2022 IEEE 20th International Conference on Industrial Informatics (INDIN)最新文献_第8页

A Win-Win Local Energy Market for Participants, Retailers, and the Network Operator : A Peer-to-Peer Trading-driven Case Study 参与者、零售商和网络运营商的双赢本地能源市场:点对点交易驱动的案例研究

2022 IEEE 20th International Conference on Industrial Informatics (INDIN) Pub Date : 2022-07-25 DOI: 10.1109/INDIN51773.2022.9976167

Liaqat Ali, M. I. Azim, Jan Peters, V. Bhandari, Anand Menon, Vinod Tiwari, Jemma Green

{"title":"A Win-Win Local Energy Market for Participants, Retailers, and the Network Operator : A Peer-to-Peer Trading-driven Case Study","authors":"Liaqat Ali, M. I. Azim, Jan Peters, V. Bhandari, Anand Menon, Vinod Tiwari, Jemma Green","doi":"10.1109/INDIN51773.2022.9976167","DOIUrl":"https://doi.org/10.1109/INDIN51773.2022.9976167","url":null,"abstract":"What are the outcomes of using a local energy market (LEM) to trade electricity between participants, retailers/suppliers and the network operator? Such a question is becoming increasingly important for electrical grids as more and more solar photovoltaics (PVs) and battery energy storage systems (BESS) are introduced. This paper presents the formulation and economic analysis of a peer-to-peer (P2P)-driven LEM to determine its suitability for each of the players in the market. To do so, a framework is proposed to define the objective function of the LEM while the financial and network parameters are considered. Then, the designed model is deployed on an actual Australian suburb containing 300 participants — 200 consumers, 50 prosumers with solar PVs, and 50 prosumers with solar PVs and BESSs. This research examines the case of two retailers/suppliers and the network operator to evaluate the financial gains which are compared to the business-as-usual (BAU), where consumers buy electricity from the grid while prosumers sell excess energy back to the grid, via feed-in-tariff (FiT) mechanism. The simulation results emphasise that with a LEM: 1) all participants save money, with prosumers owning solar PVs and BESSs gaining the most; 2) the income margin of the retailer with only consumers remains unaffected, but it is slightly increased for other retailer with prosumers; and 3) the network operator sees a slight increase in its income and grid congestion will reduce.","PeriodicalId":359190,"journal":{"name":"2022 IEEE 20th International Conference on Industrial Informatics (INDIN)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127804058","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

Hand-object Interaction Definition and Recognition for Analyzing Manual Assembly Behaviors 手工装配行为分析中的手-物交互定义与识别

2022 IEEE 20th International Conference on Industrial Informatics (INDIN) Pub Date : 2022-07-25 DOI: 10.1109/INDIN51773.2022.9976120

K. Kondo, Wang Tianyue, Yuichi Nakamura, Yuichi Sasaki, Miho Kawamura

{"title":"Hand-object Interaction Definition and Recognition for Analyzing Manual Assembly Behaviors","authors":"K. Kondo, Wang Tianyue, Yuichi Nakamura, Yuichi Sasaki, Miho Kawamura","doi":"10.1109/INDIN51773.2022.9976120","DOIUrl":"https://doi.org/10.1109/INDIN51773.2022.9976120","url":null,"abstract":"Recently, a worker’s subjective satisfaction, in other words Quality-of-Working Life (QWL), has attracted more attention than productivity or efficiency. To provide QWL-oriented working support in a factory manufacturing environment, this study proposes a framework for recognizing manual assembly behaviors that may reflect a worker’s inner state or physical condition. First, a new set of interactions is defined to describe the behavioral fluctuations and diversity that appear even in the same assembly task. We expand the conventional interaction definitions for manufacturing analysis in three ways: 1) we add primitive interactions that qualify the fundamental interactions, 2) we install a spatial attribute into the interaction definition, and 3) we allow the simultaneous occurrence of multiple interactions. Additionally, an image-based automatic recognition technique is designed to detect the newly defined interactions. Through experimental evaluations for a compressor attachment task, we found various differences in manual assembly behaviors and confirmed that they can be distinguished using the recognized interactions.","PeriodicalId":359190,"journal":{"name":"2022 IEEE 20th International Conference on Industrial Informatics (INDIN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134139364","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

Multi-Agent Deep Reinforcement Learning For Real-World Traffic Signal Controls - A Case Study 现实世界交通信号控制的多智能体深度强化学习-一个案例研究

2022 IEEE 20th International Conference on Industrial Informatics (INDIN) Pub Date : 2022-07-25 DOI: 10.1109/INDIN51773.2022.9976109

