Pub Date : 2016-10-01DOI: 10.1109/NAFIPS.2016.7851578
Marbin Pazos-Revilla, Terry N. Guo, Motoya Machida
Estimating position of moving objects have been an area of research for several decades, but challenges still remain as many of technologies or computational methods are either too costly, computationally intensive, or simply not possible to apply due to environmental, economical, or other types of constraints. In this paper we investigate a novel method for improving position estimation of a moving object using fuzzy rules in combination with Extended Kalman Filter (EKF) and Received Signal Strength Indicator (RSSI) measures. The EKF provides a recursive method for estimating internal states of nonlinear system from measured observations. The estimation performance of EKF is highly dependent on the dynamics of internal system model, which is not always available, as could be the case for the hidden locations of humans, robots, and animals moving according to their own rules. Preliminary results have shown evidence that when fuzzy rules are considered to represent the dynamical system, a reduction of error occurs, and as a result, a less conservative estimation of position is obtained when compared to the traditional weighted least-square estimate in the context of recursive approach.
{"title":"Extended Kalman Filter combined with fuzzy rules for localization using wireless transceivers","authors":"Marbin Pazos-Revilla, Terry N. Guo, Motoya Machida","doi":"10.1109/NAFIPS.2016.7851578","DOIUrl":"https://doi.org/10.1109/NAFIPS.2016.7851578","url":null,"abstract":"Estimating position of moving objects have been an area of research for several decades, but challenges still remain as many of technologies or computational methods are either too costly, computationally intensive, or simply not possible to apply due to environmental, economical, or other types of constraints. In this paper we investigate a novel method for improving position estimation of a moving object using fuzzy rules in combination with Extended Kalman Filter (EKF) and Received Signal Strength Indicator (RSSI) measures. The EKF provides a recursive method for estimating internal states of nonlinear system from measured observations. The estimation performance of EKF is highly dependent on the dynamics of internal system model, which is not always available, as could be the case for the hidden locations of humans, robots, and animals moving according to their own rules. Preliminary results have shown evidence that when fuzzy rules are considered to represent the dynamical system, a reduction of error occurs, and as a result, a less conservative estimation of position is obtained when compared to the traditional weighted least-square estimate in the context of recursive approach.","PeriodicalId":208265,"journal":{"name":"2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133456613","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 : 2016-10-01DOI: 10.1109/NAFIPS.2016.7851632
M. Naderipour, S. Bastani, M. F. Zarandi, I. Türksen
In this paper, we study a type-2 fuzzy classification method. Granular computing can help us to model the relationships between human-based system and social sciences in this field. The links in a social network often reflect social relationships among users. In this work, we investigate a classification identifying the relationships among social network users based on certain social network property, granular computing approach and Type 2 fuzzy logic. We evaluate our approach on large scale real-world data from Renren network, showing that the accuracy of the prediction of our classification algorithm is higher than the type 1 fuzzy analysis and the crisp approach.
{"title":"A fuzzy classification using a Type-2 fuzzy model in social networks","authors":"M. Naderipour, S. Bastani, M. F. Zarandi, I. Türksen","doi":"10.1109/NAFIPS.2016.7851632","DOIUrl":"https://doi.org/10.1109/NAFIPS.2016.7851632","url":null,"abstract":"In this paper, we study a type-2 fuzzy classification method. Granular computing can help us to model the relationships between human-based system and social sciences in this field. The links in a social network often reflect social relationships among users. In this work, we investigate a classification identifying the relationships among social network users based on certain social network property, granular computing approach and Type 2 fuzzy logic. We evaluate our approach on large scale real-world data from Renren network, showing that the accuracy of the prediction of our classification algorithm is higher than the type 1 fuzzy analysis and the crisp approach.","PeriodicalId":208265,"journal":{"name":"2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121760005","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 : 2016-10-01DOI: 10.1109/NAFIPS.2016.7851606
Julia Taylor Rayz, V. Raskin
The paper explores the fuzzy status of implicit information in natural language text, focusing on conceptual defaults, the routinely omitted information that readers/hearers equally routinely reconstruct. Making this information available to the natural language processing computer is essential, and fuzziness is a major issue. An analysis of 1,000 English sentences has demonstrated a diverse combination of circumstances for detecting and computing defaults with their membership function values.
