期刊文献+
任意字段
题名或关键词
题名
关键词
摘要
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息
共找到114篇文章
< 1 2 … 6 >
每页显示 20 50 100
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
显示方式：
A Novel Multi-Modal Neurosymbolic Reasoning Intelligent Algorithm for BLMP Equation 认领 引用 被引量：1
1
作者 Hanwen Zhang Runfa Zhang Qirang Liu 《Chinese Physics Letters》 SCIE EI CAS CSCD 2025年第10期13-17,共5页
The(3+1)-dimensional Boiti-Leon-Manna-Pempinelli(BLMP)equation serves as a crucial nonlinear evolution equation in mathematical physics,capable of characterizing complex nonlinear dynamic phenomena in three-dimensiona... The(3+1)-dimensional Boiti-Leon-Manna-Pempinelli(BLMP)equation serves as a crucial nonlinear evolution equation in mathematical physics,capable of characterizing complex nonlinear dynamic phenomena in three-dimensional space and one-dimensional time.With broad applications spanning fluid dynamics,shallow water waves,plasma physics,and condensed matter physics,the investigation of its solutions holds significant importance.Traditional analytical methods face limitations due to their dependence on bilinear forms.To overcome this constraint,this letter proposes a novel multi-modal neurosymbolic reasoning intelligent algorithm(MMNRIA)that achieves 100%accurate solutions for nonlinear partial differential equations without requiring bilinear transformations.By synergistically integrating neural networks with symbolic computation,this approach establishes a new paradigm for universal analytical solutions of nonlinear partial differential equations.As a practical demonstration,we successfully derive several exact analytical solutions for the(3+1)-dimensional BLMP equation using MMNRIA.These solutions provide a powerful theoretical framework for studying intricate wave phenomena governed by nonlinearity and dispersion effects in three-dimensional physical space. 展开更多
关键词 intelligent algorithm dimensional Boiti Leon Manna Pempinelli equation fluid dynamicsshallow water wavesplasma physicsand nonlinear evolution equation condensed matter physicsthe neurosymbolic reasoning characterizing complex nonlinear dynamic phenomena analytical methods
暂未订购 下载PDF
INTELLIGENT ALGORITHMS FOR SOLVING CNOP AND THEIR APPLICATIONS IN ENSO PREDICTABILITY AND TROPICAL CYCLONE ADAPTIVE OBSERVATIONS 认领 引用 被引量：5
2
作者 MU Bin ZHANG Lin-lin +4 位作者 YUAN Shi-jin QIAN Yi-wen WEN Shi-cheng YAN Jing-hao ZHOU Fei-fan 《Journal of Tropical Meteorology》 SCIE 2019年第1期63-81,共19页
Some intelligent algorithms(IAs) proposed by us, including swarm IAs and single individual IAs, have been applied to the Zebiak-Cane(ZC) model to solve conditional nonlinear optimal perturbation(CNOP) for studying El ... Some intelligent algorithms(IAs) proposed by us, including swarm IAs and single individual IAs, have been applied to the Zebiak-Cane(ZC) model to solve conditional nonlinear optimal perturbation(CNOP) for studying El Ni?o-Southern Oscillation(ENSO) predictability. Compared to the adjoint-based method(the ADJ-method), which is referred to as a benchmark, these IAs can achieve approximate CNOP results in terms of magnitudes and patterns.Using IAs to solve CNOP can avoid the use of an adjoint model and widen the application of CNOP in numerical climate and weather modeling. Of the proposed swarm IAs, PCA-based particle swarm optimization(PPSO) obtains CNOPs with the best patterns and the best stability. Of the proposed single individual IAs, continuous tabu search algorithm with sine maps and staged strategy(CTS-SS) has the highest efficiency. In this paper, we compare the validity, stability and efficiency of parallel PPSO and CTS-SS using these two IAs to solve CNOP in the ZC model for studying ENSO predictability. The experimental results show that CTS-SS outperforms parallel PPSO except with respect to stability. At the same time, we are also concerned with whether these two IAs can effectively solve CNOP when applied to more complicated models. Taking the sensitive areas identification of tropical cyclone adaptive observations as an example and using the fifth-generation mesoscale model(MM5), we design some experiments. The experimental results demonstrate that each of these two IAs can effectively solve CNOP and that parallel PPSO has a higher efficiency than CTS-SS. We also provide some suggestions on how to choose a suitable IA to solve CNOP for different models. 展开更多
关键词 CNOP single individual intelligent algorithms swarm intelligent algorithms ENSO predictability tropical cyclone adaptive observations
暂未订购 下载PDF
IMLMA:An Intelligent Algorithm for Model Lifecycle Management with Automated Retraining,Versioning,and Monitoring 认领 引用
3
作者 Yu Cao Yiyun He Chi Zhang 《Journal of Electronic Research and Application》 2025年第5期233-248,共16页
