Pub Date : 2011-10-27DOI: 10.1109/CIMSA.2011.6059921
N. Irfan, M. Yagoub, K. Hettak
Radio Frequency Identification (RFID) systems, due to recent technological advances, have been used for various advantages in industry like production facilities, supply chain management etc. However, sometimes this requires a dense deployment of readers to cover the working area. Without optimizing reader's location and number, many of them will be redundant, reducing the efficiency of the whole RFID system. There are many algorithms proposed to solve this redundant reader problem, but all existing algorithms are based on omnidirectional reader antenna pattern, which is not practical. In this paper, a genetic algorithm is used to optimize the antenna beam to eliminate redundant reader based on real directional reader antenna pattern.
{"title":"Genetic algorithm based efficient tag detection in RFID reader networks","authors":"N. Irfan, M. Yagoub, K. Hettak","doi":"10.1109/CIMSA.2011.6059921","DOIUrl":"https://doi.org/10.1109/CIMSA.2011.6059921","url":null,"abstract":"Radio Frequency Identification (RFID) systems, due to recent technological advances, have been used for various advantages in industry like production facilities, supply chain management etc. However, sometimes this requires a dense deployment of readers to cover the working area. Without optimizing reader's location and number, many of them will be redundant, reducing the efficiency of the whole RFID system. There are many algorithms proposed to solve this redundant reader problem, but all existing algorithms are based on omnidirectional reader antenna pattern, which is not practical. In this paper, a genetic algorithm is used to optimize the antenna beam to eliminate redundant reader based on real directional reader antenna pattern.","PeriodicalId":422972,"journal":{"name":"2011 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA) Proceedings","volume":"32 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":"128271480","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.2011.6059927
Alessandro Quarto, V. Di Lecce, R. Dario, J. Uva
The Italian Legislative Decree dated April 26, 2010 no. 81/08 also refers to the European directive no. 2006/25/EC on the limit values for the exposure of workers to artificial optical radiation (AOR). The main damages caused by higher exposure to AOR regard in particular eyes and all of the body (e.g. skin). Recent studies concern the health effects on retinal photoreceptors after exposure to wavelength range between 380 nm and 500 nm, named "blue light”. The aim of this paper is to present an innovative personal dosimeter for AOR detection. The proposed system can be used not only for evaluating AOR, but also providing the operator's position and attitude in relation to the natural or artificial source of radiation. The acquired Data are then processed by a Fuzzy Inference System (FIS). The FIS main target is the accurate evaluation of risk levels associated to each light radiation striking the operator's retina.
{"title":"Personal dosimeter for the measurement of artificial optical radiation (AOR) exposure","authors":"Alessandro Quarto, V. Di Lecce, R. Dario, J. Uva","doi":"10.1109/CIMSA.2011.6059927","DOIUrl":"https://doi.org/10.1109/CIMSA.2011.6059927","url":null,"abstract":"The Italian Legislative Decree dated April 26, 2010 no. 81/08 also refers to the European directive no. 2006/25/EC on the limit values for the exposure of workers to artificial optical radiation (AOR). The main damages caused by higher exposure to AOR regard in particular eyes and all of the body (e.g. skin). Recent studies concern the health effects on retinal photoreceptors after exposure to wavelength range between 380 nm and 500 nm, named \"blue light”. The aim of this paper is to present an innovative personal dosimeter for AOR detection. The proposed system can be used not only for evaluating AOR, but also providing the operator's position and attitude in relation to the natural or artificial source of radiation. The acquired Data are then processed by a Fuzzy Inference System (FIS). The FIS main target is the accurate evaluation of risk levels associated to each light radiation striking the operator's retina.","PeriodicalId":422972,"journal":{"name":"2011 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA) Proceedings","volume":"7 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":"114078790","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.2011.6059914
Ke Qin, B. Oommen
Over the last few years, the field of Chaotic Neural Networks (CNNs) has been extensively studied because of their potential applications in the understanding/recognition of patterns and images, their associative memory properties, their relationship to complex dynamic system control, and their capabilities in the modeling and analysis of other measurement systems. However, the results concerning CNNs which can demonstrate chaos, quasi-chaos, Associative Memory (AM), and Pattern Recognition (PR) are scanty. In this paper, we consider the consequences of networking a set of Logistic Neurons (LNs). By appropriately defining the input/output characteristics of a fully connected network of LNs, and by defining their set of weights and output functions, we have succeeded in designing a Logistic Neural Network (LNN) possessing some of these properties. The chaotic properties of a single-neuron have been formally proven, and those of the entire network have also been alluded to. Indeed, by appropriately setting the parameters of the LNN, we show that the LNN can yield AM, chaotic and PR properties for different settings. As far as we know, the results presented here are novel, and the chaotic PR properties of such a network are unreported.