{"title":"Conceptual defaults in fuzzy ontology","authors":"Julia Taylor Rayz, V. Raskin","doi":"10.1109/NAFIPS.2016.7851606","DOIUrl":"https://doi.org/10.1109/NAFIPS.2016.7851606","url":null,"abstract":"The paper explores the fuzzy status of implicit information in natural language text, focusing on conceptual defaults, the routinely omitted information that readers/hearers equally routinely reconstruct. Making this information available to the natural language processing computer is essential, and fuzziness is a major issue. An analysis of 1,000 English sentences has demonstrated a diverse combination of circumstances for detecting and computing defaults with their membership function values.","PeriodicalId":208265,"journal":{"name":"2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS)","volume":"129 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124273372","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 : 2016-10-01DOI: 10.1109/NAFIPS.2016.7851634
Ali Akbar Sadatasl, M. F. Zarandi, Abolfazl Sadeghi
Recently so many researches are concerned with the combined facility location and network design models for facility location and coverage problems. In this models we want to find the optimum location of facility by constructing an underlying network. We can use this for distribution network, transportation networks, health centers and emergency allocations, etc. At this study a mathematical programming model is introduced that facilities are opened on the nodes and it is assumed for connecting demand nodes and facilities there are different links with different quality that just one of them should be selected. Also if a facility in a node can't satisfy demand the demand is sent to a facility in other node and satisfied by this facility called backup facility. Also decision process is affected by uncertainty and concept of information inherently is mixed with uncertainty. Fuzzy logic can introduce mathematical models for hazy concepts and variables and systems and also showing a way for argument, control and making decision in uncertainty condition. In complex systems with high uncertainty fuzzy logic is best way for the modeling. At this study demands are considered in uncertain form and are introduced in the form of fuzzy numbers. The problem is modeled for different size and the computational results are compared.
{"title":"A combined facility location and network design model with multi-type of capacitated links and backup facility and non-deterministic demand by fuzzy logic","authors":"Ali Akbar Sadatasl, M. F. Zarandi, Abolfazl Sadeghi","doi":"10.1109/NAFIPS.2016.7851634","DOIUrl":"https://doi.org/10.1109/NAFIPS.2016.7851634","url":null,"abstract":"Recently so many researches are concerned with the combined facility location and network design models for facility location and coverage problems. In this models we want to find the optimum location of facility by constructing an underlying network. We can use this for distribution network, transportation networks, health centers and emergency allocations, etc. At this study a mathematical programming model is introduced that facilities are opened on the nodes and it is assumed for connecting demand nodes and facilities there are different links with different quality that just one of them should be selected. Also if a facility in a node can't satisfy demand the demand is sent to a facility in other node and satisfied by this facility called backup facility. Also decision process is affected by uncertainty and concept of information inherently is mixed with uncertainty. Fuzzy logic can introduce mathematical models for hazy concepts and variables and systems and also showing a way for argument, control and making decision in uncertainty condition. In complex systems with high uncertainty fuzzy logic is best way for the modeling. At this study demands are considered in uncertain form and are introduced in the form of fuzzy numbers. The problem is modeled for different size and the computational results are compared.","PeriodicalId":208265,"journal":{"name":"2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124623572","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 : 2016-10-01DOI: 10.1109/NAFIPS.2016.7851631
M. Es-haghi, S. Bastani
Emergency management will not achieve in its goals unless it prepares members in the emergency response team. Achievement of sufficient preparedness requires effective coordination among all team members. This study aimed to use the fuzzy approach and social network analysis for the evaluation of the coordination level as the most important stage in preparedness of emergency management. The evaluation of coordination with fuzzy approach was conducted by using the findings of density indicator of social network analysis and a standard questionnaire. The density indicator was used in order to evaluate the cohesion of coordination through trust and information interchange measures. Also, a standard questionnaire was applied to evaluate the involvement of members in issues related to emergency management. The findings showed that there was a low level of coordination among the whole team members and slightly related to be coordinated for confronting emergencies. Finally, this article argues that it is necessary to turn into a predictive approach in order to provide a suitable relationship for increasing coordination through creative planning and programming in order to have an ideal emergency management.
