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IG-3D:Integrated-Gradients 3D Optimization for Private Transformer Inference 认领 引用
1
作者 Lei Sun Jingwen Wang +3 位作者 Peng Hu Xiuqing Mao Cuiyun Hu Zhihong Wang 《Computers, Materials & Continua》 SCIE EI 2026年第5期1158-1176,共19页
Transformer models face significant computational challenges in private inference(PI).Existing optimization methods often rely on isolated techniques,neglecting joint structural and operational improvements.We propose... Transformer models face significant computational challenges in private inference(PI).Existing optimization methods often rely on isolated techniques,neglecting joint structural and operational improvements.We propose IG-3D,a unified framework that integrates structured compression and operator approximation through accurate importance assessment.Our approach first evaluates attention head importance using Integrated Gradients(IG),offering greater stability and theoretical soundness than gradient-based methods.We then apply a threedimensional optimization:(1)structurally pruning redundant attention heads;(2)replacing Softmax with adaptive polynomial approximation to avoid exponential computations;(3)implementing layer-wise GELU substitution to accommodate different layer characteristics.A joint thresholdmechanism coordinates compression across dimensions under accuracy constraints.Experimental results on the GLUE benchmark show that our method achieves an average 2.9×speedup in inference latency and a 50%reduction in communication cost,while controlling the accuracy loss within 2.3%,demonstrating significant synergistic effects and a superior accuracy-efficiency trade-off compared to single-technique optimization strategies. 展开更多
关键词 Private inference transformer attention-head pruning integrated gradients transformer model optimization
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A Unified Feature Selection Framework Combining Mutual Information and Regression Optimization for Multi-Label Learning 认领 引用
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作者 Hyunki Lim 《Computers, Materials & Continua》 SCIE EI 2026年第4期1262-1281,共20页
High-dimensional data causes difficulties in machine learning due to high time consumption and large memory requirements.In particular,in amulti-label environment,higher complexity is required asmuch as the number of ... High-dimensional data causes difficulties in machine learning due to high time consumption and large memory requirements.In particular,in amulti-label environment,higher complexity is required asmuch as the number of labels.Moreover,an optimization problem that fully considers all dependencies between features and labels is difficult to solve.In this study,we propose a novel regression-basedmulti-label feature selectionmethod that integrates mutual information to better exploit the underlying data structure.By incorporating mutual information into the regression formulation,the model captures not only linear relationships but also complex non-linear dependencies.The proposed objective function simultaneously considers three types of relationships:(1)feature redundancy,(2)featurelabel relevance,and(3)inter-label dependency.These three quantities are computed usingmutual information,allowing the proposed formulation to capture nonlinear dependencies among variables.These three types of relationships are key factors in multi-label feature selection,and our method expresses them within a unified formulation,enabling efficient optimization while simultaneously accounting for all of them.To efficiently solve the proposed optimization problem under non-negativity constraints,we develop a gradient-based optimization algorithm with fast convergence.Theexperimental results on sevenmulti-label datasets show that the proposed method outperforms existingmulti-label feature selection techniques. 展开更多
关键词 feature selection multi-label learning regression model optimization mutual information
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Coupling-based optimization method of gradient boosting machine for landslide susceptibility mapping 认领 引用
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作者 ZHOU Liwei SHAO Ji +4 位作者 WANG Fei LIU Yanling ZHENG Chen HONG Wei CHEN Chieh-Hung 《Journal of Mountain Science》 SCIE CSCD 2026年第4期1668-1681,共14页
The complex interaction of multiple geological and environmental factors significantly influences the reliability and precision of landslide susceptibility mapping,posing challenges to both the generalization capabili... The complex interaction of multiple geological and environmental factors significantly influences the reliability and precision of landslide susceptibility mapping,posing challenges to both the generalization capability and interpretability of Gradient Boosting Machines(GBM).To address these challenges,this study focuses on Yongjia County in southeastern China,where frequent landslides are driven by rugged topography and intensive anthropogenic activities.Using Spearman’s rank correlation coefficient and Shapley values,ten key conditioning factors were identified.Four hybrid models were subsequently developed by integrating