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A comparative analysis of hybrid RF models for efficient lithology prediction in hard rock tunneling using TBM working parameters

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ACTA GEOPHYSICA 2024年第3期72卷 1847-1866页

作者： Zhou, Jian Yang, Peixi Yong, Weixun Khandelwal, Manoj Huang, Shuai Cent South Univ Sch Resources & Safety Engn Changsha 410083 Peoples R China Federat Univ Australia Inst Innovat Sci & Sustainabil Ballarat Vic 3350 Australia

With the escalating demand for underground mining and infrastructure construction, the optimization of tunnel construction has emerged as a primary concern for researchers. The geological conditions encountered during the excavation of hard rock tunnels using tunnel boring machines (TBM) significantly impact construction efficiency and cost-effectiveness. The existing lithology testing methods need to be more efficient in aligning with TBM operational efficiency. In recent years, the rapid advancement of artificial intelligence has paved the way for its integration into numerous domains, including tunnel engineering. To address this issue, this study proposes three innovative hybrid RF-based intelligent models, namely PSO-RF, ALO-RF, and GWO-RF, for the precise prediction of lithology in hard rock tunnels using TBM working parameters. The TBM operating parameters of the Jilin Yinsong Water Supply Project serve as the basis for this investigation. Twelve distinct characteristic parameters relevant to the lithology of the tunnel working face were carefully selected as input parameters for lithology prediction. Comparative analysis of the three hybrid models reveals that GWO-RF demonstrates exceptional lithology prediction performance (ACC = 0.999924;PREA = 0.0.9999976;RECA = 0.999775;F1A = 0.999876;Kappa = 0.999911), whereas PSO-RF and ALO-RF exhibit slightly inferior performance. Nonetheless, all three hybrid models exhibit a significant improvement in prediction accuracy compared to the unoptimized RF model. The research findings presented herein facilitate the swift determination of TBM working surface lithology, enabling timely adjustment of TBM working parameters, reducing equipment wear and tear, and enhancing construction efficiency.

关键词： Tunnel boring machine Lithology prediction Random forest particle swarm optimization algorithm Ant-lion optimization algorithm Gray wolf optimization algorithm

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Research on the Bi-level Programming Model for Ticket Fare Pricing of Urban Rail Transit based on particle swarm optimization algorithm

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Procedia - Social and Behavioral Sciences 2013年 96卷 633-642页

作者： Zhao Xueyu Yang Jiaqi Wuhan University of Technology School of Transportation Heping Avenue 1040 Wuchang District Wuhan City 430063

As a public service facility, the social and economic benefits of urban rail transit ticket fare are both important, so reasonable ticket fare is a key for the solid development of urban rail transit. The social and economic benefits should be taken into account under the competitive condition led by various modes of transportation in order to get an optimal strategy in ticket fare pricing of urban rail transit on the premise of meeting the service quality standard. Here, the factors considered in the ticket fares fare pricing of urban rail transit in the domestic and foreign cities are summarized, after which the Logit model of the mode split within the public transit system is established. With considering both the respective benefits of the urban rail transit company and the travellers, a bi-level programming model is established together with the solution idea to the model with the particle swarm optimization algorithm. The example demonstrates the feasibility and effectiveness of the bi-level programming model and the related measures and the particle swarm ooptimization aalgorithm is fittable for the urban rail transit fare pricing. The suggestions proposed from the result of the example are helpful for the decision making of ticket fare pricing of urban rail transit.

关键词： Ticket fare pricing Urban rail transit Logit model Bi-level programming model particle swarm optimization algorithm

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MICE-PSO-RF Model for Predicting Coal Spontaneous Combustion Temperature Based on Multiple Imputation by Chained Equations

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COMBUSTION SCIENCE AND TECHNOLOGY 2025年

作者： Zheng, Xuezhao Li, Peihua Cai, Guobin Guo, Jun Liu, Yin Xian Univ Sci & Technol Coll Safety Sci & Engn 58 Yanta Mid Rd Xian 710054 Shaanxi Peoples R China Shaanxi Xikuang Zhitong Technol Co Ltd Xian Peoples R China Key Lab Urban Secur & Emergency Rescue Higher Educ Xian Peoples R China

