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Using improved density peak clustering algorithm for flower cluster identification and apple central and peripheral flower detection

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2025年 232卷

作者： Geng, Mingyang Shang, Yuying Xiang, Shiyu Wang, Jiachen Wang, Lei Song, Huaibo Northwest A&F Univ Coll Mech & Elect Engn Yangling 712100 Shaanxi Peoples R China Minist Agr & Rural Affairs Key Lab Agr Internet Things Yangling 712100 Shaanxi Peoples R China Shaanxi Key Lab Agr Informat Percept & Intelligent Yangling 712100 Shaanxi Peoples R China

Apple flower detection and positioning are crucial for the mechanical and chemical thinning of flowers, where typically only one or two of the strongest flowers in each cluster are retained. An improved method is proposed that leverages the YOLOv8n model for accurate flower detection. The DPC algorithm is enhanced to automatically determine the number of flower clusters and accurately identify the central flowers within those clusters. To evaluate the performance of the enhanced Single-Layer DPC algorithm, it was compared with several other clustering methods, including DPC, DPC with Shared Nearest Neighbors (DPC-SNN), K-means, K-medoids, Gaussian Mixture Model (GMM), density-Based Spatial clustering of Applications with Noise (DBSCAN), Spectral clustering (SC), minibatch and 3W-PEDP. The results demonstrated that the proposed method achieved that the Adjusted Mutual Information (AMI) and Adjusted Rand Index (ARI) were 0.7037 and 0.6043, respectively, on the Flame dataset, surpassing the highest scores obtained by other methods (0.5886 and 0.5116, respectively). Additionally, the improved algorithm reduced the deviation between the clustering center produced by the Single-Layer DPC and the true central flower. Overall, the algorithm effectively reduces clustering center deviations, showcasing its capability to accurately detect and position apple flowers.

关键词： Apple Flower Deep Learning Flower Cluster Identification density peak clustering algorithm

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Improved density peak clustering algorithm Based on Choosing Strategy Automatically for Cut-off Distance and Cluster Centre

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TEHNICKI VJESNIK-TECHNICAL GAZETTE 2018年第2期25卷 536-545页

作者： Wang, Limin Li, Mingyang Han, Xuming Zhou, Ruihong Zheng, Kaiyue Liu, Meihan Jilin Univ Finance & Econ Sch Management Sci & Informat Engn Jilin Prov Key Lab Fintech Changchun 130117 Jilin Peoples R China Changchun Univ Technol Sch Comp Sci & Engn Changchun 130012 Jilin Peoples R China

Due to the defect of quick search density peak clustering algorithm required an artificial attempt to determine the cut-off distance and circle the clustering centres, density peak clustering algorithm based on choosing strategy automatically for cut-off distance and cluster center (CSA-DP) is proposed. The algorithm introduces the improved idea of determining cut-off distance and clustering centres, according to the approximate distance that maximum density sample point to minimum density sample point and the variation of similarity between the points which may be clustering centres. First, obtaining the sample point density according to the k-nearest neighbour samples and tapping the sample sorting of the distance to the maximum density point;then finding the turning position of density trends and determining the cutoff distance on the basis of the turning position;finally, in view of the density peak clustering algorithm, finding the data points which may be the centres of the cluster, comparing the similarity between them and determining the final clustering centres. The simulation results show that the improved algorithm proposed in this paper can automatically determine the cut-off distance, circle the centres, and make the clustering results become more accurate. In the end, this paper makes an empirical analysis on the stock of 147 bio pharmaceutical listed companies by using the improved algorithm, which provides a reliable basis for the classification and evaluation of listed companies. It has a wide range of applicability.

关键词： clustering center cut-off distance density peak clustering algorithm maximum density similarity

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New density clustering-based approach for failure mode and effect analysis considering opinion evolution and bounded confidence

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Journal of Systems Engineering and Electronics 2024年第6期35卷 1491-1506页

作者： WANG Jian ZHU Jingyi SHI Hua LIU Huchen School of Management Shanghai UniversityShanghai 200444China School of Materials Shanghai Dianji UniversityShanghai 201306China School of Economics and Management Tongji UniversityShanghai 200092China

Failure mode and effect analysis(FMEA)is a preven-tative risk evaluation method used to evaluate and eliminate fail-ure modes within a ***,the traditional FMEA method exhibits many deficiencies that pose challenges in prac-tical *** improve the conventional FMEA,many modified FMEA models have been ***,the majority of them inadequately address consensus issues and focus on achieving a complete ranking of failure *** this research,we propose a new FMEA approach that integrates a two-stage consensus reaching model and a density peak clus-tering algorithm for the assessment and clustering of failure ***,we employ the interval 2-tuple linguistic vari-ables(I2TLVs)to express the uncertain risk evaluations provided by FMEA ***,a two-stage consensus reaching model is adopted to enable FMEA experts to reach a ***,failure modes are categorized into several risk clusters using a density peak clustering ***,the proposed FMEA is illustrated by a case study of load-bearing guidance devices of subway *** results show that the proposed FMEA model can more easily to describe the uncertain risk information of failure modes by using the I2TLVs;the introduction of an endogenous feedback mechanism and an exogenous feedback mechanism can accelerate the process of consensus reaching;and the density peak clustering of failure modes successfully improves the practical applicability of FMEA.