With the rapid adoption of artificial intelligence(AI)in domains such as power,transportation,and finance,the number of machine learning and deep learning models has grown exponentially.However,challenges such as dela... With the rapid adoption of artificial intelligence(AI)in domains such as power,transportation,and finance,the number of machine learning and deep learning models has grown exponentially.However,challenges such as delayed retraining,inconsistent version management,insufficient drift monitoring,and limited data security still hinder efficient and reliable model operations.To address these issues,this paper proposes the Intelligent Model Lifecycle Management Algorithm(IMLMA).The algorithm employs a dual-trigger mechanism based on both data volume thresholds and time intervals to automate retraining,and applies Bayesian optimization for adaptive hyperparameter tuning to improve performance.A multi-metric replacement strategy,incorporating MSE,MAE,and R2,ensures that new models replace existing ones only when performance improvements are guaranteed.A versioning and traceability database supports comparison and visualization,while real-time monitoring with stability analysis enables early warnings of latency and drift.Finally,hash-based integrity checks secure both model files and datasets.Experimental validation in a power metering operation scenario demonstrates that IMLMA reduces model update delays,enhances predictive accuracy and stability,and maintains low latency under high concurrency.This work provides a practical,reusable,and scalable solution for intelligent model lifecycle management,with broad applicability to complex systems such as smart grids. 展开更多
关键词 Model lifecycle management Intelligent algorithms Hyperparameter optimization Versioning and traceability Power metering
暂未订购 下载PDF
Image Forgery Detection Using Segmentation and Swarm Intelligent Algorithm 认领 引用 被引量：2
4
作者 ZHAO Fei SHI Wenchang +1 位作者 QIN Bo LIANG Bin 《Wuhan University Journal of Natural Sciences》 CAS CSCD 2017年第2期141-148,共8页
Small or smooth cloned regions are difficult to be detected in image copy-move forgery （CMF） detection. Aiming at this problem, an effective method based on image segmentation and swarm intelligent （SI） algorithm ... Small or smooth cloned regions are difficult to be detected in image copy-move forgery （CMF） detection. Aiming at this problem, an effective method based on image segmentation and swarm intelligent （SI） algorithm is proposed. This method segments image into small nonoverlapping blocks. A calculation of smooth degree is given for each block. Test image is segmented into independent layers according to the smooth degree. SI algorithm is applied in finding the optimal detection parameters for each layer. These parameters are used to detect each layer by scale invariant features transform （SIFT）-based scheme, which can locate a mass of keypoints. The experimental results prove the good performance of the proposed method, which is effective to identify the CMF image with small or smooth cloned region. 展开更多
关键词 copy-move forgery detection scale invariant features transform （SIFT） swarm intelligent algorithm particle swarm optimization
暂未订购 下载PDF
Research on an Intelligent Prediction Model for Construction Project Cost Estimation 认领 引用
5
作者 SHU Juan 《外文科技期刊数据库（文摘版）工程技术》 2026年第6期001-005,共5页
Accurate cost forecasting for construction projects is crucial for making investment decisions and controlling expenses. However, conventional forecasting methods often prove inadequate when dealing with the complexit... Accurate cost forecasting for construction projects is crucial for making investment decisions and controlling expenses. However, conventional forecasting methods often prove inadequate when dealing with the complexity of engineering projects and evolving market conditions. This study aims to develop a construction project cost prediction system utilizing intelligent computing technologies to enhance forecast accuracy and adaptability to various changes. The research first comprehensively identifies the primary components of construction project costs and key factors influencing them. It then evaluates the applicability of traditional linear regression methods, assessing their effectiveness in handling nonlinear scenarios and multiple variables, while highlighting existing limitations such as complex data correlations and slow responsiveness to market fluctuations. Subsequently, the study compares support vector machines and artificial neural networks as intelligent computational approaches for cost prediction, providing detailed explanations of BP neural network mechanisms and demonstrating its superior performance in addressing complex nonlinear relationships compared to traditional methods. Additionally, a comprehensive integration framework is designed to consolidate data from diverse sources—including bill of quantities specifications, market price indices, and macroeconomic indicators—as unified input parameters. In the final testing phase, researchers selected relevant data from actual construction project cases for repeated training and practical validation to ensure the newly developed system functioned reliably under various conditions. The improved intelligent prediction model demonstrated excellent accuracy and stability in forecasting, significantly outperforming traditional regression methods and effectively identifying the underlying causes of cost fluctuations. 展开更多