GBM with Frequency Ratio(FR),Information Value(IV),Certainty Factor(CF),and Weight of Evidence(WOE).The results reveal that the dominant contributors to landslide occurrence in the study area are,in descending order of importance:elevation,normalized difference vegetation index,distance to roads,slope,distance to rivers,land use,stream power index,vegetation,lithology,and rainfall.Model validation indicates that the CF-Logit Boost model outperforms the others,achieving the highest Area Under the Curve(AUC)and Kappa coefficient.Compared to the standalone GBM model,it improved the Kappa coefficient by 4%to 14%and AUC by 1%to 7%.These results underscore the effectiveness of CFbased variable weighting in enhancing GBM performance and provide a robust framework for improving landslide susceptibility assessments. 展开更多
关键词 Landslide Gradient boosting machine Landslide susceptibility Coupling method Model optimization
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Dual Layer Source Grid Load Storage Collaborative Planning Model Based on Benders Decomposition: Distribution Network Optimization Considering Low-Carbon and Economy 认领 引用
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作者 Jun Guo Maoyuan Chen +2 位作者 Yuyang Li Sibo Feng Guangyu Fu 《Energy Engineering》 EI 2026年第2期104-133,共30页
Theauthor proposes a dual layer source grid load storage collaborative planning model based on Benders decomposition to optimize the low-carbon and economic performance of the distribution network.The model plans the ... Theauthor proposes a dual layer source grid load storage collaborative planning model based on Benders decomposition to optimize the low-carbon and economic performance of the distribution network.The model plans the configuration of photovoltaic(3.8 MW),wind power(2.5 MW),energy storage(2.2 MWh),and SVC(1.2 Mvar)through interaction between upper and lower layers,and modifies lines 2–3,8–9,etc.to improve transmission capacity and voltage stability.The author uses normal distribution and Monte Carlo method to model load uncertainty,and combines Weibull distribution to describe wind speed characteristics.Compared to the traditional three-layer model(TLM),Benders decomposition-based two-layer model(BLBD)has a 58.1%reduction in convergence time(5.36 vs.12.78 h),a 51.1%reduction in iteration times(23 vs.47 times),a 8.07%reduction in total cost(12.436 vs.13.528 million yuan),and a 9.62%reduction in carbon emissions(12,456 vs.13,782 t).After optimization,the peak valley difference decreased from4.1 to 2.9MW,the renewable energy consumption rate reached 93.4%,and the energy storage efficiency was 87.6%.Themodel has been validated in the IEEE 33 node system,demonstrating its superiority in terms of economy,low-carbon,and reliability. 展开更多
关键词 Benders decomposition source grid load storage distribution network planning low-carbon economy optimization model
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Research on the Control of Construction Period Risks by BIM Modeling Optimization in the Pre- construction Stage of Industrial Factory Buildings 认领 引用
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作者 Zhixiong Huang 《Journal of Architectural Research and Development》 2025年第6期43-50,共8页
This research focuses on using BIM modeling optimization to control construction-period risks in the pre-construction stage of industrial factory buildings.It analyzes common risk factors and limitations of traditiona... This research focuses on using BIM modeling optimization to control construction-period risks in the pre-construction stage of industrial factory buildings.It analyzes common risk factors and limitations of traditional approaches.BIM-based methods like collision detection,4D simulation,multi-dimensional data integration,etc.,can effectively mitigate risks.Stakeholder collaboration,digital twin testing,and lean BIM integration is also crucial.Case studies show BIM can reduce risks by 32-41%,with a three phase roadmap provided. 展开更多
关键词 BIM modeling optimization Construction period risk Industrial factory building
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Optimization Strategies for the Dispatch Management of Vertical Transportation Equipment in High-Rise Building Construction 认领 引用
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作者 WU Xiao 《外文科技期刊数据库（文摘版）工程技术》 2026年第4期150-154,共5页
During the construction of high-rise buildings, the scheduling and management of vertical transportation equipment have become crucial components for ensuring smooth project progress and effective cost control. Howeve... During the construction of high-rise buildings, the scheduling and management of vertical transportation equipment have become crucial components for ensuring smooth project progress and effective cost control. However, current practices face significant challenges, including substantial discrepancies between equipment configurations and actual construction requirements, uneven task distribution across work zones, and difficulties in promptly adjusting schedules when construction conditions change abruptly. This paper provides a detailed analysis of the types, operational mechanisms, core aspects, and