{"title":"Networking logistic neurons can yield chaotic and pattern recognition properties","authors":"Ke Qin, B. Oommen","doi":"10.1109/CIMSA.2011.6059914","DOIUrl":"https://doi.org/10.1109/CIMSA.2011.6059914","url":null,"abstract":"Over the last few years, the field of Chaotic Neural Networks (CNNs) has been extensively studied because of their potential applications in the understanding/recognition of patterns and images, their associative memory properties, their relationship to complex dynamic system control, and their capabilities in the modeling and analysis of other measurement systems. However, the results concerning CNNs which can demonstrate chaos, quasi-chaos, Associative Memory (AM), and Pattern Recognition (PR) are scanty. In this paper, we consider the consequences of networking a set of Logistic Neurons (LNs). By appropriately defining the input/output characteristics of a fully connected network of LNs, and by defining their set of weights and output functions, we have succeeded in designing a Logistic Neural Network (LNN) possessing some of these properties. The chaotic properties of a single-neuron have been formally proven, and those of the entire network have also been alluded to. Indeed, by appropriately setting the parameters of the LNN, we show that the LNN can yield AM, chaotic and PR properties for different settings. As far as we know, the results presented here are novel, and the chaotic PR properties of such a network are unreported.","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":"129249375","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.2011.6059918
F. Barghi, A. Safavi
An accurate model of Air to Fuel Ratio (AFR) dynamics is critical for high-quality AFR control in SI engines. These modeling and control problems are very sensitive because the dynamics of intake manifold air-fuel flow is severely nonlinear and multivariable. This study focuses on Recurrent Neuro-Fuzzy Network (RNFN) estimation and control of AFR nonlinear dynamics in SI engines. First, a nonlinear autoregressive with exogenous inputs (NARX) model is chosen for modeling the AFR nonlinear dynamics in the fuel injection system. Then, the strategy based on RNFN, is employed to fine-tune the model parameters. A controller is also designed based on inverse model-based method. The objective of control scheme is to keep the AFR constraint conditions by providing the proper fuel injection commands. This strategy is performed on an informative data-set obtained by a real-time in-vehicle experimental test. The effectiveness of the proposed approach is evaluated and validated by the resulting improvement in comparison with ECU performance.