关键词 Construction project cost forecasting Intelligent algorithms BP neural network Genetic algorithm optimization Model validation
暂未订购 下载PDF
Intelligent Technologies and Applications for Vibration Control in Building Structures 认领 引用
6
作者 WAN Jie 《外文科技期刊数据库（文摘版）工程技术》 2026年第5期011-015,共5页
Vibration control of building structures serves as a critical technical approach to enhance structural safety and operational performance. With the increasing prevalence of complex structures such as super-tall buildi... Vibration control of building structures serves as a critical technical approach to enhance structural safety and operational performance. With the increasing prevalence of complex structures such as super-tall buildings and large-span bridges, traditional methods—whether passive, actively triggered, or partially active control—are increasingly inadequate in adapting to environmental changes, exhibiting insufficient precision and poor resistance to disturbances. The application of intelligent technologies has introduced groundbreaking solutions for structural vibration control, leveraging advanced computational algorithms, smart materials, and sensor systems to enable autonomous problem detection, automated decision-making, and real-time adjustments. This paper first examines the fundamental principles and limitations of conventional vibration control methods, then underscores the importance of intelligent technologies. It thoroughly analyzes the advantages of core techniques—including fuzzy reasoning, neural networks, genetic algorithms, and deep learning—in system modeling and controller parameter optimization, while exploring their practical applications in engineering. Furthermore, it highlights the pivotal role of innovative materials (e.g., piezoelectric materials and shape-memory alloys) and advanced sensing technologies (e.g., fiber optic sensors and wireless sensor networks) in achieving precise dynamic response measurement and efficient operation, and discusses integrated design approaches combining material properties with sensing capabilities. The paper concludes with a detailed explanation of the overall architecture of the intelligent monitoring and control system, as well as the operational processes for real-time data acquisition and feedback control. Through several practical engineering cases, it further examines the specific application effectiveness of this technology in high-rise buildings and large-span bridges. 展开更多
关键词 Building structure vibration control Intelligent technology Intelligent algorithms Intelligent materials Intelligent monitoring and control systems
暂未订购 下载PDF
Intelligent prediction of deep rock strength based on modified three-dimensional Hoek-Brown criterion 认领 引用
7
作者 Jiaxin Wang Hongru Jiang +4 位作者 Shunchuan Wu Shihuai Zhang Chaoqun Chu Xiaoping Zhang Yingming Xiao 《International Journal of Minerals,Metallurgy and Materials》 SCIE EI CAS CSCD 2026年第7期2454-2472,I0020-I0030,共19页
To study the deep rock strength,this paper proposes a five-parameter deviatoric function to modify the deviatoric function of the Hoek-Brown(HB)criterion introduces an intelligent optimization algorithm(IOA)to determi... To study the deep rock strength,this paper proposes a five-parameter deviatoric function to modify the deviatoric function of the Hoek-Brown(HB)criterion introduces an intelligent optimization algorithm(IOA)to determine the material parameters,thereby constructing a modified three-dimensional(3D)HB criterion,namely MMCHB criterion.The MMCHB criterion avoids the defects of the traditional HB criterion,which neither considers the Intermediate principal stress(IPS)nor meets the smoothness requirement,and overcomes the shortcomings of parameter determination based on conventional methods,which can lead to a single deviatoric plane envelope shape.This modified criterion can be degenerated into the HB criterion under triaxial compression and tension.The proposed criterion is verified using true triaxial test data for six types of intact rock,and the modified 3D HB criteria are selected for comparative study.The results show that the proposed criterion under the IOA has the best prediction error for the six rock types,ranging from 1.6636% to 3.4023%.Overall,the MMCHB criterion outperforms the existing modified 3D HB criteria in prediction.Based on the proposed MMCHB criterion,an intelligent prediction system is developed,which provides a new approach for intelligent prediction of deep rock strength and dynamic construction of rock material parameters. 展开更多
关键词 rock strength Hoek-Brown criterion Intermediate principal stress intelligent optimization algorithm intelligent predictionsystem
暂未订购 下载PDF
Intelligent algorithms: new avenues for designing nanophotonic devices [Invited] 认领 引用 被引量：8
8
作者 Lifeng Ma Jing Li +4 位作者 Zhouhui Liu Yuxuan Zhang Nianen Zhang Shuqiao Zheng Cuicui Lu 《Chinese Optics Letters》 SCIE EI CAS CSCD 2021年第1期31-63,共33页
