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The Classification System of Literary Works Based on k-means clustering

JOURNAL OF INTERCONNECTION NETWORkS 2022年第SUPP01期22卷 2141001-2141001页

作者： Wei, Sanqiang Hou, Hongxia Sun, Hua Li, Wei Song, Wenxia Xingtai Univ Xingtai 054001 Hebei Peoples R China

The plots in certain literary works are very complicated and hinder readers from understanding them. Therefore tools should be proposed to support readers;comprehension of complex literary works supports their understanding by providing the most important information to readers. A human reader must capture multiple levels of abstraction and meaning to formulate an understanding of a document. Hence, in this paper, an Improved k-means clustering algorithm (IkCA) has been proposed for literary word classification. For text data, the words that can express exact semantic in a class are generally better features. This paper uses the proposed technique to capture numerous cluster centroids for every class and then select the high-frequency words in centroids the text features for classification. Furthermore, neural networks have been used to classify text documents and k-mean to cluster text documents. To develop the model based on unsupervised and supervised techniques to meet and identify the similarity between documents. The numerical results show that the suggested model will enhance to increases quality comparison of the existing algorithm and k-means algorithm, accuracy comparison of ALA and IkCA (95.2%), time is taken for clustering is less than 2 hours, success rate (97.4%) and performance ratio (98.1%).

关键词： Literary works k-means clustering algorithm text classification

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Research on Communication Site Planning and Regional clustering Based on Optimal Multivariate Optimization Model

Research on Communication Site Planning and Regional Cluster...

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作者： Yanliang Chen Haoyan Pei Yan Pei Tongyan Li College of Communacation Engineering Chengdu University of Information Technology

In recent years,in the context of the continuous development of 5G networks,the construction of mobile communication mobile base stations is extremely important for the overall network environment construction in my *** sites in the objective function are planned multiple times through the immune genetic algorithm,and finally the optimal site is re-planned and the weak coverage points are clustered.

关键词： Optimal multivariate model simulated annealing algorithm immune genetic algorithm k-means clustering algorithm DBSCAN density clustering algorithm class algorithm

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Detection and diagnosis of process fault using unsupervised learning methods and unlabeled data

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INTERNATIONAL JOURNAL OF ADVANCES IN ENGINEERING SCIENCES AND APPLIED MATHEMATICS 2023年第1期15卷 24-36页

作者： Rahoma, Abdalhamid Imtiaz, Syed Ahmed, Salim khan, Faisal Mem Univ Newfoundland Fac Engn & Appl Sci Ctr Risk Integr & Safety Engn St John NF Canada Texas A&M Univ Mary Kay OConnor Proc Safety Ctr Dept Chem Engn Artie McFerrin College Stn TX USA Mem Univ Newfoundland Fac Engn & Appl Sci Ctr Risk Integr & Safety Engn C RISE 240 Prince Philip Dr St John NF A1B 3X5 Canada

Supervised learning methods, commonly used for process monitoring, require labeled historical datasets for normal condition as well for each faulty condition, which demands significant effort in data mining. This article proposes a methodology combining principal component analysis (PCA) with the k-means clustering algorithm to automate detection and diagnosis of fault from unlabeled data. The k-means algorithm is used for fault detection and diagnosis by exploiting PCA for data mining. PCA is able to precisely detect and diagnose fault from large set of unlabeled historical data. The proposed method improves the online diagnosis by using clustering algorithm in the monitoring stage. Based on the Euclidean distance between each dataset and cluster centroid of the training data, the k-means clustering algorithm is able to decide if the process is at a normal state or belongs to a particular faulty state. To illustrate the effectiveness of the methodology, the proposed method is applied to two industrial processes: (i) a separator unit from an offshore gas processing platform and (ii) a distillation column of a crude refining unit. The results show that the proposed method is able to avoid the data labeling exercise and is effective in detecting and diagnosing fault in large-scale industrial processes.

关键词： Principal component analysis (PCA) Fault detection and diagnosis (FDD) k-means clustering algorithm

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Public Management Model Based on Mathematical Equation of Statistics of Probability Theory

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APPLIED MATHEMATICS AND NONLINEAR SCIENCES 2023年

作者： Dong, Jiansu China Univ Geosci Wuhan Sch Publ Adm Wuhan 430070 Peoples R China

Firstly, this paper establishes a cloud computing public opinion management business model based on distributed collection analysis, consultative decision and concurrent collaborative behavior. At the same time, this paper studies the public opinion management system from the logical framework, network structure and security index. Then this paper uses the weak correlation mining technology of probability theory statistics to establish a public health and safety public opinion prediction system composed of three dimensions. The content includes three levels of public opinion bonding, thermal and trend. Then, this paper forecasts the development of public opinion events by combining k-means clustering and grey correlation degree. The results show that this method is suitable for public opinion prediction in public health and safety.

