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Hybrid Lithology Identification Method Based on Isometric Feature Mapping Manifold Learning and particle swarm optimization-Optimized LightGBM

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PROCESSES 2024年第8期12卷 1593页

作者： Wang, Guo Deng, Song Xu, Shuguo Li, Chaowei Wei, Wan Zhang, Haolin Li, Changsheng Gong, Wenhao Pan, Haoyu SINOPEC Res Inst Petr Engn Co Ltd Beijing 100000 Peoples R China Changzhou Univ Coll Petr Engn Changzhou 213000 Peoples R China

Accurate identification of lithology in petroleum engineering is very important for oil and gas reservoir evaluation, drilling decisions, and petroleum geological exploration. Using a cross-plot to identify lithology only considers two logging parameters, causing the accuracy of lithology identification to be insufficient. With the continuous development of artificial intelligence technology, machine learning has become an important means to identify lithology. In this study, the cutting logging data of the Junggar Basin were collected as lithologic samples, and the identification of argillaceous siltstone, mudstone, gravel mudstone, silty mudstone, and siltstone was established by logging and logging parameters at corresponding depths. Aiming at the non-equilibrium problem of lithologic data, this paper proposes using equilibrium accuracy to evaluate the model. In this study, manifold learning is used to reduce logging and logging parameters to three dimensions. Based on balance accuracy, four dimensionality reductions including isometric feature mapping (ISOMAP), principal component (PCA), independent component (ICA), and non-negative matrix factorization (NMF) are compared. It is found that ISOMAP improves the balance accuracy of the LightGBM model to 0.829, which can effectively deal with unbalanced lithologic data. In addition, the particle swarm optimization (PSO) algorithm is used to automatically optimize the super-parameters of the lightweight gradient hoist (LightGBM) model, which effectively improves the balance accuracy and generalization ability of the lithology identification model and provides strong support for fast and accurate lithology identification.

关键词： lithology identification manifold learning lightweight gradient hoist particle swarm optimization algorithm disequilibrium

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Design of College Community Management and Service System Founded on particle swarm optimization Arithmatic in Cloud Platform 3

Design of College Community Management and Service System Fo...

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3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Wang, Ke Liaoning Normal University Dalian Department of Education Liaoning China College of Food Industry Shandong Institute of Commerce and Technology Jinan Shandong China

ISBN: (纸本)9798350317022

College student community has increasingly become an important force to promote and prosper campus culture, and student community has a good appeal among the student masses. The quality of student community work directly determines the level of student education management. College student community leaders need high ability and level to make good use of the community and serve the community well. The management service system of student community based on particle swarm optimization algorithm can effectively improve the management level and service level of student community. The positioning of the college student community should not only be limited to the cultivation of students' knowledge and ability exercise, but should be scientifically positioned according to its own characteristics, combined with the needs of society and reality, and actively expand new functions. Provide necessary financial and technical support for associations, provide necessary guarantees for student associations, and encourage associations to actively obtain financial support by means of awards and allocations. The college can proceed from the actual situation, gradually plan and build the activity venue of student associations, equip corresponding activity equipment, use existing resources, provide services for associations, and improve resource utilization. © 2023 IEEE.

关键词： campus culture college community particle swarm optimization algorithm resource utilization service system

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The particle swarm optimization multi-kernel relevance vector machine for remaining useful life prediction of lithium-ion batteries 6

The particle swarm optimization multi-kernel relevance vecto...

