Pub Date : 2015-03-09DOI: 10.1109/COGSIMA.2015.7108174
D. Lafond, J. Gagnon, S. Tremblay, N. Derbentseva, M. Lizotte
The present work describes the application of a multi-criteria assessment tool called MYRIAD to the evaluation of a methodology that was developed to improve team understanding of a complex situation. The methodology aims to support a multi-disciplinary team working collaboratively on the development of a mission plan during an expeditionary stability operation. We conducted a case study assessing collaborative understanding and team performance while subject matter experts employed the methodology as a complement to the standard planning process. Results were analyzed using MYRIAD, a preference modeling system that allows combining disparate measures into a coherent assessment capturing several key logical relationships between metrics - ones that may not be modeled using the traditional weighted sum approach. MYRIAD was also used to perform a sensitivity analysis in order to derive from the preference model which aspects of the planning methodology would lead to the greatest overall improvement. Results helped identifying priority areas for future development.
{"title":"Multi-criteria assessment of a whole-of-government planning methodology using MYRIAD","authors":"D. Lafond, J. Gagnon, S. Tremblay, N. Derbentseva, M. Lizotte","doi":"10.1109/COGSIMA.2015.7108174","DOIUrl":"https://doi.org/10.1109/COGSIMA.2015.7108174","url":null,"abstract":"The present work describes the application of a multi-criteria assessment tool called MYRIAD to the evaluation of a methodology that was developed to improve team understanding of a complex situation. The methodology aims to support a multi-disciplinary team working collaboratively on the development of a mission plan during an expeditionary stability operation. We conducted a case study assessing collaborative understanding and team performance while subject matter experts employed the methodology as a complement to the standard planning process. Results were analyzed using MYRIAD, a preference modeling system that allows combining disparate measures into a coherent assessment capturing several key logical relationships between metrics - ones that may not be modeled using the traditional weighted sum approach. MYRIAD was also used to perform a sensitivity analysis in order to derive from the preference model which aspects of the planning methodology would lead to the greatest overall improvement. Results helped identifying priority areas for future development.","PeriodicalId":373467,"journal":{"name":"2015 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124934605","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 : 2015-03-09DOI: 10.1109/COGSIMA.2015.7107971
Florian Fortmann, H. Muller, A. Ludtke, Susanne CJ Boll
In complex supervisory control settings, inadequate monitoring behavior is a substantial source of human error and a risk factor threatening human life and the environment. In this paper, we present a problem-oriented approach to support a commander supervising multiple highly-automated unmanned aerial vehicles (UAV) to perform adequate monitoring behavior. Our approach utilizes an ambient light display (ALD) to continuously externalize the commander's monitoring performance using ambient visual cues in the peripheral field. We designed and evaluated our prototype with experts. Our results indicate that our prototype can support adequate monitoring behavior of a commander supervising multiple UAVs, and does not affect workload.
{"title":"Expert-based design and evaluation of an ambient light display to improve monitoring performance during multi-UAV supervisory control","authors":"Florian Fortmann, H. Muller, A. Ludtke, Susanne CJ Boll","doi":"10.1109/COGSIMA.2015.7107971","DOIUrl":"https://doi.org/10.1109/COGSIMA.2015.7107971","url":null,"abstract":"In complex supervisory control settings, inadequate monitoring behavior is a substantial source of human error and a risk factor threatening human life and the environment. In this paper, we present a problem-oriented approach to support a commander supervising multiple highly-automated unmanned aerial vehicles (UAV) to perform adequate monitoring behavior. Our approach utilizes an ambient light display (ALD) to continuously externalize the commander's monitoring performance using ambient visual cues in the peripheral field. We designed and evaluated our prototype with experts. Our results indicate that our prototype can support adequate monitoring behavior of a commander supervising multiple UAVs, and does not affect workload.","PeriodicalId":373467,"journal":{"name":"2015 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123065275","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 : 2015-03-09DOI: 10.1109/cogsima.2015.7108197
Kevin A. White, P. Thulasiraman
A tactical wireless sensor network (WSN) is a distributed network that facilitates wireless information gathering within a region of interest. A challenge in the deployment of WSNs is the limited battery power of each sensor node. This has a significant impact on the service life of the network. In order to improve the lifespan of the network, load balancing techniques using efficient routing mechanisms must be employed such that traffic is distributed between sensor nodes and gateway(s). In this paper, we study load balancing from a cross-layer point of view, specifically considering energy efficiency. We investigate the impact of deploying single and multiple gateways on the following established energy aware load balancing routing techniques: direct routing, minimum transmission energy, low energy adaptive cluster head routing, and zone clustering. Based on the node die out statistics observed with these algorithms, we develop a novel, energy efficient zone clustering algorithm called EZone. Via extensive simulations using MATLAB, we analyze the effectiveness of these algorithms on network performance for single and multiple gateway scenarios and show that the EZone algorithm maximizes network lifetime and service area coverage.