关键词： Network public opinion Public health safety incidents k-means clustering algorithm Grey correlation analysis The warning level

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Lithium battery surface defect detection based on the YOLOv3 detection algorithm 10

Lithium battery surface defect detection based on the YOLOv3...

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10th International Symposium on Precision Mechanical Measurements

作者： Lang, Xianli Zhang, Yu Shu, Shuangbao Liang, Huajun Zhang, Yuzhong Hefei Univ Technol Sch Instrument Sci & Optoelect Engn Anhui Prov Key Lab Measuring Theory & Precis Inst Hefei 230009 Peoples R China

ISBN: (数字)9781510649941

ISBN: (纸本)9781510649941;9781510649934

With the continuous development of science and technology, cylindrical lithium batteries, as new energy batteries, are widely used in many fields. In the production process of lithium batteries, various defects may occur. To detect the defects of lithium batteries, a detection algorithm based on convolutional neural networks is proposed in this paper. Firstly, image preprocessing is introduced on the collected lithium battery dataset. Secondly, the k-means clustering algorithm is used on the preprocessed dataset to generate anchor boxes for lithium battery defect detection. Then the detection network YOLOv3 is trained with the given dataset. Finally, the detection network YOLOv3 is applied to output the type and location information of the defect. The experimental results show that the mean average precision (mAP) value of the detection algorithm on the lithium battery validation dataset reaches 94% and the detection speed is 25 frames per second. The proposed algorithm can effectively locate and classify the bottom defects of the lithium battery.

关键词： Defect detection Convolutional neural network Image processing k-means clustering algorithm YOLOv3

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RETRACTED: Pressure source of athletes in field based on improved hierarchical k-means algorithm (Retracted Article)

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INTERNATIONAL JOURNAL OF ELECTRICAL ENGINEERING EDUCATION 2021年第1-Sup期60卷 2300-2314页

作者： Wu, Qian Jilin Sport Univ Coll Changchun 130000 Jilin Peoples R China

Nowadays, with the development of economy, sports industry attracts more and more people's attention, and the number and scale of sports events continue to expand. But the fierce competition, for athletes, is not only physical competition, psychological competition is particularly important. Psychological pressure is an important factor that affects athletes to play at a high level in the competition field. How to analyze the pressure source of athletes in the competition field is very important for athletes to overcome the competition pressure. Therefore, this paper designs a method based on the improved hierarchical k-means clustering algorithm (kMCA) to analyze the pressure source of athletes in the competition field, so as to help athletes overcome the pressure on the competition field and promote their high-level performance. In this method, firstly, 182 athletes were investigated by questionnaire to obtain the data of related psychological pressure sources. Secondly, because kMCA is sensitive to the selection of initial class center, the performance of kMCA is directly related to the selection of initial class. Aiming at the problem of kMCA, this paper proposes an improved hierarchical kMCA. Finally, the improved hierarchical kMCA is applied to the clustering analysis of sports competition pressure source data. Through simulation analysis, compared with kMCA, the improved layered kMCA proposed in this paper has a good performance improvement. The improved hierarchical kMCA proposed in this paper can be applied to the analysis of the pressure source of athletes in the competition field, which can analyze the pressure of athletes and get the characteristics of the pressure of athletes, so as to help athletes overcome the pressure in the competition field and achieve the mental health of the competition field?

关键词： Sports events psychological stress stress source analysis k-means clustering algorithm

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The application of landscape character classification for spatial zoning management in mountainous protected areas-A case study of Laoshan national park, China

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HELIYON 2023年第3期9卷 e13996页

作者： Zhao, Ye Huang, Xinyi Zhao, Yijun Liu, Xinyu Zhou, Ranjiamian Qingdao Univ Technol Coll Architecture & Urban Planning Qingdao Peoples R China

National parks provide a considerable number of co-benefits to society, including the balance of ecosystems, conservation of heritage values, and tourism. However, studies on zoning approaches for the management of national parks are lacking. The landscape characterization approach is a holistic method for identifying regional landscapes and helps improve zoning management, thus promoting sustainable planning. Here, we propose a landscape character classification (LCC) approach for national parks by integrating a k-means clustering algorithm and geographic information system (GIS). We used Laoshan National Park (LNP) as a case study and aimed to (1) quantify the major landscape factors (altitude, topography relief, soil type, and heritage impact intensity) that influence the landscape classification of mountainous protected areas;(2) create a map of landscape character types and areas to guide a zoning boundary;and (3) further examine how decision makers assign different conservation strategies to each landscape character area. Our results indicate that different landscape character areas reflect distinct ecological environments and heritage values and that differentiated zoning management can effectively mitigate the impact of natural disasters and human activities. Our study suggests that national parks require scientific landscape character zoning, rational descriptions of landscape character types, and targeted management measures to achieve the dual objectives of zoning and landscape conservation.

