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Underdetermined blind source separation method based on quantum Archimedes optimization algorithm

APPLIED INTELLIGENCE 2023年第11期53卷 13763-13800页

作者： Gao, Hongyuan Zhang, Zhiwei Wang, Shihao Sun, Helin Harbin Engn Univ Coll Informat & Commun Engn Harbin 150001 Peoples R China

The performance of the existing underdetermined blind source separation methods is very sensitive to the initial parameters, meanwhile, the existing setting or selection methods of initial parameters need to be improved. Consequently, an effective underdetermined blind source separation method is proposed in this paper to solve the above engineering problems. Based on the Archimedes optimization algorithm and quantum computing theory, this paper proposes a novel intelligent algorithm named quantum Archimedes optimization algorithm, which solves the objective functions for engineering problems. Then the optimal solution obtained through the quantum Archimedes optimization algorithm is used as the initial clustering centers of the k-means clustering algorithm to achieve mixing matrix estimation. In addition, the original initial estimation signal setting of the source recovery based on radial basis function network is converted into an initial solution in population for quantum Archimedes optimization algorithm. The optimal solution obtained through the quantum Archimedes optimization algorithm is used as the new initial estimation signal setting to achieve source recovery. The simulation results show that the proposed underdetermined blind source separation method has higher accuracy than previous methods. The proposed method that is more robust and applicable makes the setting and selection of initial parameters more reasonable so that the performance is no longer limited to the initial parameters.

关键词： Underdetermined blind source separation Quantum Archimedes optimization algorithm k-means clustering algorithm Radial basis function network

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Optimizing risk budgets in portfolio selection problem: A bi-level model and an efficient gradient-based algorithm

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IISE TRANSACTIONS 2024年第8期56卷 841-854页

作者： Bayat, Maryam Hooshmand, Farnaz MirHassani, Seyed Ali Amirkabir Univ Technol Tehran Polytech Dept Math & Comp Sci Tehran Iran

Risk budgeting is one of the recent and successful strategies for asset portfolio selection. In this strategy, risk budgets are associated with assets, and the amount of investment is adjusted so that the contribution of each asset to the portfolio risk is proportional to its risk budget. To the best of our knowledge, no specific method has been presented in the literature to systematically determine the value of risk budgets. To fill this research gap, in this article, we consider the risk budgets as decision variables and present a bi-level programming model where the upper level decides the risk budgets and the lower level determines the risk budgeting portfolio. Three approaches are introduced to solve the model. The first is a single-level reformulation of the bi-level model, the second is a novel gradient-based algorithm, and the third is the particle swarm optimization algorithm. Moreover, the k-means clustering method is utilized to determine the assets involved in the portfolio. Computational results over real-world datasets demonstrate the significance of the bi-level model. In addition, the results confirm the proficiency of our gradient-based algorithm from both solution quality and running time.

关键词： Risk budgeting portfolio bi-level optimization gradient descent method k-means clustering algorithm

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Swarm Intelligence-Based Improved Adaptive Chirp Mode Decomposition algorithm for Suppression of Ocular Artifacts from EEG Signal

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IEEE SENSORS JOURNAL 2024年第6期24卷 8314-8325页

作者： Silpa, Bommala Hota, Malaya kumar Vellore Inst Technol Sch Elect Engn Dept Commun Engn Vellore 632014 Tamil Nadu India