关键词： failure mode and effect analysis(FMEA) interval 2-tuple linguistic variable(I2TLV) consensus reaching density peak clustering algorithm

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TMsDP: two-stage density peak clustering based on multi-strategy optimization

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DATA TECHNOLOGIES AND APPLICATIONS 2022年第3期58卷 1-27页

作者： Ma, Jie Hao, Zhiyuan Hu, Mo Jilin Univ Sch Business & Management Changchun Peoples R China Jilin Univ Informat Resource Res Ctr Changchun Peoples R China Nanjing Normal Univ Sch Journalism & Commun Dept Network & New Media Nanjing Peoples R China

Purpose The density peak clustering algorithm (DP) is proposed to identify cluster centers by two parameters, i.e. rho value (local density) and delta value (the distance between a point and another point with a higher rho value). According to the center-identifying principle of the DP, the potential cluster centers should have a higher rho value and a higher delta value than other points. However, this principle may limit the DP from identifying some categories with multi-centers or the centers in lower-density regions. In addition, the improper assignment strategy of the DP could cause a wrong assignment result for the non-center points. This paper aims to address the aforementioned issues and improve the clustering performance of the DP. Design/methodology/approach First, to identify as many potential cluster centers as possible, the authors construct a point-domain by introducing the pinhole imaging strategy to extend the searching range of the potential cluster centers. Second, they design different novel calculation methods for calculating the domain distance, point-domain density and domain similarity. Third, they adopt domain similarity to achieve the domain merging process and optimize the final clustering results. Findings The experimental results on analyzing 12 synthetic data sets and 12 real-world data sets show that two-stage density peak clustering based on multi-strategy optimization (TMsDP) outperforms the DP and other state-of-the-art algorithms. Originality/value The authors propose a novel DP-based clustering method, i.e. TMsDP, and transform the relationship between points into that between domains to ultimately further optimize the clustering performance of the DP.

关键词： Data clustering density peak clustering algorithm Merging strategy Pinhole imaging strategy Point-domain Point-domain similarity

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Selecting and prioritizing the electricity customers for participating in demand response programs

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IET GENERATION TRANSMISSION & DISTRIBUTION 2022年第10期16卷 2086-2096页

作者： Asghari, Pardis Zakariazadeh, Alireza Siano, Pierluigi Univ Sci & Technol Mazandaran Dept Elect & Comp Engn POB 48518-78195 Behshahr Iran Univ Salerno Dept Management & Innovat Syst Via Giovanni Paolo II132 Fisciano Italy Univ Johannesburg Dept Elect & Elect Engn Sci Johannesburg South Africa

Demand response (DR) provides an opportunity for customers to play an important role in the operation of the electricity grid by reducing or shifting their electricity usage during peak periods. However, selecting customers to participate in DR programs is challenging. To solve this problem, typical load profiles should be characterized by data mining techniques such as clustering algorithms. Traditional clustering algorithms manually determine the centre of clusters and that the selected centre of clusters may fall into a local optimum. Here, to overcome these issues, a new clustering algorithm based on the density peak clustering algorithm (DPC) and Artificial Bee Colony algorithm (ABC) which is called A-DPC, is implemented to optimally determine the representative load curves. Moreover, by introducing a new priority index, the eligible residential customers are selected for participating in DR programs. Also, to meet the sufficient load reduction in a DR event, the proposed approach suggests a plenty number of residential customers to be called. The result evidence that A-DPC has a stronger global search ability to optimally select the centre of clusters if compared to other clustering algorithms.