implementation steps of major vertical transportation systems such as construction elevators and tower cranes, emphasizing that scheduling effectiveness directly impacts construction efficiency, worker safety, and cost control. It examines common challenges in scheduling practices—including insufficient equipment availability or improper distribution leading to task completion difficulties, uneven workload distribution across zones causing resource waste and delays—and investigates key factors affecting scheduling performance, including equipment compatibility with complex construction processes, spatial constraints in site layouts, manpower allocation limitations, and inadequate communication mechanisms between departments. To address these issues, a comprehensive improvement plan has been developed, utilizing operations research theories to establish a scientifically sound scheduling optimization model and formulate tailored strategies for handling unexpected construction scenarios. 展开更多
关键词 High-rise buildings Vertical transportation equipment Dispatch management Optimization model Internet of Things
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Knowledge expression,numerical modeling and optimization application of ethylene thermal cracking:From the perspective of intelligent manufacturing 认领 引用 被引量：3
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作者 Kexin Bi Shuyuan Zhang +4 位作者 Chen Zhang Haoran Li Xinye Huang Haoyu Liu Tong Qiu 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2021年第10期1-17,共17页
Applications of process systems engineering(PSE)in plants and enterprises are boosting industrial reform from automation to digitization and intelligence.For ethylene thermal cracking,knowledge expression,numerical mo... Applications of process systems engineering(PSE)in plants and enterprises are boosting industrial reform from automation to digitization and intelligence.For ethylene thermal cracking,knowledge expression,numerical modeling and intelligent optimization are key steps for intelligent manufacturing.This paper provides an overview of progress and contributions to the PSE-aided production of thermal cracking;introduces the frameworks,methods and algorithms that have been proposed over the past10 years and discusses the advantages,limitations and applications in industrial practice.An entire set of molecular-level modeling approaches from feedstocks to products,including feedstock molecular reconstruction,reaction-network auto-generation and cracking unit simulation are described.Multilevel control and optimization methods are exhibited,including at the operational,cycle,plant and enterprise level.Relevant software packages are introduced.Finally,an outlook in terms of future directions is presented. 展开更多
关键词 Ethylene thermal cracking PSE Intelligent manufacturing Molecularization and digitization Modeling and optimization
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A review of optimization modeling and solution methods in renewable energy systems 认领 引用 被引量：1
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作者 Shiwei YU Limin YOU Shuangshuang ZHOU 《Frontiers of Engineering Management》 CSCD 2023年第4期640-671,共32页
The advancement of renewable energy(RE)represents a pivotal strategy in mitigating climate change and advancing energy transition efforts.A current of research pertains to strategies for fostering RE growth.Among the ... The advancement of renewable energy(RE)represents a pivotal strategy in mitigating climate change and advancing energy transition efforts.A current of research pertains to strategies for fostering RE growth.Among the frequently proposed approaches,employing optimization models to facilitate decision-making stands out prominently.Drawing from an extensive dataset comprising 32806 literature entries encompassing the optimization of renewable energy systems(RES)from 1990 to 2023 within the Web of Science database,this study reviews the decision-making optimization problems,models,and solution methods thereof throughout the renewable energy development and utilization chain(REDUC)process.This review also endeavors to structure and assess the contextual landscape of RES optimization modeling research.As evidenced by the literature review,optimization modeling effectively resolves decisionmaking predicaments spanning RE investment,construction,operation and maintenance,and scheduling.Predominantly,a hybrid model that combines prediction,optimization,simulation,and assessment methodologies emerges as the favored approach for optimizing RES-related decisions.The primary framework prevalent in extant research solutions entails the dissection and linearization of established models,in combination with hybrid analytical strategies and artificial intelligence algorithms.Noteworthy advancements within modeling encompass domains such as uncertainty,multienergy carrier considerations,and the refinement of spatiotemporal resolution.In the realm of algorithmic solutions for RES optimization models,a pronounced focus is anticipated on the convergence of analytical techniques with artificial intelligence-driven optimization.Furthermore,this study serves to facilitate a comprehensive understanding of research trajectories and existing gaps,expediting the identification of pertinent optimization models conducive to enhancing the efficiency of REDUC development endeavors. 展开更多
