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Image modeling with combined optimization techniques for image semantic annotation

NEURAL COMPUTING & APPLICATIONS 2011年第7期20卷 1001-1015页

作者： Yang, Dong Guo, Ping Beijing Normal Univ Lab Image Proc & Pattern Recognit Beijing 100875 Peoples R China

Image semantic annotation can be viewed as a multi-class classification problem, which maps image features to semantic class labels, through the procedures of image modeling and image semantic mapping. Bayesian classifier is usually adopted for image semantic annotation which classifies image features into class labels. In order to improve the accuracy and efficiency of classifier in image annotation, we propose a combined optimization method which incorporates affinity propagation algorithm, optimizing training data algorithm, and modeling prior distribution with Gaussian mixture model to build Bayesian classifier. The experiment results illustrate that the classifier performance is improved for image semantic annotation with proposed method.

关键词： Image annotation Training data selection Gaussian mixture model affinity propagation algorithm

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An improved density-based approach to risk assessment on railway investment

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DATA TECHNOLOGIES AND APPLICATIONS 2022年第3期56卷 382-408页

作者： Guo, Jingwei Zhang, Ji Zhang, Yongxiang Xu, Peijuan Li, Lutian Xie, Zhongqi Li, Qinglin Henan Polytech Univ Jiaozuo Henan Peoples R China Southwest Jiaotong Univ Chengdu Peoples R China Changan Univ Xian Peoples R China

Purpose Density-based spatial clustering of applications with noise (DBSCAN) is the most commonly used density-based clustering algorithm, while it cannot be directly applied to the railway investment risk assessment. To overcome the shortcomings of calculation method and parameter limits of DBSCAN, this paper proposes a new algorithm called Improved Multiple Density-based Spatial clustering of Applications with Noise (IM-DBSCAN) based on the DBSCAN and rough set theory. Design/methodology/approach First, the authors develop an improved affinity propagation (AP) algorithm, which is then combined with the DBSCAN (hereinafter referred to as AP-DBSCAN for short) to improve the parameter setting and efficiency of the DBSCAN. Second, the IM-DBSCAN algorithm, which consists of the AP-DBSCAN and a modified rough set, is designed to investigate the railway investment risk. Finally, the IM-DBSCAN algorithm is tested on the China-Laos railway's investment risk assessment, and its performance is compared with other related algorithms. Findings The IM-DBSCAN algorithm is implemented on China-Laos railway's investment risk assessment and compares with other related algorithms. The clustering results validate that the AP-DBSCAN algorithm is feasible and efficient in terms of clustering accuracy and operating time. In addition, the experimental results also indicate that the IM-DBSCAN algorithm can be used as an effective method for the prospective risk assessment in railway investment. Originality/value This study proposes IM-DBSCAN algorithm that consists of the AP-DBSCAN and a modified rough set to study the railway investment risk. Different from the existing clustering algorithms, AP-DBSCAN put forward the density calculation method to simplify the process of optimizing DBSCAN parameters. Instead of using Euclidean distance approach, the cutoff distance method is introduced to improve the similarity measure for optimizing the parameters. The developed AP-DBSCAN is used to cla

关键词： Density-based clustering affinity propagation algorithm Rough set Risk assessment Railway investment

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SDN-Based Load Balancing Scheme for Multi-Controller Deployment

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IEEE ACCESS 2019年 7卷 39612-39622页

作者： Li, Guoyan Wang, Xinqiang Zhang, Zhigang Tianjin Chengjian Univ Sch Comp & Informat Engn Tianjin 300384 Peoples R China Tianjin Sino German Univ Appl Sci Sch Software & Commun Tianjin 300350 Peoples R China

Software-defined networking (SDN) separates the control plane from the data forwarding plane and realizes the flexible management of the network resources. With the explosive growth of network traffic and scale, multi-controllers need to be deployed to improve the scalability and reliability of the control plane. However, unreasonable subdomain partitioning of SDN controllers may cause the unbalanced distribution of controller loads and reduces the communication performance of the network. Therefore, in this paper, a dynamic multi-controller deployment scheme based on load balancing is proposed. We transform the flow requests into a queuing model and consider the traffic propagation delay and the capacity of controllers as two main factors affecting the deployment of the multi-controllers. In the initial static network, a modified affinity propagation algorithm (PSOAP) based on particle swarm optimization is proposed to solve the problem of clustering performance being affected by the initial values of the bias parameters and convergence coefficients, getting the reasonable network planning. With the dynamic traffic network, switches in different sub-domains are reassigned by breadth-first search (BFS) algorithm to achieve controller load balancing. The extensive evaluations demonstrate that the scheme can provide better stable, accurate, and load balancing multi-controller deployment when compared with affinity propagation (AP) and genetic algorithms.

