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Double-Layer K-Means plus plus clustering Method for Evaluation of Dispatchable Potential of Massive Regional 5G Base Stations

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IEEE ACCESS 2022年 10卷 82870-82882页

作者： Zhong, Lijun Zhu, Mengting Chen, Shuiyao Shi, Minda Zhang, Xianglong Chen, Ding State Grid Zhejiang Elect Power Co Jiaxing Power Supply Co Jiaxing 314000 Peoples R China Zhejiang Univ Coll Elect & Engn Hangzhou 310027 Peoples R China State Grid Zhejiang Elect Power Co Ltd Hangzhou 310000 Peoples R China State Grid Econ & Technol Res Inst Co Ltd Beijing 102209 Peoples R China

5G base stations (BSs), which are the essential parts of the 5G network, are important user-side flexible resources in demand response (DR) for electric power system. However, a 5G BS has little and difference dispatchable potential, how to make massive 5G BSs participate in DR conveniently is an urgent problem to be solved. clustering is an effective solution. Aiming at the special requirements of big data analysis and dispatching difficulties brought by the massive and ultra-dense distribution of 5G BSs, this paper proposes a double-layer K-means++ clustering method for regional 5G BSs considering the main characteristics of 5G BSs. The proposed clustering method considers both the geographical location and power consumption characteristics of the regional 5G BSs to partition the 5G BSs into appropriate number of clusters. Besides, the comprehensive charging and discharging dispatchable potential evaluation indices are established based on the clustering results to evaluate the dispatchable potential of each cluster. The effectiveness of the proposed method and the dispatchable potential evaluation indices are verified through a case of 5000 5G BSs in Jiaxing, China. The simulation findings reveal that the performance of the proposed clustering method is better than classical clustering algorithm and the dispatchable potential of the 5G BSs clusters can be accurately analyzed by the proposed comprehensive indices, which lay the foundation for friendly and fine interaction between 5G BSs and the power grid.

关键词： 5G mobile communication Power demand Batteries clustering methods Base stations clustering algorithms Indexes 5G base station K-means plus plus clustering dispatchable potential evaluation big data analysis demand response

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Unsupervised Feature Selection Via Orthogonal Basis clustering and Local Structure Preserving

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022年第11期33卷 6881-6892页

作者： Lin, Xiaochang Guan, Jiewen Chen, Bilian Zeng, Yifeng Xiamen Univ Dept Automat Xiamen 361005 Peoples R China Xiamen Key Lab Big Data Intelligent Anal & Decis Xiamen 361005 Peoples R China Northumbria Univ Dept Comp & Informat Sci Newcastle Upon Tyne NE1 8ST Tyne & Wear England

Due to the ``curse of dimensionality'' issue, how to discard redundant features and select informative features in high-dimensional data has become a critical problem, hence there are many research studies dedicated to solving this problem. Unsupervised feature selection technique, which does not require any prior category information to conduct with, has gained a prominent place in preprocessing high-dimensional data among all feature selection techniques, and it has been applied to many neural networks and learning systems related applications, e.g., pattern classification. In this article, we propose an efficient method for unsupervised feature selection via orthogonal basis clustering and reliable local structure preserving, which is referred to as OCLSP briefly. Our OCLSP method consists of an orthogonal basis clustering together with an adaptive graph regularization, which realizes the functionality of simultaneously achieving excellent cluster separation and preserving the local information of data. Besides, we exploit an efficient alternative optimization algorithm to solve the challenging optimization problem of our proposed OCLSP method, and we perform a theoretical analysis of its computational complexity and convergence. Eventually, we conduct comprehensive experiments on nine real-world datasets to test the validity of our proposed OCLSP method, and the experimental results demonstrate that our proposed OCLSP method outperforms many state-of-the-art unsupervised feature selection methods in terms of clustering accuracy and normalized mutual information, which indicates that our proposed OCLSP method has a strong ability in identifying more important features.

