Pub Date : 2013-07-15DOI: 10.1109/CIVEMSA.2013.6617404
P. Häfner, Christina Vinke, Victor Häfner, J. Ovtcharova, Wolfgang Schotte
Virtual environments are more and more used for educational and training purposes. In order to design virtual environments for these applications in particular, it is very important to get a deep understanding of the relevant design features supporting the user's process of learning and comprehension. Relevance and implementation of these features as well as the benefits of virtual learning environments over traditional educational approaches in general are rarely explored. Focusing on modes of interaction in this work, we examined the effect of different motion types on the knowledge acquisition of users in various virtual environments. For our study we chose a simple memorization task as approximation of low cognitive knowledge acquirement. We hypothesized motion types and immersion levels influence memorization performance in virtual environments. The memorization task was conducted in two virtual environments with different levels of immersion: A high-immersive Cave Automatic Virtual Environment (CAVE) and a low-immersive desktop virtual environment. Two motion types in virtual environments were explored: Physical and virtual walking. In the CAVE physical walking was implemented by using motion capturing and virtual walking was realized using a joystick-like input device. The results indicate neither motion types nor immersion levels in virtual environments affect memorization performance significantly.
{"title":"The impact of motion in virtual environments on memorization performance","authors":"P. Häfner, Christina Vinke, Victor Häfner, J. Ovtcharova, Wolfgang Schotte","doi":"10.1109/CIVEMSA.2013.6617404","DOIUrl":"https://doi.org/10.1109/CIVEMSA.2013.6617404","url":null,"abstract":"Virtual environments are more and more used for educational and training purposes. In order to design virtual environments for these applications in particular, it is very important to get a deep understanding of the relevant design features supporting the user's process of learning and comprehension. Relevance and implementation of these features as well as the benefits of virtual learning environments over traditional educational approaches in general are rarely explored. Focusing on modes of interaction in this work, we examined the effect of different motion types on the knowledge acquisition of users in various virtual environments. For our study we chose a simple memorization task as approximation of low cognitive knowledge acquirement. We hypothesized motion types and immersion levels influence memorization performance in virtual environments. The memorization task was conducted in two virtual environments with different levels of immersion: A high-immersive Cave Automatic Virtual Environment (CAVE) and a low-immersive desktop virtual environment. Two motion types in virtual environments were explored: Physical and virtual walking. In the CAVE physical walking was implemented by using motion capturing and virtual walking was realized using a joystick-like input device. The results indicate neither motion types nor immersion levels in virtual environments affect memorization performance significantly.","PeriodicalId":159100,"journal":{"name":"2013 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","volume":"151 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116390458","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 : 2013-07-15DOI: 10.1109/CIVEMSA.2013.6617416
J. Botía, Hernan D. Yepes, A. Cárdenas, G. Quintero
In optical communications systems, the optical combs spectrum has been used to generate multiple carriers from a continuous wave laser or other optical source. Due to different parameters to find the best spectrum, one alternative is necessary to define. This paper proposes three fuzzy entropy measures by adequacy and non-adequacy in order to evaluate the optical combs spectrum performance. The goal is to group a set of data of optical combs spectra using fuzzy clustering, specify LAMDA, and to calculate the amount of information of each class. The fuzzy measures are used to analyze the phase and voltage bias variations for the second arm of Mach-Zehnder modulator and the changes of RF signal frequency in both arms. Obtained results show little changes of fuzzy entropies by adequacy average for phase and bias voltage cases, but an increase for frequency variation cases, as expected. With this result, the approach could be applied to the stability detection in Optical Frequency Comb Generation (OFCG) used for high capacity optical systems.