关键词 renewable energy system bibliometrics mathematical programming optimization models solution methods
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Multi-Timescale Optimization Scheduling of Distribution Networks Based on the Uncertainty Intervals in Source-Load Forecasting 认领 引用 被引量：1
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作者 Huanan Yu Chunhe Ye +3 位作者 Shiqiang Li He Wang Jing Bian Jinling Li 《Energy Engineering》 EI 2025年第6期2417-2448,共32页
With the increasing integration of large-scale distributed energy resources into the grid,traditional distribution network optimization and dispatch methods struggle to address the challenges posed by both generation ... With the increasing integration of large-scale distributed energy resources into the grid,traditional distribution network optimization and dispatch methods struggle to address the challenges posed by both generation and load.Accounting for these issues,this paper proposes a multi-timescale coordinated optimization dispatch method for distribution networks.First,the probability box theory was employed to determine the uncertainty intervals of generation and load forecasts,based on which,the requirements for flexibility dispatch and capacity constraints of the grid were calculated and analyzed.Subsequently,a multi-timescale optimization framework was constructed,incorporating the generation and load forecast uncertainties.This framework included optimization models for dayahead scheduling,intra-day optimization,and real-time adjustments,aiming to meet flexibility needs across different timescales and improve the economic efficiency of the grid.Furthermore,an improved soft actor-critic algorithm was introduced to enhance the uncertainty exploration capability.Utilizing a centralized training and decentralized execution framework,a multi-agent SAC network model was developed to improve the decision-making efficiency of the agents.Finally,the effectiveness and superiority of the proposed method were validated using a modified IEEE-33 bus test system. 展开更多
关键词 Renewable energy distribution networks source-load uncertainty interval flexible scheduling soft actor-critic algorithm optimization model
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Modeling and Optimization of Surface Roughness of Epoxy/Nanoparticles Composite Coating 认领 引用
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作者 A.F.Mohamed J.Abu Alsoud +2 位作者 Mujahed Al-Dhaifallah Hegazy Rezk Mohamed K.Hassan 《Computers, Materials & Continua》 SCIE EI 2022年第4期71-83,共13页
In power plants,flue gases can cause severe corrosion damage in metallic parts such as flue ducts,heat exchangers,and boilers.Coating is an effective technique to prevent this damage.A robust fuzzy model of the surfac... In power plants,flue gases can cause severe corrosion damage in metallic parts such as flue ducts,heat exchangers,and boilers.Coating is an effective technique to prevent this damage.A robust fuzzy model of the surface roughness(Ra and Rz)of flue gas ducts coated by protective composite coating from epoxy and nanoparticles was constructed based on the experimental dataset.The proposed model consists of four nanoparticles(ZnO,ZrO2,SiO2,and NiO)with 2%,4%,6%,and 8%,respectively.Response surface methodology(RSM)was used to optimize the process parameters and identify the optimal conditions for minimum surface roughness of this coated duct.To prove the superiority of the proposed fuzzy model,the model results were compared with those obtained by ANOVA,with the coefficient of determination and the root-mean-square error(RMSE)used as metrics.For Ra,for the first output response,using ANOVA,the coefficient-of-determination values were 0.9137 and 0.4037,respectively,for training and prediction.Similarly,for Rz,the second output response,the coefficient-of-determination results were 0.9695 and 0.4037,respectively,for training and prediction.In the fuzzy modeling of Ra,for the first output response,the RMSE values were 0.0 and 0.1455,respectively,for training and testing.The values for the coefficient of determination were 1.00 and 0.9807,respectively,for training and testing.The results prove the superiority of fuzzy modeling.For modeling the second output response Rz,the RMSE values were 0.0 and 0.0421,respectively,for training and testing,and the coefficient-of-determination values were 1.00 and 0.9959,respectively,for training and testing. 展开更多
关键词 Materials coating nanoparticles modeling and optimization
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A Comprehensive Review of Sizing and Allocation of Distributed Power Generation:Optimization Techniques,Global Insights,and Smart Grid Implications 认领 引用 被引量：1
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作者 Abdullrahman A.Al-Shamma’a Hassan M.Hussein Farh +4 位作者 Ridwan Taiwo Al-Wesabi Ibrahim Abdulrhman Alshaabani Saad Mekhilef Mohamed A.Mohamed 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第11期1303-1347,共45页
