Pub Date : 2022-07-25DOI: 10.1109/INDIN51773.2022.9976167
Liaqat Ali, M. I. Azim, Jan Peters, V. Bhandari, Anand Menon, Vinod Tiwari, Jemma Green
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.
{"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}
Pub Date : 2022-07-25DOI: 10.1109/INDIN51773.2022.9976120
K. Kondo, Wang Tianyue, Yuichi Nakamura, Yuichi Sasaki, Miho Kawamura
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.
{"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}
Pub Date : 2022-07-25DOI: 10.1109/INDIN51773.2022.9976109
Maxim Friesen, Tian Tan, J. Jasperneite, Jie Wang
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.
{"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}
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.
{"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}
The tracking control of hypersonic flight vehicle (HFV) is discussed in this paper, and the nonlinear model of HFV is assumed to be completely unknown. This problem is surely challenging because of the missing prior knowledge, but is more closer to reality since the exact mode of HFV is difficult to be obtained. A reinforcement learning (RL) based optimal controller is proposed for the tracking control of HFV. A model based RL algorithm is firstly proposed and then, based on this algorithm, a model free algorithm is constructed. For relaxing the environmental conditions, neural network (NN) is adopted for the approximation of Critic and Actor, and then a Greedy Policy based updated learning law for NN is derived. The presented RL based control strategy is carried on the nonlinear model of HFV to show its effectiveness.
{"title":"Reinforcement Learning based Optimal Tracking Control for Hypersonic Flight Vehicle: A Model Free Approach","authors":"Xiaoxiang Hu, Kejun Dong, Teng-Chieh Yang, Bing Xiao","doi":"10.1109/INDIN51773.2022.9976071","DOIUrl":"https://doi.org/10.1109/INDIN51773.2022.9976071","url":null,"abstract":"The tracking control of hypersonic flight vehicle (HFV) is discussed in this paper, and the nonlinear model of HFV is assumed to be completely unknown. This problem is surely challenging because of the missing prior knowledge, but is more closer to reality since the exact mode of HFV is difficult to be obtained. A reinforcement learning (RL) based optimal controller is proposed for the tracking control of HFV. A model based RL algorithm is firstly proposed and then, based on this algorithm, a model free algorithm is constructed. For relaxing the environmental conditions, neural network (NN) is adopted for the approximation of Critic and Actor, and then a Greedy Policy based updated learning law for NN is derived. The presented RL based control strategy is carried on the nonlinear model of HFV to show its effectiveness.","PeriodicalId":359190,"journal":{"name":"2022 IEEE 20th International Conference on Industrial Informatics (INDIN)","volume":"44 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":"122642068","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}
Pub Date : 2022-07-25DOI: 10.1109/INDIN51773.2022.9976068
Abdul Rehan Khan Mohammed, Jiayi Zhang, Ahmad Bilal
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.
{"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}
Aspect-based Sentiment Classification (ASC) task is a challenge in Natural Language Processing (NLP) and is especially important for fields that require detailed analysis like finance. It aims to identify the sentiment polarity of specific aspects in sentences. In addition to tweets and posts directly related to finance, news from such as restaurants and e-commerce may also indirectly affect its stock prices. In previous approaches, attention-based neural network models were mostly adopted to implicitly connect aspects with opinion words for better aspect representations. However, due to the complexity of language and the presence of multiple aspects in a single sentence, these existing models often confuse connections. To tackle this problem, we propose a model named GAS-CL which encodes syntactical structure into aspect representations and refines it with a contrastive loss. Experiments on several datasets confirm that our approach can have better aspect representations and achieve a significant improvement.
{"title":"Graph Attention Network for Financial Aspect-based Sentiment Classification with Contrastive Learning","authors":"Zhenhuan Huang, Guansheng Wu, Xiang Qian, Baochang Zhang","doi":"10.1109/INDIN51773.2022.9976125","DOIUrl":"https://doi.org/10.1109/INDIN51773.2022.9976125","url":null,"abstract":"Aspect-based Sentiment Classification (ASC) task is a challenge in Natural Language Processing (NLP) and is especially important for fields that require detailed analysis like finance. It aims to identify the sentiment polarity of specific aspects in sentences. In addition to tweets and posts directly related to finance, news from such as restaurants and e-commerce may also indirectly affect its stock prices. In previous approaches, attention-based neural network models were mostly adopted to implicitly connect aspects with opinion words for better aspect representations. However, due to the complexity of language and the presence of multiple aspects in a single sentence, these existing models often confuse connections. To tackle this problem, we propose a model named GAS-CL which encodes syntactical structure into aspect representations and refines it with a contrastive loss. Experiments on several datasets confirm that our approach can have better aspect representations and achieve a significant improvement.","PeriodicalId":359190,"journal":{"name":"2022 IEEE 20th International Conference on Industrial Informatics (INDIN)","volume":"29 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":"114800672","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}
Pub Date : 2022-07-25DOI: 10.1109/indin51773.2022.9976147
Hoa Tran-Dang, Dong-Seong Kim
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.