{"title":"Energy efficient cross layer load balancing in tactical multigateway wireless sensor networks","authors":"Kevin A. White, P. Thulasiraman","doi":"10.1109/cogsima.2015.7108197","DOIUrl":"https://doi.org/10.1109/cogsima.2015.7108197","url":null,"abstract":"A tactical wireless sensor network (WSN) is a distributed network that facilitates wireless information gathering within a region of interest. A challenge in the deployment of WSNs is the limited battery power of each sensor node. This has a significant impact on the service life of the network. In order to improve the lifespan of the network, load balancing techniques using efficient routing mechanisms must be employed such that traffic is distributed between sensor nodes and gateway(s). In this paper, we study load balancing from a cross-layer point of view, specifically considering energy efficiency. We investigate the impact of deploying single and multiple gateways on the following established energy aware load balancing routing techniques: direct routing, minimum transmission energy, low energy adaptive cluster head routing, and zone clustering. Based on the node die out statistics observed with these algorithms, we develop a novel, energy efficient zone clustering algorithm called EZone. Via extensive simulations using MATLAB, we analyze the effectiveness of these algorithms on network performance for single and multiple gateway scenarios and show that the EZone algorithm maximizes network lifetime and service area coverage.","PeriodicalId":373467,"journal":{"name":"2015 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132228279","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 : 2015-03-09DOI: 10.1109/COGSIMA.2015.7107968
Kevin Durkee, Scott M. Pappada, Andres Ortiz, J. Feeney, S. Galster
The high volume of information available to human operators and increasing scale of work can become unmanageable due to the complexity found in a variety of domains. The need for precise, continuous assessment of human operator performance and state is important to identify when, and how, interventions should be delivered. One challenge that requires attention is the need for intelligent model-driven systems that identify specifically when some form of augmentation is needed while work is performed. Our current research and development efforts seek to fill this need by following the Sense-Assess-Augment (S-A-A) framework. We utilize the Performance Measurement Engine (PM Engine™) and the Functional State Estimation Engine (FuSE2) to derive second-by-second measurements of performance and human operator state to identify the specific points in time where performance decrements occur due to high workload. These human state patterns can be computationally modeled via the Performance Augmentation Cueing Engine in Real-time (PACER) to provide the decision logic necessary to predict when performance decrements are likely to occur. In this paper, we describe the methods used to collect our initial data set and explore the complex relationships between cognitive workload and primary task performance.
{"title":"System decision framework for augmenting human performance using real-time workload classifiers","authors":"Kevin Durkee, Scott M. Pappada, Andres Ortiz, J. Feeney, S. Galster","doi":"10.1109/COGSIMA.2015.7107968","DOIUrl":"https://doi.org/10.1109/COGSIMA.2015.7107968","url":null,"abstract":"The high volume of information available to human operators and increasing scale of work can become unmanageable due to the complexity found in a variety of domains. The need for precise, continuous assessment of human operator performance and state is important to identify when, and how, interventions should be delivered. One challenge that requires attention is the need for intelligent model-driven systems that identify specifically when some form of augmentation is needed while work is performed. Our current research and development efforts seek to fill this need by following the Sense-Assess-Augment (S-A-A) framework. We utilize the Performance Measurement Engine (PM Engine™) and the Functional State Estimation Engine (FuSE2) to derive second-by-second measurements of performance and human operator state to identify the specific points in time where performance decrements occur due to high workload. These human state patterns can be computationally modeled via the Performance Augmentation Cueing Engine in Real-time (PACER) to provide the decision logic necessary to predict when performance decrements are likely to occur. In this paper, we describe the methods used to collect our initial data set and explore the complex relationships between cognitive workload and primary task performance.","PeriodicalId":373467,"journal":{"name":"2015 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision","volume":"09 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127198770","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 : 2015-03-09DOI: 10.1109/COGSIMA.2015.7108178
A. Angelopoulou, K. Mykoniatis, W. Karwowski
Over the last decades there has been a growing interest in modeling human performance and analyzing human activity and human operator behavior to improve system design. A variety of tools and approaches which are based on task analysis methods and tools have been proposed. As technology advances and tasks become more demanding, human work changes increasing the need to create new methodologies and tools for task analysis to face the new challenges. The main aim of this research is to develop a new framework and model for simulation of human tasks performed by individuals with varying levels of skills, considering operator workload and human errors. In order to achieve this goal and fill the current gaps, a review of existing task analysis methods and tools is conducted and a model named UTASiMo is proposed. In addition, the Unified Modeling Language is used to describe the conceptual model and illustrate the different constructs and concepts included in the model. Finally, we present a case study and a preliminary simulation model designed in AnyLogicTM.
{"title":"A framework for simulation-based task analysis - The development of a universal task analysis simulation model","authors":"A. Angelopoulou, K. Mykoniatis, W. Karwowski","doi":"10.1109/COGSIMA.2015.7108178","DOIUrl":"https://doi.org/10.1109/COGSIMA.2015.7108178","url":null,"abstract":"Over the last decades there has been a growing interest in modeling human performance and analyzing human activity and human operator behavior to improve system design. A variety of tools and approaches which are based on task analysis methods and tools have been proposed. As technology advances and tasks become more demanding, human work changes increasing the need to create new methodologies and tools for task analysis to face the new challenges. The main aim of this research is to develop a new framework and model for simulation of human tasks performed by individuals with varying levels of skills, considering operator workload and human errors. In order to achieve this goal and fill the current gaps, a review of existing task analysis methods and tools is conducted and a model named UTASiMo is proposed. In addition, the Unified Modeling Language is used to describe the conceptual model and illustrate the different constructs and concepts included in the model. Finally, we present a case study and a preliminary simulation model designed in AnyLogicTM.","PeriodicalId":373467,"journal":{"name":"2015 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision","volume":"220 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123356891","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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