{"title":"Experimental validation of recurrent Neuro-Fuzzy Networks for AFR estimation and control in SI engines","authors":"F. Barghi, A. Safavi","doi":"10.1109/CIMSA.2011.6059918","DOIUrl":"https://doi.org/10.1109/CIMSA.2011.6059918","url":null,"abstract":"An accurate model of Air to Fuel Ratio (AFR) dynamics is critical for high-quality AFR control in SI engines. These modeling and control problems are very sensitive because the dynamics of intake manifold air-fuel flow is severely nonlinear and multivariable. This study focuses on Recurrent Neuro-Fuzzy Network (RNFN) estimation and control of AFR nonlinear dynamics in SI engines. First, a nonlinear autoregressive with exogenous inputs (NARX) model is chosen for modeling the AFR nonlinear dynamics in the fuel injection system. Then, the strategy based on RNFN, is employed to fine-tune the model parameters. A controller is also designed based on inverse model-based method. The objective of control scheme is to keep the AFR constraint conditions by providing the proper fuel injection commands. This strategy is performed on an informative data-set obtained by a real-time in-vehicle experimental test. The effectiveness of the proposed approach is evaluated and validated by the resulting improvement in comparison with ECU performance.","PeriodicalId":422972,"journal":{"name":"2011 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA) Proceedings","volume":"124 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":"132647845","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.2011.6059931
Zhiliang Liu, M. Zuo, Hongbing Xu
Recently Li et al. proposed a parameter selection method for Gaussian radial basis function (GRBF) in support vector machine (SVM). In his paper cosine similarity was calculated between two vectors based on the properties of GRBF kernel function. Li's method can determine an optimal sigma in SVM and thus efficiently improve its performance, yet it is limited by only focusing on a fixed original feature space and may suffer if the space contains some irrelevant and redundant features, especially in a high-dimensional feature space. In this paper, Li's method is extended to a flexible feature space so that feature selection and parameter selection are conducted at the same time. A feature subset and sigma are determined by minimizing the objective function that considers both within-class and between-class cosine similarities. Our experimental results demonstrate that the proposed method has a better performance than Li's method and traditional SVM in terms of classification accuracy.
{"title":"A Gaussian radial basis function based feature selection algorithm","authors":"Zhiliang Liu, M. Zuo, Hongbing Xu","doi":"10.1109/CIMSA.2011.6059931","DOIUrl":"https://doi.org/10.1109/CIMSA.2011.6059931","url":null,"abstract":"Recently Li et al. proposed a parameter selection method for Gaussian radial basis function (GRBF) in support vector machine (SVM). In his paper cosine similarity was calculated between two vectors based on the properties of GRBF kernel function. Li's method can determine an optimal sigma in SVM and thus efficiently improve its performance, yet it is limited by only focusing on a fixed original feature space and may suffer if the space contains some irrelevant and redundant features, especially in a high-dimensional feature space. In this paper, Li's method is extended to a flexible feature space so that feature selection and parameter selection are conducted at the same time. A feature subset and sigma are determined by minimizing the objective function that considers both within-class and between-class cosine similarities. Our experimental results demonstrate that the proposed method has a better performance than Li's method and traditional SVM in terms of classification accuracy.","PeriodicalId":422972,"journal":{"name":"2011 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA) Proceedings","volume":"10 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":"125295494","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.2011.6059926
V. Di Lecce, M. Calabrese
This work presents a two-step heuristic that employs extremely low-cost sensors for gaseous emission event discrimination. These events are triggered by particular patterns of sensor responses possibly occurring when a certain gas is emitted; patterns are then used to produce human-understandable inference rules describing the kind of emission measured. The technique, challenged by the high cross-sensitivity of the employed sensors, is based on two steps: first, sensor response patterns are extracted (unsupervisedly) from measurement signals by means of a recently proposed computational intelligence technique; second, a ‘credibility index’ is applied (supervisedly) to each pattern via fuzzy membership functions. The outcome is a set of IF THEN statements weighted by fuzzy constraints. Experiments show that such inferences allow for accurate gaseous emission event discrimination.
