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Characterization of Vertical Wind Speed Profiles Based on Ward's Agglomerative clustering algorithm

Journal of Modern Power Systems and Clean Energy 2023年第5期11卷 1437-1449页

作者： M.C.Bueso A.Molina-García A.P.Ramallo-González A.Fernández-Guillamón Department of Applied Mathematics and Statistics Universidad Politécnica de Cartagena30202 CartagenaSpain Department of Automatics Electrical Engineering and Electronic TechnologyUniversidad Politécnica de Cartagena30202 CartagenaSpain Department of Information and Communications Engineering Computer Science FacultyUniversity of Murcia30100 MurciaSpain Universidad de Castilla La-Mancha Campus Universitario02071 AlbaceteSpain

Wind turbine blades have been constantly increasing since wind energy becomes a popular renewable energy source to generate electricity. Therefore, the wind sector requires a more efficient and representative characterization of vertical wind speed profiles to assess the potential for a wind power plant site. This paper proposes an alternative characterization of vertical wind speed profiles based on Ward's agglomerative clustering algorithm, including both wind speed module and direction data. This approach gives a more accurate incoming wind speed variation around the rotor swept area, and subsequently, provides a more realistic and complete wind speed vector characterization for vertical profiles. Real wind database collected for 2018 in the Forschungsplattformen in Nordund Ostsee(FINO) research platform is used to assess the methodology. A preliminary pre-processing stage is proposed to select the appropriated number of heights and remove missing or incomplete data. Finally, two locations and four heights are selected, and 561588 wind data are characterized. Results and discussion are also included in this paper. The methodology can be applied to other wind database and locations to characterize vertical wind speed profiles and identify the most likely wind data vector patterns.

关键词： clustering algorithm wind speed data management power generation

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Maximum Correntropy Criterion-Based UKF for Loosely Coupling INS and UWB in Indoor Localization

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Computer Modeling in Engineering & Sciences 2024年第6期139卷 2673-2703页

作者： Yan Wang You Lu Yuqing Zhou Zhijian Zhao Department of Computer and Communication Engineering Northeastern UniversityQinhuangdao066004China

Indoor positioning is a key technology in today’s intelligent environments,and it plays a crucial role in many application areas.This paper proposed an unscented Kalman filter(UKF)based on the maximum correntropy criterion(MCC)instead of the minimummean square error criterion(MMSE).This innovative approach is applied to the loose coupling of the Inertial Navigation System(INS)and Ultra-Wideband(UWB).By introducing the maximum correntropy criterion,the MCCUKF algorithm dynamically adjusts the covariance matrices of the system noise and the measurement noise,thus enhancing its adaptability to diverse environmental localization requirements.Particularly in the presence of non-Gaussian noise,especially heavy-tailed noise,the MCCUKF exhibits superior accuracy and robustness compared to the traditional UKF.The method initially generates an estimate of the predicted state and covariance matrix through the unscented transform(UT)and then recharacterizes the measurement information using a nonlinear regression method at the cost of theMCC.Subsequently,the state and covariance matrices of the filter are updated by employing the unscented transformation on the measurement equations.Moreover,to mitigate the influence of non-line-of-sight(NLOS)errors positioning accuracy,this paper proposes a k-medoid clustering algorithm based on bisection k-means(Bikmeans).This algorithm preprocesses the UWB distance measurements to yield a more precise position estimation.Simulation results demonstrate that MCCUKF is robust to the uncertainty of UWB and realizes stable integration of INS and UWB systems.

关键词： Maximum correntropy criterion unscented Kalman filter inertial navigation system ultra-wideband bisecting kmeans clustering algorithm

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AWeb Application Fingerprint Recognition Method Based on Machine Learning

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Computer Modeling in Engineering & Sciences 2024年第7期140卷 887-906页

作者： Yanmei Shi Wei Yu Yanxia Zhao Yungang Jia School of Integrated Circuit Science and Engineering Tianjin University of TechnologyTianjin300382China College of International Business Zhejiang Yuexiu UniversityShaoxing312030China School of Public Administration Zhejiang Gongshang UniversityHangzhou310018China Tianjin Branch of National Computer Network Emergency Response Technical Team/Coordination Center of China Tianjin300100China