The research on nanophotonic devices has made great progress during the past decades. It is the unremitting pursuit of researchers that realize various device functions to meet practical applications. However, most of... The research on nanophotonic devices has made great progress during the past decades. It is the unremitting pursuit of researchers that realize various device functions to meet practical applications. However, most of the traditional methods rely on human experience and physical inspiration for structural design and parameter optimization, which usually require a lot of resources, and the performance of the designed device is limited. Intelligent algorithms, which are composed of rich optimized algorithms, show a vigorous development trend in the field of nanophotonic devices in recent years. The design of nanophotonic devices by intelligent algorithms can break the restrictions of traditional methods and predict novel configurations, which is universal and efficient for different materials, different structures, different modes, different wavelengths, etc. In this review, intelligent algorithms for designing nanophotonic devices are introduced from their concepts to their applications, including deep learning methods, the gradient-based inverse design method, swarm intelligence algorithms, individual inspired algorithms, and some other algorithms. The design principle based on intelligent algorithms and the design of typical new nanophotonic devices are reviewed. Intelligent algorithms can play an important role in designing complex functions and improving the performances of nanophotonic devices, which provide new avenues for the realization of photonic chips. 展开更多
关键词 intelligent algorithms nanophotonic devices deep learning methods swarm intelligence genetic algorithm
暂未订购 下载PDF
Performance evaluation for intelligent optimization algorithms in self-potential data inversion 认领 引用 被引量：7
9
作者 崔益安 朱肖雄 +2 位作者 陈志学 刘嘉文 柳建新 《Journal of Central South University》 SCIE EI CAS CSCD 2016年第10期2659-2668,共10页
The self-potential method is widely used in environmental and engineering geophysics. Four intelligent optimization algorithms are adopted to design the inversion to interpret self-potential data more accurately and e... The self-potential method is widely used in environmental and engineering geophysics. Four intelligent optimization algorithms are adopted to design the inversion to interpret self-potential data more accurately and efficiently: simulated annealing, genetic, particle swarm optimization, and ant colony optimization. Using both noise-free and noise-added synthetic data, it is demonstrated that all four intelligent algorithms can perform self-potential data inversion effectively. During the numerical experiments, the model distribution in search space, the relative errors of model parameters, and the elapsed time are recorded to evaluate the performance of the inversion. The results indicate that all the intelligent algorithms have good precision and tolerance to noise. Particle swarm optimization has the fastest convergence during iteration because of its good balanced searching capability between global and local minimisation. 展开更多
关键词 self-potential inversion intelligent algorithm
暂未订购 下载PDF
Modelling and reviewing the reliability and multi-objective optimization of wind-turbine system and photovoltaic panel with intelligent algorithms 认领 引用 被引量：4
10
作者 Reza Alayi Mehdi Jahangiri +3 位作者 John William Grimaldo Guerrero Ravil Akhmadeev Rustem Adamovich Shichiyakh Sara Abbasi Zanghaneh 《Clean Energy》 EI CSCD 2021年第4期713-730,共18页
One of the options for non-dependence on fossil fuels is the use of renewable energy,which has not grown significantly due to the variable nature of this type of energy.The combined use of wind and solar energy as ene... One of the options for non-dependence on fossil fuels is the use of renewable energy,which has not grown significantly due to the variable nature of this type of energy.The combined use of wind and solar energy as energy sources can be a good solution to the problem of variable energy output.Therefore,the purpose of this research is to model a combination of the wind-turbine system and photovoltaic cell,which is needed to investigate their ability to supply electrical energy.To determine this important power production,real data of solar-radiation intensity and wind are used and,in modelling photovoltaic cells,the effects of ambient temperature are also considered.In order to generalize the studied system in all dimensions,different scenarios have been considered.According to the amount of electrical power generated,during the evaluation of these scenarios,two economic parameters,namely the selected scenario of a wind/solar system with diesel-generator support,was determined. 展开更多
关键词 reliability wind turbine photovoltaic cell intelligent algorithm economic analysis
暂未订购 下载PDF
Research on tasks schedule and data transmission of video sensor networks based on intelligent agents and intelligent algorithms 认领 引用 被引量：2
11
作者 HUANG Hai-ping WANG Ru-chuan +2 位作者 SUN Li-juan WANG Hai-yuan XIAO Fu 《The Journal of China Universities of Posts and Telecommunications》 EI 2009年第6期84-91,112,共8页