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6th IEEE Advanced Information Technology, Electronic and Automation Control Conference (IEEE IAEAC)

作者： Tian, Qiancheng Liu, Xiping Ding, Shuai Chen, Haitao Huang, Jun Yang, Ziwei State Key Lab Space Power Sources Technol Shangha Peoples R China Shanghai Inst Space Power Sources Shanghai Peoples R China

ISBN: (数字)9781665458641

ISBN: (纸本)9781665458641

The long life of spacecraft has put forward higher requirements for the prediction of the remaining useful life (RUL) of the lithium-ion battery, and the prediction method based on relevance vector machine (RVM) has also received extensive attention. The kernel function is the main factor affecting the prediction of the RVM. Single-kernel RVM has only one kernel function, and the RUL prediction effect is ordinary. Multi-kernel RVM has many types of kernel functions, but the weight coefficient of the kernel function is relatively difficult to determine. Therefore, this paper proposes a multi-kernel RVM based on a particle swarm optimization (PSO) algorithm to predict the RUL of the lithium-ion battery. Firstly the capacity degradation data of the lithium-ion battery is reconstructed in phase space. Then Gaussian, polynomial, Sigmoid, and linear kernel functions are used to establish a multi-kernel RVM model. Finally, the particle swarm optimization algorithm is used to perform parameter self-optimization. Taking the lithium-ion battery capacity degradation data set as an example, according to the prediction evaluation index, the PSO multi-kernel RVM prediction method is better than the single-kernel and multi-kernel RVM prediction method. This method increases the types of kernel functions, solves the problem of difficulty in determining the weight coefficients of the kernel function, and improves the accuracy of predicting the RUL of the lithium-ion battery.

关键词： Relevance vector machine particle swarm optimization algorithm The phase of space reconstruction Lithium-ion battery Remaining useful life prediction

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Research on identification of unbalance parameters of rotor with multi-plane using improved particle swarm optimization

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JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING 2021年第2期43卷 1-12页

作者： Yun, Xialun Pang, Zhekai Jiang, Gedong Mei, Xuesong Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian 710048 Peoples R China Xi An Jiao Tong Univ Shaanxi Key Lab Intelligent Robots Xian 710049 Peoples R China Xi An Jiao Tong Univ Sch Mech Engn Xian 710049 Peoples R China

Based on Bentley RK4 test rig, the method of nonlinear particle swarm optimization (PSO) is used to solve inverse problem instead of previous methods, which reduces identification complexity and avoids the ill posed problem caused by the singular equation in the process of solving. This paper realized model-based standard particle swarm optimization (SPSO) algorithm after modelling of the finite element model of rotor system and creating the optimization variables and individual fitness function, and then established asynchronous adaptive particle swarm optimization with optimizing speed weight and search scope. In order to further improve algorithm accuracy, chaos weighted particle swarm optimization and double chaos particle swarm optimization (DCPSO) are established. The simulation results of four methods show that DCPSO can realize accurate identification of unbalanced parameters with an average error of 2.86%. The effectiveness and rationality of this algorithm are verified from the speed of 240-3000 r/min. Particularly, at 2040 r/min, the percentage of amplitude decrease in two measuring points is 94.07% and 95.93%, which has achieved excellent vibration suppression effect.

关键词： Multi-plane rotor system Finite element model particle swarm optimization algorithm Identification of unbalanced parameters

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ADVANTAGES OF THERMAL INDUSTRY CLUSTER AND APPLICATION OF particle swarm optimization MODEL

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THERMAL SCIENCE 2021年第2期25卷 977-987页

作者： Chen, Yiwei Song, Xiaoli Bai, Yakai Hu, Chunyu Southeast Univ Sch Econ & Management Nanjing Peoples R China Zhejiang Agr & Forestry Univ Jiyang Coll Sch Humanities Hangzhou Zhejiang Peoples R China Chinese Acad Sci Beijing Peoples R China Southeast Univ Coll Civil Engn Nanjing Peoples R China

This paper first analyzes that the waste heat before the flue gas of thermal power company enters the desulfurizationwer can be heated by the low pressure economizer, and then the heated condensate can be incorporated into the regenerative system, so as to recover the waste heat and improve the unit efficiency. In this paper, the possible lay-out scheme of the low pressure economizer in the thermal system is constructed firstly, and then the mathematical model of the equivalent enthalpy drop increment of the unit is established with the water partition coefficient as the independent variable. particle swarm optimization and co-ordinate rotation are used to optimize the model, and the results show that particle swarm optimization is more effective in solving the problem. After the low pressure economizer is added to the unit, the heat consumption of the whole plant of the unit is reduced by 61.131, the unit efficiency is increased by 0.783%, and the coal consumption of the whole plant is reduced by 2.293. Finally, from the perspective of industrial agglomeration, industrial diversification and economic resilience, this paper analyzes the advantages of diversified agglomeration of thermal power companies.

