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Real-time progressive compression method of massive data based on improved clustering algorithm

CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2023年第6期26卷 3781-3791页

作者： Yang, Hengxiang Li, Lumin Li, Kai State Grid Xinjiang Informat & Telecommun Co Urumqi 830000 Xinjiang Peoples R China

In order to realize the real-time progressive compression of massive data and ensure the quality of compressed data, a real-time progressive compression method of massive data based on improved clustering algorithm is proposed in this paper. Through the micro clustering stage of birch method based on K-Medoids clustering, clustering Feature Tree hierarchy is constructed and numerical clustering features are extracted;Taking this feature as the input of macro clustering order, the clustering Feature Tree leaf nodes are clustered based on the improved K-Medoids clustering method, and the clustering data cluster set is output;The set is used as the original data of real-time progressive compression, and the data is denoised and compressed by lifting format wavelet transform. On this basis, Huffman coding is used to compress the data losslessly. The test results show that this method has good clustering effect under the optimal number of clustering centers, can complete the real-time progressive compression of a large number of data, and the availability of compressed data is more than 92%.

关键词： improved clustering algorithm Massive data Real-time progressive Data compression clustering characteristics Huffman coding

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Malware recognition approach based on self-similarity and an improved clustering algorithm

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IET SOFTWARE 2022年第5期16卷 527-541页

作者： Chen, Jinfu Zhang, Chi Cai, Saihua Zhang, Zufa Liu, Lu Huang, Longxia Jiangsu Univ Sch Comp Sci & Commun Engn Zhenjiang 202013 Jiangsu Peoples R China Jiangsu Univ Jiangsu Key Lab Secur Technol Ind Cyberspace Zhenjiang Jiangsu Peoples R China Univ Leicester Sch Comp & Math Sci Leicester Leics England

The recognition of malware in network traffic is an important research problem. However, existing solutions addressing this problem rely heavily on the source code and misrecognise vulnerabilities (i.e. incur a high false positive rate (FPR)) in some cases. In this paper, we initially use the K-means clustering algorithm to extract malware patterns under user to root attacks in network traffic. Since the traditional K-means algorithm needs to determine the number of clusters in advance and it is easily affected by the initial cluster centres, we propose an improved K-means clustering algorithm (NIKclustering algorithm) for cluster analysis. Furthermore, we propose the use of self-similarity and our improved clustering algorithm to recognise buffer overflow vulnerabilities for malware in network traffic. This motivates us to design and implement a recognition approach for buffer overflow vulnerabilities based on self-similarity and our improved clustering algorithm, called Reliable Self-Similarity with improved K-means clustering (RSS-IKclustering). Extensive experiments conducted on two different datasets demonstrate that the RSS-IKclustering can achieve much fewer false positives than other notable approaches while increasing accuracy. We further apply our RSS-IKclustering approach on a public dataset (Center for Applied Internet Data Analysis), which also exhibited a high accuracy and low FPR of 96% and 1.5%, respectively.

关键词： pattern clustering improved clustering algorithm invasive software improved K-means clustering algorithm data analysis malware recognition approach cluster analysis cluster centres malware patterns NIKclustering algorithm network traffic traditional K-means algorithm

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Design of an Intelligent Processing System for Business Data Analysis Based on improved clustering algorithm

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Procedia Computer Science 2023年 228卷 1215-1224页

作者： Ning Wang Central South University Changsha 410083 Hunan China

In order to further optimize the processing of business data and increase the effectiveness and accuracy of business data analysis, this research investigated the design of an intelligent processing system for business data analysis based on improved clustering algorithms. Some issues with business data analysis can be efficiently resolved by enhancing clustering algorithms, resulting in the provision of more intelligent, thorough, and precise business data analysis services for businesses and organizations. The improved clustering algorithm is compared with the traditional clustering algorithm to analyze the experimental data, the results showed that the maximum clustering effect of the improved clustering algorithm was above 80%, the highest robustness was 91.65%, and the highest point in algorithm performance was 90.97%. From these three aspects, it can be seen that improved clustering algorithms have more advantages than traditional clustering algorithms. Therefore, improving clustering algorithms can make business data processing more efficient, accurate, and reliable, providing enterprises and organizations with higher quality business data analysis services.

