Pub Date : 2011-10-27DOI: 10.1109/CIMSA.2011.6059919
M. Agarwal, K. K. Biswas, M. Hanmandlu
In intuitionistic fuzzy soft set (IFSS), a user can indicate his confidence in the data provided by him by including the hesitancy. In this paper we introduce the concept of relation in generalized intuitionistic fuzzy soft sets (RGIFSS) which allows to compose two generalized intuitionistic fuzzy soft sets (GIFSSs) [13] through intuitionistic fuzzy soft relations. GIFSS along with RGIFSS provides a robust provision for a moderator to ratify the individual hesitancy of all the users supplying data to the system. An application of RGIFSS and the score function is demonstrated through case studies involving multi-criteria decision making.
{"title":"Relations in generalized intuitionistic fuzzy soft sets","authors":"M. Agarwal, K. K. Biswas, M. Hanmandlu","doi":"10.1109/CIMSA.2011.6059919","DOIUrl":"https://doi.org/10.1109/CIMSA.2011.6059919","url":null,"abstract":"In intuitionistic fuzzy soft set (IFSS), a user can indicate his confidence in the data provided by him by including the hesitancy. In this paper we introduce the concept of relation in generalized intuitionistic fuzzy soft sets (RGIFSS) which allows to compose two generalized intuitionistic fuzzy soft sets (GIFSSs) [13] through intuitionistic fuzzy soft relations. GIFSS along with RGIFSS provides a robust provision for a moderator to ratify the individual hesitancy of all the users supplying data to the system. An application of RGIFSS and the score function is demonstrated through case studies involving multi-criteria decision making.","PeriodicalId":422972,"journal":{"name":"2011 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA) Proceedings","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127972427","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 : 2011-10-27DOI: 10.1109/CIMSA.2011.6059924
R. Falcon, A. Nayak, R. Abielmona
Individual units in a wireless sensor network (WSN) are exposed to multiple risks, either during or after their deployment. The identification of the risk sources and their watchful monitoring in dynamic, unpredictable environments is pivotal to ensure a smooth, long-term functioning of the WSN. We introduce an evolving risk management framework for WSNs that captures multiple risk features and provides both a visual depiction of the corporate network threats at any time and a numerical assessment of any sensor's overall risk. The visualization module is embodied through an evolving clustering architecture which heavily relies on shadowed sets. The risk assessment module embraces fuzzy and shadowed evaluations of the risk sources and incorporates a simple adaptive learning process that weights the risk sources proportionally to their observed impact on failed sensors. A distinctive trait of the proposed framework is its highly automated yet still human-centric nature. Experiments utilizing different sensor models and deployment scenarios confirm the feasibility of the risk management platform under consideration.
{"title":"An evolving risk management framework for wireless sensor networks","authors":"R. Falcon, A. Nayak, R. Abielmona","doi":"10.1109/CIMSA.2011.6059924","DOIUrl":"https://doi.org/10.1109/CIMSA.2011.6059924","url":null,"abstract":"Individual units in a wireless sensor network (WSN) are exposed to multiple risks, either during or after their deployment. The identification of the risk sources and their watchful monitoring in dynamic, unpredictable environments is pivotal to ensure a smooth, long-term functioning of the WSN. We introduce an evolving risk management framework for WSNs that captures multiple risk features and provides both a visual depiction of the corporate network threats at any time and a numerical assessment of any sensor's overall risk. The visualization module is embodied through an evolving clustering architecture which heavily relies on shadowed sets. The risk assessment module embraces fuzzy and shadowed evaluations of the risk sources and incorporates a simple adaptive learning process that weights the risk sources proportionally to their observed impact on failed sensors. A distinctive trait of the proposed framework is its highly automated yet still human-centric nature. Experiments utilizing different sensor models and deployment scenarios confirm the feasibility of the risk management platform under consideration.","PeriodicalId":422972,"journal":{"name":"2011 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA) Proceedings","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123658286","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 : 2011-10-27DOI: 10.1109/CIMSA.2011.6059928
M. Abdallah, E. Petriu, K. Kennedy, R. Narbaitz, M. Warith
One booming concept that has recently gained significant attention in waste management is the “bioreactor landfill”. Despite the potential benefits of operating landfills as bioreactors, there are no standardized operational guidelines and procedures for the system due to the numerous processes and site-specific variables involved. This paper introduces an innovative technology that employs automated monitoring and expert control in the operation of bioreactor landfills. The proposed control system combines multiple interacting hardware and software components, and is coined as SMART (Sensor-based Monitoring and Remote-control Technology). SMART features a fuzzy logic decision engine that mimics the control actions of an experienced human operator. This technology aims to provide optimum conditions for the biodegradation of municipal solid waste, and also, to improve the profitability of the bioreactor landfill in terms of biogas production and space recovery.
