2007 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications最新文献_第2页

Ensuring the identity of a user in time: a multi-modal fuzzy approach 及时确保用户身份:多模态模糊方法

2007 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications Pub Date : 2007-06-27 DOI: 10.1109/CIMSA.2007.4362546

A. Azzini, Ernesto Damiani, S. Marrara

引用次数: 17

Dynamic Possibilistic Networks: Representation and Exact Inference 动态可能性网络:表示与精确推理

2007 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications Pub Date : 2007-06-27 DOI: 10.1109/CIMSA.2007.4362528

Abdelkader Heni, N. B. Amor, S. Benferhat, Adel M. Alimi

引用次数: 7

Face Recognition by Observation-Sequence-Based Methods Based on Pseudo 2D HMM and Neural Networks 基于伪二维HMM和神经网络的观察序列人脸识别方法

2007 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications Pub Date : 2007-06-27 DOI: 10.1109/CIMSA.2007.4362535

G. Mastronardi, Vitoantonio Bevilacqua, D. Daleno, L. Cariello, Riccardo Attimonelli, M. Castellano, I. J < N

{"title":"Face Recognition by Observation-Sequence-Based Methods Based on Pseudo 2D HMM and Neural Networks","authors":"G. Mastronardi, Vitoantonio Bevilacqua, D. Daleno, L. Cariello, Riccardo Attimonelli, M. Castellano, I. J < N","doi":"10.1109/CIMSA.2007.4362535","DOIUrl":"https://doi.org/10.1109/CIMSA.2007.4362535","url":null,"abstract":"Face recognition is an obviously interesting research area, due to its applicability in a biometric system both in commercial both in security fields. In this paper a Pseudo 2- Dimension Hidden Markov Model (P2D-HMM) combined with three different observation-sequence-based methods is introduced for face recognition. The P2D-HMM proposed, is applied to five Rol (Region of Interest) of images, one for each significant facial area in which the input frontal images are sequenced: forehead, eyes, nose mouth and chin. It has been trained by coefficients of an Artificial Neural Network used to compress a bitmap image in order to represent it with a reduced number of significant coefficients manipulated by the three observation-sequence-based methods. The introduced system, applied to the input set consisting of the Olivetti Research Lab. face database integrated with others photos, allows to obtain an high rate of recognition, up to 100% in particular with the P2D-HMM realised by the 'Strip '-like sequencing method.","PeriodicalId":392846,"journal":{"name":"2007 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications","volume":"53 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120900568","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}

引用次数: 5

A Universal Ontology for Sensor Networks Data 传感器网络数据的通用本体

2007 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications Pub Date : 2007-06-27 DOI: 10.1109/CIMSA.2007.4362539

M. Eid, R. Liscano, Abdulmotaleb El Saddik

引用次数: 120

Neural Techniques to Improve the Formative Evaluation Procedure in Intelligent Tutoring Systems 改进智能辅导系统形成性评价过程的神经技术

2007 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications Pub Date : 2007-06-27 DOI: 10.1109/CIMSA.2007.4362540

M. Castellano, G. Mastronardi, Di Giuseppe, Vito Dicensi

{"title":"Neural Techniques to Improve the Formative Evaluation Procedure in Intelligent Tutoring Systems","authors":"M. Castellano, G. Mastronardi, Di Giuseppe, Vito Dicensi","doi":"10.1109/CIMSA.2007.4362540","DOIUrl":"https://doi.org/10.1109/CIMSA.2007.4362540","url":null,"abstract":"Nowadays a special attention is paid to the quality of teaching valued in terms of efficacy of students' knowledge at the exit of its formative period. This efficacy depends both on the quality of formative iter and on the way formative activities are planned in a single teaching. Not since a lot of time has re-emerged the importance of tutoring as ad hoc regulator in student's learning process. Taking care of a student means design both of diagnostic instruments and of suitable formative interventions. That is the moment in which having the diagnosis to be preceded by a survey of performance indicators becomes important the study of measuring system models to be applied to each student. Intelligent tutoring systems (ITS), are any computer system with the ability to adopt pedagogical activities to individual student needs providing customized instruction and feedback. A correct and effective use of such system is obtained through a careful planning of formative deficit diagnostic measuring. In this paper we propose a model of formative evaluation procedure based on the use of neural techniques qualified to improve its accuracy.","PeriodicalId":392846,"journal":{"name":"2007 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124497645","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}

引用次数: 4

Linguistic description of human body posture using fuzzy logic and several levels of abstraction 运用模糊逻辑和多层次抽象对人体姿态进行语言描述

2007 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications Pub Date : 2007-06-27 DOI: 10.1109/CIMSA.2007.4362548

G. Trivio, Gonzalo Bailador

引用次数: 9

A New Fuzzy Algorithm for Global Job Scheduling in Multiclusters and Grids 多集群和网格下全局作业调度的一种新的模糊算法

2007 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications Pub Date : 2007-06-27 DOI: 10.1109/CIMSA.2007.4362538

H. Vahdat-Nejad, R. Monsefi, Mahmoud Naghibzadeh

引用次数: 4

Reducing Computational Complexity in k-NN based Adaptive Classifiers 降低基于k-NN的自适应分类器的计算复杂度

2007 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications Pub Date : 2007-06-27 DOI: 10.1109/CIMSA.2007.4362541

C. Alippi, M. Roveri

引用次数: 6

Estimating Uncertainty of a Measurement Process 测量过程的不确定度估计

2007 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications Pub Date : 2007-06-27 DOI: 10.1109/CIMSA.2007.4362529

N. Chang, Jim Lambert

引用次数: 0

Prediction of Polymer Optical Fiber Properties Using Artificial Neural Networks 基于人工神经网络的聚合物光纤性能预测

2007 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications Pub Date : 2007-06-27 DOI: 10.1109/CIMSA.2007.4362530

Xi Chen, L. Sztandera, Hugh Cartwright

{"title":"Prediction of Polymer Optical Fiber Properties Using Artificial Neural Networks","authors":"Xi Chen, L. Sztandera, Hugh Cartwright","doi":"10.1109/CIMSA.2007.4362530","DOIUrl":"https://doi.org/10.1109/CIMSA.2007.4362530","url":null,"abstract":"Polymer fibers are finding increasing applications in commercial optical communication systems. Polymer optical fibers, with specified desirable consumer characteristics, can be computationally designed. Through the use of an extensive structure - property correlation database, properties of polymers can be predicted by a Neural Network. In this paper we are focusing on glass transition temperature (Tg) that influences a desired outcome in polymeric optical fibers. Performance of such fibers can be optimized by engineering a polymer to exhibit a lower refractive index and Tg. This paper compares and discusses a neural network model and a linear model that have been developed to correlate Tg and repeating units of polymers. A comprehensive neural network model with 28 descriptors was developed to predict T values of 6 g randomly selected polymers from a database containing 71 polymers. The network was trained with the remaining 65 polymers and had an average training RMSE of 17 K (R2 = 0.95) and prediction average error of 17 K (R2 =0.85) based on 10-time experiments. A linear regression model developed for comparison had an average error of 32 K (R2 = 0.81).","PeriodicalId":392846,"journal":{"name":"2007 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications","volume":"41 03","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120990690","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}

引用次数: 0