Electroencephalogram (EEG) signals are mostly contaminated with ocular artifacts (OAs) due to eye movements and eye blinks. These artifacts make the EEG recordings difficult to analyze and diagnose neurological diseases. Therefore, in this work, we propose a hybrid framework that consists of two stages to remove OAs from the EEG signals. A correlation-based variational mode decomposition (VMD) method in the first stage removes baseline wander (BW) noise. Then, an improved adaptive chirp mode decomposition (IACMD) with continuous wavelet transform (CWT) and k-means clustering algorithm in the second stage detects and removes OAs. The IACMD, in the second stage, is implemented by optimizing ACMD parameters using an improved grey wolf optimization (IGWO) algorithm with a correlation waveform index (Cwi) as a fitness function. The IACMD extracts the modes, and the noisy mode is identified based on the energy value. However, the direct subtraction of the identified noisy mode may eliminate some information from the EEG signal. Hence, in this work, CWT and k-means clustering algorithms are used to estimate the OA-affected interval by separating EEG elements from the noisy mode. Finally, the denoised EEG signal is determined by subtracting the estimated OA signal without impacting the non-artifactual regions. The analysis is carried out on the MIT-BIH polysomnographic and EEG during mental arithmetic tasks (EEGMAT) databases. Compared to the existing techniques, the proposed method shows superior performance in terms of efficiency and preservation of EEG data. Further, the subject-wise and length-wise analysis reveals the robustness of the proposed framework.

关键词： Adaptive chirp mode decomposition (ACMD) continuous wavelet transform (CWT) electroencephalogram (EEG) k-means clustering algorithm ocular artifacts (OAs)

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Factor Modeling for clustering High-Dimensional Time Series

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JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION 2024年第546期119卷 1252-1263页

作者： Zhang, Bo Pan, Guangming Yao, Qiwei Zhou, Wang Univ Sci & Technol China Int Inst Finance Sch Management Dept Stat & Finance Hefei Peoples R China Nanyang Technol Univ Sch Phys & Math Sci Singapore Singapore London Sch Econ & Polit Sci Dept Stat London England Natl Univ Singapore Dept Stat & Data Sci Singapore Singapore

We propose a new unsupervised learning method for clustering a large number of time series based on a latent factor structure. Each cluster is characterized by its own cluster-specific factors in addition to some common factors which impact on all the time series concerned. Our setting also offers the flexibility that some time series may not belong to any clusters. The consistency with explicit convergence rates is established for the estimation of the common factors, the cluster-specific factors, and the latent clusters. Numerical illustration with both simulated data as well as a real data example is also reported. As a spin-off, the proposed new approach also advances significantly the statistical inference for the factor model of Lam and Yao. for this article are available online.

关键词： Eigenanalysis Idiosyncratic components k-means clustering algorithm Strong and weak factors

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Design of New Media Event Warning Method Based on k-means and Seasonal Optimization algorithm

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EAI ENDORSED TRANSACTIONS ON SCALABLE INFORMATION SYSTEMS 2024年第3期11卷 1-12页

作者： Gao, Zhenghan Zheng, Anzhu Qingdao Univ Technol Qingdao 266000 Shandong Peoples R China Shandong Univ Jinan 250000 Shandong Peoples R China

INTRODUCTION: Timely and effective early warning of new media events not only provides academic value to the study of new media events, but also can play a positive role in promoting the resolution of public opinion. OBJECTIVES: Aiming at the current research on early warning of new media events, there are problems such as the theoretical research is not in-depth and the early warning model is not comprehensive. METHOD: In this paper, k-means and seasonal optimization algorithm are used to construct new media event early warning method. Firstly, by analyzing the construction process of new media event early warning system, extracting text feature vector and carrying out text feature dimensionality reduction;then, combining with the random forest algorithm, the new media event early warning method based on intelligent optimization algorithm optimizing k-means clustering algorithm is proposed;finally, the validity and superiority of the proposed method is verified through the analysis of simulation experiments. RESULTS: The method developed in this paper improves the accuracy, time performance of new media event warning techniques. CONCLUSION:Addresses the lack of comprehensiveness of current approaches to early warning of new media events.

关键词： new media event warning methods k-means clustering algorithm seasonal optimization algorithm random forests

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Classification of Tropical Cyclone Tracks in the Northwest Pacific Based on the SD-k-means Model

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APPLIED SCIENCES-BASEL 2025年第11期15卷 6160-6160页

作者： Xu, Nan Yang, Baisong Ren, Jia City Univ Macau Fac Data Sci Macau 999078 Peoples R China Hainan Univ Sch Informat & Commun Engn Haikou 570228 Peoples R China

