2022 IEEE 18th International Conference on Automation Science and Engineering (CASE) Pub Date : 2022-08-20 DOI: 10.1109/CASE49997.2022.9926500

Lingchen Liu, Chaoqi Yan

引用次数: 0

AggCrack: An Aggregated Attention Model for Robotic Crack Detection in Challenging Airport Runway Environment AggCrack:机场跑道环境下机器人裂纹检测的聚合注意力模型

2022 IEEE 18th International Conference on Automation Science and Engineering (CASE) Pub Date : 2022-08-20 DOI: 10.1109/CASE49997.2022.9926470

Haifeng Li, J. Zong, Rui Huang, Zhongcheng Gui, Dezhen Song

{"title":"AggCrack: An Aggregated Attention Model for Robotic Crack Detection in Challenging Airport Runway Environment","authors":"Haifeng Li, J. Zong, Rui Huang, Zhongcheng Gui, Dezhen Song","doi":"10.1109/CASE49997.2022.9926470","DOIUrl":"https://doi.org/10.1109/CASE49997.2022.9926470","url":null,"abstract":"Crack detection is essential for guaranteeing airport runway structural reliability. An efficient solution we take is to employ a robot equipped with a camera to perform inspection task. However, automatic crack detection for airport runway is challenging, as the runway surface is seriously polluted by fuel stain and aircraft wheel mark, and the cracks need to be detected luare usually extremely thin. Thus, we propose a CNN model, AggCrack, to perform the crack detection task. AggCrack adopts an innovative semantic-level attention mechanism on the edges of the targets to focus the model on crucial features, and combines edge information and semantic segmentation for more accurate crack detection. We have implemented the algorithm and have it extensively tested on an airport runway dataset collected by our inspection robot from four different airport runways. Compared with four existing deep learning methods, experimental results show that our algorithm outperforms all counterparts. Specifically, it achieves the Precision, Recall and F1-measure at 84.24%, 70.36% and 76.68%, respectively.","PeriodicalId":325778,"journal":{"name":"2022 IEEE 18th International Conference on Automation Science and Engineering (CASE)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115679486","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

Efficient and Accurate Simulation of Origin-Destination Flow in Telecommunication Systems 电信系统始发流的高效准确仿真

2022 IEEE 18th International Conference on Automation Science and Engineering (CASE) Pub Date : 2022-08-20 DOI: 10.1109/CASE49997.2022.9926660

Mingsheng Ma, Shuaipeng Li, Yuan-Chih Chang, Sheng Zhang, Chenhong Li, Xu Gong, Huiying Xu, F. Gao, Xiaoyu Cao, Chao-Bo Yan

{"title":"Efficient and Accurate Simulation of Origin-Destination Flow in Telecommunication Systems","authors":"Mingsheng Ma, Shuaipeng Li, Yuan-Chih Chang, Sheng Zhang, Chenhong Li, Xu Gong, Huiying Xu, F. Gao, Xiaoyu Cao, Chao-Bo Yan","doi":"10.1109/CASE49997.2022.9926660","DOIUrl":"https://doi.org/10.1109/CASE49997.2022.9926660","url":null,"abstract":"Performance evaluation of telecommunication systems is challenging due to the complexity and large-scale of this problem. However, research on obtaining network measures is still scarce and difficult. Majority of previous research mainly applies the traditional simulation method to solve this problem. But in fact, the result of such traditional simulation methods may suffer from many shortcomings. To solve this problem, we adopt the max-plus method to the telecommunication system and establish simulation processes, inspired by the success of using max-plus algebra on the simulation of the production line. In this paper, we focus on the simulation of an origin-destination flow telecommunication system. The efficiency and accuracy of the proposed method are verified by comparative simulation with other simulation methods. On the premise of ensuring the accuracy of simulation, our method performs 20 times faster than traditional event scheduling method. Moreover, we analyze simulation results under different scheduling strategies to illustrate the accuracy of the simulation.","PeriodicalId":325778,"journal":{"name":"2022 IEEE 18th International Conference on Automation Science and Engineering (CASE)","volume":"234 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123118232","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

Social-Aware Decision Algorithm for On-ramp Merging Based on Level-k Gaming 基于Level-k博弈的入匝道合并社会意识决策算法