关键词： Business Data Analysis Intelligent Processing System improved clustering algorithm Traditional clustering algorithm

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Application of improved clustering algorithm in investment recommendation in embedded system

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MICROPROCESSORS AND MICROSYSTEMS 2020年 75卷 103066-103066页

作者： Luo, Wei Chongqing Technol & Business Univ Dept Finance Rongzhi Coll Chongqing 400067 Peoples R China

The user's investment behaviour is individual, and group-oriented, which can reflect the user's cognitive background and interest on a certain extent. The user investment group can help users to find similar investment partners. Users can view the investment or other related people's interests. With the development of the Internet financial industry, people's demand for Internet financial knowledge services has become increasingly strong. Accessing financial information and conducting financial transactions through online financial platforms has become normal for investors. As a popular research area, the recommendation system can help users to better use Internet information, improve user loyalty, and promote products. In this paper, an improved kernel cluster-based incremental clustering method is proposed, and the stock information of the Shanghai Stock Exchange is used as the experimental data for cluster mining. The experimental results show that the improved kernel-based incremental clustering algorithm proposed in this paper can complete the investment recommendation for financial users. For a certain extent, it reduces the risk of financial investment, enhances the stability of the financial market, and has a strong positive effect. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Embedded system improved clustering algorithm Incremental clustering Recommendation system Stock information

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improved clustering algorithm based on energy consumption in wireless sensor networks

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IET NETWORKS 2017年第3期6卷 1-7页

作者： Wu, Wenliang Xiong, Naixue Wu, Chunxue Univ Shanghai Sci & Technol Sch Opt Elect & Comp Engn Mil Rd 334 Shanghai Peoples R China Univ Shanghai Sci & Technol Sch Opt Elect & Comp Engn Mil Rd 516 Shanghai Peoples R China

Wireless sensor networks (WSNs) are widely used in military, traffic, medical and so on. The design of routing protocol for WSNs is limited by the single nature of the local topology information. Meanwhile, the power supply of sensor networks node, communication ability and storage capacity are limited, so how to improve the efficient energy of nodes and extend the networks life cycle is the focus of current research. This study proposes the improved algorithm for the LEACH (Low Energy Adaptive clustering Hierarchy) clustering algorithm, considering the residual energy of the nodes and the factors of the long distance node, the T(n) is readjusted and the new method is proposed. Then the data fusion rate is introduced to allow the cluster-heads to fuse data before sending the data, and send the data to the base station. Finally, the free-space model and the multi-path fading model are adopted to avoid the excessive consumption of energy caused by the node d(4). The authors' simulation results show that the improved algorithm can reduce the energy consumption of the networks and prolongs life cycle.

关键词： wireless sensor networks pattern clustering power consumption energy conservation routing protocols telecommunication network topology sensor fusion fading telecommunication power management energy consumption reduction multipath fading model free-space model data fusion rate low energy adaptive clustering hierarchy clustering algorithm LEACH clustering algorithm network life cycle efficient energy improvement storage capacity communication ability sensor network node power supply local topology information routing protocol WSN wireless sensor networks improved clustering algorithm

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Stochastic Disturbance Propagation Model Analysis of Power Grids Based on Epidemic Model and improved clustering

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2022年第12期20卷 3883-3891页

作者： Jin, Jiahui Liu, Yonghui Shanghai Dianji Univ Sch Elect Engn Shanghai 201306 Peoples R China

Based on epidemic model, a stochastic disturbance propagation model of power grids is proposed in this paper. Firstly, compared with epidemic model, some concepts in power grids are introduced. Then, by analyzing parameters of the nodes, some key nodes are extracted. Hence, the complex power grids are divided into some simple power grids. For the simple power grids, taking Markovian chain into account, the probability of the propagation is solved backward. Combined with the probability and the parameters of the power grids, a model describing importance of the nodes is established. Finally, to demonstrate the effectiveness of the model, an improved clustering algorithm is proposed. The simulation results show that the model has high accuracy in selecting the key nodes. Moreover, it provides an effective method in prediction of power grids.

关键词： Epidemic model importance of the nodes improved clustering algorithm stochastic propagation model

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Path Planning of Laser Soldering System Based on Intelligent algorithm

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SENSORS 2022年第21期22卷 8120-8120页

作者： Zhang, Cong Fan, Zige Dai, Yaonan Chen, Hang Wang, Sikai Chen, Xubing Wuhan Inst Technol Sch Mech & Elect Engn Wuhan 430205 Peoples R China Wuhan Inst Technol Hubei Prov Engn Technol Res Ctr Green Chem Equipm Sch Mech & Elect Engn Wuhan 430205 Peoples R China