关键词： flue gas waste heat low pressure economizer economic resilience particle swarm optimization algorithm industrial agglomeration industrial diversification

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An efficient task scheduling in a cloud computing environment using hybrid Genetic algorithm - particle swarm optimization (GA-PSO) algorithm

An efficient task scheduling in a cloud computing environmen...

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International Conference on Intelligent Sustainable Systems (ICISS)

作者： Kumar, A. M. Senthil Parthiban, K. Shankar, Siva S. Koneru Lakshmaiah Educ Fdn Dept CSE Guntur 522502 Andhra Pradesh India

ISBN: (纸本)9781538677995

Cloud computing provides the computational machines as a support of the clients utilizing cloud organize. In cloud computing, the user inputs are executed with required machines to convey the administrations. Numerous task scheduling methods are utilized to plan the client tasks to the machines. In this paper, another successful hybrid task scheduling is proposed to minimize the total execution time using Genetic algorithm (GA) and particle swarm optimization (PSO) algorithms. In hybrid Genetic algorithm - particle swarm optimization (GA-PSO) algorithm, PSO helped GA to obtain better results compare to a standard genetic algorithm, Min-Min, and Max-Min algorithms results.

关键词： Cloud computing Task scheduling algorithm Genetic algorithm particle swarm optimization algorithm

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A Weighted Ensemble Learning algorithm Based on Diversity Using a Novel particle swarm optimization Approach

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algorithmS 2020年第10期13卷 255-255页

作者： You, Gui-Rong Shiue, Yeou-Ren Yeh, Wei-Chang Chen, Xi-Li Chen, Chih-Ming Fujian Business Univ Dept Management Engn Fuzhou 350012 Peoples R China Fujian Business Univ Informat Technol Ctr Fuzhou 350012 Peoples R China Natl Tsing Hua Univ Dept Ind Engn & Management Engn Hsinchu 300 Taiwan Fujian Business Univ Dept Finance Fuzhou 350012 Peoples R China

In ensemble learning, accuracy and diversity are the main factors affecting its performance. In previous studies, diversity was regarded only as a regularization term, which does not sufficiently indicate that diversity should implicitly be treated as an accuracy factor. In this study, a two-stage weighted ensemble learning method using the particle swarm optimization (PSO) algorithm is proposed to balance the diversity and accuracy in ensemble learning. The first stage is to enhance the diversity of the individual learner, which can be achieved by manipulating the datasets and the input features via a mixed-binary PSO algorithm to search for a set of individual learners with appropriate diversity. The purpose of the second stage is to improve the accuracy of the ensemble classifier using a weighted ensemble method that considers both diversity and accuracy. The set of weighted classifier ensembles is obtained by optimization via the PSO algorithm. The experimental results on 30 UCI datasets demonstrate that the proposed algorithm outperforms other state-of-the-art baselines.

