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fuzzy clustering algorithm-based teaching resource recommendation method for automotive engine overhaul

JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2024年第6期24卷 3762-3771页

作者： Huang, Chaoqun Chongqing Technol & Business Inst Intelligent Mfg & Automobile Dept 15Siyuan RdUniv Pk Chongqing Hechuan Peoples R China Chongqing Open Univ Intelligent Mfg & Automobile Dept Chongqing Peoples R China

At present, the traditional teaching resource recommendation algorithm mainly constructs the evaluation matrix of teaching resources and determines the recommendation order according to the evaluation value, which lacks the grasp of user preferences and leads to poor recommendation effect. Therefore, a method of recommending teaching resources for automobile engine overhaul based on fuzzy clustering algorithm is proposed. This paper constructs the algorithm flow of automobile engine overhaul teaching resources recommendation, describes the K-means clustering algorithm and the improved K-means clustering algorithm, analyzes the user characteristics by using the clustering fuzzy algorithm, calculates the similarity between user interests and resources, and completes the recommendation of automobile engine overhaul teaching resources. Experiments verify the recommendation accuracy of this method, and the results show that this method has low MAE value and high recommendation accuracy when it is used to recommend teaching resources.

关键词： fuzzy clustering algorithm automotive engines teaching resources recommendation algorithm

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A fuzzy clustering algorithm with Local Contextual Information and Gaussian Function for Simultaneous Brain MR Image Segmentation and Intensity Inhomogeneity Estimation 2

A Fuzzy Clustering Algorithm with Local Contextual Informati...

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2nd International Conference on Man and Machine Interfacing (MAMI)

作者： Mahata, Nabanita Kahali, Sayan Sing, Jamuna Kanta Adhikari, Sudip Kumar Jadavpur Univ Dept Comp Sci & Engn Kolkata 700032 India Cooch Behar Govt Engn Coll Cooch Behar India

ISBN: (纸本)9781538629895

In this paper, we present a fuzzy clustering algorithm by integrating local contextual information and a Gaussian function for simultaneous brain MR image segmentation and intensity inhomogeneity estimation. For each pixel, a local contextual information is integrated due to highly correlation between the image pixels and used to define its fuzzy membership function to belong into a tissue type. Whereas, a Gaussian surface is fitted over each tissue region using the local image gradients to estimate the intensity inhomogeneity (IIH). In doing so, we have introduced global and local membership functions for each pixel. The combined IIH is iteratively removed from the image and the final segmentation result is obtained based on the global membership values. The simulation results on brain MR images show its superiority over other fuzzy-based clustering algorithms.

关键词： Brain MR image segmentation fuzzy clustering algorithm intensity inhomogeneity bias field

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Video Image Analysis of Sports Skill Training on Account of fuzzy clustering algorithm 4th

Video Image Analysis of Sports Skill Training on Account of ...

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4th International Conference on Machine Learning, Image Processing, Network Security and Data Sciences (MIND)

作者： Lu, Zhongwen Yili Normal Univ Sch Phys Educ Yining Xinjiang Peoples R China

ISBN: (纸本)9783031243660;9783031243677

With the development of Internet and audio and video technology, a large amount of training in the field of sports is stored in audio and video formats in various image analysis files. Professionals edit and optimize the audio and video of various activities in a large number of sports fields, which allows most netizens to check andwatch, and refine the processing of this kind of video. In order tomeet themarket demand, all kinds of audio and video resources are modified, processed, and carefully packaged, so that the product has a new look. Sports skills training video image analysis application in the development of the Internet and audio and video technology to break through the obstacles of various important social issues in the field of video image analysis and application. This article studies a series of theories and knowledge of image analysis application in sports field, reveals the concept and definition of fuzzy clustering algorithm, and carries on application case disposal to sports skills training video image analysis application. By analyzing the actual effect of video images, based on fuzzy clustering algorithm for sports skills training video image analysis application study, test result shows that sports skills training based on fuzzy clustering algorithm video image analysis application in sports skill training video image analysis applications in image processing complexity, collaborative filterability, The efficiency of denoising and spatial accuracy is 83.32%, 90.01%, 92.25% and 98.10%.

关键词： fuzzy clustering algorithm Sports skills Training video Image analysis application

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An Approach for Construction of Fault Trees Using Rough Set and fuzzy clustering algorithm

An Approach for Construction of Fault Trees Using Rough Set ...

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International Conference on Mechanical Engineering and Green Manufacturing (MEGM) 2010

作者： Tu Ji-liang Duan Rong-xing Dong De-cun Tongji Univ Sch Transportat Engn Shanghai 201804 Peoples R China

ISBN: (纸本)9780878492329

This paper presents a novel methodology for the construction of fault tree using rough set and clustering algorithm. Taking advantage of the strong ability of RS theory in processing large data and eliminating redundant information, this method can remove irrelevant factors from the original fault data and reduce the dimension of processing data which helps to overcome fuzzy clustering algorithm's defect when process large database. The method is introduced and explained in details and its correctness and completeness is validated by Fault tree construction of oil-immersed traction transformers.