Web application fingerprint recognition is an effective security technology designed to identify and classify web applications,thereby enhancing the detection of potential threats and attacks.Traditional fingerprint recognition methods,which rely on preannotated feature matching,face inherent limitations due to the ever-evolving nature and diverse landscape of web applications.In response to these challenges,this work proposes an innovative web application fingerprint recognition method founded on clustering techniques.The method involves extensive data collection from the Tranco List,employing adjusted feature selection built upon Wappalyzer and noise reduction through truncated SVD dimensionality reduction.The core of the methodology lies in the application of the unsupervised OPTICS clustering algorithm,eliminating the need for preannotated labels.By transforming web applications into feature vectors and leveraging clustering algorithms,our approach accurately categorizes diverse web applications,providing comprehensive and precise fingerprint recognition.The experimental results,which are obtained on a dataset featuring various web application types,affirm the efficacy of the method,demonstrating its ability to achieve high accuracy and broad coverage.This novel approach not only distinguishes between different web application types effectively but also demonstrates superiority in terms of classification accuracy and coverage,offering a robust solution to the challenges of web application fingerprint recognition.

关键词： Web application fingerprint recognition unsupervised learning clustering algorithm feature extraction automated testing network security

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A hybrid data-mining framework for train rescheduling strategy pattern discovery

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Transportation Safety and Environment 2024年第1期6卷 56-67页

作者： Ruirui Chen Xuekai Ge Ping Huang Chao Wen School of Transportation&Logistics Southwest Jiaotong UniversityChengdu 611756China Department of IT Operational Technology CN RailWaterloo N2V2R1Canada

This study presents a hybrid data-mining framework based on feature selection algorithms and clustering methods to perform the pattern discovery of high-speed railway train rescheduling strategies(RSs).The proposed model is composed of two states.In the first state,decision tree,random forest,gradient boosting decision tree(GBDT)and extreme gradient boosting(XGBoost)models are used to investigate the importance of features.The features that have a high influence on RSs are first selected.In the second state,a K-means clustering method is used to uncover the interdependences between RSs and the influencing features,based on the results in the first state.The proposed method can determine the quantitative relationships between RSs and influencing factors.The results clearly show the influences of the factors on RSs,the possibilities of different train operation RSs under different situations,as well as some key time periods and key trains that the controllers should pay more attention to.The research in this paper can help train traffic controllers better understand the train operation patterns and provides direction for optimizing rail traffic RSs.

关键词： high-speed railway rescheduling strategy pattern discovery feature identification clustering algorithm

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Method of neural network modulation recognition based on clustering and Polak-Ribiere algorithm

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Journal of Systems Engineering and Electronics 2014年第5期25卷 742-747页

作者： Faquan Yang Zan Li Hongyan Li Haiyan Huang Zhongxian Pan State Key Laboratory of Integrated Service Networks Xidian University School of Electrical and Information Engineering Foshan University

To improve the recognition rate of signal modulation recognition methods based on the clustering algorithm under the low SNR, a modulation recognition method is proposed. The characteristic parameter of the signal is extracted by using a clustering algorithm, the neural network is trained by using the algorithm of variable gradient correction （Polak-Ribiere） so as to enhance the rate of convergence, improve the performance of recognition under the low SNR and realize modulation recognition of the signal based on the modulation system of the constellation diagram. Simulation results show that the recognition rate based on this algorithm is enhanced over 30% compared with the methods that adopt clustering algorithm or neural network based on the back propagation algorithm alone under the low SNR. The recognition rate can reach 90% when the SNR is 4 dB, and the method is easy to be achieved so that it has a broad application prospect in the modulating recognition.

关键词： clustering algorithm feature extraction algorithm of Polak-Ribiere neural network （NN） modulation recognition.

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PHC：A Fast Partition and Hierarchy—Based clustering algorithm

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Journal of Computer Science & Technology 2003年第3期18卷 407-410,F003页

作者：周皓峰袁晴晴程尊平施伯乐 Department of Computing and Information Technology Fudan University Shanghai 200433 P.R. China

Cluster analysis is a process to classify data in a specified data set. In this field,much attention is paid to high-efficiency clustering algorithms. In this paper, the features in thecurrent partition-based and hierarchy-based algorithms are reviewed, and a new hierarchy-basedalgorithm PHC is proposed by combining advantages of both algorithms, which uses the cohesionand the closeness to amalgamate the clusters. Compared with similar algorithms, the performanceof PHC is improved, and the quality of clustering is guaranteed. And both the features were provedby the theoretic and experimental analyses in the paper.

