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IEEE 95th Vehicular Technology Conference: (VTC-Spring)

作者： Wang, Yu Wang, Xin Shen, Bin Cui, Taiping Chongqing Univ Posts & Telecommun CQUPT Sch Commun & Informat Engn SCIE Chongqing 400065 Peoples R China

ISBN: (数字)9781665482431

ISBN: (纸本)9781665482431

Recognizing the transmission activity of primary users (PU) is one of the basic prerequisites for enabling secondary access to the licensed spectrum. In this paper, by converting the spectrum energy observations into images, we propose to recognize the PU activity scene via machine learning based image classification methods. To reduce the cost of manually labeling the training data, we obtain the PU scene labels through a clustering optimization algorithm, and the histograms of oriented gradients (HOG) feature extraction algorithm is applied to extract and vectorize the existing labeled scene images, which are used as the input of support vector machine (SVM) to train the PU scene classifier model. Experimental results show that the proposed scheme is capable of identifying the active PU transmitters in the recognized scenes and it has better performance in low PU transmitting power environment, compared with some typical classification methods.

关键词： clustering optimization scene classification feature extraction support vector machine machine learning

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Transductive clustering optimization learning for few-shot image classification

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JOURNAL OF ELECTRONIC IMAGING 2023年第4期32卷

作者： Wang, Yi Bian, Xiong Zhu, Songhao Nanjing Univ Posts & Telecommun Coll Automat & Artificial Intelligence Nanjing Peoples R China

Few-shot image classification aims to perform image classification on new categories with only a small amount of labeled training data. However, it is difficult to complete such a task under the existing conditions. Therefore, the current few-shot learning methods are built on the paradigm of transfer learning, where the core idea is to learn a priori that can solve unknown new tasks. The idea of meta-learning is fast learning, which is similar to the idea of few-shot learning. Therefore, conventional few-shot learning methods generally adopt a meta-training approach with episodic training to construct knowledge prior. However, research work indicates that an embedding model with powerful feature representations is simpler and more effective than the existing sophisticated few-shot learning methods. Inspired by this insight, we propose a few-shot image classification method based on the transductive clustering optimization learning. First, memory block is introduced to store the internal feature structure of each category, namely comprehensive representation of various features. Second, during the training process, sample features are compared with the memory content to further expand the differences between different categories and improve the performance of feature extractor. Finally, in the transductive few-shot setting, a clustering optimization module has been introduced to select appropriate samples for clustering to alleviate the fundamental problem of sample scarcity. A large number of experimental results show that the proposed few-shot image classification method based on the transductive clustering optimization learning effectively improves the classification accuracy under various training conditions. (c) 2023 SPIE and IS&T

关键词： few-shot image classification transductive learning clustering optimization

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Historical knowledge-based MBO for global optimization problems and its application to clustering optimization

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SOFT COMPUTING 2021年第5期25卷 3485-3501页

作者： Rahbar, Mahdi Yazdani, Samaneh Islamic Azad Univ North Tehran Branch Dept Elect & Comp Engn Tehran Iran

Monarch butterfly optimization (MBO), which is a simple and widely used algorithm, has some limitations, such as utilizing the obtained experiential knowledge about the search space inefficiently, the lack of exploration, and being rotational variance. This paper proposes a new variation of MBO, which is called knowledge-based MBO (KMBO), to address these limitations. KMBO is proposed by introducing new operators that are linearized and can utilize the population's experimental knowledge. Furthermore, KMBO adopts the re-initialization operator to enhance the exploration ability and increase the diversity of the population. To verify KMBO's performance, it is tested on 23 well-known optimization benchmark functions and compared with MBO and five other state-of-the-art evolutionary algorithms. Experimental results confirm the superior performance of our proposed algorithm compared with MBO in terms of solution accuracy and convergence speed. Also, results demonstrate that KMBO performs better than or provides competitive performance with the other six algorithms. In addition, the real-world application of KMBO on clustering optimization is presented. The results prove that KMBO is applicable to solve real-world problems and achieve superior results.

关键词： Monarch butterfly optimization Numerical optimization clustering optimization Experiential knowledge

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clustering optimization for Triple-Frequency Combined Observations of BDS-3 Based on Improved PSO-FCM Algorithm

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REMOTE SENSING 2022年第15期14卷

作者： Qian, Zhaoyong Cao, Yuhua Sun, Xiaoshuang Ni, Lei Wang, Zhiyu Chen, Xiaowei Space Engn Univ Dept Space Support Beijing 101416 Peoples R China Natl Def Univ Joint Serv Coll Beijing 100858 Peoples R China

