检索结果-内蒙古大学图书馆

第29届中国控制与决策会议

作者： DONG Haiying XU kelei YANG Lixia GU Yaoqin CHEN Ningning School of New Energy and Power Engineering Lanzhou Jiaotong University School of Automation and Electrical Engineering Lanzhou Jiaotong University

ISBN: (纸本)9781509046584

Due to the complicated and variable operating environment of wind turbines,a method based on k-means clustering algorithm is proposed to divide the operating condition for wind turbines and supplies the forceful foundation for making a maintenance *** this method,the fault characteristic analysis and the characteristic frequency calculation for the gear box and the generator are *** then,the vibration characteristics of the two subsystems are extracted by using the wavelet packet ***,the k-means clustering algorithm is used to classify the measured data from a wind *** to the central-limit theorem,the criterion is established to evaluate whether the state characteristic parameters are normal or ***'s beneficial to the early warning under the different working conditions of wind *** results show that this division is accurate and it is beneficial to evaluate the operating states of wind turbines.

关键词： Wind Turbines Feature Extraction k-means clustering algorithm Operational Conditions Division

来源：评论

学校读者我要写书评

暂无评论

Visual based laser speckle pattern recognition method for structural health monitoring

Visual based laser speckle pattern recognition method for st...

引用

Conference on Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems

作者： Park, kyeongtaek Torbol, Marco Ulsan Natl Inst Sci & Technol Sch Urban & Environm Engn 50 UNIST Gil Ulsan 44919 South Korea

ISBN: (数字)9781510608221

ISBN: (纸本)9781510608221

This study performed the system identification of a target structure by analyzing the laser speckle pattern taken by a camera. The laser speckle pattern is generated by the diffuse reflection of the laser beam on a rough surface of the target structure. The camera, equipped with a red filter, records the scattered speckle particles of the laser light in real time and the raw speckle image of the pixel data is fed to the graphic processing unit (GPU) in the system. The algorithm for laser speckle contrast analysis (LASCA) computes: the laser speckle contrast images and the laser speckle flow images. The k-mean clustering algorithm is used to classify the pixels in each frame and the clusters' centroids, which function as virtual sensors, track the displacement between different frames in time domain. The fast Fourier transform (FFT) and the frequency domain decomposition (FDD) compute the modal properties of the structure: natural frequencies and damping ratios. This study takes advantage of the large scale computational capability of GPU. The algorithm is written in Compute Unifies Device Architecture (CUDA C) that allows the processing of speckle images in real time.

关键词： System identification structural health monitoring speckle contrast imaging k-means clustering algorithm parallel computing

来源：评论

学校读者我要写书评

暂无评论

Error Prediction method of Electronic Transformers Based on RBF Neural Network 13

Error Prediction method of Electronic Transformers Based on ...

引用

2017 13th IEEE International Conference on Electronic Measurement & Instruments (ICEMI)

作者： Wu Qiao Xu Xin Yang Shihai Lu Shufeng Chen Gang Electric Power Research Institute Jiangsu Electric Power Company School of Electrical Engineering Southeast University

ISBN: (纸本)9781509050369;9781509050352

With the gradual application of electronic transformers in the intelligent substation, the influence of the typical disturbing source on the electronic transformers is gradually to the industry’s attention. In order to ensure the accuracy and reliability of electronic transformers, it is necessary to determine the error of the electronic transformers accurately through the study of the influence factors of the measurement error of the electronic transformers. In this regard, this paper proposes a transformer error prediction method which combines RBF neural network prediction with k-means clustering algorithm. First, the historical data are classified by k-means clustering. Then, train the prediction model with the historical data clustered. Take the influencing factors such as temperature, load capacity and magnetic field as the independent variables and the error as the prediction target to establish the prediction model. The prediction model is suitable for the analysis of a large number of data and can accurately predict the error. After using this model to analyze the long-term experimental data of Dongshan substation in 2016, the relative error of the error result is around 5%, which is relatively and verifies the validity of the model.

关键词： Electronic transformers k-means clustering algorithm RBF neural network long-term experimental data

来源：评论

学校读者我要写书评

暂无评论

An improved k-means clustering algorithm based on the optimization of initial centroids and iterations

An improved k-means clustering algorithm based on the optimi...

