2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)最新文献_第9页

Heuristic and Meta-heuristic Algorithms for the Online Scheduling on Unrelated Parallel Machines with Machine Eligibility Constraints 具有机器资格约束的不相关并行机器在线调度的启发式和元启发式算法

2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) Pub Date : 2018-07-01 DOI: 10.1109/FSKD.2018.8687264

Shuang Cai, Qifeng Xun, Ke Liu, Ao Liu

引用次数: 0

Particle Filtering Based Visual Attention Model for Moving Target Detection 基于粒子滤波的运动目标检测视觉注意模型

2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) Pub Date : 2018-07-01 DOI: 10.1109/FSKD.2018.8686873

Long Liu, Danyang Jing, Xiaojun Chang

引用次数: 1

An Attribute Reduction Algorithm Based on the Maximum Dependency and Minimum Redundancy of Attribute 一种基于属性最大依赖和最小冗余的属性约简算法

2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) Pub Date : 2018-07-01 DOI: 10.1109/FSKD.2018.8687131

Chenxi Wang, Jiancong Fan

引用次数: 0

Design of Structural Active Control with Disturbance Attenuation 扰动衰减的结构主动控制设计

2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) Pub Date : 2018-07-01 DOI: 10.1109/FSKD.2018.8687130

Guo-sheng Wang, Bing Liang

引用次数: 0

Fuzzy Assignment Model Of Server In Finite Source Queuing System 有限源排队系统中服务器的模糊分配模型

2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) Pub Date : 2018-07-01 DOI: 10.1109/FSKD.2018.8687166

Yanli Meng, Y. Liu, Lidong Wang, Xiaodong Liu

引用次数: 0

2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) Pub Date : 2018-07-01 DOI: 10.1109/FSKD.2018.8687280

Shidong Chen, Xiangqun Chen, Junhua Hu, Jun Lu, Zesheng Hu

{"title":"Self-similarity Modeling Research on Information Gathering Service for Power Utilization of Smart Grid","authors":"Shidong Chen, Xiangqun Chen, Junhua Hu, Jun Lu, Zesheng Hu","doi":"10.1109/FSKD.2018.8687280","DOIUrl":"https://doi.org/10.1109/FSKD.2018.8687280","url":null,"abstract":"Given the self-similarity characteristic of the communication traffic-flow in the current information gathering service traffic in power utilization network, a self-similarity modeling method is proposed to quantify the feature analysis of the communication flow for the power information gathering service. Firstly, the flow's characteristic and related course of the information gathering service traffic is analyzed, and a self-similarity model is built by means of combining the multiple ON/OFF flows. Secondly, the Hurst coefficient of the service simulation traffic is estimated using Variance-Time method to quantify the flow's self-similarity with the estimation results of Hurst coefficient. Finally, it discusses the influence of Hurst coefficient is discussed according to the two factors including the acquisition accuracy and the gathering periodic. Experimental results illustrate that the proposed method is effective to model the self-similarity of the information gathering service for the power utilization in Smart Grid.","PeriodicalId":235481,"journal":{"name":"2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124377076","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

Improved Heuristic Algorithms for UAVs Path Planning in Hazardous Environment 危险环境下无人机路径规划的改进启发式算法

2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) Pub Date : 2018-07-01 DOI: 10.1109/FSKD.2018.8687134

Zhenghao Li, Peng Yang, Cen Tong, Jiaqi Shen

引用次数: 0

A New Bionic Model and Its Application to Epileptic Electroencephalograph Recognition 一种新的仿生模型及其在癫痫脑电图识别中的应用

2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) Pub Date : 2018-07-01 DOI: 10.1109/FSKD.2018.8687226

