2010 International Conference on Electronics and Information Engineering最新文献_第5页

The optimum flattening for undeveloped 3-D body surface based on energy 基于能量的未开发三维体表优化展平

2010 International Conference on Electronics and Information Engineering Pub Date : 2010-09-02 DOI: 10.1109/ICEIE.2010.5559827

Meiling Zhuang, Xiaofeng Zhang, Jianan Fang

引用次数: 1

Dynamic K-Nearest-Neighbor with Distance and attribute weighted for classification 基于距离和属性加权的动态k -最近邻分类

2010 International Conference on Electronics and Information Engineering Pub Date : 2010-09-02 DOI: 10.1109/ICEIE.2010.5559858

Jia Wu, Z. Cai, Zhechao Gao

{"title":"Dynamic K-Nearest-Neighbor with Distance and attribute weighted for classification","authors":"Jia Wu, Z. Cai, Zhechao Gao","doi":"10.1109/ICEIE.2010.5559858","DOIUrl":"https://doi.org/10.1109/ICEIE.2010.5559858","url":null,"abstract":"K-Nearest-Neighbor (KNN) as an important classification method based on closest training examples has been widely used in data mining due to its simplicity, effectiveness, and robustness. However, the class probability estimation, the neighborhood size and the type of distance function confronting KNN may affect its classification accuracy. Many researchers have been focused on improving the accuracy of KNN via distance weighted, attribute weighted, and dynamically selected methods et al. In this paper, we first reviewed some improved algorithms of KNN in three categories mentioned above. Then, we singled out an improved algorithm called dynamic k-nearest-neighbor with distance and attribute weighted, simply DKNDAW. In DKNDAW, we mixed dynamic selected, distance weighted and attribute weighted methods. We experimentally tested our new algorithm in Weka system, using the whole 36 standard UCI data sets which are downloaded from the main website of Weka. In our experiment, we compared it to KNN, WAKNN, KNNDW, KNNDAW, and DKNN. The experimental results show that DKNDAW significantly outperforms KNN, WAKNN, KNNDW, KNNDAW, and DKNN in terms of the classification accuracy.","PeriodicalId":211301,"journal":{"name":"2010 International Conference on Electronics and Information Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131860237","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

A fusion of Functional Networks and Type-2 Fuzzy Logic for the characterization of oil and gas reservoirs 一种融合功能网络和2型模糊逻辑的油气藏表征方法

2010 International Conference on Electronics and Information Engineering Pub Date : 2010-09-02 DOI: 10.1109/ICEIE.2010.5559796

F. Anifowose, A. Abdulraheem

{"title":"A fusion of Functional Networks and Type-2 Fuzzy Logic for the characterization of oil and gas reservoirs","authors":"F. Anifowose, A. Abdulraheem","doi":"10.1109/ICEIE.2010.5559796","DOIUrl":"https://doi.org/10.1109/ICEIE.2010.5559796","url":null,"abstract":"This paper presents a hybrid model consisting of a fusion of Functional Networks and Type-2 Fuzzy Logic. The model capitalizes on the capability of Functional Networks, using its least square fitting algorithm, to reduce the dimensionality of the input data by selecting the most relevant variables for the prediction of porosity and permeability of oil and gas reservoirs. It also attempts to improve the performance of Type-2 Fuzzy Logic whose complexity is increased and performance degraded with increased dimensionality of input data. The Functional Networks block was used to select the dominant variables from six datasets. The dimensionally-reduced datasets were then divided into training and testing subsets using the stratified sampling approach. Hence, the Type-2 Fuzzy Logic block is trained and tested with the best and dimensionally-reduced variables from the input data. The results showed that the hybrid model performed better in terms of training and testing with higher correlation coefficients, lower root mean square errors and reduced execution times than the original Type-2 Fuzzy Logic system. This work has confirmed the possibility and bright prospect for more hybrid models with better performance indices.","PeriodicalId":211301,"journal":{"name":"2010 International Conference on Electronics and Information Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134590406","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: 2

A fixed bits LDPC decoder 一个固定位LDPC解码器

2010 International Conference on Electronics and Information Engineering Pub Date : 2010-09-02 DOI: 10.1109/ICEIE.2010.5559810

Feng Xu, Liang Zhou, H. Wen, Chen Huang, Qian Zhao

引用次数: 0

Ribbed Smoked Sheet Grading System (RSSGS) 烟熏肋板分级系统

2010 International Conference on Electronics and Information Engineering Pub Date : 2010-09-02 DOI: 10.1109/ICEIE.2010.5559907

C. Pornpanomchai, Naret Chantharangsikul

{"title":"Ribbed Smoked Sheet Grading System (RSSGS)","authors":"C. Pornpanomchai, Naret Chantharangsikul","doi":"10.1109/ICEIE.2010.5559907","DOIUrl":"https://doi.org/10.1109/ICEIE.2010.5559907","url":null,"abstract":"Currently in the rubber factories employ many experts or scientists to grade the Ribbed Smoked Sheet (RSS). They grade the RSS by using their eyes and experience. The objective of this research is to build a computer system that can help rubber experts to grade the RSS. This system is called “Ribbed Smoked Sheet Grading System (RSSGS)”. The system consists of 4 main components, which are 1) Image Acquisition, 2) Image Preprocessing, 3) RSS Grading, and 4) Display of Result. In the image acquisition component, we use a digital camera to take an RSS image in a controlled environment box. In the image preprocessing component, we apply several image processing methods to prepare a suitable RSS image for a grading process. In the RSS grading component, we apply the k-Mean Clustering and the Euclidean Distance method to classify the RSS into five grades, which are RSS1, RSS2, RSS3 RSS4 and RSS5. In the Result Display component, we create a graphic user interface (GUI) for displaying results of the RSS grading. We test the system by using 398 RSS images for a training dataset and another 322 RSS images for an un-training dataset. The precision rates of our RSSGS are 80.90 percent for an untraining dataset. The average access time for the RSSGS is around 10.88 seconds per RSS image.","PeriodicalId":211301,"journal":{"name":"2010 International Conference on Electronics and Information Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133789006","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: 2

