检索结果-内蒙古大学图书馆

IEEE International Conference on Mechatronics and Automation

作者： Peng Zhang Lin Wang Shoupeng Han Naval Academy of Armament Beijing 10016 China College of Automation Harbin Engineering University Harbin Heilongjiang Province 150001 China

ISBN: (纸本)9781509023974

A side-scan sonar image often contains a lot of noises and its resolution is very low, the edge of side-scan sonar image is blurred, so these characteristics make it difficult to segment objects. The current common segmentation algorithms generally require setting parameters manually, and these parameters are closely related with the images they collected. Consequently, it is difficult to achieve universality and autonomy. According to the properties of segmentation algorithm and characteristics of side-scan sonar image, this paper proposes an algorithm based on unsupervised feature learning. We use sparse auto-encoder to learn the local binary patterns and Haar-like feature of the side-scan sonar images, then we build an exclusive feature for them. In the end, we test this method by using clustering algorithm based on features we built previously to segment several side-scan sonar images. The results show that the algorithm we proposed has better performance in both adaptability and effectiveness.

关键词： Local binary pattern Haar-Like feature Unsupervised feature learning clustering algorithm

来源：评论

学校读者我要写书评

暂无评论

A clustering-based Sales Forecasting Scheme Using Support Vector Regression for Computer Server

引用

Procedia Manufacturing 2015年 2卷 82-86页

作者： Dai, Wenseng Chuang, Yang-Yu Lu, Chi-Jie International Monetary Institute Financial School Renmin University of China Beijing 100872 China Department of Industrial Management Chien Hsin University of Science and Technology Taoyuan County 32097 Taiwan

In this study, a clustering-based sales forecasting scheme based on support vector regression (SVR) is proposed. The proposed scheme first uses k-means algorithm to partition the whole training sales data into several disjoint clusters. Then, for each group, the SVR is applied to construct forecasting model. Finally, for a given testing data, three similarity measurements are used to find the cluster which the testing data belongs to and then employee the corresponding trained SVR model to generate prediction result. A real aggregate sales data of computer server is used as an illustrative example to evaluate the performance of the proposed model. Experimental results revealed that the proposed clustering-based sales forecasting scheme outperforms the single SVR without data clustering and hence is an effective alternative for computer server sales forecasting. © 2015

关键词： clustering algorithm computer server Sales forecasting support vector regression

来源：评论

学校读者我要写书评

暂无评论

Systematic mapping study on granular computing

引用

KNOWLEDGE-BASED SYSTEMS 2015年第May期80卷 78-97页

作者： Salehi, Saber Selamat, Ali Fujita, Hamido Univ Teknol Malaysia UTM IRDA Digital Media Ctr Excellence Skudai 81310 Johor Malaysia Univ Teknol Malaysia Fac Comp Skudai 81310 Johor Malaysia Iwate Prefectural Univ Takizawa Iwate 0200193 Japan

Granular computing has attracted many researchers as a new and rapidly growing paradigm of information processing. In this paper, we apply systematic mapping study to classify the granular computing researches to discover relative derivations to specify its research strength and quality. Our search scope is limited to the Science Direct and IEEE Transactions papers published between January 2012 and August 2014. We defined four perspectives of classification schemes to map the selected studies that are focus area, contribution type, research type and framework. Results of mapping the selected studies show that almost half of the research focused area belongs to category of data analysis. In addition, most of the selected papers belong to proposing the solutions in research type scheme. Distribution of papers between tool, method and enhancement categories of contribution type are almost equal. Moreover, 39% of the relevant papers belong to the rough set framework. The results show that there is little attention paid to cluster analysis in existing frameworks to discover granules for classification. We applied five clustering algorithms on three datasets from UCI repository to compare the form of information granules, and then classify the patterns and define them to a specific class based on their geometry and belongings. The clustering algorithms are DBSCAN, c-means, k-means, GAk-means and Fuzzy-GrC and the comparison of information granules are based on the coverage, misclassification and accuracy. The survey of experimental results mostly shows Fuzzy-GrC and GAk-means algorithm superior to other clustering algorithms;while, c-means clustering algorithm shows inferior to other clustering algorithms. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Granular computing Granular classifier clustering algorithm Systematic mapping DBSCAN k-means c-means

来源：评论

学校读者我要写书评

暂无评论

Fast and accurate lithography simulation using cluster analysis in resist model building

