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A novel hybrid method of lithology identification based on k-means plus plus algorithm and fuzzy decision tree

JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2022年第PartD期208卷 109681-109681页

作者： Ren, Quan Zhang, Hongbing Zhang, Dailu Zhao, Xiang Yan, Lizhi Rui, Jianwen Hohai Univ Coll Earth Sci & Engn Nanjing 210098 Peoples R China

Lithology identification methods based on conventional logging data are essential in reservoir geological evaluation. Due to the highly non-linear relationship between lithology and various logging parameters, conventional methods cannot meet the requirements. In recent years, machine learning methods such as Neural Networks and decision tree have been applied to the field of lithology identification and achieved good effects. However, there is no obvious difference in logging parameters for various types of lithology, and at the same time, there is a large amount of information redundancy between each logging curve. Therefore, its uncertainty and fuzziness are high, which interferes with the result of lithology identification. Combining fuzzy theory, decision tree and k-means++ algorithm, this paper proposes a novel hybrid technique of lithology identification which can better overcome the ambiguity and uncertainty of logging data. In the actual data test, we select six logging parameters: density (RHOB), neutron porosity (NPHI), natural gamma (GR), longitudinal wave velocity (VP), shallow formation resistivity (LLS), and deep formation resistivity (LLD). Then k-means++ clustering algorithm was used for clustering analysis on logging data. Finally, the triangular membership function is selected to fuzz the logging data according to the obtained clustering center points, and a fuzzy decision tree lithology identification model is constructed. The prediction accuracy of the model reached 93.92%. The fuzzy decision tree algorithm was also compared with five machine learning algorithms, including decision tree, extremely randomized trees (ET), Adaboost, random forest (RF) and gradient boosting decision tree (GBDT). The results show that the modeling results of fuzzy decision tree algorithm outperform other algorithms. In summary, the fuzzy decision tree model developed in the study is a practical and effective model for complex lithology identification, providing a new

关键词： Lithology classification Fuzzy decision tree k-means plus plus algorithm Logging data Machine learning

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Extracting most significant data about the user queries from the search engine by k-means plus plus algorithm 1

Extracting most significant data about the user queries from...

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1st International Conference on Emerging Trends in Engineering, Technology and Science (ICETETS)

作者： Gomathi, A. Raja, k. Narasus Sarathy Inst Technol Dept Comp Sci & Engn Salem Tamil Nadu India Alpha Coll Engn Dept Comp Sci & Engn Chennai Tamil Nadu India

ISBN: (纸本)9781467367257

Today every process requires very important data as well as updated data about that process or work. In order to acquire those data, employees from various fields are searching through different search engines. Only very few times search engines are helping to get the users expected data but many times they are providing only the approximate data about the user expectation. To avoid this state, we used the algorithm named k-means++ over the search engine documents to list the most relevant information, because this algorithm uses the special mathematical method to find the out the successive cluster center of each cluster documents and only the first center is random selection from the data unlike k-means algorithm, which randomly selects all the cluster centers. Segmentation fusion is applied to provide the most resultant list, which is accepting as input of each cluster's documents gradually increasing manner. Performance of the k-means++ algorithm is compared and evaluated with the measures like Mean Average Precision and F-measure, which shows the novel algorithm has providing the better relevant result than other traditional algorithms. Finally, this algorithm also works well for large data sets. So, the researchers can utilize this algorithm to their innovative ideas comes to reality with the performance matrices of high relevancies such as precision and recall.

关键词： k-means plus plus algorithm Search engines Segmentation fusion Precision Recall

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Hybrid MRk-means plus plus RBM Model: An Efficient Heart Disease Predicting System Using ModifiedRoughk-means plus plus algorithm and Restricted Boltzmann Machine

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INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND kNOWLEDGE-BASED SYSTEMS 2023年第SUPP01期31卷 65-99页

作者： Prasanna, kamepalli S. L. Challa, Nagendra Panini VIT AP Univ Sch Comp Sci & Engn SCOPE Amaravati 522237 Andhra Pradesh India