Behavior checking of web applications after testing 测试后web应用程序的行为检查

2010 International Conference on Electronics and Information Engineering Pub Date : 2010-09-02 DOI: 10.1109/ICEIE.2010.5559739

Mingyue Jiang, Zuohua Ding, Q. Ge

引用次数: 0

A biofeedback portable system for panic disorder 治疗恐慌症的便携式生物反馈系统

2010 International Conference on Electronics and Information Engineering Pub Date : 2010-09-02 DOI: 10.1109/ICEIE.2010.5559840

Ching-Yi Chen, Chekmarev LIu, Ya-Ling Chang, Chih-Yuan Wang, Shih-Tsang Tang, Jiun-Hung Lin

引用次数: 0

Fault-tolerant data storage in Wireless Sensor Networks 无线传感器网络中的容错数据存储

2010 International Conference on Electronics and Information Engineering Pub Date : 2010-09-02 DOI: 10.1109/ICEIE.2010.5559716

Taehee Kim, Jung-Hun Kim, Hee Jeong, M. Lee

{"title":"Fault-tolerant data storage in Wireless Sensor Networks","authors":"Taehee Kim, Jung-Hun Kim, Hee Jeong, M. Lee","doi":"10.1109/ICEIE.2010.5559716","DOIUrl":"https://doi.org/10.1109/ICEIE.2010.5559716","url":null,"abstract":"In Wireless Sensor Networks (WSNs), each node performs multiple roles, such as sensor, router, and storage. It gathers data from an environment and does not only transfer the data to destinations such as sinks but also store the data in its storage. Therefore, just one node failure can cause malfunctions of various facilities of a WSN. So far, many schemes are proposed to overcome such malfunctions, however, most of them give less consideration to storage-role of nodes, that is, to reserve data safely. Besides, even schemes paying attention to storage-role of nodes are based on impractical assumptions or are suitable to particular cases. In this paper, first, we investigate the data preserving methods of existing schemes and their limitations. Then, we suggest a backup scheme as a solution to overcome such limitations and considering factors for developing a new backup scheme. The considering factors include the followings; the number of backup data, the location of backup data, and data recovery method. Finally, we propose the backup scheme reflecting the considering factors. The proposed scheme can safely preserves data in spite of node failures and consumes reasonable energy. Furthermore, it is suitable well to common WSNs and does not need any impractical assumptions.","PeriodicalId":211301,"journal":{"name":"2010 International Conference on Electronics and Information Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121708129","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

A real-time portable analyzer for anger emotion 实时便携式愤怒情绪分析仪

2010 International Conference on Electronics and Information Engineering Pub Date : 2010-09-02 DOI: 10.1109/ICEIE.2010.5559845

Aibing. Lin, Fan-Yun Yen, Mu-Hui Sun, Feng-Shuo Shang, Shih-Tsang Tang, Jiun-Hung Lin

引用次数: 3

Artificial Neural Network based cardiac arrhythmia classification using ECG signal data 基于人工神经网络的心电信号心律失常分类

2010 International Conference on Electronics and Information Engineering Pub Date : 2010-09-02 DOI: 10.1109/ICEIE.2010.5559887

S. Jadhav, S. Nalbalwar, A. Ghatol

{"title":"Artificial Neural Network based cardiac arrhythmia classification using ECG signal data","authors":"S. Jadhav, S. Nalbalwar, A. Ghatol","doi":"10.1109/ICEIE.2010.5559887","DOIUrl":"https://doi.org/10.1109/ICEIE.2010.5559887","url":null,"abstract":"In this paper we proposed a automated Artificial Neural Network (ANN) based classification system for cardiac arrhythmia using standard 12 lead ECG recordings. In this study, we are mainly interested in producing high confident arrhythmia classification results to be applicable in diagnostic decision support systems. In arrhythmia analysis, it is unavoidable that some attribute values of a person would be missing. Therefore we have replaced these missing attributes by closest column value of the concern class. Multilayer percepron (MLP) feedforward neural network model with static backpropagation algorithm is used to classify arrhythmia cases into normal and abnormal classes. Networks models are trained and tested for UCI ECG arrhythmia data set. This data set is a good environment to test classifiers as it is incomplete and ambiguous bio-signal data collected from total 452 patient cases. The classification performance is evaluated using six measures; sensitivity, specificity, classification accuracy, mean squared error (MSE), receiver operating characteristics (ROC) and area under curve (AUC). Our experimental results give 86.67% testing classification accuracy.","PeriodicalId":211301,"journal":{"name":"2010 International Conference on Electronics and Information Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125194105","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: 76