{"title":"Discriminating gaseous emission patterns in low-cost sensor setups","authors":"V. Di Lecce, M. Calabrese","doi":"10.1109/CIMSA.2011.6059926","DOIUrl":"https://doi.org/10.1109/CIMSA.2011.6059926","url":null,"abstract":"This work presents a two-step heuristic that employs extremely low-cost sensors for gaseous emission event discrimination. These events are triggered by particular patterns of sensor responses possibly occurring when a certain gas is emitted; patterns are then used to produce human-understandable inference rules describing the kind of emission measured. The technique, challenged by the high cross-sensitivity of the employed sensors, is based on two steps: first, sensor response patterns are extracted (unsupervisedly) from measurement signals by means of a recently proposed computational intelligence technique; second, a ‘credibility index’ is applied (supervisedly) to each pattern via fuzzy membership functions. The outcome is a set of IF THEN statements weighted by fuzzy constraints. Experiments show that such inferences allow for accurate gaseous emission event discrimination.","PeriodicalId":422972,"journal":{"name":"2011 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA) Proceedings","volume":"48 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":"132813235","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.2011.6059923
Weiwei Yang, Yong Zhao, Yiyong Huang, Xiaoqian Chen, Zhenguo Wang
A new laboratory test bed of on-orbit servicing system is introduced that enables simulation of the autonomous approach and docking of a chaser spacecraft to a target spacecraft with similar mass. The test bed system consists of a chaser and a target spacecraft simulator floating via air pads on a marble platform. Relative navigation of the chaser spacecraft is obtained by the united measurements with a single-camera visual sensor and IMU, through Kalman filters. Six cold-gas on-off thrusters and a flywheel are used for the translation and rotation of the chaser simulator. Considering the uncertainties in model, two nonlinear control algorithms, sliding mode control and PID control based on Back-Propagation Neural Network are adopted. Numerical simulations and ground experimental results are presented with comparison for an autonomous proximity maneuver and docking of the chaser simulator to the nonfloating target, which valid the efficiency of the sliding mode control and the test bed capabilities.
{"title":"Research on nonlinear control methods for on-orbit servicing with visual positioning system","authors":"Weiwei Yang, Yong Zhao, Yiyong Huang, Xiaoqian Chen, Zhenguo Wang","doi":"10.1109/CIMSA.2011.6059923","DOIUrl":"https://doi.org/10.1109/CIMSA.2011.6059923","url":null,"abstract":"A new laboratory test bed of on-orbit servicing system is introduced that enables simulation of the autonomous approach and docking of a chaser spacecraft to a target spacecraft with similar mass. The test bed system consists of a chaser and a target spacecraft simulator floating via air pads on a marble platform. Relative navigation of the chaser spacecraft is obtained by the united measurements with a single-camera visual sensor and IMU, through Kalman filters. Six cold-gas on-off thrusters and a flywheel are used for the translation and rotation of the chaser simulator. Considering the uncertainties in model, two nonlinear control algorithms, sliding mode control and PID control based on Back-Propagation Neural Network are adopted. Numerical simulations and ground experimental results are presented with comparison for an autonomous proximity maneuver and docking of the chaser simulator to the nonfloating target, which valid the efficiency of the sliding mode control and the test bed capabilities.","PeriodicalId":422972,"journal":{"name":"2011 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA) Proceedings","volume":"515 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":"131916801","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.2011.6059922
Lu Cao, Tao Sheng, Xiaoqian Chen
The technology of high precision attitude determination with low precision sensors based on data fusion is the objective requirement of modern small satellite. This paper presents a new attitude determination algorithm termed Pre-process EKF(PP-EKF) based on preprocess of sensor data. It can enhance the overall modeling accuracy by using the quadratic penalty function to correct the kinematics model error and angular velocity error in real-time. The measurement model of EKF is linearized by introducing q method the solution error of which is also corrected to futher improve the accuracy of the measurement model and make better use of measurement data from low precision sensors, so as to ultimately obtain good attitude determinination results. At last, the simulation results demonstrate the high reliability and advantages of the proposed algorithm.