2022 IEEE 18th International Conference on Automation Science and Engineering (CASE) Pub Date : 2022-08-20 DOI: 10.1109/CASE49997.2022.9926461

Daofei Li, Hao Pan, Yang Xiao, Bo Li, Linhui Chen, Houjian Li, Hao Lyu

引用次数: 3

Analysis of Paint Film Thickness Distribution Based on Particle Method Considering Time Series Change of Flow 考虑流量时间序列变化的粒子法漆膜厚度分布分析

2022 IEEE 18th International Conference on Automation Science and Engineering (CASE) Pub Date : 2022-08-20 DOI: 10.1109/CASE49997.2022.9926433

Yoshinobu Takahashi, Fangshou Chang, F. Kato, H. Iwata

引用次数: 0

Analysis of Process Data for Remote Health Prediction in Distributed Automation Systems 分布式自动化系统中远程健康预测过程数据分析

2022 IEEE 18th International Conference on Automation Science and Engineering (CASE) Pub Date : 2022-08-20 DOI: 10.1109/CASE49997.2022.9926576

Yu-Ming Hsieh, Jan Wilch, Chin-Yi Lin, B. Vogel‐Heuser, F. Cheng

{"title":"Analysis of Process Data for Remote Health Prediction in Distributed Automation Systems","authors":"Yu-Ming Hsieh, Jan Wilch, Chin-Yi Lin, B. Vogel‐Heuser, F. Cheng","doi":"10.1109/CASE49997.2022.9926576","DOIUrl":"https://doi.org/10.1109/CASE49997.2022.9926576","url":null,"abstract":"Predictive Maintenance (PdM) is a one of the core topics for Industry 4.0 and entitled as “Predictive Maintenance 4.0.” The main tasks of PdM are to monitor production tool health and then issue an alert when a maintenance is necessary. PdM has become a top priority as it can optimize tool utility. The so-called iFA system platform, realized by integrating several intelligent services including Intelligent Predictive Maintenance (IPM), was proposed to accomplish the goal of Zero-Defect Manufacturing. However, the current algorithm in IPM did not provide a feasible aging feature extraction procedure. Thus, once the aging features cannot be acquired adequately, the monitoring accuracy will become poor. To remedy the above-mentioned problem, the automated Aging Feature Extraction Scheme (AFES) is proposed in this paper to perform analysis of process data for remote health prediction. This automated AFES is packed as an application module and plugged in the cyber physical agent of iFA. The proposed architecture, which integrates iFA, Resource Agent (RA), message broker, and automated Production System, is also designed to effectively monitor tool health status and predict the remaining useful life via the automated AFES. The experimental results indicate that the proposed architecture can not only enhance the performance of the IPM algorithm, but also feed-back the tool health indexes to RA via comprehensive system integration, such that the goal of optimized/maximum OEE can be accomplished. This work was submitted alongside another paper to CASE2022, conceptualizing a data exchange infrastructure and its impact on dependability characteristics of the technical process.","PeriodicalId":325778,"journal":{"name":"2022 IEEE 18th International Conference on Automation Science and Engineering (CASE)","volume":"267 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125817808","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 Learning-based Iterated Local Search Algorithm for Order Batching and Sequencing Problems 基于学习的有序批处理和排序问题的迭代局部搜索算法

2022 IEEE 18th International Conference on Automation Science and Engineering (CASE) Pub Date : 2022-08-20 DOI: 10.1109/CASE49997.2022.9926486

Lijie Zhou, Chengran Lin, Qian Ma, Zhengcai Cao

引用次数: 2

Real-Time OF-based Trajectory Control of a UAS Rotorcraft Based on Integral Extended-State LQG 基于积分扩展状态LQG的UAS旋翼机实时轨迹控制

2022 IEEE 18th International Conference on Automation Science and Engineering (CASE) Pub Date : 2022-08-20 DOI: 10.1109/CASE49997.2022.9926730

Tariq Zioud, J. Escareño, O. Labbani-Igbida

引用次数: 0

Deep Learning Based Sustainable Material Attribution for Apparels 基于深度学习的服装可持续材料归因

2022 IEEE 18th International Conference on Automation Science and Engineering (CASE) Pub Date : 2022-08-20 DOI: 10.1109/CASE49997.2022.9926684

Yaswanth Kumar Nicherala, Srikrishna Sadula, V. P. Shrinivas

引用次数: 0