Pub Date : 2017-06-01DOI: 10.1109/CIVEMSA.2017.7995300
R. Precup, C. Dragos, Elena-Lorena Hedrea, I. Borlea, E. Petriu
This paper suggests evolving Takagi-Sugeno-Kang (T-S-K) fuzzy models that characterize the nonlinear dynamics phenomena occurring in the longitudinal slip of Anti-lock Braking Systems (ABSs). The rule bases and the parameters of the T-S-K fuzzy models are evolved by an incremental online identification algorithm (IOIA). A set of real-time experiments is conducted in order to validate the evolving T-S-K fuzzy models that describe the dynamics of the longitudinal slip in an ABS laboratory equipment setup aiming the longitudinal slip control. The experimental results prove the very good performance of the T-S-K fuzzy models in terms of fast output responses and small root mean square error values. The performance comparison with similar T-S-K fuzzy models evolved by another IOIA and three nature-inspired optimization algorithms is included.
{"title":"Evolving fuzzy models for Anti-lock Braking Systems","authors":"R. Precup, C. Dragos, Elena-Lorena Hedrea, I. Borlea, E. Petriu","doi":"10.1109/CIVEMSA.2017.7995300","DOIUrl":"https://doi.org/10.1109/CIVEMSA.2017.7995300","url":null,"abstract":"This paper suggests evolving Takagi-Sugeno-Kang (T-S-K) fuzzy models that characterize the nonlinear dynamics phenomena occurring in the longitudinal slip of Anti-lock Braking Systems (ABSs). The rule bases and the parameters of the T-S-K fuzzy models are evolved by an incremental online identification algorithm (IOIA). A set of real-time experiments is conducted in order to validate the evolving T-S-K fuzzy models that describe the dynamics of the longitudinal slip in an ABS laboratory equipment setup aiming the longitudinal slip control. The experimental results prove the very good performance of the T-S-K fuzzy models in terms of fast output responses and small root mean square error values. The performance comparison with similar T-S-K fuzzy models evolved by another IOIA and three nature-inspired optimization algorithms is included.","PeriodicalId":123360,"journal":{"name":"2017 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121525112","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 : 2017-06-01DOI: 10.1109/CIVEMSA.2017.7995324
Frédéric Pourraz, H. Verjus, G. Mauris
During a ski touring outing, practitioners evolve in an uncertain environment which is subject to snow avalanches. To make this evolution as serene as possible, the choice of the itinerary is of upmost importance and must be based on an aggregation of several heterogeneous criteria. The paper proposes a fuzzy rule-based system for aggregating criteria evaluations. Such fuzzy rule-based system produces results that are then rendered in a numerical virtual model using a 3D visual application. The aim of this application is to assist the ski tourer for itinerary decision making thanks to a visual rendering of the snow avalanche risk.
{"title":"Visual analytics for aiding decisions of ski touring itinerary in a risky snow avalanche environment","authors":"Frédéric Pourraz, H. Verjus, G. Mauris","doi":"10.1109/CIVEMSA.2017.7995324","DOIUrl":"https://doi.org/10.1109/CIVEMSA.2017.7995324","url":null,"abstract":"During a ski touring outing, practitioners evolve in an uncertain environment which is subject to snow avalanches. To make this evolution as serene as possible, the choice of the itinerary is of upmost importance and must be based on an aggregation of several heterogeneous criteria. The paper proposes a fuzzy rule-based system for aggregating criteria evaluations. Such fuzzy rule-based system produces results that are then rendered in a numerical virtual model using a 3D visual application. The aim of this application is to assist the ski tourer for itinerary decision making thanks to a visual rendering of the snow avalanche risk.","PeriodicalId":123360,"journal":{"name":"2017 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116845606","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 : 2017-06-01DOI: 10.1109/CIVEMSA.2017.7995320
M. Chew, S. Demidenko, M. Ooi, Y. Kuang
This paper presents a set of low-cost desktop IC test systems developed for engineering education. The set is implemented using the National Instrument (NI) Educational Laboratory Virtual Instrumentation Suite (ELVIS) and custom-made load boards. The software is based on the NI LabVIEW development environment. The set has been developed and employed for teaching electronic testing, instrumentation and measurement, and advanced electronic circuits courses within several undergraduate and graduate engineering programs at three universities in Malaysia, Vietnam, and New Zealand.
