Pub Date : 2012-09-24DOI: 10.1109/ISRCS.2012.6309288
B. Caldwell, J. Onken
This paper describes a recently completed project to develop a human factors informed simulation of team-based expert coordination and knowledge sharing tasks in a complex and resilient control system. The project explores processes of anomaly response in NASA spaceflight mission control teams, using as a baseline example the mission profile and anomalies experienced during the final Space Shuttle flight, STS-135. While controllers in this simulation work to detect and resolve anomalies using technical decision criteria, their performance is subject to stochastic and non-rational dynamics of information availability and flow affecting situation awareness and hypothesis generation. The initial goal of this work is to assist in analysis of alternatives for future mission control room designs, and to develop increased simulation capability in the area of distributed expertise and problem solving in teams.
{"title":"Simulation and human factors in modeling of spaceflight mission control teams","authors":"B. Caldwell, J. Onken","doi":"10.1109/ISRCS.2012.6309288","DOIUrl":"https://doi.org/10.1109/ISRCS.2012.6309288","url":null,"abstract":"This paper describes a recently completed project to develop a human factors informed simulation of team-based expert coordination and knowledge sharing tasks in a complex and resilient control system. The project explores processes of anomaly response in NASA spaceflight mission control teams, using as a baseline example the mission profile and anomalies experienced during the final Space Shuttle flight, STS-135. While controllers in this simulation work to detect and resolve anomalies using technical decision criteria, their performance is subject to stochastic and non-rational dynamics of information availability and flow affecting situation awareness and hypothesis generation. The initial goal of this work is to assist in analysis of alternatives for future mission control room designs, and to develop increased simulation capability in the area of distributed expertise and problem solving in teams.","PeriodicalId":227062,"journal":{"name":"2012 5th International Symposium on Resilient Control Systems","volume":"189 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115841615","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 : 2012-09-24DOI: 10.1109/ISRCS.2012.6309289
J. Hewlett, Milos Manic, C. Rieger
When utilized properly, energy management systems (EMS) can offer significant energy savings by optimizing the efficiency of heating, ventilation, and air-conditioning (HVAC) systems. However, difficulty often arises due to the constraints imposed by the need to maintain an acceptable level of comfort for a building's occupants. This challenge is compounded by the fact that human comfort is difficult to define in a measurable way. One way to address this problem is to provide a building manager with direct feedback from the building's users. Still, this data is relative in nature, making it difficult to determine the actions that need to be taken, and while some useful comfort correlations have been devised, such as ASHRAE's Predicted Mean Vote index, they are rules of thumb that do not connect individual feedback with direct, diverse feedback sensing. As they are a correlation, quantifying effects of climate, age of buildings and associated defects such as draftiness, are outside the realm of this correlation. Therefore, the contribution of this paper is the Wireless Embedded Smart Block for Environment Sensing (WESBES); an affordable wireless sensor platform that allows subjective human comfort data to be directly paired with temporospatially correlated objective sensor measurements for use in EMS. The described device offers a flexible research platform for analyzing the relationship between objective and subjective occupant feedback in order to formulate more meaningful measures of human comfort. It could also offer an affordable and expandable option for real world deployment in existing EMS.