{"title":"The algorithm of high precision attitude determination with low precision sensors based on data fusion","authors":"Lu Cao, Tao Sheng, Xiaoqian Chen","doi":"10.1109/CIMSA.2011.6059922","DOIUrl":"https://doi.org/10.1109/CIMSA.2011.6059922","url":null,"abstract":"The technology of high precision attitude determination with low precision sensors based on data fusion is the objective requirement of modern small satellite. This paper presents a new attitude determination algorithm termed Pre-process EKF(PP-EKF) based on preprocess of sensor data. It can enhance the overall modeling accuracy by using the quadratic penalty function to correct the kinematics model error and angular velocity error in real-time. The measurement model of EKF is linearized by introducing q method the solution error of which is also corrected to futher improve the accuracy of the measurement model and make better use of measurement data from low precision sensors, so as to ultimately obtain good attitude determinination results. At last, the simulation results demonstrate the high reliability and advantages of the proposed algorithm.","PeriodicalId":422972,"journal":{"name":"2011 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA) Proceedings","volume":"24 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":"125222954","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.2011.6059910
M. Neshat, Ali Adeli
The aim of this study is to design a Fuzzy Expert System to determine the concrete mix design. In the civil engineering, the determination of concrete mix design is so difficult and usually results in imprecision. Fuzzy logic is a way to represent a sort of uncertainty which is understandable for human. So, we can use the fuzzy logic to easily determine the concrete mix designs in a descriptive form. The input fields of system are Slump, Maximum Size of Aggregate (Dmax), Concrete Compressive Strength (CCS) and Fineness Modulus (FM). The output fields are quantities of water, Cement, Fine Aggregate (F.A) and Course Aggregate (C.A). The experimental results show that the average error of predicted compressive strength for FIS is 6.43%, the minimum error of which is 4.73%.
{"title":"Designing a fuzzy expert system to predict the concrete mix design","authors":"M. Neshat, Ali Adeli","doi":"10.1109/CIMSA.2011.6059910","DOIUrl":"https://doi.org/10.1109/CIMSA.2011.6059910","url":null,"abstract":"The aim of this study is to design a Fuzzy Expert System to determine the concrete mix design. In the civil engineering, the determination of concrete mix design is so difficult and usually results in imprecision. Fuzzy logic is a way to represent a sort of uncertainty which is understandable for human. So, we can use the fuzzy logic to easily determine the concrete mix designs in a descriptive form. The input fields of system are Slump, Maximum Size of Aggregate (Dmax), Concrete Compressive Strength (CCS) and Fineness Modulus (FM). The output fields are quantities of water, Cement, Fine Aggregate (F.A) and Course Aggregate (C.A). The experimental results show that the average error of predicted compressive strength for FIS is 6.43%, the minimum error of which is 4.73%.","PeriodicalId":422972,"journal":{"name":"2011 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA) Proceedings","volume":"33 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":"115582016","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.2011.6059912
Blerim Qela, H. Mouftah
In this paper, an adaptable system model for energy management in intelligent buildings is investigated. The application of wireless sensor networks and adaptive learning techniques, in order to bring forward an “Adaptable Systemic Solution” is described. Furthermore, conceptual model and high level architecture of an adaptable system for energy management in “Intelligent Buildings” is proposed. The importance of an adaptable system encompassing few subsystems, sharing knowledge and data is described. The analytical model of a novel Adaptive Learning System capable to learn and adapt by exploiting a rules-based expert system and adaptive learning principles, is proposed and described. Its use for an enhanced version of a Programmable Communicating Thermostat is discussed.
{"title":"An adaptable system for energy management in intelligent buildings","authors":"Blerim Qela, H. Mouftah","doi":"10.1109/CIMSA.2011.6059912","DOIUrl":"https://doi.org/10.1109/CIMSA.2011.6059912","url":null,"abstract":"In this paper, an adaptable system model for energy management in intelligent buildings is investigated. The application of wireless sensor networks and adaptive learning techniques, in order to bring forward an “Adaptable Systemic Solution” is described. Furthermore, conceptual model and high level architecture of an adaptable system for energy management in “Intelligent Buildings” is proposed. The importance of an adaptable system encompassing few subsystems, sharing knowledge and data is described. The analytical model of a novel Adaptive Learning System capable to learn and adapt by exploiting a rules-based expert system and adaptive learning principles, is proposed and described. Its use for an enhanced version of a Programmable Communicating Thermostat is discussed.","PeriodicalId":422972,"journal":{"name":"2011 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA) Proceedings","volume":"39 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":"129381928","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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