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The Design and Implementation of Chinese-Uighur-English Online Dictionary Based on knowledge Graph 20

The Design and Implementation of Chinese-Uighur-English Onli...

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20th IEEE International Conference on Computational Science and Engineering (CSE) / 15th IEEE/IFIP International Conference on Embedded and Ubiquitous Computing (EUC)

作者： Qiu, Lirong Yang, Huan Zhou, Rong Minzu Univ China Informat Technol Sch Beijing Peoples R China

ISBN: (纸本)9781538632215

Designing Chinese-Uighur-English online dictionary is very important for the development of ethnic scientific research and education, which is the basis for the work of Uighur semantic study. Online dictionary with a huge thesaurus can be implemented by knowledge graph, existing works have not addressed in much detail. In order to design the thesaurus of the online dictionary, this paper is based on the word-formation characteristics of Uighur vocabulary to create the Uighur knowledge graph, and proposes k-means clustering algorithm for the classification of Uighur vocabulary.

关键词： Chinese-Uighur-English online dictionary knowledge graph k-means clustering algorithm

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Hierarchical Evaluation and Fault Diagnose Strategy for the Active Distribution Network Using the Incomplete Monitoring Information 7

Hierarchical Evaluation and Fault Diagnose Strategy for the ...

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7th International Conference on Control Automation and Information Sciences (ICCAIS)

作者： Fan, Feilong Tai, Nengling Ni, Jiayun Sun, Qiu Ma, Zhoujun Chen, Peiyu Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai Peoples R China East China Normal Univ Fac Econ & Management Dept Stat Shanghai Peoples R China Hohai Univ Sch Elect Engn Nanjing Jiangsu Peoples R China State Grid Tianjin Elect Power Co Elect Power Res Inst Tianjin Peoples R China

ISBN: (纸本)9781538660201

The cost of monitoring facilities for the whole active distribution network challenges the electric power companies. This paper proposes a hierarchical evaluation and fault diagnose strategy using the incomplete monitoring information to decrease the cost of the monitoring facilities. The proposed strategy consists of three levels: the primary level, the middle level and the output level. The k-means clustering algorithm is employed at the primary level. Considering the local incomplete history information and the history fault record information, the k-means clustering algorithm assesses the failure probability of the target area in each cluster. A regional composite failure probability calculation algorithm is proposed at the middle level. Considering the distance from the monitoring area real-time sampling data matrix to each cluster, the proposed algorithm calculates the probability of the real-time monitoring data belonging to each cluster. Combining the cluster belonging probability and the failure probability of each cluster, the proposed algorithm calculate the composite failure probability of the each target area in the active distribution network. An evaluation algorithm based on the maximum composite probability is proposed at the output level. The maximum composite failure probability of each region is used as the overall operation state evaluation coefficient to evaluate the operation status level of the active distribution network. The proposed strategy is tested by the Matlab/Simulink model of a distribution network in Nanjing. Test results show that the proposed strategy can effectively detect the weak areas of the active distribution network. The real-time operating state of the active distribution network is evaluated scientifically.

关键词： Active distribution network hierarchical evaluation fault diagnose incomplete information k-means clustering algorithm

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Fuzzy Granular Classifier Approach for Spam Detection 7th

Fuzzy Granular Classifier Approach for Spam Detection

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7th International Conference on Computational Collective Intelligence (ICCCI)

作者： Salehi, Saber Selamat, Ali krejcar, Ondrej kuca, kamil Univ Teknol Malaysia UTM IRDA Digital Media Ctr Skudai 81310 Johor Malaysia Univ Teknol Malaysia Fac Comp Skudai 81310 Johor Malaysia Univ Hradec Kralove Ctr Basic & Appl Res Fac Informat & Management Hradec Kralove 50003 Czech Republic

ISBN: (纸本)9783319243061;9783319243054

Spam email problem is a major shortcoming of email technology for computer security. In this research, a granular classifier model is proposed to discover hyperboxes in the geometry of information granules for spam detection in three steps. In the first step, the k-means clustering algorithm is applied to find the seed_points to build the granular structure of the spam and non-spam patterns. Moreover, applying the interval analysis through the high homogeneity of the patterns captures the key part of the spam and non-spam classifiers' structure. In the second step, PSO algorithm is hybridized with the k-means to optimize the formalized information granules' performance. The proposed model is evaluated based on the accuracy, misclassification and coverage criteria. Experimental results reveal that the performance of our proposed model is increased through applying Particle Swarm Optimization and fuzzy set.