{"title":"Family of low-cost NI ELVIS/LabVIEW-based semiconductor testers for engineering education","authors":"M. Chew, S. Demidenko, M. Ooi, Y. Kuang","doi":"10.1109/CIVEMSA.2017.7995320","DOIUrl":"https://doi.org/10.1109/CIVEMSA.2017.7995320","url":null,"abstract":"This paper presents a set of low-cost desktop IC test systems developed for engineering education. The set is implemented using the National Instrument (NI) Educational Laboratory Virtual Instrumentation Suite (ELVIS) and custom-made load boards. The software is based on the NI LabVIEW development environment. The set has been developed and employed for teaching electronic testing, instrumentation and measurement, and advanced electronic circuits courses within several undergraduate and graduate engineering programs at three universities in Malaysia, Vietnam, and New Zealand.","PeriodicalId":123360,"journal":{"name":"2017 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128615144","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 : 2017-06-01DOI: 10.1109/CIVEMSA.2017.7995327
P. Mazurek, Jakub Wagner, A. Miekina, R. Morawski
The research reported in this paper is related to the fusion of measurement data from the impulse-radar sensors and infrared depth sensors, i.e. significantly different sensors that may be employed for unobtrusive monitoring of elderly and disabled persons. The performance of monitoring systems, based on both types of sensors - when used separately and when used in combination - has been compared in a series of experiments which involved the tracking of a moving person. The results have shown that the combined use of sensors, followed by the adequate fusion of measurement data, alleviates the problems occurring if only single-type sensors are used for monitoring, viz. the bias and dispersion of the estimates decreases and the blind spots in the monitored area disappear. Since the sequence of the position estimates can be used for derivation of many healthcare-related parameters, e.g. mean walking velocity, the application of data fusion may considerably increase the reliability of the unobtrusive monitoring of elderly and disabled persons.
{"title":"Fusion of measurement data from impulse-radar sensors and depth sensors when applied for patients monitoring","authors":"P. Mazurek, Jakub Wagner, A. Miekina, R. Morawski","doi":"10.1109/CIVEMSA.2017.7995327","DOIUrl":"https://doi.org/10.1109/CIVEMSA.2017.7995327","url":null,"abstract":"The research reported in this paper is related to the fusion of measurement data from the impulse-radar sensors and infrared depth sensors, i.e. significantly different sensors that may be employed for unobtrusive monitoring of elderly and disabled persons. The performance of monitoring systems, based on both types of sensors - when used separately and when used in combination - has been compared in a series of experiments which involved the tracking of a moving person. The results have shown that the combined use of sensors, followed by the adequate fusion of measurement data, alleviates the problems occurring if only single-type sensors are used for monitoring, viz. the bias and dispersion of the estimates decreases and the blind spots in the monitored area disappear. Since the sequence of the position estimates can be used for derivation of many healthcare-related parameters, e.g. mean walking velocity, the application of data fusion may considerably increase the reliability of the unobtrusive monitoring of elderly and disabled persons.","PeriodicalId":123360,"journal":{"name":"2017 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","volume":"283 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131704649","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 : 2017-06-01DOI: 10.1109/CIVEMSA.2017.7995310
Thomas Calloway, D. Megherbi, Hongsheng Zhang
Reliably localizing and tracking displays moving relative to content in the physical world is one of the primary technical challenges facing all augmented reality systems. While significant progress has been made in recent years, all approaches remain limited to functioning only in certain environments and situations. Attempts to improve solution generality via additional sensors (e.g., depth sensors, multiple cameras) add significant size, weight and power to wearable solutions sensitive to these attributes. In this work, we propose an approach to tracking and localization using a single camera and inertial chip. Through a combination of visual-inertial navigation, point cloud mapping and dynamically correlating building faces and edges with sparse OpenStreetMap datasets, we achieved a typical global localization precision of less than 0.25 meters and 1 degree heading relative to the map. All motion tracking calculations are performed on a local mobile device with less than 10 milliseconds of latency while global localization and drift correction is performed remotely.
