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2nd International Conference on Measurement, Instrumentation and Automation (ICMIA 2013)

作者： Zhong, Yingchun Huang, Guochen Lin, Ruisheng Li, Fang Guangdong Univ Technol Automat Sch Guangzhou 510006 Guangdong Peoples R China Guangdong Univ Business Studies Informat Sch Guangzhou 510006 Guangdong Peoples R China

ISBN: (纸本)9783037857502

Aim to the weak intelligence and humanity of current electric nursing-bed control modes, the recommended movement control mode is proposed, which is based on the existing manual, timing and speech control pattern. First, on the basis of accumulating some control data, the affinity propagation algorithm (AP algorithm) is employed to cluster in order to acquire the clustering centers, which reflect the prefer movement and corresponding value at special time of the special user. Then, according to the mechanical and electric constraints, some rules are established to adjust the clustering centers. And the nursing-bed movement sequence is obtained, which is logical. Finally, the rationality of the recommended movement sequence is analyzed according to the distribution characteristic of the dataset. The movement sequence that passes the rationality analysis will be recommended to the user and automatically saved as the recommended pattern. The experimental results show that the recommended movement sequence can basically reflect the user's habits, which is more intelligent and human than other control modes.

关键词： Recommended pattern affinity propagation algorithm Nursing-bed Movement control

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Multi-Level Clustering algorithm for Star/Galaxy Separation 12

Multi-Level Clustering Algorithm for Star/Galaxy Separation

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12th International Conference on Computational Intelligence and Security (CIS)

作者： Du, Hui Wang, Yuping Ren, Chuchu Zhong, Junkun Gao, Xiao-Zhi Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China Northwest Normal Univ Coll Comp Sci & Engn Lanzhou 730070 Peoples R China Lappeenranta Univ Technol Sch Engn Sci Lappeenranta Finland

ISBN: (纸本)9781509048403

With the coming era of astronomic data with mass, high dimensionality and nonlinearity, clustering astronomic data becomes more and more important. This paper proposed a new clustering algorithm, which reduces the space and time complexity and the sensitivity to the parameters. It is suitable for processing large scale astronomic data sets. The new algorithm consists of three phases: coarsening clustering, representative data clustering and merging. First, we use affinity propagation (AP) algorithm for coarsening. Specifically, in order to save the space and computational cost, we only compute the similarity between each point and its t nearest neighbors, and get a coarsened similarity matrix (with only t columns, where t << N and N is the number of data points). Second, to further improve the efficiency and effectiveness of the proposed algorithm, the Find of Density Peaks Clustering (FDP) is used to divide the representative points gotten in the first phase. Third, we can get the classes of all data by merging the results of the first two steps. The experimental results show the proposed algorithm can realize the clusters quickly and precisely for the classification of stars/ galaxies using Sloan Digital Sky Survey (SDSS), and is more efficient than the compared algorithms AP, and FDP.

关键词： Star/Galaxy classification SDSS affinity propagation algorithm Find of Density Peaks Multi-Level Clustering algorithm

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Load Forecasting Method Based on SSA-DAP-TCN 2

Load Forecasting Method Based on SSA-DAP-TCN

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2nd Asian Conference on Frontiers of Power and Energy (ACFPE)

作者： Yan, Lu Liu, Wei Liu, Han Wang, Xuechun Hua, Fan Cui, Yong Nanjing Univ Sci & Technol Sch Automat Nanjing Peoples R China State Grid Shanghai Elect Power Co Ltd Shanghai Peoples R China

ISBN: (纸本)9798350303896

As the sampling frequency of electricity load increases, noise is difficult to filter at high sampling rates, which can lead to deviation in load forecasting and affect the performance of prediction algorithms. Enhancing the accuracy of load forecasting is crucial to formulate production plans, guide economic dispatch and maintain the balance of power supply and demand. This study proposes a power load forecasting method based on multi-scale decomposition and reconstruction. Initially, the load data is decomposed into trend, periodic, and noise components by singular spectrum analysis (SSA), effectively eliminating noise. Then, the affinity propagation algorithm based on dynamic time warping distance (DAP) is utilized to reconstruct the components to obtain new subsequences. Time convolutional network (TCN) is utilized to predict the reconstructed subsequences, and finally the forecasted results of each subsequence are accumulated to get the final forecasted results. To verify the superiority of the proposed method, this study take actual manufacturing industry load data as an example and comparing it with various prediction models. Compared with TCN, the root mean squared error of the proposed method is improved by 55.68%.

关键词： Singular spectrum analysis Dynamic time warping distance affinity propagation algorithm Time convolutional network Load forecasting

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Localização de danos em estruturas isotrópicas com a utilização de aprendizado de máquina

Localização de danos em estruturas isotrópicas com a util...