关键词： affinity propagation algorithm load balancing queuing model software-defined networking

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Combining LVQ with SVM technique for image semantic annotation

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NEURAL COMPUTING & APPLICATIONS 2012年第4期21卷 735-746页

作者： Guo, Ping Jiang, Ziheng Lin, Song Yao, Yao Beijing Inst Technol Sch Comp Sci & Technol Beijing 100081 Peoples R China

When support vector machine (SVM) classifier is applied to image semantic annotation, it usually encounters the problem of excessive training samples. In this paper, we propose a novel method, which is by combining learning vector quantization (LVQ) technique and SVM classifier, to improve annotation accuracy and speed. affinity propagation algorithm-based LVQ technique is used to optimize the training set, and a few number of optimized representative feature vectors are used to train SVM. This approach not only meets the small sample size characteristic of SVM, but also greatly accelerates the training and annotating process. Comparative experimental studies confirm the validity of the proposed method.

关键词： Image semantic annotation Learning vector quantization Support vector machine affinity propagation algorithm

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Temporal-Structure-Aware Interference Cancellation for Asynchronous Cognitive IoT

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IEEE WIRELESS COMMUNICATIONS LETTERS 2022年第2期11卷 293-297页

作者： Liu, Yi Yuan, Xiaojun Univ Elect Sci & Technol China Natl Key Lab Sci & Technol Commun Chengdu 611731 Peoples R China

In this letter, we investigate the interference cancellation problem for asynchronous cognitive Internet of Things (C-IoT) in the concurrent spectrum access (CSA) model. An iterative receiver together with a new clustering algorithm is employed to cancel the interference and recovers the desired signal. Since the C-IoT device and the primary user (PU) are non-cooperative, they are generally asynchronous at the symbol level and the interference signal received by the secondary user (SU) receiver (Rx) possesses a first-order Markov property over time. Therefore, we propose a new clustering algorithm named hidden Morkov model (HMM) based affinity propagation (AP) algorithm that combines the HMM and the AP algorithm to realize the clustering module. The proposed HMM-AP algorithm exploits the temporal-correlation information to improve the performance of the iterative receiver. We show that the receiver with the HMM-AP algorithm can work well in non-cooperative system and has a much better performance than the original AP.

关键词： Interference Hidden Markov models Clustering algorithms Receivers Markov processes Synchronization Transmitters Machine learning non-coordination affinity propagation algorithm hidden Markov model

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Quality relevant over-complete independent component analysis based monitoring for non-linear and non-Gaussian batch process

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2020年 205卷

作者： Peng, Chang Kai, Wang Pu, Wang Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China

A large number of multivariate statistical methods have been applied to process monitoring, but conventional methods only extract limited feature information that often cannot effectively monitor the quality related changes of production characteristics in batch processes. In order to improve the effect of the process monitoring, this paper proposes a batch process monitoring method based on Multistage Over-complete Independent Component Analysis (OICA) algorithm. Firstly, the affinity propagation algorithm (AP) is used to divide the batch production process. Secondly, the extra quality information extracted by Partial Least Squares (PLS) algorithm is input into OICA algorithm. Finally, a monitoring model is established for process monitoring in each sub-stage. The effectiveness of the proposed method has been verified by comparing with the conventional methods in the fed-batch penicillin fermentation process.

关键词： Batch process Fault monitoring affinity propagation algorithm Over-complete independent component analysis Partial least squares

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Social welfare evaluation during demand response programs execution considering machine learning-based load profile clustering

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APPLIED ENERGY 2024年 357卷

作者： Moazzen, Farid Alikhani, Majid Aghaei, Jamshid Hossain, M. J. Univ Technol Sydney Sch Elect & Data Engn Ultimo Australia Cent Queensland Univ Sch Engn & Technol Rockhampton Australia