As a novel application technology,wireless video sensor networks become the current research focus,especially on target tracking and surveillance scenario.Based on multiple agents＇ technique,this article introduces a... As a novel application technology,wireless video sensor networks become the current research focus,especially on target tracking and surveillance scenario.Based on multiple agents＇ technique,this article introduces a series of intelligent algorithms such as simulated annealing algorithm（SA）,genetic algorithm（GA）,and ant colony optimization algorithm（ACO） or their mixed algorithms,to resolve the optimization of tasks schedule and data transmission.This article analyzes the performance of abovementioned algorithms and verifies their feasibility associated with agents.The simulations demonstrates that the mixed algorithms based on SA and GA obtain the optimal solution to tasks schedule,and those combined with SA-ACO show advantages on multimedia sensor networks routing optimization. 展开更多
关键词 video sensor networks intelligent algorithm tasks schedule optimal routing agent
暂未订购 下载PDF
Overview of research on intelligent swarm munitions 认领 引用 被引量：4
12
作者 Guannan Chang Wenxing Fu +4 位作者 Junmin Zhao Jinping Li Haochun Miao Xiaofeng Zhang Peng Dong 《Defence Technology（防务技术）》 SCIE EI CAS CSCD 2025年第12期225-244,共20页
This article started with an overview of the current technological status and engineering developments in the field of swarm munitions.It first introduced swarm behaviors and related swarm algorithms,and then provided... This article started with an overview of the current technological status and engineering developments in the field of swarm munitions.It first introduced swarm behaviors and related swarm algorithms,and then provided a comprehensive summary of the research progress in the field of swarm munitions from four aspects:Collaborative perception and detection,collaborative positioning and navigation,task allocation for swarms,and path planning for swarms.In summary,future developments in collaborative perception,planning,positioning,navigation,and decision-making for swarm munitions will trend towards intelligence,adaptability,and collaboration.It can enable swarm munitions to be better adapted to complex and dynamic battlefields,improving operational effectiveness and mission capabilities. 展开更多
关键词 Swarm munitions Collaborative control Intelligent algorithms
暂未订购 下载PDF
A novel intelligent optimization and guidance method for blast furnace oriented to increasing yield of hot metal 认领 引用
13
作者 Yi-fan Duan Ran Liu +6 位作者 Xiao-jie Liu Hong-wei Li Xin Li Hong-yang Li Jun Zhao Hao-nan Wang Qing Lv 《Journal of Iron and Steel Research International》 SCIE EI CAS CSCD 2025年第11期3790-3816,共27页
In the traditional blast furnace(BF)ironmaking process in China,a notable deviation exists between the theoretical and actual yield of hot metal,leading to unexpected iron loss and restricting the improvement of produ... In the traditional blast furnace(BF)ironmaking process in China,a notable deviation exists between the theoretical and actual yield of hot metal,leading to unexpected iron loss and restricting the improvement of production capacity,which cannot adapt to the increasingly intensified smelting rhythm.Focusing on a BF in a Chinese steel enterprise,a deep neural network algorithm was designed to model the impact of multiple parameters on actual yield of hot metal in a single BF smelting cycle,successfully accomplishing the theoretical computation and real-time prediction of yield of hot metal for subsequent,unknown BF smelting cycle.Test results show that the proposed algorithm demonstrates an impressive prediction accuracy of 86.7% within an error range of±10 t and can swiftly complete the training and convergence process in 32.5 s.By integrating prediction results with Nomogram,a regulatory mechanism was engineered to minimize the deviation between theoretical and actual yield of hot metal.This mechanism ensures the yield enhancement of hot metal through dynamic adjustments of BF operational parameters.Industrial-scale application experiments confirmed that the intelligent operation and optimization system,developed in the laboratory,can maintain the yield deviation of hot metal within a stable range of 30 t,achieving a maximum reduction in iron loss rate of 17.65%compared to that before system operation.The findings provide robust support for the yield increase and efficiency improvement of the experimental BF. 展开更多
关键词 Blast furnace Production increase Dynamic adjustment Blast furnace operational parameter Hot metal yield Intelligent algorithm
暂未订购 下载PDF
An intelligent task offloading algorithm(iTOA)for UAV edge computing network 认领 引用 被引量：11
14
作者 Jienan Chen Siyu Chen +3 位作者 Siyu Luo Qi Wang Bin Cao Xiaoqian Li 《Digital Communications and Networks》 SCIE EI 2020年第4期433-443,共11页