引用

2011 3rd IEEE International Conference on Information Management and Engineering(ICIME 2011)

作者： Li Lei Pattern Recognition and Intelligence System, College of Automation, Nanjing University of Post and Telecommunications Nanjing, China Hang Xin Pattern Recognition and Intelligence System, College of Automation, Nanjing University of Post and Telecommunications Nanjing, China

This paper presents a new method to preprocess the datasets, and an efficient way for assigning data points to clusters. The improved method avoids k-means algorithm sensitive to the initial centroids and instability, and it avoids the large number of iterations, saving the running time. Experimental results show that the improved method can effectively improve the speed of clustering and accuracy, reducing the computational complexity of the k-means.

关键词： k-means clustering algorithm initial centroids iterations computational complexity data mining

来源：评论

学校读者我要写书评

暂无评论

Error Prediction method of Electronic Transformers Based on RBF Neural Network

Error Prediction method of Electronic Transformers Based on ...

引用

13th IEEE International Conference on Electronic Measurement & Instruments (ICEMI)

作者： Wu Qiao Xu Xin Yang Shihai Lu Shufeng Chen Gang Jiangsu Elect Power Co Elect Power Res Inst Nanjing 211103 Jiangsu Peoples R China Southeast Univ Sch Elect Engn Nanjing 210096 Jiangsu Peoples R China

ISBN: (纸本)9781509050352

With the gradual application of electronic transformers in the intelligent substation, the influence of the typical disturbing source on the electronic transformers is gradually to the industry's attention. In order to ensure the accuracy and reliability of electronic transformers, it is necessary to determine the error of the electronic transformers accurately through the study of the influence factors of the measurement error of the electronic transformers. In this regard, this paper proposes a transformer error prediction method which combines RBF neural network prediction with k-means clustering algorithm. First, the historical data are classified by k-means clustering. Then, train the prediction model with the historical data clustered. Take the influencing factors such as temperature, load capacity and magnetic field as the independent variables and the error as the prediction target to establish the prediction model. The prediction model is suitable for the analysis of a large number of data and can accurately predict the error. After using this model to analyze the long-term experimental data of Dongshan substation in 2016, the relative error of the error result is around 5%, which is relatively and verifies the validity of the model.

关键词： Electronic transformers k-means clustering algorithm RBF neural network long-term experimental data

来源：评论

学校读者我要写书评

暂无评论

Research on Intrusion Detection Based on Feature Extraction of Autoencoder and the Improved k-means algorithm

Research on Intrusion Detection Based on Feature Extraction ...

引用

10th International Symposium on Computational Intelligence and Design (ISCID)

作者： Wang, Xingang Wang, Linlin Qilu Univ Technol Sch Informat Jinan Shandong Peoples R China

ISBN: (纸本)9781538636749

Nowadays, intrusion detection is a technology to effectively avoid a number of risks of network intrusion. The k-means algorithm is widely used in intrusion detection. But, the algorithm has some shortcomings, such as random selection of k value, sensitive selection of initial cluster centers, and low accuracy in clustering high-dimensional data. In order to make up for these shortcomings of the k-means algorithm, this paper proposes an AE-kmeans architecture which combines an autoencoder with the improved k-means algorithm. The AE-kmeans architecture realizes the dimension reduction and feature extraction of these original data by introducing an autoencoder, and uses the improved k-means algorithm to cluster these processed data. These improvements of the improved k-means algorithm mainly include two aspects: the first aspect, a new method is introduced to select initial cluster centers;the second aspect, the algorithm calculates the weight of each attribute with the coefficient of variation, and then uses these weights in Euclidean distance formula, resulting in a weighted Euclidean distance formula. Finally, the AE-kmeans architecture uses kDD CUP99 data set for intrusion detection simulation experiments. The Experimental results show that the AE-kmeans architecture not only enhances the ability to deal with high-dimensional data, but also improves the detection rate and reduces the error-detection rate compared with k-means algorithm.

关键词： intrusion detection k-means clustering algorithm autoencoder AE-kmeans architecture feature extraction weighted Euclidean distance formula

来源：评论

学校读者我要写书评

暂无评论

Hybrid clustering-Time Series-Bayesian Neural Network Short-Term Load Forecasting Method 48

Hybrid Clustering-Time Series-Bayesian Neural Network Short-...