{"title":"Fuzzy entropies by adequacy and non-adequacy applied to the analysis of combs spectra stability","authors":"J. Botía, Hernan D. Yepes, A. Cárdenas, G. Quintero","doi":"10.1109/CIVEMSA.2013.6617416","DOIUrl":"https://doi.org/10.1109/CIVEMSA.2013.6617416","url":null,"abstract":"In optical communications systems, the optical combs spectrum has been used to generate multiple carriers from a continuous wave laser or other optical source. Due to different parameters to find the best spectrum, one alternative is necessary to define. This paper proposes three fuzzy entropy measures by adequacy and non-adequacy in order to evaluate the optical combs spectrum performance. The goal is to group a set of data of optical combs spectra using fuzzy clustering, specify LAMDA, and to calculate the amount of information of each class. The fuzzy measures are used to analyze the phase and voltage bias variations for the second arm of Mach-Zehnder modulator and the changes of RF signal frequency in both arms. Obtained results show little changes of fuzzy entropies by adequacy average for phase and bias voltage cases, but an increase for frequency variation cases, as expected. With this result, the approach could be applied to the stability detection in Optical Frequency Comb Generation (OFCG) used for high capacity optical systems.","PeriodicalId":159100,"journal":{"name":"2013 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125351248","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 : 2013-07-15DOI: 10.1109/CIVEMSA.2013.6617409
J. McCausland, George Di Nardo, R. Falcon, R. Abielmona, V. Groza, E. Petriu
In this paper, a risk-aware robotic sensor network (RSN) is proposed in the context of Critical Infrastructure Protection. Such a network will be comprised of mobile sensor nodes that perceive various aspects of their environment and topologically reconfigure in order to secure a strategic area of interest. Risk awareness is provided through the application of a recently developed Risk Management Framework to the RSN. The risk level of each node is assessed in terms of their degree of distress, proximity factor, and terrain maneuverability. Risk monitoring alerts are issued whenever any given sensor node's quantitative risk metric exceeds a user-defined threshold value. At this point, a node-in-distress (NID) has been identified as the weak point of the securing structure around which the RSN is deployed. The NID can no longer be used with confidence and the effective perimeter coverage of the RSN has been reduced, thus creating potential security breaches in the area of interest. In response, the remaining nodes will self-organize to maximize the perimeter coverage while minimizing the cost of doing so. A limited set of contingency network topologies is produced via evolutionary multi-objective optimization using the Non-Dominated Sorting Genetic Algorithm (NSGA-II) and then ranked according to a human-guided alternative selection algorithm. The security operator picks the most suitable topology, which is then effectuated upon the environment. Results indicate that NSGA-II is capable of producing feasible network topologies to satisfy maximum perimeter coverage, while reducing the energy required for topology reconfiguration. As far as we are concerned, this is the first time a RSN applied to a CIP scenario is self-organized in response to a risk analysis conducted on every sensor node on the basis of multiple risk features.
{"title":"A proactive risk-aware robotic sensor network for Critical Infrastructure Protection","authors":"J. McCausland, George Di Nardo, R. Falcon, R. Abielmona, V. Groza, E. Petriu","doi":"10.1109/CIVEMSA.2013.6617409","DOIUrl":"https://doi.org/10.1109/CIVEMSA.2013.6617409","url":null,"abstract":"In this paper, a risk-aware robotic sensor network (RSN) is proposed in the context of Critical Infrastructure Protection. Such a network will be comprised of mobile sensor nodes that perceive various aspects of their environment and topologically reconfigure in order to secure a strategic area of interest. Risk awareness is provided through the application of a recently developed Risk Management Framework to the RSN. The risk level of each node is assessed in terms of their degree of distress, proximity factor, and terrain maneuverability. Risk monitoring alerts are issued whenever any given sensor node's quantitative risk metric exceeds a user-defined threshold value. At this point, a node-in-distress (NID) has been identified as the weak point of the securing structure around which the RSN is deployed. The NID can no longer be used with confidence and the effective perimeter coverage of the RSN has been reduced, thus creating potential security breaches in the area of interest. In response, the remaining nodes will self-organize to maximize the perimeter coverage while minimizing the cost of doing so. A limited set of contingency network topologies is produced via evolutionary multi-objective optimization using the Non-Dominated Sorting Genetic Algorithm (NSGA-II) and then ranked according to a human-guided alternative selection algorithm. The security operator picks the most suitable topology, which is then effectuated upon the environment. Results indicate that NSGA-II is capable of producing feasible network topologies to satisfy maximum perimeter coverage, while reducing the energy required for topology reconfiguration. As far as we are concerned, this is the first time a RSN applied to a CIP scenario is self-organized in response to a risk analysis conducted on every sensor node on the basis of multiple risk features.","PeriodicalId":159100,"journal":{"name":"2013 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116645116","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 : 2013-07-15DOI: 10.1109/CIVEMSA.2013.6617415
F. López-Peña, M. M. Gonzalez, V. Casás, R. Duro, D. P. Agras
An ANN based system has been developed for forecasting the roll motion of a ship and predicting the onset of parametric roll resonance. This kind of instability can be devastating for the ship and is a phenomenon that is difficult to predict when using classical mathematical modeling approaches. In the present investigation the ANNs are trained using data obtained from a mathematical model of ship roll motion while the performance of the whole system is verified with realistic towing tank tests. The results achieved are quite promising and support the claim that it can be implemented in any ship without the need for any kind of water tank or real ship tests.
{"title":"An ANN based system for forecasting ship roll motion","authors":"F. López-Peña, M. M. Gonzalez, V. Casás, R. Duro, D. P. Agras","doi":"10.1109/CIVEMSA.2013.6617415","DOIUrl":"https://doi.org/10.1109/CIVEMSA.2013.6617415","url":null,"abstract":"An ANN based system has been developed for forecasting the roll motion of a ship and predicting the onset of parametric roll resonance. This kind of instability can be devastating for the ship and is a phenomenon that is difficult to predict when using classical mathematical modeling approaches. In the present investigation the ANNs are trained using data obtained from a mathematical model of ship roll motion while the performance of the whole system is verified with realistic towing tank tests. The results achieved are quite promising and support the claim that it can be implemented in any ship without the need for any kind of water tank or real ship tests.","PeriodicalId":159100,"journal":{"name":"2013 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115211389","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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