In the last decade, with the introduction of smart meters to smart grids, demand response programs (DRPs) have been widely adopted to establish a generation and consumption balance. DRPs provide many benefits for efficient grid management. However, these programs are conducive to higher levels of dissatisfaction by changing grid customers' consumption patterns. This paper aims to investigate the effects of DRPs on social welfare (SW). To this end, the paper presents a mathematical model for SW during the implementation of DRPs. In the proposed model, the level of customer satisfaction is assumed the main factor contributing to SW. This mathematical model considers different types of DRPs in terms of their impacts on SW. The paper also seeks to obtain linear and nonlinear models of DRPs and the coefficient of participation (CoP). CoP as an indicator shows the percentage of customers who actively participate in each DRP and plays a significant role in the assessment of the SW level. Moreover, owing to the sparsity and variety of distribution network customers, load patterns are classified into different clusters to take the load types into account. As a matter of fact, this process aims to identify similar patterns and thus, the same level of satisfaction for each separate cluster. The classification process is performed by using a machine learning-based clustering method known as the affinity propagation (AP) algorithm. Then, the model calculates the level of SW for the clusters based on the usage of electrical equipment and the time of day when they are turned on. The obtained levels of SW help operators select the best programs for every cluster in terms of customer satisfaction, and achieve the highest performance of DRPs. Lastly, the model is evaluated using real data of a distribution network to ensure the effectiveness and accuracy of the model.

关键词： Demand response programs Smart grids affinity propagation algorithm Machine learning Load classification

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Track Association for Dynamic Target Tracking System Based on AP algorithm

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Transactions of Nanjing University of Aeronautics and Astronautics 2014年第6期31卷 643-651页

作者：储岳中徐波高有涛 College of Astronautics Nanjing University of Aeronautics and Astronautics School of Computer Science Anhui University of Technology School of Astronomy & Space Science Nanjing University

Track association of multi-target has been recognized as one of the key technologies in distributed multiple-sensor data fusion system,and its accuracy directly impacts on the performance of the whole tracking system.A multi-sensor data association is proposed based on aftinity propagation(AP)*** proposed method needs an initial similarity,a distance between any two points,as a parameter,therefore,the similarity matrix is calculated by track position,velocity and azimuth of track *** approach can automatically obtain the optimal classification of uncertain target based on clustering validity ***,the same kind of data are fused based on the variance of measured data and the fusion result can be taken as a new measured data of the ***,the measured data are classified to a certain target based on the nearest neighbor ideas and its characteristics,then filtering and target tracking are *** experimental results show that the proposed method can effectively achieve multi-sensor and multi-target track association.

关键词： affinity propagation algorithm data fusion target tracking track association

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Fast and accurate single-cell RNA-seq analysis by clustering of transcript-compatibility counts

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GENOME BIOLOGY 2016年第1期17卷 1-14页

作者： Ntranos, Vasilis Kamath, Govinda M. Zhang, Jesse M. Pachter, Lior Tse, David N. Univ Calif Berkeley Dept Math Berkeley CA 94720 USA Univ Calif Berkeley Dept Mol & Cell Biol 229 Stanley Hall Berkeley CA 94720 USA Stanford Univ Dept Elect Engn Stanford CA 94305 USA Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94720 USA

Current approaches to single-cell transcriptomic analysis are computationally intensive and require assay-specific modeling, which limits their scope and generality. We propose a novel method that compares and clusters cells based on their transcript-compatibility read counts rather than on the transcript or gene quantifications used in standard analysis pipelines. In the reanalysis of two landmark yet disparate single-cell RNA-seq datasets, we show that our method is up to two orders of magnitude faster than previous approaches, provides accurate and in some cases improved results, and is directly applicable to data from a wide variety of assays.

关键词： Minimum Span Tree affinity propagation Read Alignment affinity propagation algorithm Pairwise Distance Matrix

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Clustering of News Topics Integrating the Relationship among News Elements 17th

Clustering of News Topics Integrating the Relationship among...

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17th Chinese Lexical Semantics Workshop (CLSW)

作者： Hou, Jiaying Yu, Zhengtao Hong, Xudong Li, Feng Kunming Univ Sci & Technol Sch Informat Engn & Automat Kunming 650500 Yunnan Peoples R China Kunming Univ Sci & Technol Key Lab Intelligent Informat Proc Kunming 650500 Yunnan Peoples R China Logist Sci Res Inst PLA Beijing 100166 Peoples R China

ISBN: (纸本)9783319495088;9783319495071

To make full use of news document structure and the relation among different news documents, a news topic clustering method is proposed of using the relation among document elements. First, the word characteristic weight was calculated by the TF-IDF method based on word frequency statistics to generate document space vector and news document similarity was calculated using text similarity measurement algorithm to obtain the initial news document similarity matrix. Then, the initial similarity matrix was modified with the relation among different news elements as semi-supervised constraint information, the clustering of news documents was realized using affinity propagation algorithm, and news topics were extracted from news clusters. As a result, the construction of news topic model was finished. At last, the contrast experiments were performed on manually-annotated news corpus. The results show that the affinity propagation clustering methods integrating the relation among document elements can achieve a better effect than those without constraint information.

关键词： relationship among news elements news topic affinity propagation algorithm semi-supervised constraints

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