9th IEEE International Conference on Cognitive Informatics (ICCI'10)最新文献_第2页

Application of RBF network based on Artificial Immune Algorithm to predict gas pipeline load 基于人工免疫算法的RBF网络在天然气管道负荷预测中的应用

9th IEEE International Conference on Cognitive Informatics (ICCI'10) Pub Date : 2010-07-07 DOI: 10.1109/COGINF.2010.5599769

Liu Hong, Zeng Qing-heng, Yang Min, Fan Mujiao

引用次数: 0

Rapid and automatic atlas-based approach of Alzheimer's disease assessment by positron emission tomography neuroimages 基于正电子发射断层扫描神经图像的阿尔茨海默病快速自动评估方法

9th IEEE International Conference on Cognitive Informatics (ICCI'10) Pub Date : 2010-07-07 DOI: 10.1109/COGINF.2010.5599709

G. Qian, Xuezhen Shen, S. Luo, Jesse S. Jin, W. Nowinski

{"title":"Rapid and automatic atlas-based approach of Alzheimer's disease assessment by positron emission tomography neuroimages","authors":"G. Qian, Xuezhen Shen, S. Luo, Jesse S. Jin, W. Nowinski","doi":"10.1109/COGINF.2010.5599709","DOIUrl":"https://doi.org/10.1109/COGINF.2010.5599709","url":null,"abstract":"Current Alzheimer's disease diagnosis and cognitive assessment are based on medical history assessment and evaluation of cognitive score systems. They are time-consuming and subjective. A rapid and automated method is developed by processing positron emission tomography neuroimages and performing statistical analysis. The brain areas are firstly extracted from the neuroimages by an atlas-assisted approach, and then transformed piecewise into a common atlas space by dividing the brain into 18 cubic regions based on the landmarks identified automatically. The statistical models of stepwise regressions and discriminant classification are applied to predict the cognitive scores and make a diagnosis on Alzheimer's disease or mild cognitive impairment. The proposed method is fully automatic and has been tested on 400 cases. The preliminary testing results are promising. For a group of 250 cases which are the samples of the regressions and discriminant classification, the success rates of disease diagnosis are 73.7%, 54.9%, and 79.7% for the patients with Alzheimer's disease, mild cognitive impairment, and normal subjects, respectively. The average success rate for another group of 150 cases is 61.3%.","PeriodicalId":365083,"journal":{"name":"9th IEEE International Conference on Cognitive Informatics (ICCI'10)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132066302","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

Research of scheduling strategy based on rendering pretreatment mechanism 基于渲染预处理机制的调度策略研究

9th IEEE International Conference on Cognitive Informatics (ICCI'10) Pub Date : 2010-07-07 DOI: 10.1109/COGINF.2010.5599797

Qi Wang, Yizhen Cao, Yongbin Wang

引用次数: 0

Application of statistical learning theory to predict corrosion rate of injecting water pipeline 应用统计学习理论预测注水管道腐蚀速率

9th IEEE International Conference on Cognitive Informatics (ICCI'10) Pub Date : 2010-07-07 DOI: 10.1109/COGINF.2010.5599754

H. Zhen, Liu Hong, Mujiao Fan, Chunbi Xu

{"title":"Application of statistical learning theory to predict corrosion rate of injecting water pipeline","authors":"H. Zhen, Liu Hong, Mujiao Fan, Chunbi Xu","doi":"10.1109/COGINF.2010.5599754","DOIUrl":"https://doi.org/10.1109/COGINF.2010.5599754","url":null,"abstract":"Support Vector Machines (SVM) represents a new and very promising approach to pattern recognition based on small dataset. The approach is systematic and properly motivated by Statistical Learning Theory (SLT). Training involves separating the classes with a surface that maximizes the margin between them. An interesting property of this approach is that it is an approximate implementation of Structural Risk Minimization (SRM) induction principle, therefore, SVM is more generalized performance and accurate as compared to artificial neural network which embodies the Embodies Risk Minimization (ERM) principle. In this paper, according to corrosion rate complicated reflection relation with influence factors, we studied the theory and method of Support Vector Machines based the statistical learning theory and proposed a pattern recognition method based Support Vector Machine to predict corrosion rate of injecting water pipeline. The outline of the method is as follows: First, we researched the injecting water quality corrosion influence factors in given experimental zones with Gray correlation method; then we used the LibSVM software based Support Vector Machine to study the relationship of those injecting water quality corrosion influence factors, and set up the mode to predict corrosion rate of injecting water pipeline. Application and analysis of the experimental results in Shengli oilfield proved that SVM could achieve greater accuracy than the BP neural network do, which also proved that application of SVM to predict corrosion rate of injecting water pipeline, even to the other theme in petroleum engineering, is reliable, adaptable, precise and easy to operate.","PeriodicalId":365083,"journal":{"name":"9th IEEE International Conference on Cognitive Informatics (ICCI'10)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132726742","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