{"title":"WESBES: A wireless embedded sensor for improving human comfort metrics using temporospatially correlated data","authors":"J. Hewlett, Milos Manic, C. Rieger","doi":"10.1109/ISRCS.2012.6309289","DOIUrl":"https://doi.org/10.1109/ISRCS.2012.6309289","url":null,"abstract":"When utilized properly, energy management systems (EMS) can offer significant energy savings by optimizing the efficiency of heating, ventilation, and air-conditioning (HVAC) systems. However, difficulty often arises due to the constraints imposed by the need to maintain an acceptable level of comfort for a building's occupants. This challenge is compounded by the fact that human comfort is difficult to define in a measurable way. One way to address this problem is to provide a building manager with direct feedback from the building's users. Still, this data is relative in nature, making it difficult to determine the actions that need to be taken, and while some useful comfort correlations have been devised, such as ASHRAE's Predicted Mean Vote index, they are rules of thumb that do not connect individual feedback with direct, diverse feedback sensing. As they are a correlation, quantifying effects of climate, age of buildings and associated defects such as draftiness, are outside the realm of this correlation. Therefore, the contribution of this paper is the Wireless Embedded Smart Block for Environment Sensing (WESBES); an affordable wireless sensor platform that allows subjective human comfort data to be directly paired with temporospatially correlated objective sensor measurements for use in EMS. The described device offers a flexible research platform for analyzing the relationship between objective and subjective occupant feedback in order to formulate more meaningful measures of human comfort. It could also offer an affordable and expandable option for real world deployment in existing EMS.","PeriodicalId":227062,"journal":{"name":"2012 5th International Symposium on Resilient Control Systems","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122094731","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 : 2012-09-24DOI: 10.1109/ISRCS.2012.6309304
P. Horváth, M. Yampolskiy, Yuan Xue, X. Koutsoukos, J. Sztipanovits
Cyber-Physical Systems (CPS), such as networked control systems, are increasingly deployed over wireless networks. Given the sensitivity of control systems to networking conditions such as packet drops, delays and jitters, it is important to verify and evaluate the control system properties under realistic wireless networking deployment scenarios. However, current research is often based on simplistic models of the wireless network physical layer behaviors. In this paper, we point out deficiencies in the existing simulation methods for the performance evaluation of wireless networked control systems and present a novel simulation framework for wireless network control systems. Our approach aims at capturing the effects in the physical layer more accurately than state-of-art simulators are capable of. An integrated simulation tool, based on open-source solutions, is presented and a case study of a networked control system is also provided to illustrate the capabilities of our simulation tool.
{"title":"An integrated system simulation approach for wireless networked control systems","authors":"P. Horváth, M. Yampolskiy, Yuan Xue, X. Koutsoukos, J. Sztipanovits","doi":"10.1109/ISRCS.2012.6309304","DOIUrl":"https://doi.org/10.1109/ISRCS.2012.6309304","url":null,"abstract":"Cyber-Physical Systems (CPS), such as networked control systems, are increasingly deployed over wireless networks. Given the sensitivity of control systems to networking conditions such as packet drops, delays and jitters, it is important to verify and evaluate the control system properties under realistic wireless networking deployment scenarios. However, current research is often based on simplistic models of the wireless network physical layer behaviors. In this paper, we point out deficiencies in the existing simulation methods for the performance evaluation of wireless networked control systems and present a novel simulation framework for wireless network control systems. Our approach aims at capturing the effects in the physical layer more accurately than state-of-art simulators are capable of. An integrated simulation tool, based on open-source solutions, is presented and a case study of a networked control system is also provided to illustrate the capabilities of our simulation tool.","PeriodicalId":227062,"journal":{"name":"2012 5th International Symposium on Resilient Control Systems","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125988714","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 : 2012-09-24DOI: 10.1109/ISRCS.2012.6309311
P. Hawrylak, M. Haney, M. Papa, John Hale
The Smart Grid is a large networked cyber-physical control system that is part of the critical infrastructure. This paper presents a cyber-physical attack against a substation where the attacker causes a transformer to overheat. The attack is modeled using a hybrid attack graph (HAG), which provides a means to model both the physical and cyber components of the attack. The HAG provides insight into potential attack vectors. Based on this information, key points in the system can be identified where security can be strengthened. Direction for future work to expand the capabilities of HAGs for modeling cyber-physical attacks is presented.