Unmanned Aerial Vehicle(UAV)has emerged as a promising technology for the support of human activities,such as target tracking,disaster rescue,and surveillance.However,these tasks require a large computation load of im... Unmanned Aerial Vehicle(UAV)has emerged as a promising technology for the support of human activities,such as target tracking,disaster rescue,and surveillance.However,these tasks require a large computation load of image or video processing,which imposes enormous pressure on the UAV computation platform.To solve this issue,in this work,we propose an intelligent Task Offloading Algorithm(iTOA)for UAV edge computing network.Compared with existing methods,iTOA is able to perceive the network’s environment intelligently to decide the offloading action based on deep Monte Calor Tree Search(MCTS),the core algorithm of Alpha Go.MCTS will simulate the offloading decision trajectories to acquire the best decision by maximizing the reward,such as lowest latency or power consumption.To accelerate the search convergence of MCTS,we also proposed a splitting Deep Neural Network(sDNN)to supply the prior probability for MCTS.The sDNN is trained by a self-supervised learning manager.Here,the training data set is obtained from iTOA itself as its own teacher.Compared with game theory and greedy search-based methods,the proposed iTOA improves service latency performance by 33%and 60%,respectively. 展开更多
关键词 Unmanned aerial vehicles(UAVs) Mobile edge computing(MEC) Intelligent task offloading algorithm(iTOA) Monte Carlo tree search(MCTS) Deep reinforcement learning Splitting deep neural network(sDNN)
暂未订购 下载PDF
Optimization model for performance-based warranty decision of degraded systems based on improved sparrow search algorithm 认领 引用
15
作者 DONG Enzhi CHENG Zhonghua +3 位作者 LIU Zichang ZHU Xi WANG Rongcai BAI Yongsheng 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2025年第5期1259-1280,共22页
Performance-based warranties(PBWs)are widely used in industry and manufacturing.Given that PBW can impose financial burdens on manufacturers,rational maintenance decisions are essential for expanding profit margins.Th... Performance-based warranties(PBWs)are widely used in industry and manufacturing.Given that PBW can impose financial burdens on manufacturers,rational maintenance decisions are essential for expanding profit margins.This paper proposes an optimization model for PBW decisions for systems affected by Gamma degradation processes,incorporating periodic inspection.A system performance degradation model is established.Preventive maintenance probability and corrective renewal probability models are developed to calculate expected warranty costs and system availability.A benefits function,which includes incentives,is constructed to optimize the initial and subsequent inspection intervals and preventive maintenance thresholds,thereby maximizing warranty profit.An improved sparrow search algorithm is developed to optimize the model,with a case study on large steam turbine rotor shafts.The results suggest the optimal PBW strategy involves an initial inspection interval of approximately 20 months,with subsequent intervals of about four months,and a preventive maintenance threshold of approximately 37.39 mm wear.When compared to common cost-minimization-based condition maintenance strategies and PBW strategies that do not differentiate between initial and subsequent inspection intervals,the proposed PBW strategy increases the manufacturer’s profit by 1%and 18%,respectively.Sensitivity analyses provide managerial recommendations for PBW implementation.The PBW strategy proposed in this study significantly increases manufacturers’profits by optimizing inspection intervals and preventive maintenance thresholds,and manufacturers should focus on technological improvement in preventive maintenance and cost control to further enhance earnings. 展开更多
关键词 performance-based warranty gamma process periodic inspection intelligent optimization algorithm
暂未订购 下载PDF
Dynamic Boundary Optimization via IDBO-VMD:A Novel Power Allocation Strategy for Hybrid Energy Storage with Enhanced Grid Stability 认领 引用 被引量：1
16
作者 Zujun Ding Qi Xiang +10 位作者 Chengyi Li Mengyu Ma Chutong Zhang Xinfa Gu Jiaming Shi Hui Huang Aoyun Xia Wenjie Wang Wan Chen Ziluo Yu Jie Ji 《Energy Engineering》 EI 2026年第1期527-552,共26页
In order to address environmental pollution and resource depletion caused by traditional power generation,this paper proposes an adaptive iterative dynamic-balance optimization algorithm that integrates the Improved D... In order to address environmental pollution and resource depletion caused by traditional power generation,this paper proposes an adaptive iterative dynamic-balance optimization algorithm that integrates the Improved Dung Beetle Optimizer(IDBO)with VariationalMode Decomposition(VMD).The IDBO-VMD method is designed to enhance the accuracy and efficiency of wind-speed time-series decomposition and to effectively smooth photovoltaic power fluctuations.This study innovatively improves the traditional variational mode decomposition(VMD)algorithm,and significantly improves the accuracy and adaptive ability of signal decomposition by IDBO selfoptimization of key parameters K and a.On this basis,Fourier transform technology is used to define the boundary point between high frequency and low frequency signals,and