关键词： search problems A-DPC artificial bee colony algorithm optimisation pattern clustering peak periods electricity usage representative load curves typical load profiles density peak clustering algorithm electricity grid data mining Artificial Bee Colony algorithm eligible residential customers DR event electricity customers demand response programs demand side management DR programs power engineering computing

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Evaluation on social media health information communication based on machine learning technology

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INTERNET TECHNOLOGY LETTERS 2024年第6期7卷

作者： Lian, Xiaoqing Liang, Cang Li, Jing Baoding Vocat & Tech Coll Media Arts Dept Baoding Peoples R China Baoding Vocat & Tech Coll Media Arts Dept Baoding 071051 Hebei Peoples R China

Social media is an important channel for information dissemination in today's society. All kinds of enterprises, political organization, social organizations, etc. release all kinds of information through social media. This article conducted research and analysis on information on social media and effectively managed it. Machine learning methods can effectively solve the problem of analyzing health information (HI) in social media, thereby improving analysis efficiency and accuracy. This article explored the dissemination of social media HI based on machine learning technology, elaborated on the analysis and research of social media HI dissemination, discussed the importance of social media HI for the audience, and analyzed the empowerment of machine learning in HI dissemination. Through analysis and investigation, the new social media HI dissemination has increased by 0.09% compared with the traditional social media HI dissemination pseudoscience information identification;audience involvement has increased by 0.08;audience professionalism has increased by 0.2. Introducing machine learning into the field of HI content dissemination can help achieve customized production and crowdsourcing of content, from concept to reality, and from theory to practice, and thus trigger a new content revolution, shining new youth and vitality into HI dissemination.

关键词： density peak clustering algorithm health information communication machine learning technology social media

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Speaker Diarization System based on DPCA algorithm For Fearless Steps Challenge Phase-2 21

Speaker Diarization System based on DPCA Algorithm For Fearl...

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Interspeech Conference

作者： Zhang, Xueshuai Wang, Wenchao Zhang, Pengyuan Chinese Acad Sci Key Lab Speech Acoust & Content Understanding Inst Acoust Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9781713820697

This paper describes the ASRGroup team speaker diarization systems submitted to the TRACK 2 of the Fearless Steps Challenge Phase-2. In this system, the similarity matrix among all segments of an audio recording was measured by Sequential Bidirectional Long Short-term Memory Networks (Bi-LSTM), and a clustering scheme based on density peak Cluster algorithm (DPCA) was proposed to clustering the segments. The system was compared with the Kaldi Toolkit diarization system (x-vector based on TDNN with PLDA scoring model) and the Spectral system (similarity based on Bi-LSTM with Spectral clustering algorithm). Experiments show that our system is significantly outperforms above systems and achieves a Diarization Error Rate (DER) of 42.75% and 39.52% respectively on the Dev dataset and Eval dataset of TRACK 2 (Fearless Steps Challenge Phase-2). Compared with the baseline Kaldi Toolkit diarization system and Spectral clustering algorithm with Bi-LSTM similarity models, the DER of our system is absolutely reduced 4.64%, 1.84% and 8.85%, 7.57% respectively on the two datasets.

关键词： speaker diarization speaker cluster density peak clustering algorithm

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Intradialytic blood pressure pattern recognition based on density peak clustering

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JOURNAL OF BIOMEDICAL INFORMATICS 2018年 83卷 33-39页

作者： Wang, Feng Zhou, Jing-yi Tian, Yu Wang, Yu Zhang, Ping Chen, Jiang-hua Li, Jing-song Zhejiang Univ Coll Biomed Engn & Instrument Sci Minist EducCollaborat Innovat Ctr Diag & Treatme Engn Res Ctr EMR & Intelligent Expert SystKey La 38 Zheda Rd Hangzhou 310027 Zhejiang Peoples R China Zhejiang Univ Coll Med Affiliated Hosp 1 Kidney Dis Ctr Hangzhou Zhejiang Peoples R China

End-stage renal disease (ESRD) is the final stage of chronic kidney disease (CKD) and requires hemodialysis (HD) for survival. Intradialytic blood pressure (IBP) measurements are necessary to ensure patient safety during HD treatments and have critical clinical and prognostic significance. Studies on IBP measurements, especially IBP patterns, are limited. All related studies have been based on a priori knowledge and artificially classified IBP patterns. Therefore, the results were influenced by subjective concepts. In this study, we proposed a new approach to identify IBP patterns to classify ESRD patients. We used the dynamic time warping (DTW) algorithm to measure the similarity between two series of IBP data. Five blood pressure (BP) patterns were identified by applying the density peak clustering algorithm (DPCA) to the IBP data. To illustrate the association between BP patterns and prognosis, we constructed three random survival forest (RSF) models with different covariates. Model accuracy was improved 3.7-6.3% by the inclusion of BP patterns. The results suggest that BP patterns have critical clinical and prognostic significance regarding the risk of cerebrovascular events. We can also apply this clustering approach to other time series data from electronic health records (EHRs). This work is generalizable to analyses of dense EHR data.

关键词： Hemodialysis Intradialytic blood pressure patterns Dynamic time warping density peak clustering algorithm

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