{"title":"Using hybrid attack graphs to model cyber-physical attacks in the Smart Grid","authors":"P. Hawrylak, M. Haney, M. Papa, John Hale","doi":"10.1109/ISRCS.2012.6309311","DOIUrl":"https://doi.org/10.1109/ISRCS.2012.6309311","url":null,"abstract":"The Smart Grid is a large networked cyber-physical control system that is part of the critical infrastructure. This paper presents a cyber-physical attack against a substation where the attacker causes a transformer to overheat. The attack is modeled using a hybrid attack graph (HAG), which provides a means to model both the physical and cyber components of the attack. The HAG provides insight into potential attack vectors. Based on this information, key points in the system can be identified where security can be strengthened. Direction for future work to expand the capabilities of HAGs for modeling cyber-physical attacks is presented.","PeriodicalId":227062,"journal":{"name":"2012 5th International Symposium on Resilient Control Systems","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131813467","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 : 2012-09-24DOI: 10.1109/ISRCS.2012.6309298
C. Tolle
The Control community relies heavily on good System Identification (SysID) for finding the plant models needed to develop a good controller. However over time the SysID process and controller development process have remained generally separate activities. One reason for this is that SysID and Control are disparate in their fundamental nature. For good SysID, one is faced with the challenge of persistently exciting plant dynamics; while a good control system attempts to constrain or suppress much of a plant's natural dynamics with desired dynamics. It is this inherent conflict that separates the two practices. But for many plants, their inherent instabilities makes trajectory collection difficult, thus there is a desire to perform data collection while under some simple form of control. Nevertheless, in order to perform solid SysID one must sample the very dynamics one might need to suppress; how then can this be achieved? This paper will explore the notation of persistent excitation, its relationship to phase space trajectories, and how one might recover the most nonlinear dynamics information for SysID while remaining under the linearizing based control region - the very place that those dynamics are most suppressed.
{"title":"Musings on persistent excitation prompts new weighted least squares SysID method for nonlinear differential equation based systems","authors":"C. Tolle","doi":"10.1109/ISRCS.2012.6309298","DOIUrl":"https://doi.org/10.1109/ISRCS.2012.6309298","url":null,"abstract":"The Control community relies heavily on good System Identification (SysID) for finding the plant models needed to develop a good controller. However over time the SysID process and controller development process have remained generally separate activities. One reason for this is that SysID and Control are disparate in their fundamental nature. For good SysID, one is faced with the challenge of persistently exciting plant dynamics; while a good control system attempts to constrain or suppress much of a plant's natural dynamics with desired dynamics. It is this inherent conflict that separates the two practices. But for many plants, their inherent instabilities makes trajectory collection difficult, thus there is a desire to perform data collection while under some simple form of control. Nevertheless, in order to perform solid SysID one must sample the very dynamics one might need to suppress; how then can this be achieved? This paper will explore the notation of persistent excitation, its relationship to phase space trajectories, and how one might recover the most nonlinear dynamics information for SysID while remaining under the linearizing based control region - the very place that those dynamics are most suppressed.","PeriodicalId":227062,"journal":{"name":"2012 5th International Symposium on Resilient Control Systems","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133818374","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 : 2012-09-24DOI: 10.1109/ISRCS.2012.6309292
O. Linda, M. Manic, T. Vollmer
The planned large scale deployment of smart grid network devices will generate a large amount of information exchanged over various types of communication networks. The implementation of these critical systems will require appropriate cyber-security measures. A network anomaly detection solution is considered in this paper. In common network architectures multiple communications streams are simultaneously present, making it difficult to build an anomaly detection solution for the entire system. In addition, common anomaly detection algorithms require specification of a sensitivity threshold, which inevitably leads to a tradeoff between false positives and false negatives rates. In order to alleviate these issues, this paper proposes a novel anomaly detection architecture. The designed system applies a previously developed network security cyber-sensor method to individual selected communication streams allowing for learning accurate normal network behavior models. In addition, an Interval Type-2 Fuzzy Logic System (IT2 FLS) is used to model human background knowledge about the network system and to dynamically adjust the sensitivity threshold of the anomaly detection algorithms. The IT2 FLS was used to model the linguistic uncertainty in describing the relationship between various network communication attributes and the possibility of a cyber attack. The proposed method was tested on an experimental smart grid system demonstrating enhanced cyber-security.