引用

JOURNAL OF MICRO-NANOLITHOGRAPHY MEMS AND MOEMS 2015年第2期14卷 1-6页

作者： Kumar, Pardeep Srinivasan, Babji Mohapatra, Nihar R. Indian Inst Technol Gandhinagar Dept Elect Engn Ahmadabad 382424 Gujarat India

As technology nodes continue to shrink, optical proximity correction (OPC) has become an integral part of mask design to improve the subwavelength printability. The success of lithography simulation to perform OPC on an entire chip relies heavily on the performance of lithography process models. Any small enhancement in the performance of process models can result in a valuable improvement in the yield. We propose a robust approach for lithography process model building. The proposed scheme uses the clustering algorithm for model building and thereby improves the accuracy and computational efficiency of lithography simulation. The effectiveness of the proposed method is verified by simulating some critical layers in 14- and 22-nm complementary metal oxide semiconductor technology nodes. Experimental results show that compared with a conventional approach, the proposed method reduces the simulation time by 50x with similar to 5% improvement in accuracy. (C) 2015 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： lithography simulation compact model optical proximity correction clustering algorithm K-means clustering density peak clustering randomization empirical resist model

来源：评论

学校读者我要写书评

暂无评论

A fast and flexible instance selection algorithm adapted to non-trivial database sizes

引用

INTELLIGENT DATA ANALYSIS 2015年第3期19卷 631-658页

作者： Ros, Frederic Harba, Rachid Pintore, Marco Guillaume, Serge Univ Orleans Lab Prisme Orleans Orleans France PILA St Jean De La Ruelle France IRSTEA UMR ITAP Montpellier France

In this paper, a new instance selection algorithm is proposed in the context of classification to manage non-trivial database sizes. The algorithm is hybrid and runs with only a few parameters that directly control the balance between the three objectives of classification, i.e. errors, storage requirements and runtime. It comprises different mechanisms involving neighborhood and stratification algorithms that specifically speed up the runtime without significantly degrading efficiency. Instead of applying an IS (Instance Selection) algorithm to the whole database, IS is applied to strata deriving from the regions, each region representing a set of patterns selected from the original training set. The application of IS is conditioned by the purity of each region (i.e. the extent to which different categories of patterns are mixed in the region) and the stratification strategy is adapted to the region components. For each region, the number of delivered instances is firstly limited via the use of an iterative process that takes into account the boundary complexity, and secondly optimized by removing the superfluous ones. The sets of instances determined from all the regions are put together to provide an intermediate instance set that undergoes a dedicated filtering process to deliver the final set. Experiments performed with various synthetic and real data sets demonstrate the advantages of the proposed approach.

关键词： Supervised classification instance selection clustering algorithm k-nearest neighbors

来源：评论

学校读者我要写书评

暂无评论

Visualization of Dengue Incidences using Expectation Maximization (EM) algorithm

Visualization of Dengue Incidences using Expectation Maximiz...

引用

International Conference on Intelligent and Advanced Systems

作者： Nirbhay Mathur Vijanth S. Asirvadam Sarat C. Dass Balvinder Singh Gill Department of Electrical and Electronics Engineering Centre for Intelligent Signal & Imaging Research (CISIR) Universiti Teknologi PETRONAS Department of Fundamental and Applied Science Centre for Intelligent Signal & Imaging Research (CISIR) Universiti Teknologi PETRONAS Disease Control Division Ministry of Health Malaysia (MoH)

ISBN: (纸本)9781509008469

The aim of this study was to use the geographical information system (GIS) to visualize the dengue incidences on a weekly basis in Selangor, Malaysia. Along with the prediction modeling on data using centroid model and distribution model based on K-means and Expectation Maximization (EM) algorithms respectively. The results show that weekly hotspot were mainly concentrated in the central part of Petaling district of Selangor. R-GIS(R software) and clustering algorithm were used for year 2014 with several weeks to develop the relation between the visualization and prediction of reported incidences. The results are validated for a small region (Petaling district of Selangor state) in Malaysia and they showed vulnerability hotspot in visualizing the dengue incidences. Thus, the proposed method is able to localize the nature of dengue incidence which can further be utilized for vector disease controlled process.

关键词： Dengue GIS clustering algorithm Weekly prediction K-means EM algorithm

来源：评论

学校读者我要写书评

暂无评论

Load Forecasting Method Research Based on Improved K-means algorithm and SVM

Load Forecasting Method Research Based on Improved K-means A...