关键词： Fault tree Rough set fuzzy clustering algorithm Traction transformer

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IDENTIFICATION OF KEY FACTORS OF FIRE RISK OF OIL DEPOT BASED ON fuzzy clustering algorithm

IDENTIFICATION OF KEY FACTORS OF FIRE RISK OF OIL DEPOT BASE...

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ASME Pressure Vessels and Piping Conference

作者： Xie, Shuyi Dong, Shaohua Zhang, Guangyu China Univ Petr Coll Safety & Ocean Engn Beijing Peoples R China

ISBN: (纸本)9780791859001

With the rapid development of the national economy, the demand for oil is increasing. In order to meet the increasing energy demand, China has established a number of oil depot in recent years, whose largest capacity reaching up to tens of millions of cubic meters. Due to the flammable and explosive nature of the stored medium, the risk of fire in the oil depot area has increased dramatically as the tank capacity of the storage tank area has increased. The intensification of the oil depot and the development of large-scale oil storage tanks have brought convenience to the national oil depot, but also brought many catastrophic consequences. In recent years, there have been many fires and explosions in the oil depot, causing major casualties and property losses, which seriously endangered the ecological environment and public safety. Based on the constructed oil depot fire risk index system, the fuzzy C -means algorithm (FCM) and fuzzy maximum support tree clustering algorithm is introduced. Through the two fuzzy clustering mathematical models, key factors in the established index system are identified. Firstly, the expert scoring method is used to evaluate the indicators in the oil depot fire risk index system, and the importance degree evaluation matrix of oil depot fire risk factors is constructed through the fuzzy analysis of expert comments. Then, the fuzzy C -means algorithm (FCM) and the fuzzy clustering tree algorithm are used to cluster the various risk indicators, and the key factors of the oil depot fire risk are identified. Through the comparative analysis and cross -validation of the results of the two fuzzy clustering methods, the accuracy of the recognition results is ensured. Finally, using an oil depot as a case study, it is found that passive fire prevention capability and emergency rescue capability are key factors that need to be paid attention to in the oil depot fire risk index. The fuzzy clustering algorithm used in this paper can digitize the s

关键词： Oil Depot Fire Risk Index System fuzzy clustering algorithm Identification of Key Factors of Fire Risk

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Simulation of Industrial Economic Development Model Based on fuzzy clustering algorithm 2

Simulation of Industrial Economic Development Model Based on...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Prakash, N. Arun Kumar, A. Prasad, B. Samson Isaac, J. Ramaganesh, M. Uthayakumar, P. Department of Electrical and Electronics Engineering Kumaraguru College of Technology Saravanampatti Coimbatore35 India Hyderabad Telangana Dundigal500043 India Department of Electronics and Communication Engineering Sona College of Technology India Department of Mechanical Engineering Kalasalingam Academy of Research and Education India Tamilnadu Dindigul624622 India

ISBN: (纸本)9798350305418

As human society enters the era of the knowledge economy, the "industrial economic development of fuzzy clustering algorithm (FCM), which promotes the development of science and technology and social progress, has undergone profound changes in both connotation and form, and has gradually become an important part of the national development strategy." In order to investigate the effects of various policy alternatives on the energy industry, a system simulation model of the expansion of the energy industry from the position of a low-carbon economy is created based on DPSIR theory from the perspectives of energy, economy, environment, policy, and society. There are other clustering algorithms available today, but the fuzzy c-means clustering algorithm is the most widely used. Many improved techniques are presented based on this algorithm to produce a better clustering effect. The clustering effectiveness index must be evaluated for the effect after clustering. clustering analysis is a critical component of unsupervised pattern recognition. fuzzy clustering has become the industry standard for clustering analysis because it establishes the ambiguous description of the sample for the category and can more precisely reflect reality. © 2023 IEEE.

关键词： Development model simulation fuzzy clustering algorithm Industrial economy

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Construction of Evaluation Model of Regional Economic Development Level Based on fuzzy clustering algorithm 2

Construction of Evaluation Model of Regional Economic Develo...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Shrivastava, Harshit Al-Fatlawy, Mohammed Hussien Sahaya Sheela, M. Gopalakrishnan, S. Sivasubramanian, S. Zhu, Kejia Department of Mechanical Engineering IES College Of Technology MP Bhopal462044 India College of technical engineering The Islamic university Najaf Iraq Tamil Nadu Chennai India Tamil Nadu Kelambakkam India New Prince Shri Bhavani College Of Engineering And Technology Chennai India Xi'an Fanyi University Shaanxi Xi'an710000 China