关键词： Feature extraction Matrix decomposition Optimization clustering algorithms Symmetric matrices Sparse matrices Manifolds Locality preserving orthogonal basis clustering theoretical analysis unsupervised feature selection

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Multi-View Transfer clustering Algorithm Based on Gaussian Mixture Model

Multi-View Transfer Clustering Algorithm Based on Gaussian M...

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2024 International Conference on Fuzzy Theory and Its Applications, iFUZZY 2024

作者： Jiang, Yan Li, Xingchen Zhou, Jin Han, Shiyuan Du, Tao Yang, Cheng Liu, Bowen University of Jinan Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan250022 China

ISBN: (纸本)9798350352788

It is well known that the Gaussian mixture model (GMM) is an exemplary model-based clustering approach that captures data distribution patterns to facilitate clustering. However, when it faces to multi-view clustering tasks, the classical GMM algorithm often encounters limitations in scalability. Multi-view clustering entails employing diverse views or feature sets to cluster data, thereby achieving comprehensive and accurate clustering outcomes. The classical GMM algorithm fails to fully exploit the correlated information across multiple views, resulting in a decrease in clustering accuracy. This paper proposes a new GMM-based trans-fer multi-view clustering algorithm named MT-GMM. This algorithm transfers valuable knowledge from other views to guide and enhance the clustering task of the current view, thereby improving the clustering performance specifically for the current view. Experimental results conducted on real-world datasets demonstrate that the proposed MT-GMM outperforms several other multi-view clustering methods, as it can effectively harness the complementary information between different views accurately capture the structures of data. © 2024 IEEE.

关键词： clustering algorithms

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Compositional Verification Using Geodesic Distance via Assume-Guarantee Reasoning

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IEEE ACCESS 2024年 12卷 92612-92621页

作者： Liu, Xiaoyan Henan Polytech Univ Sch Software Jiaozuo 454000 Peoples R China

Assume-guarantee reasoning is indeed an effective compositional verification technique that can help mitigate the state explosion problem in model checking. However, the biggest challenge of applying assume-guarantee reasoning is how to best decompose a system. This paper presents a novel compositional verification frame based on geodesic distance. The proposed algorithm introduces novel techniques to decompose system components into groups. The algorithm's effectiveness and efficiency are evaluated through a comparative analysis with four state-of-the-art methods commonly used in the field. The results of the comparison consistently demonstrate that the proposed algorithm outperforms the state-of-the-art methods. This implies that the new algorithm exhibits superior performance in terms of decomposition quality and verification efficiency.

关键词： Partitioning algorithms Model checking clustering algorithms Analytical models Cognition Logic WebRTC compositional verification assume-guarantee reasoning group technology combinatorial testing geodesic distance partition

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An Energy-Efficient Small-Cell Operation Algorithm for Ultra-Dense Cellular Networks

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IEEE ACCESS 2024年 12卷 191650-191660页

作者： Hasan, MD. Mehedi Haq, MD. Shayanul Hossain, MD. Maruf Tiash, Junaed Kiron Kwon, Sungoh Amer Int Univ Bangladesh Dept Comp Sci Dhaka 1229 Bangladesh Univ Ulsan Sch Elect Engn Ulsan 44610 South Korea

The ultra-dense network (UDN) incorporates a high densification of small cells, resulting in improved capacity and coverage for a fifth-generation (5G) cellular network. However, the proliferation of small cells within the 5G network has led to a notable surge in energy consumption. To improve energy efficiency in a UDN, this paper proposes a small-cell operation algorithm considering sleep mode and active mode switching for small cells. The algorithm periodically monitors the radio resource status of the macro cells and small cells for the mode-switching process. If a macro cell with a lower radio load is detected, the algorithm triggers a small cell sleep mode switching. To that end, the algorithm finds a small cell with the lowest radio load within the coverage of the macro cell. Therefore, the algorithm switches the small cell to sleep mode estimating the accumulated load in the macro. An active mode switching is triggered by the algorithm when a macro cell with a high radio load is detected. The algorithm then generates a location dataset consisting of the location information of inactive small cells and static UEs. A clustering technique is adopted on the dataset to find clusters containing at least one small cell. Considering the average reported reference signal received power (RSRP) of the UEs, a cluster is selected, and the small cells in the cluster are switched to active mode. Through system-level simulations, the performance of the proposed algorithm is evaluated. The simulation results showed that the proposed approach can ensure a higher energy efficiency than previous algorithms.