关键词： ensemble learning diversity machine learning particle swarm optimization algorithm weighted ensemble

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particle swarm optimization for the shortest path problem

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第1期41卷 1353-1373页

作者： Yang, Lehua Li, Dongmei Tan, Ruipu Fujian Jiangxia Univ Coll Elect & Informat Sci Fuzhou Fujian Peoples R China Res Inst Data Anal & Intelligent Decis Making Fuzhou Fujian Peoples R China Fujian Jiangxia Univ Coll Foreign Languages Fuzhou Fujian Peoples R China

Solving the shortest path problem is very difficult in situations such as emergency rescue after a typhoon: roaddamage caused by a typhoon causes the weight of the rescue path to be uncertain and impossible to represent using single, precise numbers. In such uncertain environments, neutrosophic numbers can express the edge distance more effectively: membership in a neutrosophic set has different degrees of truth, indeterminacy, and falsity. This paper proposes a shortest path solution method for interval-valued neutrosophic graphs using the particle swarm optimization algorithm. Furthermore, by comparing the proposed algorithm with the Dijkstra, Bellman, and ant colony algorithms, potential shortcomings and advantages of the proposed method are deeply explored, and its effectiveness is verified. Sensitivity analysis performed using a 2020 typhoon as a case study is presented, as well as an investigation on the efficiency of the algorithm under different parameter settings to determine the most reasonable settings. particle swarm optimization is a promising method for dealing with neutrosophic graphs and thus with uncertain real-world situations.

关键词： Interval-valued neutrosophic numbers neutrosophic graph particle swarm optimization algorithm shortest path problem

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Prediction of yarn strength based on an expert weighted neural network optimized by particle swarm optimization

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TEXTILE RESEARCH JOURNAL 2021年第23-24期91卷 2911-2924页

作者： Zhang, Baowei Song, Jiuxiang Zhao, Suna Jiang, Hao Wei, Jingdian Wang, Yonghua Zhengzhou Univ Light Ind Zhengzhou 450002 Henan Peoples R China

Aiming at solving the problem that existing artificial neural networks (ANNs) still have low accuracy in predicting yarn strength, this study combines traditional expert experience and an ANN to propose a hybrid network, named the expert weighted neural network. Many studies have shown that it is reliable to predict yarn strength based on ANN technology. However, most ANN training models face with problems of low accuracy and easy trapping into their local minima. The strength prediction of traditional yarns relies on expert experience. Obvious expert experience can help the model perform preliminary learning and help the algorithm model achieve higher accuracy. Therefore, this study proposes a neural network model that combines expert weights and particle swarm optimization (PSO). The model uses PSO to optimize the weights of experts and investigates its effectiveness in yarn strength prediction.

关键词： Yarn strength expert weights artificial neural network expert weighted neural network particle swarm optimization algorithm

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The Retinex-based image dehazing using a particle swarm optimization method

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MULTIMEDIA TOOLS AND APPLICATIONS 2021年第3期80卷 3425-3442页

作者： Yao, Li-Ping Pan, Zhong-liang South China Normal Univ Sch Phys & Telecommun Engn Guangzhou 510006 Guangdong Peoples R China Guangdong Inst Med Instruments Guangzhou 510000 Guangdong Peoples R China

To the best of our knowledge, currently the physical model based method is still an ill posed problem. Additionally, the image enhancement approaches also suffer from the texture preservation issue. Retinex-based approach is proved its effectiveness in image dehazing while the parameter should be turned properly. Therefore, in this paper, the particle swarm optimization (PSO) algorithm is firstly performed to optimize the parameter and the hazed image is converted into hue, saturation, intensity(HSI) for color compensation, In the other hand, the multi-scale local detail upgrading and the bilateral filtering approaches are designed to overcome the dehazing artefacts and edge preservation, which could further improve the overall visual effect of images. Experimental results on natural and synthetic images by using qualitative analysis and frequently used quantitative evaluation metrics illustrate the approving defogging effect of the proposed method. For instance, in a natural image road, our method achieves the higher e for 0.63, gamma for 3.21 andHfor 7.81, respectively and lower sigma for 0.04. In a synthetic image poster, the higher PSNR for 18.17 and SSIM for 0.78 are also acquired compared to other explored approaches in this paper. Besides, the results performed on other underwater and aerial images in this study further demonstrates its defog effectiveness.

关键词： Image enhancement Retinex particle swarm optimization algorithm Nature and synthetic images Underwater and aerial images

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