The clinical diagnosis of heart disease in most situations is based on a difficult amalgamation of pathological and clinical information. Because of this complication, there is a significant level of curiosity among many diagnostic healthcare professionals and researchers who are keenly interested in the efficient, accurate, and early-stage forecasting of heart disease. Deep Learning algorithms aid in the prediction of heart disease. The main focus of this paper is to develop a method for predicting heart disease through Modified Rough k means + + (MRk + +) clustering along with the Restricted Boltzmann Machine (RBM). This paper is categorized into two modules: (1) Propose a clustering component based on Modified Rough k-means + +;(2) disease prediction based on RBM. The input Cleveland dataset is clustered using the stochastic probabilistic rough k-means + + clustering technique in the module for clustering. The clustered data is acquired and used in the RBM, and this hybrid structure is then used in the heart disease forecasting module. Throughout the testing procedure, the most valid result is chosen from the clustered test data, and the RBM classifier that correlates to the nearest cluster in the test data is based on the smallest distance or similar parameters. Furthermore, the output value is used to predict heart disease. There are three different types of experiments that are performed: In the first experiment comprises modifying the rough k-means + + clustering algorithm, the second experiment evaluates the classification result, and the third experiment suggests hybrid model representation. When the Hybrid Modified Rough k-means + + - RBM model is compared with any single model, it provides the highest accuracy.

关键词： Heart disease Clustering Rough set k-means plus plus algorithm Classification Restricted Boltzmann machine Hybrid modified Roughk-means plus plus

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Residual Strength Prediction of Composite Laminates Subjected to Compression After Impact (CAI) Using Intelligent Reconstruction of Impact-Induced Damage

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APPLIED COMPOSITE MATERIALS 2025年 1-26页

作者： Yin, Wentao Li, Nian Wang, Luyao Zheng, Yangyan Nanjing Tech Univ Coll Mech & Power Engn Nanjing 211816 Peoples R China Natl Univ Singapore Dept Mech Engn Singapore 117575 Singapore

This paper presents an equivalent damage model for efficiently predicting compression-after-impact (CAI) behaviors of laminated composites, based on the intelligent numerical reconstruction of impact-induced damage. Using the k-means + + clustering algorithm, the 3D spatial distribution of delamination is quantitatively identified from C-scanning time-of-flight (TOF) images and then discretized into a numerical mesh along with the soft inclusion. With the incorporation of interlaminar and intralaminar damage models, the CAI residual strength, failure modes, and damage scenarios of the composite laminate after low-velocity impact are predicted, showing good agreement with the experimental results at various impact energies. The proposed model enables fast evaluation of CAI strength within 1.5 h, without requiring impact energy information and maintaining accuracy, which is beneficial for application in damage tolerance design and optimization of engineering laminated structures.

关键词： Composite laminate CAI strength Impact damage k-means plus plus algorithm TOF clustering

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Thermal error modeling of motorized spindle considering the effect of milling head heat source

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2023年第1-2期129卷 855-870页

作者： Dai, Ye Li, Yang Zhan, Shiqiang Li, Zhaolong Wang, Xin Li, Weiwei Harbin Univ Sci & Technol Sch Mech Power Key Lab Adv Mfg & Intelligent Technol Minist Educ Harbin 150000 Peoples R China Ningbo Tiankong Five Axis CNC Technol Co Ltd Yuyao 315400 Peoples R China

The thermal behavior of the machine tool is an important cause of machining errors, and the thermal error prediction model can predict the errors in real time and improve the machining accuracy of the motorized spindle. Neural networks are widely used in thermal error modeling because of their fast learning speed, excellent prediction accuracy, and robustness. Firstly, the thermal characteristics are analyzed using the motorized spindle as the study object to determine its temperature field distribution. Secondly, the measurement point locations are selected based on the temperature field distribution, and the k-means++ algorithm and gray correlation analysis are used to reduce the 11 temperature measurement points to 4, which improved the input quality of the model. Finally, this paper combines GA and ACO to search the optimal parameters of the BP neural network to build a neural network thermal error prediction model, which overcomes the problems of slow convergence and the tendency to fall into local extremes. Compared with the radial basis function neural network (RBF neural network) model and ACO-BP neural network model, the average value of root mean square error (RMSE) of the Z-directional thermal errors predicted by the GA-ACO-BP neural network model is reduced by 45.3% and 58.3%;and the average value of mean absolute error (MAE) is decreased by 65.2% and 53.8%, respectively.