To address the issue of missing indicator gas data in coal spontaneous combustion temperature prediction models in practical applications, a novel prediction model is proposed, which integrates the MICE (Multiple Imputation by Chained Equations) method with PSO (particle swarm optimization)-optimized RF (Random Forest) algorithm. A coal spontaneous combustion heating experiment was conducted, selecting O2, CO, CO2, CH4, and C2H6 as characteristic gases for coal combustion. Five sets of data with randomly introduced missing values were designed, and four imputation methods - KNN, RF, PSO-SVR, and MICE - were used to impute the missing values. The imputed data were then used to train three temperature prediction models (BP, RF, and SVR), with parameters optimized using PSO. The imputation and prediction results were compared. The results indicate that, across five different missing data rates, the MICE method outperforms the other imputation techniques. Without parameter tuning, all three models showed overfitting. However, after PSO optimization, the PSO-RF model achieved the highest prediction accuracy and demonstrated good stability. In practical applications, the MICE-PSO-RF model yielded an average temperature prediction error of 2.69 degrees C across 15 test sets, demonstrating reliable performance in predicting coal temperature.

关键词： Coal spontaneous combustion temperature prediction data imputation multiple imputation method random forest particle swarm optimization algorithm

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Simulation-Enhanced MQAM Modulation Identification in Communication Systems: A Subtractive Clustering-Based PSO-FCM algorithm Study

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INFORMATION 2024年第1期15卷 42-42页

作者： Quan, Zhi Zhang, Hailong Luo, Jiyu Sun, Haijun Zhengzhou Univ Sch Elect & Informat Engn Zhengzhou 450001 Peoples R China

Signal modulation recognition is often reliant on clustering algorithms. The fuzzy c-means (FCM) algorithm, which is commonly used for such tasks, often converges to local optima. This presents a challenge, particularly in low-signal-to-noise-ratio (SNR) environments. We propose an enhanced FCM algorithm that incorporates particle swarm optimization (PSO) to improve the accuracy of recognizing M-ary quadrature amplitude modulation (MQAM) signal orders. The process is a two-step clustering process. First, the constellation diagram of the received signal is used by a subtractive clustering algorithm based on SNR to figure out the initial number of clustering centers. The PSO-FCM algorithm then refines these centers to improve precision. Accurate signal classification and identification are achieved by evaluating the relative sizes of the radii around the cluster centers within the MQAM constellation diagram and determining the modulation order. The results indicate that the SC-based PSO-FCM algorithm outperforms the conventional FCM in clustering effectiveness, notably enhancing modulation recognition rates in low-SNR conditions, when evaluated against a variety of QAM signals ranging from 4QAM to 64QAM.

关键词： modulation recognition fuzzy c-means algorithm constellation diagram particle swarm optimization algorithm MQAM signal

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An improved retrieval method for liquid water content of the Antarctic ice sheet using SMOS data

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GEOCARTO INTERNATIONAL 2025年第1期40卷

作者： Zhou, Yi Feng, Yongjiu Cao, Yuze Chen, Shurui Wang, Rong Wang, Xiaofeng Xiao, Changjiang Ye, Zhen Tong, Xiaohua Tongji Univ Coll Surveying & Geoinformat Shanghai Peoples R China Tongji Univ Shanghai Key Lab Planetary Mapping & Remote Sensin Shanghai Peoples R China Tongji Univ Shanghai Res Inst Intelligent Autonomous Syst Shanghai Peoples R China

With global climate warming, Antarctic ice sheet melting has garnered increasing attention, as changes in liquid water content (LWC) significantly affect sea level rise and regional climate. This study integrates SMOS L-band passive microwave data with the LS-MEMLS microwave emission model and employs the particle swarm optimization algorithm to retrieve the surface LWC of the Antarctic ice sheet and its spatiotemporal variations. We analyzed LWC, surface density, and melt days across different Antarctic regions, focusing on the trends in LWC and its relationship with multi-source remote sensing products. The results indicate a rising trend in LWC and melting of the Antarctic Peninsula and coastal ice shelves from 2018 to 2020, with a notable peak in 2020, potentially related to the anomalous climatic events. This research provides new methodological and theoretical insights into Antarctic ice sheet dynamics melt and their implications for the global climate system.

