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2nd International Symposium on Instrumentation and Measurement, Sensor Network and Automation (IMSNA)

作者： Wang, Yushun Lei, Peng Xu, Hong Lanzhou Univ Sch Informat Sci & Engn Lanzhou 730000 Peoples R China

ISBN: (纸本)9781479927166

The coverage region of WLAN network is limited compare with cell phone system such as GSM and WCDMA. The favorable area to deploy WLAN is the area which has strong demand for wireless network. How to identify the needs and guide the deployment of WLAN, that isn't a easy issue. It will waste the investment if we deploy the WLAN in improper places. This paper proposes a solution which can collect customer feedback with the help of smart phone client software and affinity propagation algorithm is applied to determine which region should be deploy first according to giant user feedback from that client software. Actual results show that the method is feasible and effective. It can significantly improve the accuracy of deployment of WLAN and efficiency of operations.

关键词： component WLAN Telecom operator mobile client affinity propagation algorithm clustering algorithm GIS

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Adaptive affinity propagation algorithm Based on New Strategy of Dynamic Damping Factor and Preference

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2019年第1期14卷 97-104页

作者： Hu, Jiusong Liu, Hongli Yan, Zhi Hunan Univ Coll Elect & Informat Engn Changsha Hunan Peoples R China

The affinity propagation algorithm has been extensively applied in various fields. However, it is still faces two severe challenges in actual applications: one is that the algorithm may be nonconvergent;and the other is that the convergence speed is low. Aiming at solving these two problems, an adaptive affinity propagation algorithm based on a new strategy of dynamic damping factor and preference is proposed in this paper. On one hand, the dynamic damping factor changes the factor value according to the check state of oscillation to eliminate and escape from the oscillation. On the other hand, dynamic preference adjusts the value of the preference based on the bisection and memory tuple to reduce the search scope of the target preferences continuously. Simulation results show that the proposed algorithms can solve the potential nonconvergence problem effectively and reduce the time consumed significantly. (c) 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

关键词： affinity propagation algorithm adaptive affinity propagation algorithm damping factor preference

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Identification of rock discontinuity sets based on a modified affinity propagation algorithm

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INTERNATIONAL JOURNAL OF ROCK MECHANICS AND MINING SCIENCES 2017年 94卷 32-42页

作者： Liu, Jie Zhao, Xing-Dong Xu, Zeng-he Northeastern Univ Sch Resources & Civil Engn Shenyang 110819 Peoples R China

Identification of rock discontinuity sets is an important foundation for stability analysis of rock engineering applications. A modified affinity propagation (AP) algorithm is proposed for identifying rock discontinuity sets based on discontinuity orientations. The method considers all data points as potential clustering centers simultaneously, which could avoid the hard selection on initial clustering centers as well as achieve the global optimization. Euclidean distance measure in the original algorithm is not suitable for the clustering of discontinuity orientations and therefore a similarity measure based on the negative sine-squared value of the acute angle between discontinuity unit normal vectors was used. Moreover, the Silhouette validity index was introduced to determine the optimal clustering number. The validity of the new method was tested by using artificial data, and in-situ data compiled from the literature. Finally, the proposed method was applied to discontinuity grouping in an underground water-sealed oil storage cavern in Liaoning Province, China. The results show that the new method can effectively filter noisy data and achieve good clustering results with stronger robustness than other methods.

关键词： Rock discontinuity sets Cluster analysis affinity propagation algorithm Orientation analysis

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Research on the Market Structure Visualization of Chinese High-Liquidity Equity ETFs Based on the affinity propagation algorithm 24

Research on the Market Structure Visualization of Chinese Hi...

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16th International Conference on Machine Learning and Computing (ICMLC)

作者： Wang, Yakun Xu, Lili Mo, Liangze Nanning Univ Coll Digital Econ Nanning Peoples R China