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作者： Oliveira, Daniela Cabral de Universidade Estadual Paulista

Este trabalho introduz uma nova metodologia de Monitoramento da Integridade de Estruturas (SHM, do inglês Structural Health Monitoring) utilizando algoritmos de aprendizado de máquina não-supervisionado para localização e detecção de dano. A abordagem foi testada em material isotrópico (placa de alumínio). Os dados experimentais foram cedidos por Rosa (2016). O banco de dados disponibilizado é abrangente e inclui medidas em diversas situações. Os transdutores piezelétricos foram colados na placa de alumínio com dimensões de 500 x 500 x 2mm, que atuam como sensores e atuadores ao mesmo tempo. Para manipulação dos dados foram analisados os sinais definindo o primeiro pacote do sinal (first packet), considerando apenas o intervalo de tempo igual ao tempo da força de excitação. Neste caso, na há interferência dos sinais refletidos nas bordas da estrutura. Os sinais são obtidos na situação sem dano (baseline) e, posteriormente nas diversas situações de dano. Como método de avaliação do quanto o dano interfere em cada caminho, foram implementadas as seguintes métricas: pico máximo, valor médio quadrático (RMSD), correlação entre os sinais, normas H2 e H∞ entre os sinais baseline e sinais com dano. Logo após o cálculo das métricas para as diversas situações de dano, foi implementado o algoritmo de aprendizado de máquina não-supervisionado K-Means no matlab e também testado no toolbox Weka. No algoritmo K-Means há a necessidade da pré-determinação do número de clusters e isto pode dificultar sua utilização nas situações reais. Então, fez se necessário a implementação de um algoritmo de aprendizado de máquina não-supervisionado que utiliza propagação de afinidades, onde a determinação do número de clusters é definida pela matriz de similaridades. O algoritmo de propagação de afinidades foi desenvolvido para todas as métricas separadamente para cada *** paper introduces a new Structural Health Monitoring (SHM) methodology using unsupervised machine learning algorithms f

关键词： SHM Aprendizado de máquina não-supervisionado Algoritmo K-Means Algoritmo propagação de afinidade Unsupervised machine learning K-Means algorithm affinity propagation algorithm Dissertação de mestrado

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Discriminative Reference-Based Scene Image Categorization

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2014年第10期E97D卷 2823-2826页

作者： Li, Qun Xu, Ding An, Le Nanjing Univ Posts & Telecommun Sch Comp Sci & Techn Nanjing Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Wireless Commun Key Lab Jiangsu Prov Minist Educ Nanjing Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Key Lab Broadband Wireless Commun & Sensor Networ Minist Educ Nanjing Jiangsu Peoples R China Univ Calif VISLab Riverside CA USA

A discriminative reference-based method for scene image categorization is presented in this letter. Reference-based image classification approach combined with K-SVD is approved to be a simple, efficient, and effective method for scene image categorization. It learns a subspace as a means of randomly selecting a reference-set and uses it to represent images. A good reference-set should be both representative and discriminative. More specifically, the reference-set subspace should well span the data space while maintaining low redundancy. To automatically select reference images, we adapt affinity propagation algorithm based on data similarity to gather a reference-set that is both representative and discriminative. We apply the discriminative reference-based method to the task of scene categorization on some benchmark datasets. Extensive experiment results demonstrate that the proposed scene categorization method with selected reference set achieves better performance and higher efficiency compared to the state-of-the-art methods.

关键词： scene categorization reference-based scheme affinity propagation algorithm discriminative reference-set

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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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Intelligent scanning for optimal rock discontinuity sets considering multiple parameters based on manifold learning combined with UAV photogrammetry

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ENGINEERING GEOLOGY 2022年 309卷

作者： Liu, Yongqiang Chen, Jianping Tan, Chun Zhan, Jiewei Song, Shengyuan Xu, Wanglai Yan, Jianhua Zhang, Yansong Zhao, Mingyu Wang, Qing Jilin Univ Coll Construction Engn Changchun 130026 Peoples R China China Water Northeastern Invest Design & Res Co Lt Changchun 130000 Jilin Peoples R China Engn Technol Res Ctr Minist Water Resource Cold Re Changchun 130000 Jilin Peoples R China Changan Univ Coll Geol Engn & Geomatics Xian 710054 Peoples R China

The Sichuan-Tibet railway, which spans many alpine canyon regions, is being built in southwestern China. Investigating the characteristics of rock discontinuity sets is the basis for identifying dangerous rock masses above the tunnel portals. The traditional methods of identifying discontinuity sets usually consider orientations and ignore other parameters, which results in incorrect guidance for rock engineering. To this end, the affinity propagation (AP) algorithm based on modified isometric mapping (Isomap) is proposed for partitioning discontinuity sets based on orientation, trace length, and aperture. The new unsupervised algorithm (ISOAP) uses manifold learning to complete the transformation process for orientations from spherical vectors to scalars and avoids selecting the initial clustering center to achieve global optimization. The Silhouette Index is used to intelligently scan the optimal clustering results. The proposed algorithm is tested on a complex artificial data set and on Shanley and Mahtab's data set. Since accurately obtaining discontinuity information is impossible by traditional means (i.e., using geological compasses and measurement tapes) due to the existence of a mass of high and steep slopes, the ISOAP algorithm is combined with semiautomatic technology based on unmanned aerial vehicle (UAV) photogrammetry and applied to a rock slope located along the railway. The introduction of manifold learning is beneficial for quickly applying abundant unmodified clustering algorithms to rock engi-neering and searching the optimal algorithm suitable for analyzing the structural characteristics of a specific fractured rock mass. The proposed method can simplify rock engineering analyses and provide more reasonable results.