The triple-frequency linear combination method can provide combinations with different characteristics and is one of the important methods to improve the performance of navigation services. Due to the large number of combinations and different combination performances, combinatorial clustering optimization is very important, and the efficiency of manual screening is very low. Firstly, based on the basic model, the objective equations are derived. Secondly, based on the fuzzy c-means (FCM) algorithm, three improved PSO-FCM algorithms are proposed, namely the S-PSO-FCM algorithm, L-PSO-FCM algorithm, and LOG-PSO-FCM algorithm. Thirdly, according to the different combination characteristics, the two datasets whose combined coefficients sum to 0 and 1 are emphatically discussed. Finally, the effectiveness of the improved PSO-FCM algorithms is studied based on the public dataset and the measured BeiDou-3 navigation satellite system (BDS-3) data of short baseline, long baseline, and ultra-long baseline. The results show that the performance of the proposed algorithm is better than that of the FCM algorithm, especially in short baseline and long baseline cases.

关键词： triple frequency clustering optimization combination FCM algorithm improved PSO-FCM algorithm

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Study on A New clustering optimization Algorithm and Its Application in Network Faults Analysis

Study on A New Clustering Optimization Algorithm and Its App...

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International Conference on Information Technology and Computer Science

作者： Liu, Peiqi Li, Zengzhi Xian Univ Architecture & Technol Sch Informat & Control Engn Xian 710055 Peoples R China Xi An Jiao Tong Univ Sch Elect & Informat Engn Xian 710049 Peoples R China

ISBN: (纸本)9780769536880

the iterative optimization algorithm is a traditional classification method of the pattern recognition. In the iterative optimization algorithm, the primary center of classes is selected by random method. This choice method causes the iterative time increase greatly in the optimization at anaphase. It also has some serious defects which are the selected samples blindly, the presented a local extremum and the omitted clustering tendency of samples. By the researching and analyzing the iterative optimization algorithm, the newly algorithm, clustering optimization algorithm based on neighborhood of samples distribution, is designed according to the conception of the clustering tendency and neighborhood of patterns in this paper. The time complexity of the newly algorithm is O(n) and n is a number of samples in sets. This algorithm is applied in the faults analysis in network management based on SNMP protocol. The analysis results were consistent with faults type and this algorithm provides a feasible method for faults analysis.

关键词： clustering optimization knowledge classification faults diagnosis

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Analysis of Spatial clustering optimization

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Geo-Spatial Information Science 2008年第4期11卷 302-307页

作者： YANG Jianfeng YAN Puliu XIA Delin GENG Qing School of Electronic information Wuhan University 129 Luoyu Road Wuhan 430079 China

Spatial clustering is widely used in many fields such as WSN (Wireless Sensor Networks), web clustering, remote sensing and so on for discovery groups and to identify interesting distributions in the underlying database. By discussing the relationships between the optimal clustering and the initial seeds, a clustering validity index and the principle of seeking initial seeds were proposed, and on this principle we recommend an initial seed-seeking strategy: SSPG (Single-Shortest-Path Graph). With SSPG strategy used in clustering algorithms, we find that the result of clustering is optimized with more probability. At the end of the paper, according to the combinational theory of optimization, a method is proposed to obtain optimal reference k value of cluster number, and is proven to be efficient.

关键词： data mining spatial clustering SSPG clustering optimization

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Robust RGB: D-SLAM in highly dynamic environments based on probability observations and clustering optimization

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MEASUREMENT SCIENCE AND TECHNOLOGY 2024年第3期35卷 035405-035405页

作者： Liu, Hailin Tian, Liangfang Du, Qiliang Xu, Wenjie South China Univ Technol Sch Automat Sci & Engn Guangzhou 510641 Peoples R China Guangdong Engn Res Ctr Cloud Edge End Collaborat T Guangzhou 510640 Peoples R China South China Univ Technol Sch Mech & Automot Engn Guangzhou 510641 Peoples R China South China Univ Technol Sch Automat Sci & Engn Guangzhou 510640 Peoples R China

Visual simultaneous localization and mapping (SLAM) is the underlying support of unmanned systems. Currently, most visual SLAM methods are based on the static environment assumption so that dynamic objects in the camera's field of view will seriously disrupt its working performance. In view of this, an RGB-D SLAM approach based on probability observations and clustering optimization for highly dynamic environments is proposed, which can effectively eliminate the influence of dynamic objects and accurately estimate the ego-motion of an RGB-D camera. The method contains a dual static map point detection strategy, which is carried out simultaneously in the current and previous frames. First, to enhance tracking robustness in highly dynamic environments, the probabilities of map points being static, calculated by both reprojection deviation and intensity deviation, are used to weight the cost function for pose estimation. Meanwhile, by taking the previous frames as a reference, a static velocity probability based on sparse scene flow is acquired to preliminarily recognize static map points and further improve the tracking accuracy. Then, an improved map point optimization strategy based on K-means clustering is designed, which effectively takes advantage of the clustering algorithm to refine the static map point labels while alleviating its stubborn problem. Finally, the experimental results on the TUM dataset and real scenes compared with the state-of-the-art visual SLAM methods illustrate that the proposed method achieves an extremely robust and accurate performance for estimating camera pose in highly dynamic environments.