ISBN: (纸本)9798400709234

In today's financial markets, Exchange-Traded Funds (ETFs), particularly high-liquidity equity ETFs exhibiting robust market liquidity, have garnered increasing attention from investors. This study conducts an analysis of 50 highly liquid equity Exchange-Traded Funds (ETFs) within the Chinese securities market, covering the period from October 25, 2022, to October 26, 2023. Employing the Sparse Inverse Covariance Estimation method (GraphicalLassoCV), the research calculates the conditional correlation among ETFs. Additionally, an unsupervised learning method, the affinity propagation algorithm from machine learning, is utilized for further analysis and exploration of the market structure of Chinese high-liquidity equity ETFs. Subsequently, 'manifold' techniques are applied for data visualization. The results reveal that 50 high-liquidity equity ETFs are divided into 11 clusters, and specific clusters exhibit industry cohesion, reflecting similar intraday volatility patterns within sectors. The Broad Market Indices and Banking Cluster and the Semiconductor and Artificial Intelligence Cluster are the largest and most active categories. They account for 31.88% and 24.87% of the total market capitalization, and 8.62% and 38.66% of the total trading volume, *** represents the trend over the past year, where short-term investors have shown a preference for innovative companies in sectors such as semiconductors and artificial intelligence. On the other hand, long-term capital tends to favor ETFs associated with banking, market indices, and other sectors characterized by high dividend yields and strong risk diversification capabilities.

关键词： affinity propagation algorithm Market structure Visualization research High-liquidity equity ETFs

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affinity propagation algorithm in WLAN network deployment

Affinity propagation algorithm in WLAN network deployment

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International Symposium on Instrumentation and Measurement, Sensor Network and Automation

作者： Yushun Wang Peng Lei Hong Xu School of Information Science and Engineering Lanzhou University Lanzhou China

ISBN: (纸本)9781479927173

关键词： component WLAN Telecom operator mobile client affinity propagation algorithm clustering algorithm GIS

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Gravity Theory-Based affinity propagation Clustering algorithm and Its Applications

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TEHNICKI VJESNIK-TECHNICAL GAZETTE 2018年第4期25卷 1125-1135页

作者： Wang, Limin Hao, Zhiyuan Han, Xuming Zhou, Ruihong Jilin Univ Finance & Econ Sch Management Sci & Informat Engn Jilin Big Data Res Ctr Business Changchun 130117 Jilin Peoples R China Changchun Univ Technol Sch Comp Sci & Engn Changchun 130117 Jilin Peoples R China

The original affinity propagation clustering algorithm (AP) only used the Euclidean distance of data sample as the only standard for similarity calculation. This method of calculation had great limitations for data with high dimension and sparsity when the original algorithm was running. Due to the single calculation method of similarity, the convergence and clustering accuracy of the algorithm were greatly affected. On the other hand, in the universe, we can consider the formation of galaxies is a clustering process. In addition, the interaction between different celestial bodies are achieved through universal gravitation. This paper introduced the Density Peak clustering algorithm (DP) and gravitational thought into the AP algorithm, and constructed the density property to calculate the similarity, put forward the affinity propagation clustering algorithm based on Gravity (GAP). The proposed algorithm was more accurate to calculate similarity of simple points through the local density of corresponding points, and then used the gravity formula to update the similarity matrix. The data clustering process could be seen as the sample points spontaneously attract each other based on 'gravitation'. Experimental results showed that the convergence performance of GAP algorithm is obviously improved over the AP algorithm, and the clustering effect was better.

关键词： affinity propagation algorithm gravitation theory local density similarity matrix

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Semi-supervised affinity propagation Clustering algorithm Based On Kernel Function 27

Semi-supervised Affinity Propagation Clustering Algorithm Ba...

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27th Chinese Control and Decision Conference (CCDC)

作者： Zhao Xiaoqiang Xie Yaping Lanzhou Univ Technol Coll Elect & Informat Engn Lanzhou 730050 Peoples R China Key Lab Gansu Adv Control Ind Proc Lanzhou 730050 Peoples R China

ISBN: (纸本)9781479970162

Aiming at complex data sets, affinity propagation clustering algorithm has shortcomings of clustering inefficiency and low accuracy. A semi-supervised affinity propagation clustering algorithm based on kernel function (K-SAP Clustering algorithm) is proposed in this paper. This algorithm first maps the complex clustering space into the feature space and change the similarity measure by a kernel function. Then semi-supervised algorithm is used to adjust the similarity matrix to be neighbours of data in same cluster. Finally, AP algorithm is used to iterate and update to get, the global optimum. Simulation results show the proposed algorithm is better and more accurate than SAP algorithm for complex data sets clustering.