引用

48th North American Power Symposium (NAPS)

作者： Ghofrani, M. Carson, D. Ghayekhloo, M. Univ Washington Sch Sci Technol Engn & Math STEM Bothell WA 98011 USA Qazvin Islamic Azad Univ Dept Elect & Comp Engn Qazvin Iran

ISBN: (纸本)9781509032709

This paper offers a hybrid short-term load forecasting (STLF) model using a Bayesian neural network (BNN) with a pre-processing stage consisting of a k-means clustering algorithm and time series analysis. The data clusters are time series analyzed to provide the most accurate data sets for each hour of the day. The final forecast is provided from the BNN output. California load data is used to determine the accuracy and processing speed of the proposed method. Additionally a comparison between BNN and other intelligent algorithms is provided using the same pre-processing stage to further gauge performance benchmarks.

关键词： Bayesian neural network k-means clustering algorithm short-term load forecasting

来源：评论

学校读者我要写书评

暂无评论

Towards an EEG-based brain-computer interface for online robot control

引用

MULTIMEDIA TOOLS AND APPLICATIONS 2016年第13期75卷 7999-8017页

作者： Li, Yantao Zhou, Gang Graham, Daniel Holtzhauer, Andrew Southwest Univ Coll Comp & Informat Sci Chongqing 400715 Peoples R China Coll William & Mary Dept Comp Sci Williamsburg VA 23187 USA Mitre Corp Mclean VA USA

According to New York Times, 5.6 million people in the United States are paralyzed to some degree. Motivated by requirements of these paralyzed patients in controlling assisted-devices that support their mobility, we present a novel EEG-based BCI system, which is composed of an Emotive EPOC neuroheadset, a laptop and a Lego Mindstorms NXT robot in this paper. We provide online learning algorithms that consist of k-means clustering and principal component analysis to classify the signals from the headset into corresponding action commands. Moreover, we also discuss how to integrate the Emotiv EPOC headset into the system, and how to integrate the LEGO robot. Finally, we evaluate the proposed online learning algorithms of our BCI system in terms of precision, recall, and the F-measure, and our results show that the algorithms can accurately classify the subjects' thoughts into corresponding action commands.

关键词： EEG BCI system k-means clustering algorithm Principal component analysis

来源：评论

学校读者我要写书评

暂无评论

An approach to evaluate resource utilization in energy management systems

引用

ENERGY SOURCES PART B-ECONOMICS PLANNING AND POLICY 2016年第9期11卷 855-860页

作者： Haeri, Abdorrahman Rezaie, kamran Iran Univ Sci & Technol Sch Ind Engn POB 16846-13114 Tehran Iran Univ Tehran Coll Engn Sch Ind Engn Tehran Iran

Organizations implement management systems to achieve better performance in different functions. One important area of management systems is "energy management systems". ISO 50001: 2011 proposed a systematic framework for operation of an effective energy management system (EnMS). Resources should be used efficiently to obtain suitable performance. Thus, it is useful to evaluate resource utilization by considering the importance and efficiency of resources in processes. In this study, major functions of an EnMS are identified in accordance with ISO 50001. Then, for each function, processes and resources are specified and classified. Later, data are gathered, and appropriate measures are calculated. Finally, data analysis techniques are applied to propose strategic decisions for improvement. In addition, an oil refinery is considered as a case study.

关键词： Business process energy management system ISO 50001:2011 k-means clustering algorithm resource utilization

来源：评论

学校读者我要写书评

暂无评论

An Efficient Predictive Model for Probability of Genetic Diseases Transmission

引用

INTERNATIONAL JOURNAL OF ENGINEERING 2017年第8期30卷 1152-1159页

作者： Hamidi, H. Qaribpour, F. KN Toosi Univ Technol Dept Ind Engn Informat Technol Grp Tehran Iran

In this article, a new combined approach of a decision tree and clustering is presented to predict the transmission of genetic diseases. In this article, the performance of these algorithms is compared for more accurate prediction of disease transmission under the same condition and based on a series of measures like the positive predictive value, negative predictive value, accuracy, sensitivity and specificity. The results show that support vector machine algorithm outperformed the other two simple algorithms and the neural network and genetic algorithms offered better prediction at the end, while the proposed combined approach is developed using different parameters and outperformed the simple methods.

关键词： Genetic Disease Transmitting Genetic Diseases k-means clustering algorithm Data Mining Decision Trees

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：