A cognitive crawler using structure pattern for incremental crawling and content extraction 一个使用结构模式进行增量爬行和内容提取的认知爬虫

9th IEEE International Conference on Cognitive Informatics (ICCI'10) Pub Date : 2010-07-07 DOI: 10.1109/COGINF.2010.5599733

Shijia Xi, F. Sun, Jianmin Wang

{"title":"A cognitive crawler using structure pattern for incremental crawling and content extraction","authors":"Shijia Xi, F. Sun, Jianmin Wang","doi":"10.1109/COGINF.2010.5599733","DOIUrl":"https://doi.org/10.1109/COGINF.2010.5599733","url":null,"abstract":"In this paper, we design a cognitive crawler to dramatically reduce the website crawling cost and extract nsefnl content from web pages in an unsupervised procedure. The main idea of reducing the crawling cost is to retrieving those lately modified pages and newly added pages only. However, in reality, it is impossible for traditional crawler to judge whether a page has been modified or newly added without doing a whole crawling. We propose a method to predict those lately modified pages and newly added pages without do any actual crawling; we also find a feasible and stable feature \"structure pattern\" to better indicates the modified probability of certain page. In the meanwhile, we develop a hybrid clustering method combined with K-means and agglomerative hierarchical clustering to automatically find all the structure patterns in certain website. Using structure pattern, we developed an unsupervised algorithm to generate website's templates; using templates, crawler can extract useful information of web pages much more easily and precisely. We also introduce feasible formulas to predict pages' modified probabilities and crawling time intervals. To evaluate the performance of an incremental crawling algorithm, we proposed three new indicators. Using the algorithm proposed, we could extract content of pages with high performance. The experimental results illustrate that structure pattern is very useful and the performance of this cognitive crawler is quite promising and it can save huge amount of bandwidth and is qualified for different websites of various scales.","PeriodicalId":365083,"journal":{"name":"9th IEEE International Conference on Cognitive Informatics (ICCI'10)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131845712","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

Multi-kernel SVM based star pattern recognition for Celestial Navigation 基于多核支持向量机的天体导航星图识别

9th IEEE International Conference on Cognitive Informatics (ICCI'10) Pub Date : 2010-07-07 DOI: 10.1109/COGINF.2010.5599814

Taiyang Liu, Shicheng Wang, Zhiguo Liu, Haibo Min, R. Li

引用次数: 2

Greedy iterative DHP algorithm-based near-optimal control for a class of nonlinear descriptor systems with actuator saturating 一类具有执行器饱和的非线性广义系统的贪婪迭代DHP算法的近最优控制

9th IEEE International Conference on Cognitive Informatics (ICCI'10) Pub Date : 2010-07-07 DOI: 10.1109/COGINF.2010.5599806

Yanhong Luo, Zhenwei Liu, Dongsheng Yang

引用次数: 1

Articular models for first-degree paraconsistent systems 一级副协调系统的关节模型

9th IEEE International Conference on Cognitive Informatics (ICCI'10) Pub Date : 2010-07-07 DOI: 10.1109/COGINF.2010.5599781

R. Jennings, Yuesen Chen

引用次数: 6

A noise-insensitive LLE algorithm 一种噪声不敏感的LLE算法

9th IEEE International Conference on Cognitive Informatics (ICCI'10) Pub Date : 2010-07-07 DOI: 10.1109/COGINF.2010.5599812

Zhou Yong, Gou Hong Yun

引用次数: 0

A novel emotion recognition approach based on ensemble learning and rough set theory 基于集成学习和粗糙集理论的情感识别新方法

9th IEEE International Conference on Cognitive Informatics (ICCI'10) Pub Date : 2010-07-07 DOI: 10.1109/COGINF.2010.5599818

Yong Yang, Guoyin Wang, Zhiyu Zhang, Kan Tian

引用次数: 2