关键词： Data Mining Clustering Kernel Fraction affinity propagation algorithm

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A New Cloudlet Placement Method Based on affinity propagation for Cyber-Physical-Social Systems in Wireless Metropolitan Area Networks

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IEEE ACCESS 2020年 8卷 34313-34325页

作者： Peng, Kai Qian, Xingda Zhao, Bohai Zhang, Kejia Liu, Yang Huaqiao Univ Coll Engn Quanzhou 362021 Peoples R China Heilongjiang Univ Sch Math Sci Harbin 150080 Peoples R China Univ Sci & Technol Liaoning Sch Comp Sci & Software Engn Anshan 114051 Peoples R China

Cyber-Physical-Social Systems (CPSS) integrates the cyber, physical and social spaces together. There are a large number of mobile users in CPSS that need low latency services. Fortunately, mobile edge computing (MEC) is a novel technology which can provide such services. The edge server plays a key role in MEC, but how to manage the edge server is an important challenge. For one thing, the number of cloudlets and the resource are limited. For another, the number of mobile devices (MDs) is very large and randomly distributed. And thus, how to determine the suitable number of cloudlets while serving the maximum number of MDs is significant. To this end, a new cloudlet placement method based on improved affinity propagation (AP) algorithm is proposed to solve the above problems. More specially, the improved AP algorithm can obtain the least number of cloudlets while covering the largest number of MDs. In addition, the load balancing strategy is used to ensure that the load of each cloudlet maintains a balanced state. Last but not the least, our proposed method can be used in scenarios where users move.

关键词： Cloud computing Load management Resource management Servers Heuristic algorithms Edge computing Mobile handsets Mobile edge computing cloudlet placement affinity propagation algorithm load balancing

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Batch process fault detection for multi-stage broad learning system

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NEURAL NETWORKS 2020年 129卷 298-312页

作者： Peng, Chang RuiWei, Lu Kang, Olivia Kai, Wang Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China

In the real industrial production process, some minor faults are difficult to be detected by multivariate statistical analysis methods with mean and variance as detection indicators due to the aging equipment and catalyst deactivation. With structural characteristics, deep neural networks can better extract data features to detect such faults. However, most deep learning models contain a large number of connection parameters between layers, which causes the training time-consuming and thus makes it difficult to achieve a fast-online response. The Broad Learning System (BLS) network structure is expanded without a retraining process and thus saves a lot of training time. Considering that different stages of the batch production process have different production characteristics, we use the affinity propagation (AP) algorithm to separate the different stages of the production process. This paper conducts research on a multi-stage process monitoring framework that integrates AP and the BLS. Compared with other monitoring models, the monitoring results in the penicillin fermentation process have verified the superiority of the AP-BLS model. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： affinity propagation algorithm Broad learning system Penicillin fermentation process Fault detection

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A Hybrid Model for Nonlinear Regression with Missing Data UsingQuasilinearKernel

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2020年第12期15卷 1791-1800页

作者： Zhu, Huilin Tian, Yanling Ren, Yanni Hu, Jinglu Waseda Univ Grad Sch Informat Prod & Syst Wakamatsu Ku Hibikino 2-7 Kitakyushu Fukuoka 8080135 Japan

In both the research and engineering fields, missing data is a serious problem that cannot be overlooked. Therefore, available datasets with missing data are a challenge to be modeled by conventional global prediction models. In this paper, we propose a hybrid model consisting of an autoencoder and a gated linear network for solving the regression problem under missing value scenario. A sophisticated modeling and identifying algorithm is developed. First, an extended affinity propagation (AP) clustering algorithm is applied to obtain a self-organized competitive net dividing the datasets into several clusters. Second, a multiple imputation tool with topp%winner-take-all denoising autoencoders (DAE) is introduced to realize better predictions of missing values, in which rough estimates of missing values by using the mean imputation and similarity method within the clusters are used as teacher signals of DAE. Finally, a gated linear network is designed to construct a piecewise linear regression model with interpolations in the exact same way as a support vector regression with a quasilinear kernel composed using the cluster information obtained in the AP clustering step. Based on the experiments of five datasets, our proposed method demonstrates its effectiveness and robustness compared with other traditional kernels and state-of-the-art methods, even on datasets with a large percentage of missing values. (c) 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

关键词： nonlinear regression missing data affinity propagation algorithm denoising autoencoder support vector regression quasilinear kernel

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