{"title":"Evaluating coordination in emergency response team by using fuzzy logic through social network analysis","authors":"M. Es-haghi, S. Bastani","doi":"10.1109/NAFIPS.2016.7851631","DOIUrl":"https://doi.org/10.1109/NAFIPS.2016.7851631","url":null,"abstract":"Emergency management will not achieve in its goals unless it prepares members in the emergency response team. Achievement of sufficient preparedness requires effective coordination among all team members. This study aimed to use the fuzzy approach and social network analysis for the evaluation of the coordination level as the most important stage in preparedness of emergency management. The evaluation of coordination with fuzzy approach was conducted by using the findings of density indicator of social network analysis and a standard questionnaire. The density indicator was used in order to evaluate the cohesion of coordination through trust and information interchange measures. Also, a standard questionnaire was applied to evaluate the involvement of members in issues related to emergency management. The findings showed that there was a low level of coordination among the whole team members and slightly related to be coordinated for confronting emergencies. Finally, this article argues that it is necessary to turn into a predictive approach in order to provide a suitable relationship for increasing coordination through creative planning and programming in order to have an ideal emergency management.","PeriodicalId":208265,"journal":{"name":"2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131986329","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 : 2016-10-01DOI: 10.1109/NAFIPS.2016.7851584
H. R. D. N. Costa, A. L. Neve
ANFIS and other algorithms were used for the classification of the defects that occur in the production process of glass for packing. In this study we used the Project “Newglass” installed in Portugal. This project used a model of Manufactures to study the process of manufacturing glass packaging. The database Project “Newglass” consists of the operating variables of the furnace and the percentage of defects found in end products of the factory model. The classification obtained through the ANFIS was compared with the results obtained in the manufacture of glass for packing. The classifications obtained in the manufacture and in the ANFIS software were also compared with the classification obtained with CART (Classification and Regression Tree).
{"title":"A study on the application of regression trees and adaptive neuro-fuzzy inference system in glass manufacturing process for packaging","authors":"H. R. D. N. Costa, A. L. Neve","doi":"10.1109/NAFIPS.2016.7851584","DOIUrl":"https://doi.org/10.1109/NAFIPS.2016.7851584","url":null,"abstract":"ANFIS and other algorithms were used for the classification of the defects that occur in the production process of glass for packing. In this study we used the Project “Newglass” installed in Portugal. This project used a model of Manufactures to study the process of manufacturing glass packaging. The database Project “Newglass” consists of the operating variables of the furnace and the percentage of defects found in end products of the factory model. The classification obtained through the ANFIS was compared with the results obtained in the manufacture of glass for packing. The classifications obtained in the manufacture and in the ANFIS software were also compared with the classification obtained with CART (Classification and Regression Tree).","PeriodicalId":208265,"journal":{"name":"2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124455632","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 : 2016-10-01DOI: 10.1109/NAFIPS.2016.7851621
L. Valera, M. Ceberio
The ability to make observations of natural phenomena has played a fundamental role in our world. From what we observe, models are derived and we can get an understanding about how things work by simulating our models. This has been particularly important in areas such as medicine, physics, chemistry. However, when we do not initiate simulations but that we are simply observing a phenomenon, it is valuable to be able to understand it “on the fly” and be able to predict its future behavior. Added challenges come from the fact that observations are never 100% accurate and therefore we must deal with uncertainty. In this work, we use Interval Constraint Solving Techniques (ICST) to handle uncertainty in the observations of a given phenomenon, and to be able to determine its initial conditions and unfold the dynamic behavior further in time.
{"title":"Using Interval Constraint Solving Techniques to better understand and predict future behaviors of dynamic problems","authors":"L. Valera, M. Ceberio","doi":"10.1109/NAFIPS.2016.7851621","DOIUrl":"https://doi.org/10.1109/NAFIPS.2016.7851621","url":null,"abstract":"The ability to make observations of natural phenomena has played a fundamental role in our world. From what we observe, models are derived and we can get an understanding about how things work by simulating our models. This has been particularly important in areas such as medicine, physics, chemistry. However, when we do not initiate simulations but that we are simply observing a phenomenon, it is valuable to be able to understand it “on the fly” and be able to predict its future behavior. Added challenges come from the fact that observations are never 100% accurate and therefore we must deal with uncertainty. In this work, we use Interval Constraint Solving Techniques (ICST) to handle uncertainty in the observations of a given phenomenon, and to be able to determine its initial conditions and unfold the dynamic behavior further in time.","PeriodicalId":208265,"journal":{"name":"2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115401955","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 : 2016-10-01DOI: 10.1109/NAFIPS.2016.7851627
B. Lahijanian, M. Zarandi, F. Farahani
Planning and scheduling of the operating rooms play an undeniable role in providing appropriate services in hospitals. In this paper, a mixed-integer programming model for scheduling the operating rooms is presented in which elective patients are investigated. This model seeks to minimize the total weighted start times. Considering such as issue, three age groups are used for the weighting calculation. The setup times for this group of patients are sequence-dependent and the duration of surgical operation are analyzed through fuzzy logic. The proposed model is solved using a hybrid algorithm. The obtained results show that considering the duration of surgical operation as fuzzy numbers are in accordance with more reality. Furthermore, these fuzzy numbers lead to reaching the final solution in a shorter time.