{"title":"Intelligent control of bioreactor landfills","authors":"M. Abdallah, E. Petriu, K. Kennedy, R. Narbaitz, M. Warith","doi":"10.1109/CIMSA.2011.6059928","DOIUrl":"https://doi.org/10.1109/CIMSA.2011.6059928","url":null,"abstract":"One booming concept that has recently gained significant attention in waste management is the “bioreactor landfill”. Despite the potential benefits of operating landfills as bioreactors, there are no standardized operational guidelines and procedures for the system due to the numerous processes and site-specific variables involved. This paper introduces an innovative technology that employs automated monitoring and expert control in the operation of bioreactor landfills. The proposed control system combines multiple interacting hardware and software components, and is coined as SMART (Sensor-based Monitoring and Remote-control Technology). SMART features a fuzzy logic decision engine that mimics the control actions of an experienced human operator. This technology aims to provide optimum conditions for the biodegradation of municipal solid waste, and also, to improve the profitability of the bioreactor landfill in terms of biogas production and space recovery.","PeriodicalId":422972,"journal":{"name":"2011 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA) Proceedings","volume":"222 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121512431","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 : 2011-10-27DOI: 10.1109/CIMSA.2011.6059911
R. Pall, E. Cheung
The North Atlantic Treaty Organization (NATO) Stockpile Planning Committee (SPC) periodically determines if NATO member nations have the necessary munitions for a full range of mission types, accomplished through the use of a model that minimizes the cost of the required stockpile. We were tasked to examine how the methodology of this model could be modified to allow individual nations to better determine their requirements for Precision-Guided Munitions (PGMs). The approach we undertook involves augmenting the methodology of the model with a multi-objective optimization approach using a genetic algorithm, in which the solution is optimized along two competing objectives: total cost (which is minimized), and the usage of PGMs (which is maximized). We recommended that the SPC consider including this change in all future versions of ACROSS.
{"title":"On stockpile planning using a multi-objective genetic algorithm","authors":"R. Pall, E. Cheung","doi":"10.1109/CIMSA.2011.6059911","DOIUrl":"https://doi.org/10.1109/CIMSA.2011.6059911","url":null,"abstract":"The North Atlantic Treaty Organization (NATO) Stockpile Planning Committee (SPC) periodically determines if NATO member nations have the necessary munitions for a full range of mission types, accomplished through the use of a model that minimizes the cost of the required stockpile. We were tasked to examine how the methodology of this model could be modified to allow individual nations to better determine their requirements for Precision-Guided Munitions (PGMs). The approach we undertook involves augmenting the methodology of the model with a multi-objective optimization approach using a genetic algorithm, in which the solution is optimized along two competing objectives: total cost (which is minimized), and the usage of PGMs (which is maximized). We recommended that the SPC consider including this change in all future versions of ACROSS.","PeriodicalId":422972,"journal":{"name":"2011 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA) Proceedings","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126178601","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 : 2011-10-27DOI: 10.1109/CIMSA.2011.6059929
M. Abdallah, E. Petriu, K. Kennedy, R. Narbaitz, M. Warith
Landfill is by far the dominant and most economical method for the disposal of solid waste worldwide. The landfill ecosystem involves several physical, chemical, and biological processes that take place simultaneously. The complexity of the landfill processes as well as the uncertainty of solid waste characteristics have led to the implementation of unconventional techniques in modeling the system. In fact, no conventional model could be successfully developed for such a nonlinear ill-defined system because it is practically impossible to isolate the individual effect of its variables and satisfactorily identify its behaviour. Recently, knowledge-based techniques, such as fuzzy logic, became widely used to model complex systems based on qualitative knowledge about their behaviour. This paper presents an implementation of fuzzy logic to solve a serious operational problem in modern landfills. A typical sanitary landfill evolves through consecutive operational phases which are hard to distinguish and characterize. The identification of these phases is vital because each phase has different requirements that have to be met in order to assure safe and smooth transition from one phase to another. A fuzzy logic controller was developed to identify the operational phase of a landfill at a given time based on certain quantitative characteristics of the leachate generated and biogas produced.