{"title":"Global localization and tracking for wearable augmented reality in urban environments","authors":"Thomas Calloway, D. Megherbi, Hongsheng Zhang","doi":"10.1109/CIVEMSA.2017.7995310","DOIUrl":"https://doi.org/10.1109/CIVEMSA.2017.7995310","url":null,"abstract":"Reliably localizing and tracking displays moving relative to content in the physical world is one of the primary technical challenges facing all augmented reality systems. While significant progress has been made in recent years, all approaches remain limited to functioning only in certain environments and situations. Attempts to improve solution generality via additional sensors (e.g., depth sensors, multiple cameras) add significant size, weight and power to wearable solutions sensitive to these attributes. In this work, we propose an approach to tracking and localization using a single camera and inertial chip. Through a combination of visual-inertial navigation, point cloud mapping and dynamically correlating building faces and edges with sparse OpenStreetMap datasets, we achieved a typical global localization precision of less than 0.25 meters and 1 degree heading relative to the map. All motion tracking calculations are performed on a local mobile device with less than 10 milliseconds of latency while global localization and drift correction is performed remotely.","PeriodicalId":123360,"journal":{"name":"2017 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124348376","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 : 1900-01-01DOI: 10.1109/civemsa.2017.7995328
Julie Blumenthal, D. Megherbi, R. Lussier
It has long been known that Chlorophyll fluorescence (ChlF), a plant response to stressors in time, is a useful tool in detecting plant stress. Accurate and early plant stress detection is imperative in enabling appropriate and timely intervention. One of the major limitations of prior work in ChIF-based plant classification is that, in general, only a few key inflection points of a localized selection of a chlorophyll fluorescence signal are used to calculate single index values for classification. These values yield limited insight into stress level and especially into stressor type. In this paper, we introduce and present a new method for plant stress classification that uses supervised learning, via Hidden Markov Models (HMMs), to build accurate class profiles using global (versus local) ChlF time-varying signal data acquired via video imaging. We show how creating increased-state supervised models can in particular, classify specific stressor types as well as achieve more granularity in stressor level classification. Experimental results are presented to show the value and potential of the proposed supervised method to enable more accurate and specific classification of plant stressor types and stressor levels.
{"title":"Supervised machine learning via Hidden Markov Models for accurate classification of plant stress levels & types based on imaged Chlorophyll fluorescence profiles & their rate of change in time","authors":"Julie Blumenthal, D. Megherbi, R. Lussier","doi":"10.1109/civemsa.2017.7995328","DOIUrl":"https://doi.org/10.1109/civemsa.2017.7995328","url":null,"abstract":"It has long been known that Chlorophyll fluorescence (ChlF), a plant response to stressors in time, is a useful tool in detecting plant stress. Accurate and early plant stress detection is imperative in enabling appropriate and timely intervention. One of the major limitations of prior work in ChIF-based plant classification is that, in general, only a few key inflection points of a localized selection of a chlorophyll fluorescence signal are used to calculate single index values for classification. These values yield limited insight into stress level and especially into stressor type. In this paper, we introduce and present a new method for plant stress classification that uses supervised learning, via Hidden Markov Models (HMMs), to build accurate class profiles using global (versus local) ChlF time-varying signal data acquired via video imaging. We show how creating increased-state supervised models can in particular, classify specific stressor types as well as achieve more granularity in stressor level classification. Experimental results are presented to show the value and potential of the proposed supervised method to enable more accurate and specific classification of plant stressor types and stressor levels.","PeriodicalId":123360,"journal":{"name":"2017 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130578832","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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