关键词： Motorized spindle k-means plus plus algorithm Temperature-sensitive point BP neural network Thermal error prediction model

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SITE selection and optimization for urban epidemic prevention material distribution points 7

SITE selection and optimization for urban epidemic preventio...

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7th International Conference on Traffic Engineering and Transportation System (ICTETS)

作者： Gao, Renjie Wei, Zhexue Shandong Normal Univ Sch Business Jinan Peoples R China

ISBN: (纸本)9781510674479

During a large-scale epidemic outbreak, the strategic planning of storage and distribution locations for essential life supplies becomes of paramount importance. This article focuses on the scientific supply of epidemic-related goods, taking the case of Changchun city as an example. It considered the human resource costs involved in the supply chain and constructed an organized network diagram for the supply of essential life supplies to residents. The results indicate that through the analysis of the shortest paths, we derive the optimal number of distribution points for essential life supplies in various administrative regions and outlines detailed plans for the provision of these supplies to residents. This article establishes a solid theoretical and practical foundation for future emergency management strategies.

关键词： Epidemic Prevention site selection material transportation k-means plus plus algorithm dijkstra algorithm

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Research on the Application of Shangri-La Regional Color Extraction in the Design of Cultural and Creative Products 26th

Research on the Application of Shangri-La Regional Color Ext...

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26th International Conference on Human-Computer Interaction (HCII)

作者： Yang, Xingqiao Long, Ren Zhang, Tianyue Yang, Xiaoran Yang, Wanlin Huazhong Univ Sci & Technol Wuhan 430074 Hubei Peoples R China

ISBN: (纸本)9783031619496;9783031619502

The regional colors of Shangri-La in China have high representativeness and research value. This study aims to explore the regional color extraction method using the k-means++ clustering algorithm for the extraction of Shangri-La regional colors and apply the extracted colors to the design practice of Shangri-La cultural and creative products. The study includes: selecting representative pictures as data samples;using the k-means++ clustering algorithm to carry out scientific color extraction, out-putting a network model of Shangri-La's regional colors, a standard master color card, and a color matching design guideline, etc., and completing the design practice;and collecting users' evaluations through questionnaire surveys and quantifying the results by using a five-level Likert scale. The results show that the color extracted based on the k-means++ algorithm is very characteristic, and its application to cultural and creative product design can reflect the cultural characteristics of Shangri-La and have a good color-matching effect.

关键词： Shangri-La regional color extraction k-means plus plus algorithm cultural and creative product design

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Clustering-based improved genetic algorithm for solving rural logistics route planning problem with conventional and new energy vehicles

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING 2024年

作者： Qiu, Yanghui Wu, Zhengtian Suzhou Univ Sci & Technol Sch Elect & Informat Engn Suzhou 215009 Peoples R China

In the context of global climate change and environmental sustainability, reducing carbon emissions has become a primary focus for both businesses and governments. The current challenges in rural logistics largely stem from low adoption of new energy technologies and widespread idling of vehicles, resulting in high carbon emissions in rural logistics operations. Against this backdrop, this study addresses the issues by considering the application of new energy sources, distribution strategies, vehicle carbon emission calculations, and other low-carbon optimization methods. The objective is to establish a model that optimizes node construction costs, operational costs, transportation costs, and carbon emission costs. This research aims to tackle the problem of village-level distribution center location and routing optimization, taking into account carbon emission costs as a decisive factor to reduce environmental impact. To achieve this goal, this paper proposes a comprehensive approach that combines the k-means++ algorithm with an enhanced genetic algorithm. In this approach, the k-means++ algorithm is first employed to select optimal locations for village-level distribution centers, minimizing construction, operational, and carbon emission costs. Subsequently, the enhanced genetic algorithm is utilized to determine the optimal distribution routes, incorporating a hybrid fleet of new energy and conventional vehicles, aiming to minimize overall costs, including carbon emissions and transportation costs. Application of our algorithm in a real rural distribution network demonstrates that, compared to traditional methods, it achieves higher efficiency while reducing carbon emission costs. Additionally, our study provides valuable insights for policymakers and decision-makers regarding carbon emission reduction and sustainable development, offering robust decision support for the planning and management of village-level distribution centers.