关键词： Discontinuity sets Multiple parameters Manifold learning affinity propagation algorithm UAV photogrammetry

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An Intelligent Document Clustering Approach to Detect Crime Patterns

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Procedia Technology 2013年 11卷 1181-1187页

作者： Qusay Bsoul Juhana Salim Lailatul Qadri Zakaria Knowledge Technology Research Group Universiti Kebangsaan Malaysia 43600 Bangi Selangor Malaysia

A wide number of reports and news on crimes, increasingly conducted almost every day, have resulted in making detection of such crimes more difficult if not complex. Therefore, the need for detecting and identifying such crimes emerges as a necessary way of detecting and identifying such crime patterns on the news. Document Clustering have been increasingly becoming an important task for obtaining good results with the unsupervised learning methods. It aims to automatically group similar documents in one cluster using different types of extractions and cluster algorithms. There are ongoing works done to improve Document Clustering techniques such as Extractions and Clustering approaches to overcome the difficulty in designing a general purpose document clustering for crime investigation and the ill posed problem of extraction and clustering. This paper discusses two major sequential stages in Document Clustering “Extraction Features and Clustering algorithms” as well as the major challenges and the key issues in designing extraction features and clustering algorithms. In addition, the following approach assists the law enforcement officers and detectives to enhance performance and speed up the process of solving crimes.

关键词： Intelligent Clustering K-means affinity propagation algorithm Lemmatization algorithm Crime Detection

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Network Pruning via Weight Enhancement of Adaptive Exemplar Filters 23

Network Pruning via Weight Enhancement of Adaptive Exemplar ...

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Proceedings of the 2023 12th International Conference on Computing and Pattern Recognition

作者： Zhoufeng Liu Ke Yang Ningyu Chai Haipeng Jiao Ruimin Yang Zhongyuan University of Technology China

ISBN: (纸本)9798400707988

The redundant architecture and inefficient parameters of Convolutional Neural Networks (CNNs) can be drastically decreased by the model compression approaches while the network model's high performance is maintained. The structured model compression technique filter pruning is a persistent subject of research since it is hardware-friendly. Whereas many filter pruning methods use training data and human empirical design to determine important filters, this might result in inaccurate results after fine-tuning and the creation of suboptimal compression networks. In this paper, we propose the Weight Enhancement of Adaptive Exemplar Filter pruning algorithm (WEAEF), which merely processes the weights of filters to decide which filters to keep at each layer automatically, while strengthening the critical filter weights thereby improving the accuracy of the pruned network without destroying the model structure. affinity propagation (AP), a clustering algorithm, uses similarity information sent between data points to automatically locate grouping centers. Inspiring by this, we implement the AP algorithm to make filters as data points for discovering filters that, via the similarities of filters, represent the performance of all filters within the corresponding layer. To be able to obtain enhanced accuracy after fine-tuning, we further creatively deliver the Weight Enhancement (WD) module, which improves accuracy by boosting the utility of the filter weights yet maintaining the model's structure. Our method has been shown to be superior to other state-of-the-art methods in the datasets CIFAR-10 and ILSVRC-2012.

关键词： affinity propagation algorithm Filter pruning Model Compression Weight Enhancement module

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A Belief propagation Based Hierarchical Approach for Capacitated Network Decomposition

A Belief Propagation Based Hierarchical Approach for Capacit...

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IEEE/CIC International Conference on Communications in China

作者： Juan Liu Huaiyu Dai Department of Electrical and Computer Engineering North Carolina State University

ISBN: (纸本)9781479941452

With increasing applications of large-scale networks, fully distributed and self-organized clustering algorithms have attracted much attention recently. In this paper, we review the capacitated network decomposition problem that aims to minimize the cut weight. As an extension of our previous work [1], we propose a novel belief propagation based distributed clustering algorithm, which allows each node to select one or multiple co-cluster nodes by sending messages between pairs of nodes. Based on the max-product algorithm, we derive the message-passing procedure on the corresponding factor graph. The distributed clustering algorithm is also extended for a hierarchical structure. Simulation results show that our algorithm can efficiently find a capacitated network decomposition solution, and it outperforms the popular affinity propagation algorithm in some applications.

关键词： belief maintenance graph theory network theory (graphs) pattern clustering affinity propagation algorithm belief propagation capacitated network decomposition distributed clustering algorithm factor graph hierarchical approach hierarchical structure max-product algorithm message-passing procedure algorithm design and analysis Clustering algorithms Frequency modulation Joining processes Measurement Message passing Partitioning algorithms

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