Tropical cyclone (TC) track clustering plays a crucial role in understanding cyclone movement patterns, which is essential for risk assessment and disaster preparedness. This study proposes an improved SD-k-means clustering algorithm for classifying TC tracks. Using the best-track datasets of TCs from 2000 to 2022, provided by NOAA (National Oceanic and Atmospheric Administration) and JMA (Japan Meteorological Agency), it explores the quantitative relationships between various TC features, such as latitude, longitude, and wind speed, and their motion speed and deflection angles. Based on these analyses, clustering indicators coupled with TC tracks and motion characteristics are identified. To evaluate the model's performance, three clustering methods-standard k-means, DTW (Dynamic Time Warping)-based k-means, and DBSCAN (Density-Based Spatial clustering of Applications with Noise)-are compared using the Calinski-Harabasz (CH) index and the Davies-Bouldin Index (DBI) as evaluation metrics. The experimental results show that the SD-k-means algorithm achieved high consistency across the majority of clustering indices, with the optimal number of clusters determined to be four. The spatial distribution of the clustering results demonstrates that SD-k-means is effective in distinguishing different TC track patterns, providing valuable insights for regional disaster prevention and risk management efforts.

关键词： tropical cyclone track classification soft-DTW algorithm k-means clustering algorithm

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Topology-distance-based clustering method for water distribution network partitioning

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AQUA-WATER INFRASTRUCTURE ECOSYSTEMS AND SOCIETY 2023年第11期72卷 2186-2198页

作者： Du, kun Li, Jiangyun Xu, Wei Liu, Zilian Zheng, Feifei Kunming Univ Sci & Technol Fac Civil Engn & Mech Kunming 650500 Peoples R China Kunming Univ Sci & Technol Intelligent Infrastructure Operat & Maintenance Te Kunming 650500 Peoples R China Zhejiang Univ Coll Civil Engn & Architecture Hangzhou 310058 Peoples R China

Partitioning water distribution networks (WDNs) into district metered areas offers benefits including reduced nonrevenue water and simplified pressure management. However, current research in this field tends to narrowly focus on the topological relationships among nodes, often overlooking the influence of pressure reducing valves (PRVs) and pump stations on clustering results. To address this limitation, this study introduces a topology-distance-based clustering (TDBC) method that enhances the accuracy of partitioning by explicitly considering the impact of PRVs and pump stations. In the TDBC method, the Floyd algorithm is initially employed to construct a topological distance matrix that quantifies the degree of node connectivity. By amplifying topological distances for links including PRVs and pump stations, their effect on clustering results is incorporated. Subsequently, nodes are clustered using the k-means algorithm based on the resulting topology-distance matrix. The proposed TDBC approach is applied to four network cases, and its outcomes are compared with those of two traditional methods. The comparative analysis indicates that the TDBC algorithm achieves precise partitioning results for networks incorporating PRVs or pump stations, while ensuring a harmonious balance between modularity and the uniformity of the partitioning results, even in networks with complex structures and highly interconnected loops.

关键词： community structure detection k-means clustering algorithm partitioning water distribution network

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Integrity-Management Characteristics and Efficiency Evaluation of Oil and Gas Pipelines

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China Petroleum Processing & Petrochemical Technology 2023年第4期25卷 139-150页

作者： Zhang Xiaodong Sun Jiazheng Fu Yong Lei Shaojuan School of Economics and Management University of Science and Technology BeijingBeijing 100083China PetroChina Planning and Engineering Institute Beijing 100083 China Nuclear and Radiation Safety Center Beijing 102444China

Pipeline integrity is a cornerstone of the operation of many industrial systems, and maintaining pipeline integrity is essential for preventing economic losses and ecological damage caused by oil and gas leaks. Based on integritymanagement data published by the US Pipeline and Hazardous Materials Safety Administration, this study applied the k-means clustering and data envelopment analysis(DEA) methods to both explore the characteristics of pipeline-integrity management and evaluate its efficiency. The k-means clustering algorithm was found to be scientifically valid for classifying pipeline companies as either low-, medium-, or high-difficulty companies according to their integrity-management requirements. Regardless of a pipeline company's classification, equipment failure was found to be the main cause of pipeline failure. In-line inspection corrosion and dent tools were the two most-used tools for pipeline inspection. Among the types of repair, 180-day condition repairs were a key concern for pipeline companies. The results of the DEA analysis indicate that only three out of 34 companies were deemed to be DEA-effective. To improve the effectiveness of pipeline integrity management, we propose targeted directions and scales of improvement for non-DEA-effective companies.