关键词： visual SLAM dynamic environments static probability clustering optimization

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A new clustering method for the optimization of distribution networks layout considering energy efficiency and continuity of service

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SUSTAINABLE ENERGY GRIDS & NETWORKS 2022年 30卷

作者： Falabretti, Davide Sabbatini, Gianluca Politecn Milan Dept Energy Via La Masa 34 I-20156 Milan Italy

The electrification of final consumptions and the spreading of distributed generation have fostered the search for new and more advanced electrical grid planning techniques, considering many of the aspects affecting electrical grids operation, such as their electrical and economic performance, as well as system reliability. Distribution network clustering is defined to be the division of the utility service territory into several high voltage-medium voltage substation service areas, and it represents one of the most important steps for grids planning. In this context, the purpose of this paper is to propose an innovative clustering algorithm based on Mixed Integer Linear Programming (MILP) formulation, able to take into account some fundamental aspects of the distribution networks planning process, such as the electrical losses along lines, the existing distribution network layout, and the reliability of the system. The approach, tested on the medium voltage network of the medium-sized city of Meran in the North of Italy, has proved to effectively manage the computational burden needed to model and optimize the structure of a complex distribution grid, as those that usually cover large urban areas. (c) 2022 Elsevier Ltd. All rights reserved.

关键词： Distribution networks clustering optimization Continuity of service Energy efficiency Mixed Integer Linear Programming

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Heuristic Algorithms for Software Modules clustering

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Procedia Computer Science 2025年 258卷 2159-2169页

作者： Ali H. Ali Dujan B. Taha Software Dept. College of Computer Science and Mathematics Mosul University Iraq Computer Science Dept. College of Computer Science and Mathematics Mosul University Mosul Iraq

In the fields of software engineering, the maintenance phase takes the most effort. Software Module clustering (SMC), as a part of reverse engineering, is used for the purpose of creating structural models from the source code enhancing its comprehensibility. Finding the best clusters is an NP-hard problem. The method of software module clustering aims to extract the best clusters, which involves Increase cohesion and reduce coupling. This re-search addresses a set of problems with the aim of solving them, which are drawback found in most previous studies. This paper adopts three main methods to solve Software Modules clustering problem (SMCP), a Teaching-Learning-Based-optimization (TLBO) algorithm, a Black Widow optimization (BWO) algorithm, and the third is a hybrid algorithm combining the previous two methods. These algorithms were used to cluster software modules and were evaluated based on MQ (Modularity Quality), cohesion, coupling, and stability. To determine the optimal number of clusters for each reference program, the Louvain algorithm is used, which works on detecting communities in networks. The study also experintly examined the impact of the chaos, random, Lorenzo methods in the initial generation of solutions on the quality of the results and stability. Considering the results of the experiments conducted on eight standard applications, the hybrid algorithm performs better in SMC problems than the TLBO and BWO. The performance of these algorithms also improves significantly, when their initial populations are generated using the logistic Lorenz and Chaos method rather than a random one. 6.644548537, 6.318318013, and 4.375766992 are the average MQ of the clusters that were formed for the selected casses set using the Hybrid method, BWO, and TLBO, respectively.

关键词： Software Modules clustering Software clustering clustering optimization

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Research on the Text clustering Algorithm based on Latent Semantic Analysis and optimization

Research on the Text Clustering Algorithm based on Latent Se...

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2011 IEEE International Conference on Computer Science and Automation Engineering(CSAE 2011)

作者： Wang Chun-hong Nan Li-Li Ren Yao-Peng Computer Science and Technology ,Yun cheng University, Yun Cheng,044000

The text clustering based on Vector Space Model has problems, such as high-dimensional and sparse, unable to solve synonym and polyseme etc. And meanwhile, k-means clustering algorithm has shortcomings, which depends on the initial clustering center and needs to fix the number of clusters in advance. Aiming at these problems, in this paper, a text clustering algorithm based on Latent Semantic Analysis and optimization is proposed. This algorithm can not only overcome the problems of Vector Space Model, but also can avoid the shortcomings of k-means algorithm. And compared with the text clustering algorithm based on Latent Semantic Analysis and the text clustering algorithm based on Vector Space Model and optimization, our algorithm is proved which can preferably improve the effect of text clustering, and upgrade the precision ratio and recall ration of text.

关键词： text clustering Vector Space Model Latent Semantic Analysis clustering optimization k-means clustering algorithm

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