关键词： clustering algorithm data mining

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A proof of the convergence theorem of maximum-entropy clustering algorithm

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Science China(Information Sciences) 2010年第6期53卷 1151-1158页

作者： REN ShiJun & WANG YaDong School of Computers, Harbin Institute of Technology, Harbin 150001, China School of Computers Harbin Institute of Technology Harbin China

In this paper, we point out that the counterexample constructed by Yu et al.is incorrect by using scientific computing software Sage.This means that the example cannot negate the convergence theorem of maximum entropy clustering algorithm.Furthermore, we construct an example to negate Theorem 1 in Yu’s paper, and we propose Proposition 3 to prove that the limit of the iterative sequence is a local minimum of the objective function while v varies and u remains stable.Finally, we give a theoretical proof of the convergence theorem of maximum entropy clustering algorithm.

关键词： entropy fixed point clustering algorithm convergence

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Improved Kernel Possibilistic Fuzzy clustering algorithm Based on Invasive Weed Optimization

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Journal of Shanghai Jiaotong university(Science) 2015年第2期20卷 164-170页

作者：赵小强周金虎 College of Electrical Engineering and Information Engineering Lanzhou University of Technology

Fuzzy c-means(FCM) clustering algorithm is sensitive to noise points and outlier data, and the possibilistic fuzzy c-means(PFCM) clustering algorithm overcomes the problem well, but PFCM clustering algorithm has some problems: it is still sensitive to initial clustering centers and the clustering results are not good when the tested datasets with noise are very unequal. An improved kernel possibilistic fuzzy c-means algorithm based on invasive weed optimization(IWO-KPFCM) is proposed in this paper. This algorithm first uses invasive weed optimization(IWO) algorithm to seek the optimal solution as the initial clustering centers, and introduces kernel method to make the input data from the sample space map into the high-dimensional feature space. Then, the sample variance is introduced in the objection function to measure the compact degree of data. Finally, the improved algorithm is used to cluster data. The simulation results of the University of California-Irvine(UCI) data sets and artificial data sets show that the proposed algorithm has stronger ability to resist noise, higher cluster accuracy and faster convergence speed than the PFCM algorithm.

关键词： data mining clustering algorithm possibilistic fuzzy c-means(PFCM) kernel possibilistic fuzzy c-means algorithm based on invasiv

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Quantitative contrast of urban agglomeration colors based on image clustering algorithm:Case study of the Xia-Zhang-Quan metropolitan area

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Frontiers of Architectural Research 2021年第3期10卷 692-700页

作者： Meichen Ding Faculty of Architecture Tan Kah Kee CollegeXiamen UniversityZhangzhou363105China

Color is an important element to consider when shaping urban characteristics.However,previous studies seldom included quantitative analyses of color relationships between urban agglomerations within proximal regions and with similar cultures to distinguish and shape individual urban personalities.This study focused on Xiamen,Zhangzhou,and Quanzhou metropolitan areas,which are influenced by Minnan culture,and collected natural and cultural landscape network images that collectively represent the urban landscape in China.Color extraction,computer vision processing technologies,and clustering algorithms,such as k-means partitioning,hierarchical methods,and co-occurrence frequency,were applied using image recognition.We then established an urban color database and quantified color attributes.Finally,we conducted a comparative analysis of dominant colors and color combination associations in Xiamen,Zhangzhou,and Quanzhou metropolitan areas to explore their similarities and differences and define their characteristics.We also considered other cities of the same type for comparison.

关键词： City color Urban agglomeration Network image clustering algorithm Computer vision

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SLID: A Secure Lowest-ID clustering algorithm

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Wuhan University Journal of Natural Sciences 2005年第1期10卷 39-42页

作者： HUGuang-ming HUANGZun-guo HUHua-ping GONGZheng-hu SchoolofComputerScience NationalUniversityofDefenseTechnologyChangsha410073HunanChina

In order to solve security problem of clustering algorithm, we proposed amethod to enhance the security of the well-known lowest-ID clustering algorithm. This method isbased on the idea of the secret sharing and the (k, n) threshold cryptography, Each node, whetherclusterhead or ordinary member, holds a share of the global certificate, and any k nodes cancommunicate securely. There is no need for any clusterhead to execute extra functions more thanrouting. Our scheme needs some prior configuration before deployment, and can be used in criticalenvironment with small scale. The security-enhancement for Lowest-ID algorithm can also be appliedinto other clustering approaches with minor modification. The feasibility of this method wasverified bythe simulation results.

关键词： clustering algorithm secret sharing threshold cryptography

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