2009 International Conference of Soft Computing and Pattern Recognition最新文献

The Phylogenetic Tree of RNA Polymerase Constructed Using MOM Method 用MOM法构建RNA聚合酶系统发育树

2009 International Conference of Soft Computing and Pattern Recognition Pub Date : 2009-12-04 DOI: 10.1109/SoCPaR.2009.99

N. Muda, A. R. Othman, N. Najimudin, Z. Mohamed-Hussein

引用次数: 4

Service Design in Movie Theaters Using Auction Mechanism with Seat Reservations 基于座位预订拍卖机制的电影院服务设计

2009 International Conference of Soft Computing and Pattern Recognition Pub Date : 2009-12-04 DOI: 10.1109/SoCPaR.2009.106

N. Nishino, Koji Fukuya, K. Ueda

引用次数: 4

Correlation Research of Association Rules and Application in the Data about Coronary Heart Disease 关联规则在冠心病数据中的相关性研究及应用

2009 International Conference of Soft Computing and Pattern Recognition Pub Date : 2009-12-04 DOI: 10.1109/SoCPaR.2009.39

Z. Lin, Weiguo Yi, Mingyu Lu, Zhi Liu, Hao Xu

引用次数: 3

PSO-Based Optimization of State Feedback Tracking Controller for a Flexible Link Manipulator 基于pso的柔性连杆机械臂状态反馈跟踪控制器优化

2009 International Conference of Soft Computing and Pattern Recognition Pub Date : 2009-12-04 DOI: 10.1109/SoCPaR.2009.26

M. I. Solihin, Wahyudi, Rini Akmeliawati

引用次数: 13

Ripe Tomato Recognition and Localization for a Tomato Harvesting Robotic System 番茄收获机器人系统的成熟番茄识别与定位

2009 International Conference of Soft Computing and Pattern Recognition Pub Date : 2009-12-04 DOI: 10.1109/SoCPaR.2009.111

H. Yin, Y. Chai, Simon X. Yang, G. Mittal

引用次数: 26

System Identification for Modelling Gas Flow with a Leakage 带泄漏气体流动建模的系统识别

2009 International Conference of Soft Computing and Pattern Recognition Pub Date : 2009-12-04 DOI: 10.1109/SoCPaR.2009.46

Afif bin Md Akib, N. Saad, V. Asirvadam

引用次数: 1

A Multi-agent Model for Product Reuse Service Markets 产品重用服务市场的多智能体模型

2009 International Conference of Soft Computing and Pattern Recognition Pub Date : 2009-12-04 DOI: 10.1109/SoCPaR.2009.150

M. Matsumoto, S. Kondoh, T. Takenaka

引用次数: 1

A Simple Wearable Hand Gesture Recognition Device Using iMEMS 使用iMEMS的简单可穿戴手势识别设备

2009 International Conference of Soft Computing and Pattern Recognition Pub Date : 2009-12-04 DOI: 10.1109/SoCPaR.2009.117

A. Pandit, Dhairya Dand, Sisil Mehta, Shashank Sabesan, Ankit Daftery

{"title":"A Simple Wearable Hand Gesture Recognition Device Using iMEMS","authors":"A. Pandit, Dhairya Dand, Sisil Mehta, Shashank Sabesan, Ankit Daftery","doi":"10.1109/SoCPaR.2009.117","DOIUrl":"https://doi.org/10.1109/SoCPaR.2009.117","url":null,"abstract":"Communicating with computer involves use of touch screens, wireless/wired mouse along with keyboards. In this paper, we propose a “Human Computer Interfacing Device” mapping Hand Gestures, most intuitive communication gesture, to communicate with computers, robots and other household electronic devices. Developments in field of communication have enabled computer commands being executed using hand gestures. This proposed novel solution achieves effective interaction with Internet giving quality feedback, intuitive control with complete mobility. Simple inertial navigation sensor like an accelerometer is utilized to get dynamic/static profile of movement to navigate the mouse or gyroscope to rotate 3-D virtual objects. Accelerometer profiles are converted into wireless interactivity. The device involves non-tactile interaction with computer screen where mouse pointer reacts to and manipulates screen content in accordance with hand gestures. The applications envisioned: interaction using gesture technology for effective communication empowering physically challenged to interact with computers including 3-D graphic interactions and simulations.","PeriodicalId":284743,"journal":{"name":"2009 International Conference of Soft Computing and Pattern Recognition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130584869","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: 32

Object Recognition Using Fourier Descriptors and Genetic Algorithm 基于傅里叶描述子和遗传算法的目标识别

2009 International Conference of Soft Computing and Pattern Recognition Pub Date : 2009-12-04 DOI: 10.1109/SoCPaR.2009.70

M. Sarfraz, Mehmood-ul-Hassan, M. Iqbal

引用次数: 12

Toward Computational Modeling of the Consumer Based on a Large-Scale Dataset Observed in a Real Service 基于实际服务中大规模数据集的消费者计算建模研究

2009 International Conference of Soft Computing and Pattern Recognition Pub Date : 2009-12-04 DOI: 10.1109/SoCPaR.2009.108

Tsukasa Ishigaki, Y. Motomura

{"title":"Toward Computational Modeling of the Consumer Based on a Large-Scale Dataset Observed in a Real Service","authors":"Tsukasa Ishigaki, Y. Motomura","doi":"10.1109/SoCPaR.2009.108","DOIUrl":"https://doi.org/10.1109/SoCPaR.2009.108","url":null,"abstract":"In service industries, productivity growth requires matching the level of demand of the consumer and the level of service of the provider. This matching requires the service provider to have knowledge of consumer-related factors, such as the satisfaction level or the concept of value of the consumer. An intelligent model of the consumer is needed in order to estimate such factors because these factors cannot be observed directly by the service provider. However, obtaining knowledge of such factors in real services using conventional consumer behavior theory is difficult because the models are not designed for practical application, but rather are intended to provide a comprehensive and elaborative understanding of consumer behaviors. In addition, most conventional models are qualitative, and so cannot provide quantitative information for decision making by the providers. The present paper describes a method for computational modeling of the consumer by understanding the behavior based on large-scale datasets observed in real services. It is difficult to model the behavior or decision making process of the customer, which involves nonlinear or non-Gaussian variables, using conventional statistical modeling techniques, which assume linear or Gaussian models. We use a Bayesian network method, which can handle nonlinear and non-Gaussian variables as conditional probabilities. The models are constructed based on large-scale datasets observed in real services and present some practical applications of the models to retail and content providing services. The proposed method is efficient for many other services that use a variety of large-scale datasets.","PeriodicalId":284743,"journal":{"name":"2009 International Conference of Soft Computing and Pattern Recognition","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126005254","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: 3