关键词： Spam detection Hyperbox geometry of classifiers Granular classifier Membership functions Particle Swarm Optimization k-means clustering algorithm

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Operational Conditions Division of Wind Turbines 29

Operational Conditions Division of Wind Turbines

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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

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Optimization for Learning and Control 1

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2023年

作者： Martin Andersen Anders Hansson

ISBN: (数字)9781119809173;9781119809142

ISBN: (纸本)9781119809135

Optimization for Learning and Control Comprehensive resource providing a masters’ level introduction to optimization theory and algorithms for learning and control Optimization for Learning and Control describes how optimization is used in these domains, giving a thorough introduction to both unsupervised learning, supervised learning, and reinforcement learning, with an emphasis on optimization methods for large-scale learning and control problems. Several applications areas are also discussed, including signal processing, system identification, optimal control, and machine learning. Today, most of the material on the optimization aspects of deep learning that is accessible for students at a Masters’ level is focused on surface-level computer programming; deeper knowledge about the optimization methods and the trade-offs that are behind these methods is not provided. The objective of this book is to make this scattered knowledge, currently mainly available in publications in academic journals, accessible for Masters’ students in a coherent way. The focus is on basic algorithmic principles and trade-offs. Optimization for Learning and Control covers sample topics such as:

Optimization theory and optimization methods, covering classes of optimization problems like least squares problems, quadratic problems, conic optimization problems and rank optimization.
First-order methods, second-order methods, variable metric methods, and methods for nonlinear least squares problems.
Stochastic optimization methods, augmented Lagrangian methods, interior-point methods, and conic optimization methods.
Dynamic programming for solving optimal control problems and its generalization to reinforcement learning.
How optimization theory is used to develop theory and tools of statistics and learning, e.g., the maximum likelihood method, expectation maximization, k-means clustering, and support vector machines.
How calculus of variatio

关键词： Optimization machine learning statistics linear algebra artificial neural networks entropy probability theory signal processing system identification mathematical programming k-means clustering algorithm support vector machine reinforcement learning optimal control dynamic programming

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An Efficient Lane Markings Detection and Tracking Method based on Vanishing Point Constraints 35

An Efficient Lane Markings Detection and Tracking Method bas...

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第35届中国控制会议

作者： HOU Changzheng HOU Jin YU Chaochao School of Information Science and Technology Southwest Jiaotong University

ISBN: (纸本)9781509009107

This paper proposes an efficient lane markings detection and tracking method,which utilizes line segments as feature information combined with vanishing point *** method can be highlighted in four items as ***,the region of interest(ROI) of road image is determined,and then the edge information is extracted by Canny edge ***,edge image is scanned to calculate the orientation of edge-linking pixels and we filter out noise edges which have abnormal ***,line segments detected by Progressive Probabilistic Hough Transform(PPHT) are applied to analyze the structural information of lanes and the interferential line segments are eliminated under vanishing point ***,kmeans clustering algorithm is used to classify and fit the closest two lane ***,a kalman filter is utilized for lane markings *** experimental results demonstrate the good accuracy and robustness of our method in various complex environment.

关键词： Vanishing Point Constraint Noise Edges Progressive Probabilistic Hough Transform k-means clustering algorithm kalman Filter

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Damaged road extracting with high resolution aerial image of post-earthquake

Damaged road extracting with high resolution aerial image of...

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International Conference on Intelligent Earth Observing and Applications (IEOAs)

作者： Zheng, Zezhong Pu, Chengjun Zhu, Mingcang Xia, Jun Zhang, Xiang Liu, Yalan Li, Jiang Univ Elect Sci & Technol China Sch Resources & Environm Chengdu 611731 Sichuan Peoples R China Guangxi Key Lab Spatial Informat & Geomat Guilin 541004 Guangxi Peoples R China Wuhan Univ State Key Lab Water Resources & Hydropower Engn S Wuhan 430072 Hubei Peoples R China Chengdu Univ Technol Key Lab Geosci Spatial Informat Technol Minist Land & Resources China Chengdu 610059 Sichuan Peoples R China Beijing Normal Univ & Inst Remote Sensing & Digit State Key Lab Remote Sensing Sci Beijing 100101 Peoples R China Land & Resources Dept Sichuan Prov Chengdu 610072 Sichuan Peoples R China Chinese Acad Sci Inst Remote Sensing & Digital Earth Beijing 100094 Peoples R China Old Dominion Univ Dept Elect & Comp Engn Norfolk VA 23529 USA

ISBN: (纸本)9781510600492

With the rapid development of earth observation technology, remote sensing images have played more important roles, because the high resolution images can provide the original data for object recognition, disaster investigation, and so on. When a disastrous earthquake breaks out, a large number of roads could be damaged instantly. There are a lot of approaches about road extraction, such as region growing, gray threshold, and k-means clustering algorithm. We could not obtain the undamaged roads with these approaches, if the trees or their shadows along the roads are difficult to be distinguished from the damaged road. In the paper, a method is presented to extract the damaged road with high resolution aerial image of post-earthquake. Our job is to extract the damaged road and the undamaged with the aerial image. We utilized the mathematical morphology approach and the k-means clustering algorithm to extract the road. Our method was composed of four ingredients. Firstly, the mathematical morphology filter operators were employed to remove the interferences from the trees or their shadows. Secondly, the k-means algorithm was employed to derive the damaged segments. Thirdly, the mathematical morphology approach was used to extract the undamaged road;Finally, we could derive the damaged segments by overlaying the road networks of pre-earthquake. Our results showed that the earthquake, broken in Yaan, was disastrous for the road, Therefore, we could take more measures to keep it clear.