关键词： Landscape character classification Zoning management k-means clustering algorithm Laoshan national park Landscape conservation strategies Mountainous protected areas

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Factors and Models Influencing Value Co-Creation in the Supply Chain of Collection Resources for Library Distribution Providers Under Data Ecology

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2023年第9期14卷 388-397页

作者： Lin, Xiaoyun Guangzhou Coll Technol & Business Guangzhou 510850 Peoples R China

Under the data ecology, the advancement of relevant technology and the utilization of relevant resources have provided more efficient technical services for various industries. However, with the proliferation of data resources, problems such as information pollution and data redundancy have arisen in the process of supply chain services for collection resources. For solving such problems and enhancing the collection resource supply efficiency of librarians, the study uses data mining technology combined with improved k-means clustering algorithm to design a value co-creation model of library collection resource supply chain for librarians under data ecology. The outcomes indicate that the shortest running time of traditional k-means algorithm is 40ms and the longest running time is 115ms in Wine dataset, and the running time of improved k-means algorithm is stable at 59ms;the shortest running time of traditional k-means algorithm is 26 ms and the longest running time is 58ms in Iris dataset, and the running time of improved k-means algorithm is stable at 53 ms. The clustering accuracy of the improved k-means algorithm in the Wine data set is 98.2%, which is 0.3% exceeding the traditional k-means algorithm, which is 97.9%;the clustering accuracy in the Iris data set is 100%, which is 2.4% exceeding the traditional k-means algorithm, which is 97.6%. In summary, it can be seen that the studied data ecology has a good application of the factors and models influencing the value co-creation of the supply chain of library resources for library dispensers.

关键词： Resource supply chain data mining value co-creation k-means clustering algorithm pavilion dispenser

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STUDY OF WIRE SEGMENTING algorithm OF SUBSTATION EQUIPMENT BASED ON 3D POINT CLOUD

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2023年第6期19卷 1785-1803页

作者： Wang, Pengshuai Luo, Yong Wang, Heshan Zhang, Haiquan Zhengzhou Univ Sch Elect & Informat Engn 100 Sci Ave Zhengzhou 450001 Peoples R China

In this paper, a cone search method is proposed to segment the wire from substation equipment based on three-dimensional (3D) point cloud. On the one hand, the segmentation of the wire which connects to substation equipment can improve the recognition rate of the point cloud of substation equipment. On the other hand, it can reduce operation workload and improve the efficiency of the intelligent identification of substation equipment. In the process of wire segmentation, first, the position of substation device is located by rotating, and two endpoints of the wire are found. Second, other endpoints of the wire are found by using k-means clustering algorithm and the nearest neighborhood search algorithm. Then use linear fitting based on three-dimensional point cloud to search the data of wire and solve the problem of defective wire points. Finally, using the cone search to confine search direction, find out the wire points and remove it. The key findings of this study include the following. (a) A cone search method proposed to divide wires from substation equipment in 3D point cloud is effective, and (b) using linear fitting based on three-dimensional point cloud can solve the problem of defective wire points. Experiments show that using the method can remove the wire well in substation equipment.

关键词： Wire segmenting k-means clustering algorithm Linear fitting Substation equipment Cone search method

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Research on the Application of YOLOv3-Tiny algorithm in the Detection of Circuit Experiment Equipment 2021

Research on the Application of YOLOv3-Tiny Algorithm in the ...

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2nd International Conference on Artificial Intelligence and Information Systems (ICAIIS )

作者： Liang, Mingju Wang, Zhenguo Wang, Yang Guangdong Shunde Innovat Design Inst Foshan Peoples R China Zhengzhou Univ Sch Informat Engn Zhengzhou Peoples R China

ISBN: (纸本)9781450390200

In the physical circuit experiment teaching of junior high school, the equipment detection link before the experiment can be completed by the target detection algorithm based on embedded devices. In order to solve the problems of slow detection speed and false detection and missed detection in the traditional target detection algorithm on embedded devices, a real-time target detection model based on YOLOv3-Tiny is proposed. By data augmentation of self-made circuit experimental equipment data set, the problem of electronic equipment category imbalance is solved. k-means clustering algorithm is used to obtain the optimal Anchor size and number of circuit experimental equipment data sets, and the parameters of YOLOv3-Tiny algorithm are improved. The test results show that the improved YOLOv3-Tiny algorithm can achieve a detection accuracy of 94.61% and a detection frame rate of 39 frame/s on the NPU processor of the embedded device Rk3399Pro development board.

关键词： Target Detection YOLOv3-Tiny embedded NPU processor k-means clustering algorithm

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