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Sensor-based Training Optimization of a Cyclist Group 基于传感器的自行车组训练优化
7th International Conference on Hybrid Intelligent Systems (HIS 2007) Pub Date : 2007-09-17 DOI: 10.1109/HIS.2007.27
A. Le, T. Jaitner, L. Litz
{"title":"Sensor-based Training Optimization of a Cyclist Group","authors":"A. Le, T. Jaitner, L. Litz","doi":"10.1109/HIS.2007.27","DOIUrl":"https://doi.org/10.1109/HIS.2007.27","url":null,"abstract":"Determining the optimal exercise intensity is a crucial factor to increase performance in professional cycling. Novel sensor technologies allow to optimize the training not only for an individual cyclist but also for an entire training group. A sensor-based assisted bicycle trainer (ABT) system with a control algorithm has been developed at the University of Kaiserslautern to optimize the group training in cycling. The focus of this paper is on the development of the control algorithm, a model predictive controller (MPC) for the optimization of the group training. The controller predicts the heart rate of the cyclists based on individualized heart rate models and regulates the group training by advising cyclists to change the position in the group, to adjust the group speed, or to split the group in such a way that each cyclist can meet his training plan as exactly as possible.","PeriodicalId":359991,"journal":{"name":"7th International Conference on Hybrid Intelligent Systems (HIS 2007)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124920316","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: 10
Combining Gradient-Based With Evolutionary Online Learning: An Introduction to Learning Classifier Systems 结合基于梯度的进化在线学习:学习分类器系统的介绍
7th International Conference on Hybrid Intelligent Systems (HIS 2007) Pub Date : 2007-09-17 DOI: 10.1109/HIS.2007.66
Martin Volker Butz
{"title":"Combining Gradient-Based With Evolutionary Online Learning: An Introduction to Learning Classifier Systems","authors":"Martin Volker Butz","doi":"10.1109/HIS.2007.66","DOIUrl":"https://doi.org/10.1109/HIS.2007.66","url":null,"abstract":"Learning classifier systems (LCSs), introduced by John H. Holland in the 1970s, are rule-based evolutionary online learning systems that combine gradient-based rule evaluation with evolutionary-based rule structuring techniques. Since the introduction of the accuracy-based XCS classifier system by Stewart W. Wilson in 1995, LCSs showed to be flexible, online learning methods that are applicable to datamining, reinforcement learning, and function approximation problems. Comparisons showed that performance is competitive with state-of-the art machine learning algorithms, but the learning algorithms applied are usually more flexible and highly adaptive. Moreover, problem knowledge can be extracted easily. This tutorial provides a gentle introduction to LCSs and their general functioning. It then gives further details on the XCS classifier system and highlights various successful applications. In conclusion, promising future directions of LCS research and applications are discussed.","PeriodicalId":359991,"journal":{"name":"7th International Conference on Hybrid Intelligent Systems (HIS 2007)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129124747","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: 13
Block Encryption Using Hybrid Additive Cellular Automata 使用混合加性元胞自动机的块加密
7th International Conference on Hybrid Intelligent Systems (HIS 2007) Pub Date : 2007-09-17 DOI: 10.1109/HIS.2007.23
P. Anghelescu, S. Ionita, E. Sofron
{"title":"Block Encryption Using Hybrid Additive Cellular Automata","authors":"P. Anghelescu, S. Ionita, E. Sofron","doi":"10.1109/HIS.2007.23","DOIUrl":"https://doi.org/10.1109/HIS.2007.23","url":null,"abstract":"With the ever-increasing growth of data communication, the need for security and privacy has become a strong necessity. In these conditions, the necessity of new powerful encryption techniques becomes a crucial issue. In this paper Cellular Automata (CA) are applied to construct cryptography algorithms. We present an encryption system implemented on a structure of Hybrid Additive Cellular Automata (HACA) used for securing the medical data sent over the internet. The experimental results prove the power of cellular automata encryption systems. The method supports both software and hardware implementation. In this paper we present a fully functional software application for the data encryption of multimedia medical content.","PeriodicalId":359991,"journal":{"name":"7th International Conference on Hybrid Intelligent Systems (HIS 2007)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116660216","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: 24
Particle Detection on Election Microscopy Micrographs Using Multi-Classifier Systems 基于多分类系统的选举显微图像粒子检测
7th International Conference on Hybrid Intelligent Systems (HIS 2007) Pub Date : 2007-09-17 DOI: 10.1109/HIS.2007.51
Lucas M. Oliveira, R. B. Paradeda, Bruno M. Carvalho, A. Canuto, M. D. Souto
{"title":"Particle Detection on Election Microscopy Micrographs Using Multi-Classifier Systems","authors":"Lucas M. Oliveira, R. B. Paradeda, Bruno M. Carvalho, A. Canuto, M. D. Souto","doi":"10.1109/HIS.2007.51","DOIUrl":"https://doi.org/10.1109/HIS.2007.51","url":null,"abstract":"The determination of the three-dimensional (3D) structure of biological macromolecules at different configurations can be very important for understanding biological processes at the molecular level. The detection of individual particles from electron microscopy (EM) micrographs turns into a major labor-intensive bottleneck, when the number of particles needed starts to exceed a few tens of thousand molecular images. Multi-classifier systems have been widely investigated as tools for performing complex classifying tasks. In this work, we investigate the adequacy of using multi-classifier systems to detect particles on electron microscopy micrographs. In order to do so, we compare the performance of five algorithms for generating individual classifiers and three other ones for multi-classifier algorithms. Such results are also compared with others found in the literature. In terms of results, the multi-classifier systems generated show larger accuracy (correct classification) and lower false positive and negative rates.","PeriodicalId":359991,"journal":{"name":"7th International Conference on Hybrid Intelligent Systems (HIS 2007)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127186467","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: 1