{"title":"Improving cyber-security of smart grid systems via anomaly detection and linguistic domain knowledge","authors":"O. Linda, M. Manic, T. Vollmer","doi":"10.1109/ISRCS.2012.6309292","DOIUrl":"https://doi.org/10.1109/ISRCS.2012.6309292","url":null,"abstract":"The planned large scale deployment of smart grid network devices will generate a large amount of information exchanged over various types of communication networks. The implementation of these critical systems will require appropriate cyber-security measures. A network anomaly detection solution is considered in this paper. In common network architectures multiple communications streams are simultaneously present, making it difficult to build an anomaly detection solution for the entire system. In addition, common anomaly detection algorithms require specification of a sensitivity threshold, which inevitably leads to a tradeoff between false positives and false negatives rates. In order to alleviate these issues, this paper proposes a novel anomaly detection architecture. The designed system applies a previously developed network security cyber-sensor method to individual selected communication streams allowing for learning accurate normal network behavior models. In addition, an Interval Type-2 Fuzzy Logic System (IT2 FLS) is used to model human background knowledge about the network system and to dynamically adjust the sensitivity threshold of the anomaly detection algorithms. The IT2 FLS was used to model the linguistic uncertainty in describing the relationship between various network communication attributes and the possibility of a cyber attack. The proposed method was tested on an experimental smart grid system demonstrating enhanced cyber-security.","PeriodicalId":227062,"journal":{"name":"2012 5th International Symposium on Resilient Control Systems","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121993995","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 : 2012-09-24DOI: 10.1109/ISRCS.2012.6309313
Jonathan M. Chu, Mirko Montanari, R. Campbell
There has been a great increase in recent years as to the amount of data from the grid that has been going to online systems. As more smart meters get installed into the AMI(advanced metering infrastructure), there is a need to mitigate the potential security threats in the collection system. There are a multitude of attack vectors that an adversary may take to compromise the confidentiality of user data and it may take much time and effort for developers to securely cover all such attack vectors. In this paper, we analyze the architecture of AMI systems and how data moves from one end to the other. In particular, we discuss the need for more research in safe program validation that protects against information leaks. Security problems can arise when programs do not perform as intended and may reveal confidential information or take unexpected actions. We discuss a theoretical network architecture that could take advantage of such secure program validation. The model minimizes attack vectors by containing data in one secure location that we call a DBPC(database processing center) instead of transporting data to multiple locations through a traditional DBMS(database management system). When outside parties want access to the data, they can send verified secure applications to the DBPC to run their applications remotely without direct access to the data. We describe the design of the AMI simulator and DBPC prototype module that we implemented.
{"title":"A case for validating remote application integrity for data processing systems","authors":"Jonathan M. Chu, Mirko Montanari, R. Campbell","doi":"10.1109/ISRCS.2012.6309313","DOIUrl":"https://doi.org/10.1109/ISRCS.2012.6309313","url":null,"abstract":"There has been a great increase in recent years as to the amount of data from the grid that has been going to online systems. As more smart meters get installed into the AMI(advanced metering infrastructure), there is a need to mitigate the potential security threats in the collection system. There are a multitude of attack vectors that an adversary may take to compromise the confidentiality of user data and it may take much time and effort for developers to securely cover all such attack vectors. In this paper, we analyze the architecture of AMI systems and how data moves from one end to the other. In particular, we discuss the need for more research in safe program validation that protects against information leaks. Security problems can arise when programs do not perform as intended and may reveal confidential information or take unexpected actions. We discuss a theoretical network architecture that could take advantage of such secure program validation. The model minimizes attack vectors by containing data in one secure location that we call a DBPC(database processing center) instead of transporting data to multiple locations through a traditional DBMS(database management system). When outside parties want access to the data, they can send verified secure applications to the DBPC to run their applications remotely without direct access to the data. We describe the design of the AMI simulator and DBPC prototype module that we implemented.","PeriodicalId":227062,"journal":{"name":"2012 5th International Symposium on Resilient Control Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125852032","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 : 2012-09-24DOI: 10.1109/ISRCS.2012.6309296
K. McCarty, Milos Manic
Micro-zone analysis involves use of data fusion and data mining techniques in order to understand the relative impact of many different variables. Data Fusion requires the ability to combine or “fuse” date from multiple data sources. Data mining involves the application of sophisticated algorithms such as Neural Networks and Decision Trees, to describe micro-zone behavior and predict future values based upon past values. One of the difficulties encountered in developing generic time series or other data mining techniques for micro-zone analysis is the wide variability of the data sets available for analysis. This presents challenges all the way from the data gathering stage to results presentation. This paper presents an architecture designed and used to facilitate the collection of disparate data sets well suited for data fusion and data mining. Results show this architecture provides a flexible, dynamic framework for the capture and storage of a myriad of dissimilar data sets and can serve as a foundation from which to build a complete data fusion architecture.