Maxim Friesen, Tian Tan, J. Jasperneite, Jie Wang

{"title":"Multi-Agent Deep Reinforcement Learning For Real-World Traffic Signal Controls - A Case Study","authors":"Maxim Friesen, Tian Tan, J. Jasperneite, Jie Wang","doi":"10.1109/INDIN51773.2022.9976109","DOIUrl":"https://doi.org/10.1109/INDIN51773.2022.9976109","url":null,"abstract":"Increasing traffic congestion leads to significant costs, whereby poorly configured signaled intersections are a common bottleneck and root cause. Traditional traffic signal control (TSC) systems employ rule-based or heuristic methods to decide signal timings, while adaptive TSC solutions utilize a traffic-actuated control logic to increase their adaptability to real-time traffic changes. However, such systems are expensive to deploy and are often not flexible enough to adequately adapt to the volatility of today’s traffic dynamics. More recently, this problem became a frontier topic in the domain of deep reinforcement learning (DRL) and enabled the development of multi-agent DRL approaches that can operate in environments with several agents present, such as traffic systems with multiple signaled intersections. However, many of these proposed approaches were validated using artificial traffic grids. This paper presents a case study, where real-world traffic data from the town of Lemgo in Germany is used to create a realistic road model within VISSIM. A multi-agent DRL setup, comprising multiple independent deep Q-networks, is applied to the simulated traffic network. Traditional rule-based signal controls, modeled in LISA+ and currently employed in the real world at the studied intersections, are integrated into the traffic model and serve as a performance baseline. The performance evaluation indicates a significant reduction of traffic congestion when using the RL-based signal control policy over the conventional TSC approach with LISA+. Consequently, this paper reinforces the applicability of RL concepts in the domain of TSC engineering by employing a highly realistic traffic model.","PeriodicalId":359190,"journal":{"name":"2022 IEEE 20th International Conference on Industrial Informatics (INDIN)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123905730","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

Learning-based Automatic Report Generation for Scheduling Performance in Time-Sensitive Networking 基于学习的时间敏感网络调度性能自动报表生成

2022 IEEE 20th International Conference on Industrial Informatics (INDIN) Pub Date : 2022-07-25 DOI: 10.1109/INDIN51773.2022.9976085

Lingzhi Li, Qimin Xu, Yanzhou Zhang, Lei Xu, Yingxiu Chen, Cailian Chen

{"title":"Learning-based Automatic Report Generation for Scheduling Performance in Time-Sensitive Networking","authors":"Lingzhi Li, Qimin Xu, Yanzhou Zhang, Lei Xu, Yingxiu Chen, Cailian Chen","doi":"10.1109/INDIN51773.2022.9976085","DOIUrl":"https://doi.org/10.1109/INDIN51773.2022.9976085","url":null,"abstract":"As the global industrial upgrading requires higher reliability and real-time performance of data communication, Time-sensitive Networking (TSN) has been widely studied. Al-though many TSN scheduling algorithms are designed, there is no standardized analysis report after scheduling and comprehensive scheduling performance evaluation. This paper presents a complete automatic report generation system to analyze the scheduling performance. To standardize various data in TSN-based manufacturing, a uniform auto-generated report model is defined based on the Open Platform Communication Unified Architecture (OPC UA). A learning-based performance evaluation (LPE) method is established to comprehensively analyze the performance of TSN scheduling. In LPE, analytical hierarchy process (AHP) and entropy weight method (EWM) is adopted to optimize the weight distribution of performance indexes objectively, and convolutional neural network (CNN) is used to get the final evaluation result rapidly. Compared with the previous evaluation methods, simulations show the training time of the evaluation method is significantly reduced.","PeriodicalId":359190,"journal":{"name":"2022 IEEE 20th International Conference on Industrial Informatics (INDIN)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129209665","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

Reinforcement Learning based Optimal Tracking Control for Hypersonic Flight Vehicle: A Model Free Approach 基于强化学习的高超声速飞行器最优跟踪控制:一种无模型方法

2022 IEEE 20th International Conference on Industrial Informatics (INDIN) Pub Date : 2022-07-25 DOI: 10.1109/INDIN51773.2022.9976071

Xiaoxiang Hu, Kejun Dong, Teng-Chieh Yang, Bing Xiao

引用次数: 1

Observer-Based Robust Adaptive Tracking for Uncertain Robot Manipulators with External Force Disturbance Rejection 基于观测器的不确定机械臂抗外力干扰鲁棒自适应跟踪