关键词： Antarctic ice sheet liquid water content SMOS melt detection particle swarm optimization algorithm

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Prediction of cardiovascular disease based on multiple feature selection and improved PSO-XGBoost model

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SCIENTIFIC REPORTS 2025年第1期15卷 1-12页

作者： Cao, Kerang Liu, Chang Yang, Siqi Zhang, Yuxin Li, Lili Jung, Hoekyung Zhang, Shuo Shenyang Univ Chem Technol Coll Comp Sci & Technol Shenyang 110142 Peoples R China Key Lab Intelligent Technol Chem Proc Ind Liaoning Shenyang 110142 Peoples R China Shenyang Matern & Child Hlth Hosp Shenyang 110014 Peoples R China Paichai Univ Comp Engn Dept Daejeon 35345 South Korea

Cardiovascular disease is a common disease that threatens human health. In order to predict it more accurately, this paper proposes a cardiovascular disease prediction model that combines multiple feature selection, improved particle swarm optimization algorithm, and extreme gradient boosting tree. Firstly, the dataset is preprocessed, and an XGBoost cardiovascular disease prediction model is constructed for model training and compare it with other algorithms. Then, combined with two factor Pearson correlation analysis and feature importance ranking, multiple feature selection is performed, with the optimal feature subset as the feature input. Finally, the improved particle swarm optimization algorithm is used to adjust the hyperparameters of the extreme gradient boosting tree algorithm, and selecting the optimal hyperparameter combination to construct the MFS-DLPSO-XGBoost model. The recall, precision, accuracy, F1 score, and area under the ROC curve (AUC) of the MFS-DLPSO-XGBoost model reached 71.4%, 76.3%, 74.7%, 73.6%, and 80.8%, respectively, which increased by 3.6%, 3.2%, 2.7%, 3.2%, and 2.3% compared to XGBoost. The results indicate that the model proposed in this article has good classification performance and can provide assistance for doctors and patients in predicting and preventing heart disease.

关键词： Cardiovascular disease Machine learning XGBoost algorithm Multi feature selection particle swarm optimization algorithm Model prediction

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Temperature prediction model for a high-speed motorized spindle based on back-propagation neural network optimized by adaptive particle swarm optimization

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Journal of Southeast University(English Edition) 2022年第3期38卷 235-241页

作者： Lei Chunli Zhao Mingqi Liu Kai Song Ruizhe Zhang Huqiang School of Mechanical and Electrical Engineering Lanzhou University of TechnologyLanzhou 730050China

To predict the temperature of a motorized spindle more accurately,a novel temperature prediction model based on the back-propagation neural network optimized by adaptive particle swarm optimization(APSO-BPNN)is ***,on the basis of the PSO-BPNN algorithm,the adaptive inertia weight is introduced to make the weight change with the fitness of the particle,the adaptive learning factor is used to obtain different search abilities in the early and later stages of the algorithm,the mutation operator is incorporated to increase the diversity of the population and avoid premature convergence,and the APSO-BPNN model is ***,the temperature of different measurement points of the motorized spindle is forecasted by the BPNN,PSO-BPNN,and APSO-BPNN *** experimental results demonstrate that the APSO-BPNN model has a significant advantage over the other two methods regarding prediction precision and *** presented algorithm can provide a theoretical basis for intelligently controlling temperature and developing an early warning system for high-speed motorized spindles and machine tools.

关键词： temperature prediction high-speed motorized spindle particle swarm optimization algorithm back-propagation neural network robustness

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An Energy Efficient particle swarm optimization based VM Allocation for Cloud Data Centre: EEVMPSO

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EAI ENDORSED TRANSACTIONS ON SCALABLE INFORMATION SYSTEMS 2023年第5期10卷 1-15页

作者： Pandey, Abhishek Kumar Singh, Sarvpal Madan Mohan Malaviya Univ Technol Dept Informat Technol & Comp Applicat Gorakhpur Uttar Pradesh India