关键词： Carbon emission costs k-means plus plus algorithm improved genetic algorithm village-level distribution center location distribution route optimization

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Understanding operation patterns of urban online ride-hailing services: A case study of Xiamen

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TRANSPORT POLICY 2021年 101卷 100-118页

作者： Xiong, Ziyue Li, Jian Wu, Hangbin Tongji Univ Urban Mobil Inst 4800 Caoan Rd Shanghai 201804 Peoples R China Tongji Univ Minist Educ Key Lab Rd & Traff Engn 4800 Caoan Rd Shanghai 201804 Peoples R China Tongji Univ Coll Transportat Engn 4800 Caoan Rd Shanghai 201804 Peoples R China Tongji Univ Coll Surveying & Geoinformat 1239 Siping Rd Shanghai 200092 Peoples R China

Online ride-hailing has gradually become a popular travel choice worldwide, while it also brought policy challenges to balance the traditional taxi industry and online ride-hailing services. Understanding the operation patterns of urban online ride-hailing services is essential for government policy-making. However, insufficient attention has been paid to the operating characteristics of online ride-hailing vehicles due to limited empirical data. This paper proposes a cluster analysis framework for the identification of different operation patterns of urban online ride-hailing. The customer order and GPS data of online ride-hailing vehicles and traditional taxis in Xiamen, China is used in this study. The k-means++ clustering algorithm is used based on the proposed intensity and stability indices of ride-hailing vehicle operating characteristics. The results show that there are three types of online ride-hailing operation patterns, namely full-time (which accounts for 52.801%), part-time (29.502%), and occasional (17.697%). The operation pattern of full-time ride-hailing vehicles is similar to that of traditional taxis, but with lower intensity and stability due to a reduced workload and flexible time schedule. Part-time ride hailing vehicles are operated unsteadily and irregularly in the drivers' spare time, and the working time periods are mainly concentrated in the morning and evening peak hours. Occasional ride-hailing vehicles provide very limited service. Finally, several policy suggestions for online ride-hailing from the perspective of government management, e.g., the number of licenses and operation places and time periods, are proposed based on the results.

关键词： Online ride-hailing Cluster analysis k-means plus plus algorithm Operation patterns Policy-making

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Automatic vessel plate number recognition for surface unmanned vehicles with marine applications

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FRONTIERS IN NEUROROBOTICS 2023年 17卷 1131392页

作者： Zhang, Renran Zhang, Lei Su, Yumin Yu, Qingze Bai, Gaoyi Harbin Engn Univ Dept Sci Harbin Peoples R China Harbin Engn Univ Technol Underwater Vehicle Lab Harbin Peoples R China

In the practical application scenarios of USVs, it is necessary to identify a vessel in order to accomplish tasks. Considering the sensors equipped on the USV, visible images provide the fastest and most efficient way of determining the hull number. The current studies divide the task of recognizing vessel plate number into two independent subtasks: text localization in the image and its recognition. Then, researchers are focusing on improving the accuracy of localization and recognition separately. However, these methods cannot be directly applied to USVs due to the difference between these two application scenarios. In addition, as the two independent models are serial, there will be inevitable propagation of error between them, as well as an increase in time costs, resulting in a less satisfactory performance. In view of the above, we proposed a method based on object detection model for recognizing vessel plate number in complicated sea environments applied to USVs. The accuracy and stability of model have been promoted by recursive gated convolution structure, decoupled head, reconstructing loss function, and redesigning the sizes of anchor boxes. To facilitate this research, a vessel plate number dataset is established in this paper. Furthermore, we conducted a experiment utilizing a USV platform in the South China Sea. Compared with the original YOLOv5, the mAP (mean Average Precision) value of proposed method is increased by 6.23%. The method is employed on the "Tian Xing" USV platform and the experiment results indicates both the ship and vessel plate number can be recognized in real-time. In both the civilian and military sectors, this has a great deal of significance.

关键词： YOLOv5 vessel plate number unmanned surface vehicles (USVs) real-time recognition k-means plus plus algorithm

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