关键词： Damaged road extracting mathematical morphology k-means clustering algorithm high resolution aerial images of post-earthquake

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Data Compression Scheme for Fronthaul Based on Vector Quantization 19

Data Compression Scheme for Fronthaul Based on Vector Quanti...

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International Conference on Robotics, Intelligent Control and Artificial Intelligence (RICAI)

作者： Feng, Chenwei Lin, Mingxia Xie, Xinlin Zhang, Mingjiang Xiamen Univ Technol Sch Optoelect & Commun Engn Xiamen Peoples R China

ISBN: (纸本)9781450372985

With the rapid development of the mobile communication technology, the number of long-term evolution (LTE) users and the volume of data is continually increasing, which greatly increases the amount of data transferred by fronthaul, resulting in a huge investment in optical fiber resource. In order to control the consumption of optical fiber resource, lower the cost of operator and avoid congestion in the premise of increasing data volume, it is necessary to compress the data for fronthaul. According to the characteristics of the LTE baseband signal, this paper proposes a data compression scheme on the basis of discrete cosine transform (DCT) and vector quantization. Firstly, the time domain signal is transformed in DCT. And then, the k-means clustering algorithm is used for vector quantization on the transformed signal. Finally, the Huffman coding is applied to further increase the compression ratio (CR) under the promise of an acceptable error. The simulation results show that the scheme proposed has a good performance in both compression ratio and error vector Magnitude (EVM) for the LTE baseband signal base on orthogonal frequency division multiplexing (OFDM).

关键词： Fronthaul Data compression Vector quantization Discrete cosine transform k-means clustering algorithm Huffman coding

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A kM-Net Model based on k-means Weight Initialization for Images Classification 20

A KM-Net Model based on k-Means Weight Initialization for Im...

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20th IEEE International Conference on High Performance Computing and Communications (HPCC) / 16th IEEE International Conference on Smart City (SmartCity) / 4th IEEE International Conference on Data Science and Systems (DSS)

作者： Ma, Miao Liu, Lin Chen, Yuli Shaanxi Normal Univ Minist Educ Sch Comp Sci Key Lab Modern Teaching Technol Xian Shaanxi Peoples R China Shaanxi Normal Univ Sch Comp Sci Xian Shaanxi Peoples R China

ISBN: (纸本)9781538666142

Convolutional neural networks (CNNs) have been successfully applied in the fields of image classification. The training cost of various large CNN models increases rapidly, which has become a challenging task to balance the recognition accuracy and the convergence speed in the training procedure of CNN models for a particular dataset. In this paper, a kM-Net model with convolutional kernels in the first layer initialized by k-means clustering algorithm is proposed. Experimental results show that when compared with some other methods on the most widely-used SVHN dataset and ASL dataset, the proposed kM Net model can not only improve the recognition accuracy, but also accelerate the convergence speed of the training procedure.

关键词： convolutional neural network LeNet-5 ReLU function k-means clustering algorithm

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Development of Digital Evidence Collector and File Classification System with k-means algorithm 5

Development of Digital Evidence Collector and File Classific...

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IEEE Asia Pacific Conference on Wireless and Mobile (APWiMob)

作者： Ruriawan, Muhammad Faris Anggono, Bintaran Siahaan, Isaac Anugerah Purwanto, Yudha Telkom Univ Sch Elect Engn Bandung Indonesia

ISBN: (纸本)9781728148656

Digital forensic is a branch of forensic science that focuses on research on the usual storage media that electronic users use such as hard disks, flash drives or other devices used on computers. The output is called digital evidence. The purpose of doing digital forensics is to find an evidence that can be used in the investigation of a case, until the evidence becomes valid and could be used as evidence in court. In this research, we implement a system of digital evidence collection, recovery, and file classification application. The classification was done by k-means clustering algorithm. The system could detect the storage media, duplicate the content, and classify the output using k-means algorithm. It can help a forensic examiner in the collection, examination, analysis, and reporting phase in accordance with NIST SP 800-86. The application also can assist investigators in managing files in the storage media as digital evidence so that outputs are obtained in accordance with applicable law.

关键词： digital forensic digital evidence k-means clustering algorithm

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