{"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}
Pub Date : 2022-07-25DOI: 10.1109/INDIN51773.2022.9976103
M. Clausen, J. Schütz
The most vital requirement for the electric power system as a critical infrastructure is its security of supply. In course of the transition of the electric energy system, however, the security provided by the N-1 principle increasingly reaches its limits. The IT/OT convergence changes the threat structure significantly. New risk factors, that can lead to major blackouts, are added to the existing ones. The problem, however, the cost of security optimizations are not always in proportion to their value. Not every component is equally critical to the energy system, so the question arises, "How secure does my system need to be?". To adress the security-by-design principle, this contribution introduces a Security Metric (SecMet) that can be applied to Smart Grid architectures and its components and deliver an indicator for the "Securitisation Need" based on an individual risk assessment.
{"title":"Identifying Security Requirements for Smart Grid Components: A Smart Grid Security Metric","authors":"M. Clausen, J. Schütz","doi":"10.1109/INDIN51773.2022.9976103","DOIUrl":"https://doi.org/10.1109/INDIN51773.2022.9976103","url":null,"abstract":"The most vital requirement for the electric power system as a critical infrastructure is its security of supply. In course of the transition of the electric energy system, however, the security provided by the N-1 principle increasingly reaches its limits. The IT/OT convergence changes the threat structure significantly. New risk factors, that can lead to major blackouts, are added to the existing ones. The problem, however, the cost of security optimizations are not always in proportion to their value. Not every component is equally critical to the energy system, so the question arises, \"How secure does my system need to be?\". To adress the security-by-design principle, this contribution introduces a Security Metric (SecMet) that can be applied to Smart Grid architectures and its components and deliver an indicator for the \"Securitisation Need\" based on an individual risk assessment.","PeriodicalId":359190,"journal":{"name":"2022 IEEE 20th International Conference on Industrial Informatics (INDIN)","volume":"11 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":"121194608","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}
Pub Date : 2022-07-25DOI: 10.1109/INDIN51773.2022.9976179
Y. Berezovskaya, Chen-Wei Yang, V. Vyatkin
Contemporary data centres consume electricity on an industrial scale and require control to improve energy efficiency and maintain high availability. The article proposes an idea and structure of the framework supporting development and validation of the multi-agent control for the energy-efficient data centre. The framework comprises two subsystems: the modelling toolbox and the controlling toolbox. This work focuses on such essential components of the controlling toolbox, as an individual controller. The reinforcement learning approach is applied to the controllers’ implementation. The server fan controller, named SF agent, is implemented based on the framework infrastructure and reinforcement learning approach. The agent’s capability of energy-saving is demonstrated.
{"title":"Reinforcement learning approach to implementation of individual controllers in data centre control system","authors":"Y. Berezovskaya, Chen-Wei Yang, V. Vyatkin","doi":"10.1109/INDIN51773.2022.9976179","DOIUrl":"https://doi.org/10.1109/INDIN51773.2022.9976179","url":null,"abstract":"Contemporary data centres consume electricity on an industrial scale and require control to improve energy efficiency and maintain high availability. The article proposes an idea and structure of the framework supporting development and validation of the multi-agent control for the energy-efficient data centre. The framework comprises two subsystems: the modelling toolbox and the controlling toolbox. This work focuses on such essential components of the controlling toolbox, as an individual controller. The reinforcement learning approach is applied to the controllers’ implementation. The server fan controller, named SF agent, is implemented based on the framework infrastructure and reinforcement learning approach. The agent’s capability of energy-saving is demonstrated.","PeriodicalId":359190,"journal":{"name":"2022 IEEE 20th International Conference on Industrial Informatics (INDIN)","volume":"97 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":"121729507","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}
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