a targeted energy distribution strategy is proposed:high frequency fluctuations are allocated to supercapacitors to quickly respond to transient power fluctuations;Lowfrequency components are distributed to lead-carbon batteries,optimizing long-term energy storage and scheduling efficiency.This strategy effectively improves the response speed and stability of the energy storage system.The experimental results demonstrate that the IDBO-VMD algorithm markedly outperforms traditional methods in both decomposition accuracy and computational efficiency.Specifically,it effectively reduces the charge–discharge frequency of the battery,prolongs battery life,and optimizes the operating ranges of the state-of-charge(SOC)for both leadcarbon batteries and supercapacitors.In addition,the energy management strategy based on the algorithm not only improves the overall energy utilization efficiency of the system,but also shows excellent performance in the dynamic management and intelligent scheduling of renewable energy generation. 展开更多
关键词 Energy efficiency hybrid energy storage system intelligent algorithm power fluctuation mitigation renewable energy
暂未订购 下载PDF
Structural Optimization of a Multi-Story Frame Structure Based on a Pre-Trained Physics-Informed Neural Network(PINN)Surrogate Model 认领 引用
17
作者 An Xu Zhixiong Liu +4 位作者 Hua Rong Liang Han Wei Shi Jun Huang Jiyang Fu 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第4期279-313,共35页
In structural optimization,data-driven surrogate models are often explored as alternatives to finite element analysis to reduce computational cost.However,conventional neural networks usually fail to capture key struc... In structural optimization,data-driven surrogate models are often explored as alternatives to finite element analysis to reduce computational cost.However,conventional neural networks usually fail to capture key structural characteristics and are limited to predicting global responses(e.g.,top displacement),but usually fail to achieve accurate internal force predictions with conventional training data volumes.As a result,most existing studies involving surrogate models did not concern internal force constraints.To address this issue,this study proposes a structural optimization framework based on a pre-trained Physics-Informed Neural Network(PINN)surrogate model.By embedding static equilibrium equation into the loss function,the model achieves higher predictive accuracy,particularly for internal forces,while pre-training accelerates convergence and enhances stability.Combined with an improved multi-swarm particle swarm optimization(MPSO)algorithm,the framework enables efficient optimization of multi-story frame structures under internal force and multiple other constraints.The application to a six-story frame structure validates its effectiveness:compared with a DNN-based model,the PINN-based model improves the coefficient of determination for internal force prediction from 0.8874 to 0.9937.These results demonstrate that the proposed method offers a promising approach for efficient optimization of multi-story frame structures. 展开更多
关键词 Structural optimization intelligent algorithms physics-informed neural networks(PINN) neural networks
暂未订购 下载PDF
Performance evaluation and interpretation of hybrid models based on light gradient boosting machine to predict coal mining subsidence 认领 引用
18
作者 Xuan Cui Shengli Yang Jiachen Wang 《International Journal of Coal Science & Technology》 SCIE EI CAS CSCD 2026年第1期64-83,共20页
Accurate prediction of coal mining subsidence(CMS)is pivotal for mining design,environmental preservation,safety assurance,and the formulation of preventive measures in mining areas.This study introduces an effective ... Accurate prediction of coal mining subsidence(CMS)is pivotal for mining design,environmental preservation,safety assurance,and the formulation of preventive measures in mining areas.This study introduces an effective hybrid prediction model for coal mining subsidence based on light gradient boosting machine(LightGBM)and uses Shapley Additive Explanations(SHAP)method to reveal and explain the contribution mechanism and interaction of factors affecting mining subsidence.This study collected a dataset of 163 mining subsidence cases(covering 12 features such as coal seams,rock layers,and mining conditions)to model mining subsidence prediction,and evaluated the performance of the model through multiple performance indicators.The results indicate that the hybrid prediction model developed in this paper demonstrates remarkable performance on the test set,with HGS-LightGBM standing out,achieving the coefficient of determination(R2)of 0.9589,while the single LightGBM achieves an R2of 0.925.Finally,apply model interpretation techniques to analyze the impact of input features on mining subsidence,and explain the prediction principles and decision-making process of the model.The analysis reveals that the thickness of coal seam(m)is the most influential parameter for CMS.Furthermore,a targeted interaction analysis on key parameters was conducted to clarify the impact mechanisms of each influencing factor.In summary,the model established in this study has excellent performance and exhibits significant interpretability and transparency. 展开更多