引用

2016 International Conference on Electrical Engineering and Automation（ICEEA2016）

作者： Shu LIU Jian ZHOU Jian LU Lei XU Xiu YANG Shanghai Electric Power Research Institute College of Electric Engineering Shanghai University of Electric Power

A short-term load forecasting method combining k-means clustering algorithm and SVM is proposed. Euclidean distance and waveform similarity clustering of double standards is used in improved k-means clustering algorithm. The different load curves is accurately classified and their typical load curve is extracted, realized the classification function of different types of user. Then according to the classification results, select the same type of load curves and load factors with the predicted load as input of support vector machine prediction model. This method is used to classify and predict the actual daily load curve of shanghai. It shows that the method can greatly improve the prediction accuracy and is practical.

关键词： clustering algorithm Support vector machine(SVM) Typical load curve Load characteristics trend Load forecasting

来源：评论

学校读者我要写书评

暂无评论

An Outliers Detection Method of Time Series Data for Soft Sensor Modeling

An Outliers Detection Method of Time Series Data for Soft Se...

引用

第28届中国控制与决策会议

作者： Hui-xin Tian Xing-jun Liu Mei Han School of Electrical Engineering and Automation Tianjin Polytechnic University Key Laboratory of Advanced Electrical Engineering and Energy Technology Tianjin Polytechnic University

ISBN: (纸本)9781467397155

Aiming at the particularity of data outliers of soft sensor modeling in complex industrial processes,a new outliers detection method for time series is *** new method combines the traditional density-based clustering algorithm(DBSCAN) with soft sensor modeling *** soft sensor modeling errors are used as the guidance of outliers detection process and replace the traditional manual intervention in the clustering *** the outlier detection is completed as well as the soft sensor modeling is *** experiment shows that the new outliers detection method has good performance.

关键词： soft sensor modeling complex industrial process outlier detection time series clustering algorithm

来源：评论

学校读者我要写书评

暂无评论

Parallelizing Hot Topic Detection of Microblog On Spark

Parallelizing Hot Topic Detection of Microblog On Spark

引用

International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery

作者： Wei Ai Dapu Li College of Information Science and Engineering Hunan University

ISBN: (纸本)9781509040940

With the emergence of the big data age, how to get valuable hot topic from the vast amount of digitized textual materials quickly and accurately has attracted more and more attention. This paper proposes a parallel Two-phase Micmac Hot Topic Detection (TMHTD) method specially design for microblogging in "Big Data" environment, which is implemented based on Apache Spark cloud computing environment. TMHTD is a distributed clustering framework for documents sets with two phases, including micro-clustering and macro-clustering. In the first phase, TMHTD partitions original data sets into a group of smaller data sets, and these data subsets are clustered into many small topics, producing intermediate results. In the second phase, the intermediate results are integrated into one, further clustered, and achieve the final hot topic sets. To improve the accuracy of the hot topic detection, an optimization of TMHTD is proposed. To handle large databases, we deliberately design a group of MapReduce jobs to concretely accomplish the hot topic detection in a highly scalable way. Extensive experimental results indicate that the accuracy and performance of TMHTD algorithm can be improved significantly over existing approaches.

关键词： Big data clustering algorithm Hot topic detection Microblog Spark

来源：评论

学校读者我要写书评

暂无评论

A Novel Segmentation Correction Method for Fusion of Vision and Laser

A Novel Segmentation Correction Method for Fusion of Vision ...

引用

IEEE International Conference on Mechatronics and Automation

作者： Yawei Yuan Jianghai Zhao Shihui Fang Qunshan Xu Department of Automation University of Science and Technology of China Hefei Anhui Province 230027 China Institution of Advanced Manufacturing Technology Hefei Institution of Physical Science CAS Changzhou Jiangsu Province 213164 China

ISBN: (纸本)9781509023974

In this paper, a method is proposed to solve the displacements and distortions, which are caused by inaccurate calibration in the low-level fusion. Compared with existing methods, the proposed method does not rely on any specified environmental feature and can be applied to a variety of scenarios. To implement it, twice clustering processes are applied to segment the input point cloud, and an iterative closest point (ICP) algorithm is used to iterate and correct the corresponding partitions. Furthermore, we also quantify an index to evaluate the result of correction and provide some simplified constraints to improve the measurement accuracy. Finally, the effectiveness of the proposed methods is verified by the result of 3D reconstruction.

关键词： 2D laser scanner stereo vision low-level sensor fusion clustering algorithm distortion correction

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：