关键词： Switches Energy efficiency Base stations Quality of service clustering algorithms Heuristic algorithms Power demand Costs 5G mobile communication Ultra-dense networks Ultra-dense cellular network energy efficiency user equipment physical resource block quality of service throughput

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Adaptive Beampattern Synthesis for Partially-Clustered Array Through K-Means and Amplitude-Only MVDR

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IEEE SIGNAL PROCESSING LETTERS 2025年 32卷 2129-2133页

作者： Lee, Yinman Natl Chi Nan Univ Dept Elect Engn Nantou 54561 Taiwan

Partially-clustered arrays (PCA) are recognized for offering a balanced compromise between beamforming performance and implementation complexity. In this letter, leveraging the information of both pointing and nulling directions, we present a low-complexity method aimed at optimizing not only the phase-shift and amplitude values but also the configuration of clustering for adaptive beampattern synthesis. Simulation results underscore the efficacy of our approach, demonstrating its output signal-to-interference-plus-noise ratio (SINR) performance consistently close to that of the minimum variance distortionless response (MVDR) beamformer, and markedly superior to the conventional excitation matching (EM) based PCA approach.

关键词： Principal component analysis Antenna arrays Vectors Phase shifters Signal to noise ratio Array signal processing Tuners Optimization Directive antennas clustering algorithms Partially-clustered array (PCA) subarray conventional beamformer MVDR beamformer K-means clustering

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Energy-Saving clustering Routing Protocol for Wireless Sensor Networks Using Fuzzy Inference

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IEEE SENSORS JOURNAL 2022年第3期22卷 2845-2857页

作者： Hou, Jun Qiao, Jianhua Han, Xinglong Taiyuan Univ Sci & Technol Elect Informat Engn Dept Taiyuan 030024 Peoples R China

One of the most important researches in wireless sensor networks is energy saving. The clustering algorithm can efficiently save energy. However, most of the existing clustering algorithms use a fixed cluster head election algorithm and perform each round of clustering, so that unsuitable nodes are continuously elected as cluster heads, leading to rapid exhaustion of node energy and short network life. In order to solve these problems, this paper proposes an Energy-saving Fuzzy clustering Routing algorithm (EFCR) for wireless sensor networks,which designs two types of clustering, clustering Type1 and clustering Type2, and alternately executes them in different rounds according to the threshold. clustering Type1 considers the parameters that affect the energy consumption of the node: the distance from the node to the base station, the number of neighbor nodes of the node, and the remaining energy of the node as fuzzy inputs, and calculates the fitness of the node as a cluster head through fuzzy inference to avoid the occurrence of unsuitable nodes being successively elected as cluster heads. clustering Type2 performs data transmission by trusting the last round of cluster heads, which reduces the number of control data packets in each round of clustering and reduces the overall energy consumption of the network. This paper conducted simulations in four different scenarios, compared other clustering algorithms of the same type, and analyzed the remaining energy of the network, the number of surviving nodes, and the life of the network. The results show that EFCR algorithm is better than others.

