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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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The pH paradox

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SCIENCE OF THE TOTAL ENVIRONMENT 2024年 946卷 174099页

作者： More, kagiso Samuel Wolkersdorfer, Christian Tshwane Univ Technol SARChI Chair Mine Water Management Dept Environm Water & Earth Sci Private Bag X680 ZA-0001 Pretoria South Africa Univ South Africa Inst Nanotechnol & Water Sustainabil iNanoWS Coll Sci Engn & Technol Private Bag X6 ZA-1709 Johannesburg South Africa

This paper highlights the critical role of pH or proton activity measurements in environmental studies and emphasises the importance of applying proper statistical approaches when handling pH data. This allows for more informed decisions to effectively manage environmental data such as from mining influenced water. Both the pH and {H + } of the same system display different distributions, with pH mostly displaying a normal or bimodal distribution and {H + } showing a lognormal distribution. It is therefore a challenge of whether to use pH or {H + } to compute the mean or measures of central tendency for further environmental statistical analyses. In this study, different statistical techniques were applied to understand the distribution of pH and {H + } from four different mine sites, Metsa monttu in Finland, Felsendome Rabenstein in Germany, Eastrand and Westrand mine water treatment plants in South Africa. Based on the statistical results, the geometric mean can be used to calculate the average of pH if the distribution is unimodal. For a multimodal pH data distribution, peak identifying methods can be applied to extract the mean for each data population and use them for further statistical analyses.

关键词： Environmental parameters Statistical analyses Data distribution Multimodal distribution Geometric mean 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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Optimizing the eigenvectors of spectral data with an initial clustering approach: Spectral versus colorimetric criteria

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COLOR RESEARCH AND APPLICATION 2022年第4期47卷 866-877页

作者： Mahbadi, Ali Akbar Amirshahi, Seyed Hossein Amirkabir Univ Technol Dept Text Engn Tehran Polytech 350 Hafez Ave Tehran 15875 Iran

clustering of spectral data based on the correlation distance coefficient of the reflectance spectra and the color difference values is presented to minimize the information loss caused by the compression of spectral data when the first three most important eigenvectors are considered. The suggested criteria used a distance function to partition the reflectance spectra of Munsell chips into subgroups using a k-means algorithm. The cumulative energy contributions of the first three eigenvectors of the groups are calculated and compared to those that are computed based on the 10 principal hues of Munsell color system as well as nonclustered spectra. The performance of the suggested methods is evaluated by the colorimetric and spectrophotometric errors between the ground truth and the reconstructed spectra. While the clustering methods yield significantly better results than nonclustered data, the clustering approach based on the correlation distance coefficient of spectral data provides the best overall performance for all considered measures compared to colorimetric clustering strategies.

关键词： color difference value correlation distance k-means clustering algorithm principal component analysis spectral reconstruction

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Design and Implementation of Data Management and Visualisation Module in Financial Digital Management

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JOURNAL OF INFORMATION & kNOWLEDGE MANAGEMENT 2024年第0期

作者： Ren, Junying Henan Inst Econ & Trade Coll Intelligent Finance Zhengzhou 450000 Peoples R China

Enterprise financial data is the key indicator of enterprise development, which provides the important basis for management to analyse and make decisions. Therefore, the provision of reliable and effective information services to enterprises through visualisation technology has become an urgent problem to be solved in the construction of enterprise informatisation. At present, the common data statistics and visualisation tools in the market are difficult to meet the needs of specialised financial enterprises for data analysis. Additionally, the current financial management system has several issues, including an abundance of data and lack of observation suitability. Aiming at the deficiency of data management function in the system, this paper studies the improvement design of data management and visualisation module in financial digital management. First, k-means clustering algorithm and C4.5 decision tree algorithm are selected to improve the financial data management system. Then, through the existing hierarchical data visualisation scheme, the node link method, space filling method and Sankey chart are proposed to display the changes of financial data. Finally, the data management and visualisation module and the corresponding algorithm flow are designed. The experiment indicates a contour coefficient of 0.53 for the performance evaluation model based on the k-means algorithm, indicating a satisfactory clustering result. The employee violation prediction model, based on the C4.5 decision tree algorithm, exhibits a high prediction accuracy of 92.35% for the training dataset, demonstrating its effectiveness in predicting employee violations. The data rendering accuracy of the visualised tool is 98.46%, significantly surpassing that of traditional visualisation tools. At the same time, its visual effect and operation are better than traditional tools. Compared with the traditional data visualisation system, this research method improves the efficiency of enterprise

关键词： Digitalisation financial management data visualisation k-means clustering algorithm C4.5 decision tree algorithm

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Evaluation of black tea appearance quality using a segmentation-based feature extraction method

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FOOD BIOSCIENCE 2024年 58卷

作者： Song, Feihu Lu, Xiaolong Lin, Yiqing Zhou, Qiaoyi Li, Zhenfeng Ling, Caijin Song, Chunfang Jiangnan Univ Sch Mech Engn Jiangsu Key Lab Adv Food Mfg Equipment & Technol Wuxi 214122 Peoples R China Guangdong Acad Agr Sci Tea Res Inst Guangdong Prov Key Lab Tea Plant Resources Innovat Guangzhou 510640 Peoples R China

Compared with highly subjective manual sensory quality evaluation, the application of computer vision techniques in black tea appearance quality evaluation helps to establish an objective and efficient black tea quality evaluation system. In this study, Yinghong No. 9 black tea was taken as the research object, and the gold pekoe, color and strips were adopted as the appearance evaluation characteristics for black tea. An image segmentation method based on the improved k-means clustering algorithm was proposed to realize the segmentation of the dark background area, tea area and golden pekoe area. The CIELAB color model was used to extract color features of the tea area. The texture features extracted by GLRLM were applied to evaluate the strips. The RF, SVR and BPNN were selected to construct prediction models for evaluating tea appearance quality. The prediction accuracy and generalization ability of the RF model are superior to those of the SVR model and BP model, with R2p, RMSEP and RPD values of 0.898, 1.548 and 3.207, respectively. The proposed feature extraction method based on regional segmentation intuitively described the key evaluation characteristics of black tea appearance, and the predicted results were highly consistent with the manual sensory evaluation.

关键词： Black tea k-means clustering algorithm Image segmentation Gray-level run-length matrix Sensory evaluation

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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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An improved method for k-means clustering based on internal validity indexes and inter-cluster variance

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INTERNATIONAL JOURNAL OF COMPUTATIONAL SCIENCE AND ENGINEERING 2022年第3期25卷 253-261页

作者： Zhu, Guangli Li, Xiaoqing Zhang, Shunxiang Xu, Xin Zhang, Biao Anhui Univ Sci & Technol Sch Comp Sci & Engn Huainan Peoples R China

It is difficult to get the best cluster number of k-means clustering algorithm by using the traditional internal validity indexes. Therefore, a good clustering result cannot be obtained usually. To solve this problem, this paper proposes an improved method for k-means clustering based on internal validity indexes and inter-cluster variance. Firstly, some integers distributed in the interval [2, root n] are selected as initial cluster numbers. Further, each initial cluster number is selected to carry out k-means clustering and obtain a clustering result. Secondly, two initial cluster numbers k(D) and k(C) are extracted respectively under two optimal validity index values (i.e., DB and CH). The k(D) and k(C) are extracted by comparing and analysing the validity index values. Finally, the validity index ICS-VAR is proposed to select the best cluster number k(B), while k(D) is not equal to k(C). Experimental results show that the improved method can obtain a better cluster number under a certain condition.

关键词： k-means clustering algorithm internal validity indexes inter-cluster variance initial cluster number

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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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