Cloud computing has witnessed exponential growth in recent years, resulting in a significant surge in energy consumption and operational costs of cloud data centres. Efficiently allocating Virtual Machines (VMs) within these data centres is crucial to achieve energy efficiency and optimize resource utilization. System instability may result from repeated requests for computing resources. One of the most critical difficulties facing virtualization technology is finding the best way to stack virtual machines on top of physical devices in cloud data centers. The host must move virtual machines from overloaded to underloaded hosts as part of load balancing, which has an impact on energy consumption. We propose energy-efficient particle swarm optimization algorithm (EEVMPSO) for Virtual Machine allocation is designed to maximize the load balancing. System resources, including CPU, storage, and memory, are optimized using EEVMPSO. The energy-aware virtual machine migration using the particle swarm optimization algorithm for dynamic VMs placement and energy -efficient cloud data centers. We conducted extensive experiments and simulations to evaluate the performance of the proposed algorithm in comparison to existing VM allocation methods. The results demonstrate the superiority of our approach in achieving energy efficiency and resource optimization. The experimental result shown in the proposed method, consumption energy in comparison to the PAPSO, KHA, EALBPSO, and RACC-MDT algorithm by 10.86%, 18.22%, 25.8%, and 31.34%, respectively, demonstrated the improvements in the service level agreements violation 5.77%, 15.3%, 26.19%, and 30.4%, as well as the average CPU utilization 2.2%, 24%, 22.6%, and 14.6%.

关键词： particle swarm optimization algorithm Cloud Computing Virtual Machine Placement Cloud data center Service level agreements

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Small unmanned helicopter modeling method based on a hybrid kernel function PSO-LSSVM

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JOURNAL OF SUPERCOMPUTING 2023年第12期79卷 13889-13906页

作者： Zhou, Jian Wang, Weixin Lu, Jian Liu, Lingzhe Xian Polytech Univ Sch Elect & Informat Xian 710048 Shaanxi Peoples R China

The mathematical modeling of a small unmanned helicopter (SUH) with multivariable, highly nonlinear and complex dynamic characteristics is considered. This paper presents a modeling method for SUHs based on a particle swarm optimization least squares support vector machine (PSO-LSSVM) with a hybrid kernel function. The proposed method is based on a least square support vector machine and uses linear weighting of the polynomial kernel function (POLY) and Gaussian kernel function (RBF) to form a hybrid kernel function, and uses a particle swarm optimization algorithm to search for the optimal parameters. Finally, a mathematical model of the longitudinal and lateral passages of a SUH is established. According to the flight test data, the longitudinal and lateral channel models are trained and verified in the hover and low-speed forward flight states of a SUH. The experimental and comparison results demonstrate that the model established via this method has higher prediction accuracy and more accurate prediction results than a model established using a least squares support vector machine with a single kernel function. The identification accuracy of the SUH model is improved effectively.

关键词： Hybrid kernel function Least square support vector machines Mathematical modeling particle swarm optimization algorithm Small unmanned helicopter

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Identification of optimal maintenance parameters for best maintenance and service management system in the SMEs

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JOURNAL OF QUALITY IN MAINTENANCE ENGINEERING 2024年第1期30卷 133-152页

作者： Kumaresan, Velmurugan Saravanasankar, S. Di Bona, Gianpaolo Kalasalingam Acad Res & Educ Krishnankoil India Univ Cassino & Lazio Meridionale Dept Civil & Mech Engn Cassino Italy

PurposeThrough the use of the Markov Decision Model (MDM) approach, this study uncovers significant variations in the availability of machines in both faulty and ideal situations in small and medium-sized enterprises (SMEs). The first-order differential equations are used to construct the mathematical equations from the transition-state diagrams of the separate subsystems in the critical part manufacturing ***/methodology/approachTo obtain the lowest investment cost, one of the non-traditional optimization strategies is employed in maintenance operations in SMEs in this research. It will use the particle swarm optimization (PSO) algorithm to optimize machine maintenance parameters and find the best solutions, thereby introducing the best decision-making process for optimal maintenance and service *** major goal of this study is to identify critical subsystems in manufacturing plants and to use an optimal decision-making process to adopt the best maintenance management system in the industry. The optimal findings of this proposed method demonstrate that in problematic conditions, the availability of SME machines can be enhanced by up to 73.25%, while in an ideal situation, the system's availability can be increased by up to 76.17%.Originality/valueThe proposed new optimal decision-support system for this preventive maintenance management in SMEs is based on these findings, and it aims to achieve maximum productivity with the least amount of expenditure in maintenance and service through an optimal planning and scheduling process.

关键词： Small and medium-sized enterprise Markov decision model particle swarm optimization algorithm Optimal decision-making process

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