{"title":"Application of fuzzy logic in modern landfills","authors":"M. Abdallah, E. Petriu, K. Kennedy, R. Narbaitz, M. Warith","doi":"10.1109/CIMSA.2011.6059929","DOIUrl":"https://doi.org/10.1109/CIMSA.2011.6059929","url":null,"abstract":"Landfill is by far the dominant and most economical method for the disposal of solid waste worldwide. The landfill ecosystem involves several physical, chemical, and biological processes that take place simultaneously. The complexity of the landfill processes as well as the uncertainty of solid waste characteristics have led to the implementation of unconventional techniques in modeling the system. In fact, no conventional model could be successfully developed for such a nonlinear ill-defined system because it is practically impossible to isolate the individual effect of its variables and satisfactorily identify its behaviour. Recently, knowledge-based techniques, such as fuzzy logic, became widely used to model complex systems based on qualitative knowledge about their behaviour. This paper presents an implementation of fuzzy logic to solve a serious operational problem in modern landfills. A typical sanitary landfill evolves through consecutive operational phases which are hard to distinguish and characterize. The identification of these phases is vital because each phase has different requirements that have to be met in order to assure safe and smooth transition from one phase to another. A fuzzy logic controller was developed to identify the operational phase of a landfill at a given time based on certain quantitative characteristics of the leachate generated and biogas produced.","PeriodicalId":422972,"journal":{"name":"2011 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA) Proceedings","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126219809","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 : 2011-10-27DOI: 10.1109/CIMSA.2011.6059920
F. Peña, Marcos Miguez Gonzalez, V. Casás, R. Duro
A neural network based system has been applied for forecasting the large amplitude roll motions of a ship that appear during parametric roll resonance. Under these conditions, ship roll motion presents a highly nonlinear behavior and accurate predictions are difficult to achieve using classical mathematical modeling approaches. The results obtained present very good agreement to reality, leading to the possibility of applying the system as a base for a parametric roll warning system.
{"title":"Ship roll motion time series forecasting using neural networks","authors":"F. Peña, Marcos Miguez Gonzalez, V. Casás, R. Duro","doi":"10.1109/CIMSA.2011.6059920","DOIUrl":"https://doi.org/10.1109/CIMSA.2011.6059920","url":null,"abstract":"A neural network based system has been applied for forecasting the large amplitude roll motions of a ship that appear during parametric roll resonance. Under these conditions, ship roll motion presents a highly nonlinear behavior and accurate predictions are difficult to achieve using classical mathematical modeling approaches. The results obtained present very good agreement to reality, leading to the possibility of applying the system as a base for a parametric roll warning system.","PeriodicalId":422972,"journal":{"name":"2011 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA) Proceedings","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127860176","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 : 2011-10-27DOI: 10.1109/CIMSA.2011.6059932
Saeed Motiian, H. Soltanian-Zadeh
Particle Swarm Optimization (PSO) is an algorithm based on social intelligence, utilized in many fields of optimization. In applications like speech recognition, due to existence of high dimensional matrices, the speed of standard PSO is very low. In addition, PSO may be trapped in a local optimum. In this paper, we introduce a novel algorithm that is faster and generates superior results than the standard PSO. Also, the probability of being trapped in a local optimum is decreased. To illustrate advantages of the proposed algorithm, we use it to train a Hidden Markov Model (HMM) and find the minimum of the Ackley function.
{"title":"Improved particle swarm optimization and applications to Hidden Markov Model and Ackley function","authors":"Saeed Motiian, H. Soltanian-Zadeh","doi":"10.1109/CIMSA.2011.6059932","DOIUrl":"https://doi.org/10.1109/CIMSA.2011.6059932","url":null,"abstract":"Particle Swarm Optimization (PSO) is an algorithm based on social intelligence, utilized in many fields of optimization. In applications like speech recognition, due to existence of high dimensional matrices, the speed of standard PSO is very low. In addition, PSO may be trapped in a local optimum. In this paper, we introduce a novel algorithm that is faster and generates superior results than the standard PSO. Also, the probability of being trapped in a local optimum is decreased. To illustrate advantages of the proposed algorithm, we use it to train a Hidden Markov Model (HMM) and find the minimum of the Ackley function.","PeriodicalId":422972,"journal":{"name":"2011 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA) Proceedings","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129659003","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 : 2011-10-27DOI: 10.1109/CIMSA.2011.6059933
A. Crétu, P. Payeur, R. Laganière
The continuous rise in the amount of vehicles in circulation brings an increasing need for automatically and efficiently recognizing vehicle categories for multiple applications such as optimizing available parking spaces, balancing ferry load, planning infrastructure and managing traffic, or servicing vehicles. This paper describes the design and implementation of a vehicle classification system using a set of images collected from 6 views. The proposed computational system combines human visual attention mechanisms to identify a set of salient discriminative features and a series of binary support vector machines to achieve fast automated classification. An average classification rate of 96% is achieved for 3 vehicle categories. An improvement to 99.13% is achieved by using additional measurement on the width and height of the vehicles.