关键词： clustering algorithms Heuristic algorithms Inference algorithms Wireless sensor networks Sensors Fuzzy logic Base stations Cluster routing protocol energy-saving routing algorithm fuzzy inference system wireless sensor network

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An Online Attack Decision Method for Unmanned Aerial Vehicle Cluster in Uncertain Environments

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IEEE SENSORS JOURNAL 2024年第11期24卷 18457-18466页

作者： Su, Wenjia Gao, Min Gao, Xinbao Fang, Dan Xuan, Zhaolong Army Engn Univ PLA Natl Demonstrat Ctr Expt Ammunit Support & Safety Shijiazhuang 050003 Peoples R China Army Engn Univ PLA Key Lab Ammunit Support & Safety Evaluat Shijiazhuang 050003 Peoples R China Army Engn Univ Shijiazhuang Campus Shijiazhuang 050003 Peoples R China

Viewing stationary targets with priority order in an uncertain environment, the cooperative strike decision of distributed unmanned aerial vehicle (UAV) cluster is explored in this article. With regard to the decision for UAV cluster in dynamic environments, it has always been a difficult problem. In this article, a Q-learning-based attack decision method is proposed, and a two-layer Q-network is designed, aiming at solving the decision problem of UAVs in cruise in terms of attack time and attack plan. The upper Q network is used to find the time to attack the target during the cruise, while the lower Q network is used to determine the attack plan based on the current position of the UAV and the target after the attack time is determined. Ultimately, the effectiveness of our proposed method is validated through simulation, with comparative simulations against the dual-layer crayfish optimization algorithm (Dual-COA) demonstrating the superiority of our approach. This provides a reference for future operational endeavors of UAV clusters.

关键词： Autonomous aerial vehicles Task analysis Heuristic algorithms Mathematical models clustering algorithms Uncertainty Sensors Online attack decision Q-learning unmanned aerial vehicle (UAV) cluster

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TM-search: An Efficient and Effective Tool for Protein Structure Database Search

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JOURNAL OF CHEMICAL INFORMATION AND MODELING 2024年第3期64卷 1043-1049页

作者： Liu, Zi Zhang, Chengxin Zhang, Qidi Zhang, Yang Yu, Dong-Jun Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Peoples R China Jingdezhen Ceram Univ Comp Dept Jingdezhen 333403 Peoples R China Univ Michigan Dept Computat Med & Bioinformat Ann Arbor MI 48109 USA

The quickly increasing size of the Protein Data Bank is challenging biologists to develop a more scalable protein structure alignment tool for fast structure database search. Although many protein structure search algorithms and programs have been designed and implemented for this purpose, most require a large amount of computational time. We propose a novel protein structure search approach, TM-search, which is based on the pairwise structure alignment program TM-align and a new iterative clustering algorithm. Benchmark tests demonstrate that TM-search is 27 times faster than a TM-align full database search while still being able to identify similar to 90% of all high TM-score hits, which is 2-10 times more than other existing programs such as Foldseek, Dali, and PSI-BLAST.

关键词： clustering algorithms

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An Ensemble Learning Algorithm Based on Density Peaks clustering and Fitness for Imbalanced Data

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IEEE ACCESS 2022年 10卷 116120-116128页

作者： Xu, Hui Liu, Qicheng Yantai Univ Sch Comp & Control Engn Yantai 264005 Peoples R China

In view of the low classification accuracy of the minority class in imbalanced data, an algorithm called DPF-EL (density peaks and fitness combined with ensemble learning) based on density peaks clustering and fitness is proposed. Firstly, this method uses the density peaks clustering algorithm to divide the majority class into different sub-clusters, the local density calculated in the clustering process is used to assign weights to each sub-cluster, and the number of under-sampling is determined by the weights. Secondly, the concept of fitness is introduced into the sub-clusters, the selection probability of the samples is calculated according to the size of their fitness, and the majority class is under-sampled based on the selection probability. Finally, combined with boosting algorithm, iterative training is performed on the balanced data set. Experimental tests were conducted with KEEL imbalanced data sets, and the experimental results show that the performance of DPF-EL algorithm is better than other algorithms, which indicates the feasibility of the proposed algorithm.

关键词： Classification algorithms clustering algorithms Probability Ensemble learning Partitioning algorithms Machine learning algorithms Decision trees Imbalanced data density peaks clustering fitness under-sampling classification

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