关键词： Integrity management k-means clustering algorithm data envelopment analysis safety management

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VkECE-3D: Energy-Efficient Coverage Enhancement in Three-Dimensional Heterogeneous Wireless Sensor Networks Based on 3D-Voronoi and k-means algorithm

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SENSORS 2023年第2期23卷 573-573页

作者： Gou, Pingzhang Guo, Baoyong Guo, Miao Mao, Shun Northwest Normal Univ Coll Comp Sci & Engn Lanzhou 730070 Peoples R China

During these years, the 3D node coverage of heterogeneous wireless sensor networks that are closer to the actual application environment has become a strong focus of research. However, the direct application of traditional two-dimensional planar coverage methods to three-dimensional space suffers from high application complexity, a low coverage rate, and a short life cycle. Most methods ignore the network life cycle when considering coverage. The network coverage and life cycle determine the quality of service (QoS) in heterogeneous wireless sensor networks. Thus, energy-efficient coverage enhancement is a significantly pivotal and challenging task. To solve the above task, an energy-efficient coverage enhancement method, VkECE-3D, based on 3D-Voronoi partitioning and the k-means algorithm is proposed. The quantity of active nodes is kept to a minimum while guaranteeing coverage. Firstly, based on node deployment at random, the nodes are deployed twice using a highly destructive polynomial mutation strategy to improve the uniformity of the nodes. Secondly, the optimal perceptual radius is calculated using the k-means algorithm and 3D-Voronoi partitioning to enhance the network coverage quality. Finally, a multi-hop communication and polling working mechanism are proposed to lower the nodes' energy consumption and lengthen the network's lifetime. Its simulation findings demonstrate that compared to other energy-efficient coverage enhancement solutions, VkECE-3D improves network coverage and greatly lengthens the network's lifetime.

关键词： heterogeneous wireless sensor networks highly destructive polynomial mutation strategy 3D-Voronoi partitioning k-means clustering algorithm polling working mechanism

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Combined relevance vector machine technique and subset simulation importance sampling for structural reliability

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APPLIED MATHEMATICAL MODELLING 2023年 113卷 129-143页

作者： Xie, Bin Peng, Chong Wang, Yanzhong Beihang Univ Sch Mech Engn & Automat Beijing 100191 Peoples R China

Evaluating the failure probability of engineering structures involving implicit performance functions generally requires time-consuming finite element simulations. The computational effort is usually unbearable, especially for small failure probability. This paper proposes a new reliability analysis method combining relevance vector machine and subset simu-lation importance sampling, which guarantees the accuracy of results and improves the computational efficiency. In this method, relevance vector machine is first applied to ap-proximate relatively coarse limit states. Then, subset simulation importance sampling is executed based on the constructed relevance vector machine. To improve the prediction accuracy, samples from first and last level are used to refine relevance vector machine. Since only the samples of last level are involved in the calculation of the failure probabil-ity, and the updated relevance vector machine has a very high prediction accuracy for the samples of last level, the obtained failure probability is extremely accurate. In addition, the samples are predicted by the well-constructed relevance vector machine rather than eval-uated with real performance functions, which drastically decreases the calculation time. To reduce redundant iterations in the update process, a novel learning function based on the current design of experiment position and a stopping condition based on error estimation of reliability prediction are employed. Furthermore, the k-means clustering algorithm is utilized to implement the ability of surrogate model-based subset simulation importance sampling to handle multiple failure mode problems. The efficiency and accuracy of the proposed method are verified by four examples involving different types of features.(c) 2022 Elsevier Inc. All rights reserved.

关键词： Reliability analysis Relevance vector machine Subset simulation importance sampling k-means clustering algorithm Small failure probability

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