关键词 Coal mining subsidence LightGBM Machine learning Intelligent algorithm Sensitivity analysis
暂未订购 下载PDF
Recent Advances and Future Directions in Centrifugal Slurry Pump Design Optimization:A Lifecycle-Oriented Review 认领 引用
19
作者 Jianping Yuan Chenxin Yu +2 位作者 Yanxia Fu Weidong Wang Heng Liao 《Fluid Dynamics & Materials Processing》 EI 2026年第6期285-312,共28页
Slurry transport is a critical multiphase-flow process in mining,metallurgy,and dredging applications,where hydraulic efficiency,particle-induced wear,cavitation erosion,and structural vibration are strongly coupled.T... Slurry transport is a critical multiphase-flow process in mining,metallurgy,and dredging applications,where hydraulic efficiency,particle-induced wear,cavitation erosion,and structural vibration are strongly coupled.This topic-focused review synthesizes recent advances in centrifugal slurry pump design optimization from the perspectives of wear-resistant surface engineering,hydraulic design,structural dynamics,intelligent optimization algorithms,and multiphysics simulation.Unlike earlier reviews that primarily addressed hydraulic performance,erosion wear,flow visualization,or numerical modeling in isolation,the present work adopts a lifecycle-oriented perspective.Representative studies are critically evaluated according to reported efficiency improvements,wearrate and material-loss reduction,cavitation and net-positive-suction-head-related performance changes,validation strategies,uncertainty sources,and practical engineering feasibility.Particular attention is devoted to the integration of computational fluid dynamics with the discrete element method,fluid-structure interaction,cavitation-erosion coupling,particle-size effects,surrogate-assisted optimization,and digital-twin-enabled monitoring frameworks.The reviewed literature indicates that high-fidelity simulations and intelligent algorithms have significantly enhanced design exploration and predictive capability.However,their large-scale engineering deployment remains limited by challenges associated with model validation,data availability,computational cost,interpretability,and generalization under variable slurry conditions.Finally,digital-twin-enabled lifecycle optimization is discussed as a promising conceptual pathway rather than a fully validated industrial solution,highlighting the need for reduced-order modeling,robust sensing strategies,uncertainty-aware data assimilation,and staged experimental validation to support reliable real-world implementation. 展开更多
关键词 Slurry pump design optimization wear-resistant materials hydraulic model intelligent algorithms multiphysics simulation
暂未订购 下载PDF
Comparative Analysis for Evaluating Wind Energy Resources Using Intelligent Optimization Algorithms and Numerical Methods 认领 引用
20
作者 Musaed Alrashidi 《Computer Systems Science & Engineering》 SCIE EI 2023年第10期491-513,共23页
Statistical distributions are used to model wind speed,and the twoparameters Weibull distribution has proven its effectiveness at characterizing wind speed.Accurate estimation of Weibull parameters,the scale(c)and sha... Statistical distributions are used to model wind speed,and the twoparameters Weibull distribution has proven its effectiveness at characterizing wind speed.Accurate estimation of Weibull parameters,the scale(c)and shape(k),is crucial in describing the actual wind speed data and evaluating the wind energy potential.Therefore,this study compares the most common conventional numerical(CN)estimation methods and the recent intelligent optimization algorithms(IOA)to show how precise estimation of c and k affects the wind energy resource assessments.In addition,this study conducts technical and economic feasibility studies for five sites in the northern part of Saudi Arabia,namely Aljouf,Rafha,Tabuk,Turaif,and Yanbo.Results exhibit that IOAs have better performance in attaining optimal Weibull parameters and provided an adequate description of the observed wind speed data.Also,with six wind turbine technologies rating between 1 and 3MW,the technical and economic assessment results reveal that the CN methods tend to overestimate the energy output and underestimate the cost of energy($/kWh)compared to the assessments by IOAs.The energy cost analyses show that Turaif is the windiest site,with an electricity cost of$0.016906/kWh.The highest wind energy output is obtained with the wind turbine having a rated power of 2.5 MW at all considered sites with electricity costs not exceeding$0.02739/kWh.Finally,the outcomes of this study exhibit the potential of wind energy in Saudi Arabia,and its environmental goals can be acquired by harvesting wind energy. 展开更多
关键词 Weibull distribution conventional numerical methods intelligent optimization algorithms wind resource exploration and exploitation cost of energy($/kWh)
暂未订购 下载PDF
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
上一页 1 2 … 6 下一页 到第 页
在线咨询 使用帮助 返回顶部 意见反馈