Jin Zhang, T. Tian, Xiaofei Liu, Xuanyu Shu, Qiang Li

{"title":"A New Bionic Model and Its Application to Epileptic Electroencephalograph Recognition","authors":"Jin Zhang, T. Tian, Xiaofei Liu, Xuanyu Shu, Qiang Li","doi":"10.1109/FSKD.2018.8687226","DOIUrl":"https://doi.org/10.1109/FSKD.2018.8687226","url":null,"abstract":"Imitating the nervous system is not only an effective method to construct artificial neural networks with better performance, but also a research hotspots. In this paper, a new bionic model stimulating olfactory neural systems, KIII model, is introduced and its performance is researched based on epileptic electroencephalograph (EEG) recognition. In section 2, KIII model is introduced briefly. The structure of the KIII model is similar to that of olfactory neural systems. The model of neurons is based on the real action of neurons with the stimulus and optimized according to mathematical optimization. In section 3, KIII model was used as a classifier and the performance of the KIII model was evaluated to identify epileptic EEG. In the first group of experiments, the features are extracted based on EMD and the recognition rate of the KIII model is over 91 % with few training times. In the second group of experiments, extracting the feature of EEG is unnecessary and the raw EEG signals are used as the input directly. The KIII model gives the better recognition rate over 96 %. Experimental results show that KIII model has remarkable characteristics: (1) KIII model can learn the pattern with few training times, which is different from general deep learning models; (2) KIII model can recognize raw signals without special feature-extracting process, which is similar with deep learning model; (3) the structure of KIII model is very similar to that of real olfactory neural systems, which is partly similar with deep learning model.","PeriodicalId":235481,"journal":{"name":"2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128249162","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 Density-based method for Outlier Detecting on Numerical Massive Data 基于密度的数值海量数据离群点检测方法

2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) Pub Date : 2018-07-01 DOI: 10.1109/FSKD.2018.8687215

Keyan Cao, Anchen Miao, Ning Jin, Yuanwei Qi, Ibrahim Musa

{"title":"A Density-based method for Outlier Detecting on Numerical Massive Data","authors":"Keyan Cao, Anchen Miao, Ning Jin, Yuanwei Qi, Ibrahim Musa","doi":"10.1109/FSKD.2018.8687215","DOIUrl":"https://doi.org/10.1109/FSKD.2018.8687215","url":null,"abstract":"As large amounts of data have been generated with the development of the Internet, outliers detecting and effective information obtaining on it become an important issue. For this question, this paper presents a density-based algorithm for numerical attribute anomaly points. A high-density set is selected as the candidate set of clustering centers based on density distribution in order to decrease the times of iterations of k-means algorithm and improve the efficiency, then the initial centers are chosen by the method depending on the maximum distance product. After that, the data is preprocessed by k-means clustering algorithm. The whole process of clustering is combined with MapReduce programming model. A candidate set of abnormal points is obtained from each cluster by appropriate pruning method. At last, the ultimate outliers are determined according to the density-based LOF algorithm. The experimental results show that the maximum-distance-product-based initializing method for clustering centers improves the efficiency of clustering and the algorithm we proposed has better accuracy, expansibility and speedup ratio on outlier detection for numerical attributes.","PeriodicalId":235481,"journal":{"name":"2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130537810","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

Fast Adaptive Stacking of Ensembles Adaptation for Supporting Active Learning. a Real Case Application 支持主动学习的集成自适应快速自适应叠加。真实案例应用

2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) Pub Date : 2018-07-01 DOI: 10.1109/FSKD.2018.8686851

Agustín Alejandro Ortiz Díaz, F. Bayer, F. Baldo, Alberto Verdecia-Cabrera, L. M. Mariño, Isvani Inocencio Frías Blanco

{"title":"Fast Adaptive Stacking of Ensembles Adaptation for Supporting Active Learning. a Real Case Application","authors":"Agustín Alejandro Ortiz Díaz, F. Bayer, F. Baldo, Alberto Verdecia-Cabrera, L. M. Mariño, Isvani Inocencio Frías Blanco","doi":"10.1109/FSKD.2018.8686851","DOIUrl":"https://doi.org/10.1109/FSKD.2018.8686851","url":null,"abstract":"Algorithms for mining stream data have received a lot of attention in the last decade. In general, several of these algorithms require labeled instances for training because they are designed for supervised learning. However, in many real activities, labelled process involves overspending of resources. Due to this, active learning and other learning paradigms have proposed reducing the cost of labeling instances without a significant loss of model performance. In this paper we adapt Fast Adaptive Stacking of Ensembles (FASE), an ensemble method for learning from non-stationary data streams, to the active learning paradigm. This new version, called FASE-AL, was assessed by classifying the Joinville roads into paved and unpaved roads. The assessment data is emitted by a device placed in the public transport buses. FASE-AL obtained better percentage of correctly classified instances compared with other classical algorithms. The results are shown on a map which can be used by the public transport companies in the preparation of buses maintenance plan and route planning decisions, for instance.","PeriodicalId":235481,"journal":{"name":"2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134230651","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