Laser soldering has been gradually applied to the soldering of electronic components due to the rapid development of microelectronics. However, it is inefficient to use a mechanical shaft to move a laser beam. Here, a laser soldering system is constructed using galvanometer scanning, and an intelligent algorithm is also introduced to optimize the soldering path. Firstly, a laser soldering system for scanning of galvanometers is established, and the functions of visual monitoring, motion planning and parameter integration are presented. Secondly, the position of the laser beam and the corresponding soldering spot are determined, and the coordinate information is provided to plan a route by camera calibration and coordinate system transformation. Finally, the problem of path planning in this system is decomposed into the generation of the soldering point full coverage processing frame, and the route optimization of processing platform and laser beam motion. Furthermore, an improved clustering algorithm, based on the characteristics of system structure, and a hybrid optimization algorithm are designed to deal with the generation of the soldering point full coverage processing frame, the route optimization of processing platform and laser beam motion. In addition, the simulations and experiments are verified by test board. These findings shown that the established system and designed optimization algorithm can promote the efficiency of laser soldering.

关键词： laser soldering system improved clustering algorithm hybrid optimization algorithm path planning

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Study on Location and Route Optimization of Distribution Center of a Digital Company Based on clustering algorithm and Ant Colony algorithm Method

Study on Location and Route Optimization of Distribution Cen...

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9th International Conference on Industrial Technology and Management (ICITM)

作者： Wang Jiaojiao Li Yueyu Cao Congcong Sichuan Univ Business Sch Chengdu Peoples R China

ISBN: (数字)9781728143064

ISBN: (纸本)9781728143064

Aiming at the complex problem of logistics distribution, in order to achieve the shortest path and the least total cost, this paper analyzes and designs the logistics distribution network from the two aspects of distribution center location and path optimization. Firstly, the improved clustering algorithm is used to determine the distribution center, the corresponding supply point and distribution point of each distribution center, then the ant colony algorithm is used to optimize the path, and finally the distribution center and the corresponding distribution path are determined. In the end of this paper, the case analysis is carried out to find out the best distribution center and the shortest path, which gets an optimized distribution network, so as to achieve the purpose of the lowest cost.

关键词： distribution center path optimization improved clustering algorithm ant colony algorithm

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Porosity detection in pulsed GTA welding of 5A06 Al alloy through spectral analysis

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JOURNAL OF MATERIALS PROCESSING TECHNOLOGY 2018年 259卷 332-340页

作者： Huang, Yiming Zhao, Dejin Chen, Huabin Yang, Lijun Chen, Shanben Tianjin Univ Sch Mat Sci & Engn Tianjin Key Lab Adv Joining Technol Tianjin 300350 Peoples R China Shanghai Jiao Tong Univ Sch Mat Sci & Engn Intelligentized Robot Welding Technol Lab Shanghai 200240 Peoples R China

The influence of line-broadening and zero shift of the sensor on identifying spectral lines was eliminated by an improved K-medoids clustering algorithm after performing principal component analysis on the spectral band of interest. The intensity ratio of the acquired spectral lines was calculated and dealt with by empirical mode decomposition to predict the existence of porosity based on the method given by our previous study. Furthermore, the effect of sheet gap on porosity formation was investigated by the proposed method. The analysis results were verified by X-ray detection, which demonstrated the distribution of porosity as well. The results showed that an appropriate gap could contribute significantly to reducing the porosity. The spectral sensor system and analysis method proposed in this study may help decrease the testing costs and increase productivity, while maintaining high weld quality.

关键词： Sensor system Data mining improved clustering algorithm Empirical mode decomposition Porosity detection X-ray detection

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Massive Intelligent Power Consumption Information Mining Research

Massive Intelligent Power Consumption Information Mining Res...

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2nd International Conference on Simulation and Modeling Methodologies,Technologies and Applications(SMTA 2015)

作者： H.L.Sun J.Ye X.M.Chen H.F.Luo State Grid Chongqing Electric Power Research Institute School of Electrical Engineering Chongqing University

With the development of national unified strong smart grid construction, intelligent community are developing positively, which made various information systems comprehensive on-line. There will be a large number of electrical terminal equipment and new energy access in electricity consumption link at the end of the grid, which will surely generate a lot of basic electricity information and data. Electricity consumption data of smart home in intelligent community is the conduct result of the daily life of each family, which can exactly reflect the user‘s real information needs. The history electrical behavior of user is hidden behind the information. How to transform information into knowledge is the most important issue faced recently. This paper analyzed the intelligent power consumption data mining scope, built the framework of data mining based on cloud computing, proposed by the improved clustering algorithm for parallel data mining.

关键词： Electric intelligent information Price Data mining Cloud computing improved clustering algorithm

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