{"title":"Salient features based on visual attention for multi-view vehicle classification","authors":"A. Crétu, P. Payeur, R. Laganière","doi":"10.1109/CIMSA.2011.6059933","DOIUrl":"https://doi.org/10.1109/CIMSA.2011.6059933","url":null,"abstract":"The continuous rise in the amount of vehicles in circulation brings an increasing need for automatically and efficiently recognizing vehicle categories for multiple applications such as optimizing available parking spaces, balancing ferry load, planning infrastructure and managing traffic, or servicing vehicles. This paper describes the design and implementation of a vehicle classification system using a set of images collected from 6 views. The proposed computational system combines human visual attention mechanisms to identify a set of salient discriminative features and a series of binary support vector machines to achieve fast automated classification. An average classification rate of 96% is achieved for 3 vehicle categories. An improvement to 99.13% is achieved by using additional measurement on the width and height of the vehicles.","PeriodicalId":422972,"journal":{"name":"2011 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA) Proceedings","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123609004","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 : 2011-10-27DOI: 10.1109/CIMSA.2011.6059915
M. Radac, R. Precup, E. Petriu, P. Ianc, S. Preitl, C. Dragos
This paper discusses low-cost optimal Takagi-Sugeno state feedback fuzzy controllers for the position control of servo systems where the process is modeled by second-order linear dynamics with an integral component, and saturation and dead zone input static nonlinearity. The state feedback gain matrices in the rule consequents of the fuzzy controllers are obtained by the combination of the parallel distributed compensation and linear-quadratic regulator applied to each rule. An example concerning the position control of a DC servo system laboratory equipment is offered and experimental results are included.
{"title":"Low-cost optimal state feedback fuzzy control of nonlinear second-order servo systems","authors":"M. Radac, R. Precup, E. Petriu, P. Ianc, S. Preitl, C. Dragos","doi":"10.1109/CIMSA.2011.6059915","DOIUrl":"https://doi.org/10.1109/CIMSA.2011.6059915","url":null,"abstract":"This paper discusses low-cost optimal Takagi-Sugeno state feedback fuzzy controllers for the position control of servo systems where the process is modeled by second-order linear dynamics with an integral component, and saturation and dead zone input static nonlinearity. The state feedback gain matrices in the rule consequents of the fuzzy controllers are obtained by the combination of the parallel distributed compensation and linear-quadratic regulator applied to each rule. An example concerning the position control of a DC servo system laboratory equipment is offered and experimental results are included.","PeriodicalId":422972,"journal":{"name":"2011 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA) Proceedings","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116994156","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 : 2011-10-27DOI: 10.1109/CIMSA.2011.6059936
Yisu Zhao, Xin Wang, E. Petriu
Psychologists found that human eye gaze direction has a very strong influence on facial expression. However, there exist few researches that took eye gaze direction into consideration while recognizing facial expressions. This paper makes use of the combination of eye gaze and facial expression information to detect human emotion. First, eye gaze direction is categorized into direct gaze and avert gaze. We then carry on facial expression recognition based on the eye gaze analysis results. This could provide the confusion between some of the facial expressions therefore improve the recognition rate of emotion detection. Experimental results show that our proposed method can provide more accurate recognition rate then recognizing facial expression alone.
{"title":"Facial expression anlysis using eye gaze information","authors":"Yisu Zhao, Xin Wang, E. Petriu","doi":"10.1109/CIMSA.2011.6059936","DOIUrl":"https://doi.org/10.1109/CIMSA.2011.6059936","url":null,"abstract":"Psychologists found that human eye gaze direction has a very strong influence on facial expression. However, there exist few researches that took eye gaze direction into consideration while recognizing facial expressions. This paper makes use of the combination of eye gaze and facial expression information to detect human emotion. First, eye gaze direction is categorized into direct gaze and avert gaze. We then carry on facial expression recognition based on the eye gaze analysis results. This could provide the confusion between some of the facial expressions therefore improve the recognition rate of emotion detection. Experimental results show that our proposed method can provide more accurate recognition rate then recognizing facial expression alone.","PeriodicalId":422972,"journal":{"name":"2011 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA) Proceedings","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132028315","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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