How to Obtain Fair Managerial Decisions in Sugar Cane Harvest Using NSGA-II 如何利用NSGA-II获得公平的甘蔗收获管理决策
7th International Conference on Hybrid Intelligent Systems (HIS 2007) Pub Date : 2007-09-01 DOI: 10.1109/HIS.2007.53
D. Pacheco, T. Lucas, Fernando Buarque de Lima-Neto
{"title":"How to Obtain Fair Managerial Decisions in Sugar Cane Harvest Using NSGA-II","authors":"D. Pacheco, T. Lucas, Fernando Buarque de Lima-Neto","doi":"10.1109/HIS.2007.53","DOIUrl":"https://doi.org/10.1109/HIS.2007.53","url":null,"abstract":"The world's demand for sugar and particularly for renewable fuels such as ethanol requires an increase in production in sugar mills. The use of artificial neural networks (ANN) posed as a predictive core associated with the algorithm NSGA-II aims at helping decision makers to optimize the multi-objective harvest problem. This paper presents two approaches and the good results achieved as compared with other classical techniques.","PeriodicalId":359991,"journal":{"name":"7th International Conference on Hybrid Intelligent Systems (HIS 2007)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130344076","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: 6
An Immune Genetic Algorithm for Software Test Data Generation 软件测试数据生成的免疫遗传算法
7th International Conference on Hybrid Intelligent Systems (HIS 2007) Pub Date : 2007-09-01 DOI: 10.1109/HIS.2007.37
A. Bouchachia
{"title":"An Immune Genetic Algorithm for Software Test Data Generation","authors":"A. Bouchachia","doi":"10.1109/HIS.2007.37","DOIUrl":"https://doi.org/10.1109/HIS.2007.37","url":null,"abstract":"This paper aims at incorporating immune operators in genetic algorithms as an advanced method for solving the problem of test data generation. The new proposed hybrid algorithm is called immune genetic algorithm (IGA). A full description of this algorithm is presented before investigating its application in the context of software test data generation using some benchmark programs. Moreover, the algorithm is compared with other evolutionary algorithms.","PeriodicalId":359991,"journal":{"name":"7th International Conference on Hybrid Intelligent Systems (HIS 2007)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130893131","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: 61
A Hybrid Content-Collaborative Recommender System Integrated into an Electronic Performance Support System 集成到电子性能支持系统中的混合内容-协作推荐系统
7th International Conference on Hybrid Intelligent Systems (HIS 2007) Pub Date : 2007-09-01 DOI: 10.1109/HIS.2007.30
L. Iaquinta, Anna Lisa Gentile, P. Lops, M. Degemmis, G. Semeraro
{"title":"A Hybrid Content-Collaborative Recommender System Integrated into an Electronic Performance Support System","authors":"L. Iaquinta, Anna Lisa Gentile, P. Lops, M. Degemmis, G. Semeraro","doi":"10.1109/HIS.2007.30","DOIUrl":"https://doi.org/10.1109/HIS.2007.30","url":null,"abstract":"An electronic performance support system (EPSS) introduces challenges on contextualized and personalized information delivery. Recommender systems aim at delivering and suggesting relevant information according to users preferences, thus EPSSs could take advantage of the recommendation algorithms that have the effect of guiding users in a large space of possible options. The JUMP project aims at integrating an EPSS with a hybrid recommender system. Collaborative and content-based filtering are the recommendation techniques most widely adopted to date. The main contribution of this paper is a content- collaborative hybrid recommender which computes similarities between users relying on their content- based profiles, in which user preferences are stored, instead of comparing their rating styles. A distinctive feature of our system is that a statistical model of the user interests is obtained by machine learning techniques integrated with linguistic knowledge contained in WordNet. This model, named \"semantic user profile\", is exploited by the hybrid recommender in the neighborhood formation process.","PeriodicalId":359991,"journal":{"name":"7th International Conference on Hybrid Intelligent Systems (HIS 2007)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116857903","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: 12
A Clustering Method for Mixed Feature-Type Symbolic Data using Adaptive Squared Euclidean Distances 基于自适应平方欧氏距离的混合特征型符号数据聚类方法
7th International Conference on Hybrid Intelligent Systems (HIS 2007) Pub Date : 1900-01-01 DOI: 10.1109/ichis.2007.4344046
R. D. de Souza, F. D. de Carvalho
{"title":"A Clustering Method for Mixed Feature-Type Symbolic Data using Adaptive Squared Euclidean Distances","authors":"R. D. de Souza, F. D. de Carvalho","doi":"10.1109/ichis.2007.4344046","DOIUrl":"https://doi.org/10.1109/ichis.2007.4344046","url":null,"abstract":"This work presents a clustering method for mixed feature-type symbolic data. The presented method needs a previous pre-processing step to transform mixed symbolic data into modal symbolic data. The dynamic clustering algorithm with adaptive distances has then as input a set of vectors of modal symbolic data (weight distributions) and furnishes a partition and a prototype to each class by optimizing an adequacy criterion that measures the fitting between the clusters and their representatives based on adaptive squared Euclidean distances. Examples with synthetic symbolic data sets and an application with a real symbolic data sets show the usefulness of this method.","PeriodicalId":359991,"journal":{"name":"7th International Conference on Hybrid Intelligent Systems (HIS 2007)","volume":"1 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":"134159687","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
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