{"title":"A proposed data fusion architecture for micro-zone analysis and data mining","authors":"K. McCarty, Milos Manic","doi":"10.1109/ISRCS.2012.6309296","DOIUrl":"https://doi.org/10.1109/ISRCS.2012.6309296","url":null,"abstract":"Micro-zone analysis involves use of data fusion and data mining techniques in order to understand the relative impact of many different variables. Data Fusion requires the ability to combine or “fuse” date from multiple data sources. Data mining involves the application of sophisticated algorithms such as Neural Networks and Decision Trees, to describe micro-zone behavior and predict future values based upon past values. One of the difficulties encountered in developing generic time series or other data mining techniques for micro-zone analysis is the wide variability of the data sets available for analysis. This presents challenges all the way from the data gathering stage to results presentation. This paper presents an architecture designed and used to facilitate the collection of disparate data sets well suited for data fusion and data mining. Results show this architecture provides a flexible, dynamic framework for the capture and storage of a myriad of dissimilar data sets and can serve as a foundation from which to build a complete data fusion architecture.","PeriodicalId":227062,"journal":{"name":"2012 5th International Symposium on Resilient Control Systems","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127124809","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 : 2012-09-24DOI: 10.1109/ISRCS.2012.6309297
O. Linda, Dumidu Wijayasekara, Milos Manic, C. Rieger
In the past several decades Building Energy Management Systems (BEMSs) have become vital components of most modern buildings. BEMSs utilize advanced microprocessor technology combined with extensive sensor data collection and communication to minimize energy consumption while maintaining high human comfort levels. When properly tuned and operated, BEMSs can provide significant energy savings. However, the complexity of the acquired sensory data and the overwhelming amount of presented information renders them difficult to adjust or even understand by responsible building managers. This inevitably results in suboptimal BEMS operation and performance. To address this issue, this paper reports on a research effort that utilizes Computational Intelligence techniques to fuse multiple heterogeneous sources of BEMS data and to extract relevant actionable information. This actionable information can then be easily understood and acted upon by responsible building managers. In particular, this paper describes the use of anomaly detection algorithms for improving the understandability of BEMS data and for increasing the state-awareness of building managers. The developed system utilizes modified nearest neighbor clustering algorithm and fuzzy logic rule extraction technique to automatically build a model of normal BEMS operations and detect possible anomalous behavior. In addition, linguistic summaries based on fuzzy set representation of the input values are generated for the detected anomalies which increase the understandability of the presented results.
{"title":"Computational intelligence based anomaly detection for Building Energy Management Systems","authors":"O. Linda, Dumidu Wijayasekara, Milos Manic, C. Rieger","doi":"10.1109/ISRCS.2012.6309297","DOIUrl":"https://doi.org/10.1109/ISRCS.2012.6309297","url":null,"abstract":"In the past several decades Building Energy Management Systems (BEMSs) have become vital components of most modern buildings. BEMSs utilize advanced microprocessor technology combined with extensive sensor data collection and communication to minimize energy consumption while maintaining high human comfort levels. When properly tuned and operated, BEMSs can provide significant energy savings. However, the complexity of the acquired sensory data and the overwhelming amount of presented information renders them difficult to adjust or even understand by responsible building managers. This inevitably results in suboptimal BEMS operation and performance. To address this issue, this paper reports on a research effort that utilizes Computational Intelligence techniques to fuse multiple heterogeneous sources of BEMS data and to extract relevant actionable information. This actionable information can then be easily understood and acted upon by responsible building managers. In particular, this paper describes the use of anomaly detection algorithms for improving the understandability of BEMS data and for increasing the state-awareness of building managers. The developed system utilizes modified nearest neighbor clustering algorithm and fuzzy logic rule extraction technique to automatically build a model of normal BEMS operations and detect possible anomalous behavior. In addition, linguistic summaries based on fuzzy set representation of the input values are generated for the detected anomalies which increase the understandability of the presented results.","PeriodicalId":227062,"journal":{"name":"2012 5th International Symposium on Resilient Control Systems","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115166956","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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