ISBN: (纸本)9798350305418

In recent years, economic exchanges between China and the world market have become more and more close, and China's economic development has also directly affected the stability of the world economy. It even directly determines the major policies of the country, and its importance is obvious. For example, before China's reform and opening up, it had not yet implemented the transition of its economic system. At this time, more emphasis was placed on military strategy and political considerations. The coordinated development of regional economy does not mean simultaneous development or the same development speed. Rather, it is necessary to evaluate according to a certain index system, establish a scientific and systematic evaluation model, and analyze the data of coordinated regional economic development according to the system model. And it is becoming more and more obvious, manifested in the imbalance of society, economy and ecology in the system of the region itself, the continuous expansion of regional disparity, the serious regional blockade and local protection, the continuous economic friction between regions, and the unreasonable division of labor between regions. At the same time, help develop the central region, improve grain production capacity, make economical and effective use of coal resources in the central region, and promote the transformation of production organization structure in the central region, so as to make the central region develop better. Secondly, the division of economic regions from the perspective is convenient for policy-making departments to better study the regional heterogeneity and spatial spillover effect of policies. How to take effective measures to correctly solve the problem of regional gap and realize the coordinated development of regional economy is an important topic in front of us. This paper mainly focuses on the detailed division of economic regions based on fuzzy clustering algorithm, which is the premise and basis of

关键词： Development level fuzzy clustering algorithm Regional division Regional economy

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Development of Primary School Mathematics Education Technology Resources based on fuzzy clustering algorithm 2

Development of Primary School Mathematics Education Technolo...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Gloria Jeyaraj, Josephine Pon Uthayakumar, P. Muthuraman, M.S. Surendran, S. Bhuvana, R. Sheeba, M.R. Department of ECE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Tamilnadu Chennai India Department of Mathematics PSNA College of Engineering and Technology Tamilnadu Dindigul India Department of Computer Science and Engineering Tagore Engineering College Tamil Nadu Chennai India Dept of Computer Science AM JAIN College Tamilnadu Chennai India Department of Computer Science Government Arts and Science College for Women Thoothukudi District Sathankulum India

ISBN: (纸本)9798350305418

India's compulsory primary education emphasizes mathematics. Math helps boost students' logical and rational thinking. It supports our nation's scientific development and modern students' scientific and cultural growth. Students learn elementary school mathematics by facing a lot of data, thinking, and checking results. Teaching materials are not the only educational technology in the primary school mathematics teaching reform. Math teachers have started to focus on the need for more teaching resources and new tools and technology. Due to the lack of complete and systematic teaching materials, primary school mathematics technical resource development has new goals and requirements. Resource mining with fuzzy clustering is great. fuzzy clustering algorithm can introduce fuzzy mathematics into cluster analysis, use membership function clustering to determine sample affinity, and analyze and aggregate a large number of text resource information into meaningful classes or clusters for primary school mathematics education technology resource mining and development. An optimization strategy for cluster number is given to meet classic fuzzy clustering algorithm initialization requirements. The optimized fuzzy clustering algorithm optimizes the initial clustering center, overcoming its sensitivity to the beginning value. Thus, the data set is fuzzy-divided unsupervised. This article analyzes created resources to inform primary school campus mathematics education development. © 2023 IEEE.

关键词： educational technology fuzzy clustering algorithm primary school mathematics resource development

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Computer-Aided Audit Software Based on fuzzy clustering algorithm 2

Computer-Aided Audit Software Based on Fuzzy Clustering Algo...

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2nd IEEE International Conference on Image Processing and Computer Applications, ICIPCA 2024

作者： Fen, Yuan Wuhan Qingchuan University Hubei Province Wuhan City430000 China

ISBN: (纸本)9798350360240

According to the needs of auditing work, efforts are made to develop suitable computer-aided auditing software to gradually change or enhance traditional auditing techniques and methods, which can better play the role of audit supervision. The purpose of this paper is to study the design and optimisation of computer-aided audit software based on fuzzy clustering algorithms. By analysing the technical difficulties of computer-aided auditing, this paper proposes the design concept of the project software. The improved fuzzy C-mean clustering algorithm DFCM and the fast audit steps of the project based on improved fuzzy clustering are specifically introduced, and the data processing methods of the system are described in detail, and the specific application of the project software is also presented. The experimental results show that the computer-aided audit software in this paper can be applied to calculate the audit price of the project to be audited and the software is feasible. © 2024 IEEE.

关键词： Audit Software Computer-aided fuzzy clustering algorithm Software Design

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Research on Application of Sports Video Image Analysis Based on fuzzy clustering algorithm 3

Research on Application of Sports Video Image Analysis Based...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Liu, Tianming Shandong Polytechnic College Shandong Jining272000 China

ISBN: (纸本)9798350319125

With the wide application of computer vision technology in sports field, how to analyze sports video efficiently and accurately has become an important research direction. fuzzy clustering algorithm as a nonlinear data mining method can deal with uncertainty and noise provides a new idea for the analysis of sports video images. This paper mainly discusses the application of fuzzy clustering algorithm in sports video image processing, and analyzes its influence on sports video image © 2023 IEEE.

关键词： athletic sports fuzzy clustering algorithm Video image analysis

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