2022 IEEE 20th International Conference on Industrial Informatics (INDIN) Pub Date : 2022-07-25 DOI: 10.1109/INDIN51773.2022.9976068

Abdul Rehan Khan Mohammed, Jiayi Zhang, Ahmad Bilal

{"title":"Observer-Based Robust Adaptive Tracking for Uncertain Robot Manipulators with External Force Disturbance Rejection","authors":"Abdul Rehan Khan Mohammed, Jiayi Zhang, Ahmad Bilal","doi":"10.1109/INDIN51773.2022.9976068","DOIUrl":"https://doi.org/10.1109/INDIN51773.2022.9976068","url":null,"abstract":"With the rapid growth in technology, the industries are fast-moving from the current automation standing into robotisation to increase productivity and deliver uniform quality. This requirement, in turn, has escalated the demand for robot control schemes. This paper proposes an observer-based robust adaptive tracking control scheme to minimise model uncertainties and external force disturbance effect to control the robot manipulator. No considerations are required for the upper bound of system uncertainties and disturbances in the control design. Plus, the speed of variation and the magnitude of unknown parameters and perturbations are assumed to have no limitations. The proposed control scheme uses an adaptation mechanism for a high gain nonlinear observer along with simplicity and universality properties to ensure robust tracking and make the system follow the desired reference model. Simulation results show that the proposed robust adaptive control scheme achieves boundedness for all the closed-loop signals and convergence of the tracking error.","PeriodicalId":359190,"journal":{"name":"2022 IEEE 20th International Conference on Industrial Informatics (INDIN)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121820392","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

Graph Attention Network for Financial Aspect-based Sentiment Classification with Contrastive Learning 基于对比学习的金融方面情感分类图注意网络

2022 IEEE 20th International Conference on Industrial Informatics (INDIN) Pub Date : 2022-07-25 DOI: 10.1109/INDIN51773.2022.9976125

Zhenhuan Huang, Guansheng Wu, Xiang Qian, Baochang Zhang

引用次数: 0

Dynamic Task Offloading Approach for Task Delay Reduction in the IoT-enabled Fog Computing Systems 基于物联网的雾计算系统中降低任务延迟的动态任务卸载方法

2022 IEEE 20th International Conference on Industrial Informatics (INDIN) Pub Date : 2022-07-25 DOI: 10.1109/indin51773.2022.9976147

Hoa Tran-Dang, Dong-Seong Kim

{"title":"Dynamic Task Offloading Approach for Task Delay Reduction in the IoT-enabled Fog Computing Systems","authors":"Hoa Tran-Dang, Dong-Seong Kim","doi":"10.1109/indin51773.2022.9976147","DOIUrl":"https://doi.org/10.1109/indin51773.2022.9976147","url":null,"abstract":"Fog computing systems (FCS) have been widely integrated in the IoT-based applications aiming to improve the quality of services (QoS) such as low response service delay by performing the task computation nearby the task generation sources (i.e., IoT devices) on behalf of remote cloud servers. However, to achieve the objective of delay reduction remains challenging for offloading strategies due to the resource limitation of fog devices. In addition, a high rate of task requests combined with heavy tasks (i.e., large task size) may cause a high imbalance of workload distribution among the heterogeneous fog devices. To cope with the situation, this paper proposes a dynamic task offloading (DTO) approach, which is based on the resource states of fog devices to derive the task offloading policy dynamically. Accordingly, a task can be executed by either a single fog or multiple fog devices through parallel computation of subtasks to reduce the task execution delay. Through the extensive simulation analysis, the proposed approaches show potential advantages in reducing the average delay significantly in the systems with high rate of service requests and heterogeneous fog environment compared with the existing solutions.","PeriodicalId":359190,"journal":{"name":"2022 IEEE 20th International Conference on Industrial Informatics (INDIN)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114604675","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

Identifying Security Requirements for Smart Grid Components: A Smart Grid Security Metric 识别智能电网组件的安全需求:一个智能电网安全度量

2022 IEEE 20th International Conference on Industrial Informatics (INDIN) Pub Date : 2022-07-25 DOI: 10.1109/INDIN51773.2022.9976103

M. Clausen, J. Schütz

引用次数: 0

Reinforcement learning approach to implementation of individual controllers in data centre control system 数据中心控制系统中单个控制器的强化学习实现方法

2022 IEEE 20th International Conference on Industrial Informatics (INDIN) Pub Date : 2022-07-25 DOI: 10.1109/INDIN51773.2022.9976179

Y. Berezovskaya, Chen-Wei Yang, V. Vyatkin

引用次数: 0