{"title":"Proposing a model for operating room scheduling based on fuzzy surgical duration","authors":"B. Lahijanian, M. Zarandi, F. Farahani","doi":"10.1109/NAFIPS.2016.7851627","DOIUrl":"https://doi.org/10.1109/NAFIPS.2016.7851627","url":null,"abstract":"Planning and scheduling of the operating rooms play an undeniable role in providing appropriate services in hospitals. In this paper, a mixed-integer programming model for scheduling the operating rooms is presented in which elective patients are investigated. This model seeks to minimize the total weighted start times. Considering such as issue, three age groups are used for the weighting calculation. The setup times for this group of patients are sequence-dependent and the duration of surgical operation are analyzed through fuzzy logic. The proposed model is solved using a hybrid algorithm. The obtained results show that considering the duration of surgical operation as fuzzy numbers are in accordance with more reality. Furthermore, these fuzzy numbers lead to reaching the final solution in a shorter time.","PeriodicalId":208265,"journal":{"name":"2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114506712","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 : 2016-10-01DOI: 10.1109/NAFIPS.2016.7851599
Emer Bernal, O. Castillo, J. Soria
In this paper we propose the use of fuzzy systems for dynamic adjustment of parameters of the imperialist competitive algorithm (ICA). This algorithm is inspired by the concept of imperialism; where powerful countries try to make a colony of other countries. We developed different fuzzy systems one with the Beta parameter and another using a combination with the Beta and Xi parameters to measure the performance of the algorithm with 10 benchmark mathematical functions with different number of decades and 30 times each. In this paper a comparison was made between both variants for demonstrate the efficiency of the algorithm in optimization problems.
{"title":"Fuzzy logic for dynamic adaptation in the imperialist competitive algorithm","authors":"Emer Bernal, O. Castillo, J. Soria","doi":"10.1109/NAFIPS.2016.7851599","DOIUrl":"https://doi.org/10.1109/NAFIPS.2016.7851599","url":null,"abstract":"In this paper we propose the use of fuzzy systems for dynamic adjustment of parameters of the imperialist competitive algorithm (ICA). This algorithm is inspired by the concept of imperialism; where powerful countries try to make a colony of other countries. We developed different fuzzy systems one with the Beta parameter and another using a combination with the Beta and Xi parameters to measure the performance of the algorithm with 10 benchmark mathematical functions with different number of decades and 30 times each. In this paper a comparison was made between both variants for demonstrate the efficiency of the algorithm in optimization problems.","PeriodicalId":208265,"journal":{"name":"2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS)","volume":"37 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114110127","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 : 2016-10-01DOI: 10.1109/NAFIPS.2016.7851579
Ernesto León-Castro, Ezequiel Avilés-Ochoa, J. M. Lindahl, A. M. G. Lafuente
The paper presents the Heavy Ordered Weighted Moving Average (HOWMA) operator. It is a new aggregation operator that takes the advantage of the moving average to solve time series smoothing problems and the heavy OWA to provide more robust formulation and take into account the expectations of the future scenarios. It includes a wide range of particular cases such as the olympic moving aggregation and centered moving aggregation. The study also includes a new application to forecast the exchange rate USD/MXN combining econometric models and the HOWMA operator.
{"title":"Heavy Moving Averages in exchange rate forecasting","authors":"Ernesto León-Castro, Ezequiel Avilés-Ochoa, J. M. Lindahl, A. M. G. Lafuente","doi":"10.1109/NAFIPS.2016.7851579","DOIUrl":"https://doi.org/10.1109/NAFIPS.2016.7851579","url":null,"abstract":"The paper presents the Heavy Ordered Weighted Moving Average (HOWMA) operator. It is a new aggregation operator that takes the advantage of the moving average to solve time series smoothing problems and the heavy OWA to provide more robust formulation and take into account the expectations of the future scenarios. It includes a wide range of particular cases such as the olympic moving aggregation and centered moving aggregation. The study also includes a new application to forecast the exchange rate USD/MXN combining econometric models and the HOWMA operator.","PeriodicalId":208265,"journal":{"name":"2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126429856","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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