Optimal sizing and allocation of distributed generators(DGs)have become essential computational challenges in improving the performance,efficiency,and reliability of electrical distribution networks.Despite extensive ... Optimal sizing and allocation of distributed generators(DGs)have become essential computational challenges in improving the performance,efficiency,and reliability of electrical distribution networks.Despite extensive research,existing approaches often face algorithmic limitations such as slow convergence,premature stagnation in local minima,or suboptimal accuracy in determining optimal DG placement and capacity.This study presents a comprehensive scientometric and systematic review of global research focused on computer-based modelling and algorithmic optimization for renewable DG sizing and placement.It integrates both quantitative and qualitative analyses of the scholarly landscape,mapping influential research domains,co-authorship structures,the articles’citation networks,keyword clusters,and international collaboration patterns.Moreover,the study classifies and evaluates the most prominent objective functions,key computational models and optimization algorithms,DG technologies,and strategic approaches employed in the field.The findings reveal that advanced algorithmic frameworks substantially enhance network stability,minimize real power losses,and improve voltage profiles under various operational constraints.This review serves as a foundational resource for researchers and practitioners,highlighting emerging algorithmic trends,modelling innovations,and data-driven methodologies that can guide future development of intelligent,optimization-based DG integration strategies in smart distribution systems. 展开更多
关键词 Systematic and scientometric global trends distributed generation sizing and allocation multiobjectives modelling and algorithmic optimization
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Torsional stiffness modeling and optimization of the rubber torsion bushing for a light tracked vehicle 认领 引用
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作者 姚寿文 郑鑫 +2 位作者 程海涛 黄友剑 莫容利 《Journal of Beijing Institute of Technology》 EI CAS 2015年第3期361-368,共8页
Taking the rubber torsion bushing of a certain type of all-terrain tracked vehicle as the research object,a theoretical model of torsional stiffness was proposed according to the non-linear characteristics of rubber c... Taking the rubber torsion bushing of a certain type of all-terrain tracked vehicle as the research object,a theoretical model of torsional stiffness was proposed according to the non-linear characteristics of rubber components and structural feature of the suspension. Simulations were carried out under different working conditions to obtain root mean square of vertical weighted acceleration as the evaluation index for ride performance of the all-terrain tracked vehicle,with a dynamics model of the whole vehicle based on the theoretical model of the torsional stiffness and standard road roughness as excitation input. Response surface method was used to establish the parametric optimization model of the torsional stiffness. The evaluation index showed that ride performance of the vehicle with optimized torsional stiffness model of suspension was improved compared with previous model fromexperiment. The torsional stiffness model of rubber bushing provided a theoretical basis for the design of the rubber torsion bushing in light tracked vehicles. 展开更多
关键词 rubber torsion bushing torsional stiffness model dynamics modeling ride performance optimization
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Oilfield sustainability and management:An optimization model for the reconstruction of oil and gas gathering systems based on potential location mining 认领 引用
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作者 Jie Chen Wei Wang +2 位作者 Wen-Yuan Sun Dong Li Yu-Bo Jiao 《Petroleum Science》 SCIE EI CAS CSCD 2025年第2期935-955,共21页
The carbon emissions and cost during the construction phase are significant contributors to the oilfield lifecycle.As oilfields enter the late stage,the adaptability of facilities decreases.To achieve sustainable deve... The carbon emissions and cost during the construction phase are significant contributors to the oilfield lifecycle.As oilfields enter the late stage,the adaptability of facilities decreases.To achieve sustainable development,oilfield reconstruction was usually conducted in discrete rather than continuous space.Motivated by economic and sustainability goals,a 3-phase heuristic model for oilfield reconstruction was developed to mine potential locations in continuous space.In phase 1,considering the process characteristics of the oil and gas gathering system,potential locations were mined in continuous space.In phase 2,incorporating comprehensive reconstruction measures,a reconstruction model was established in discrete space.In phase 3,the topology was further adjusted in continuous space.Subsequently,the model was transformed into a single-objective mixed integer linear programming model using the augmented ε-constraint method.Numerical experiments revealed that the small number of potential locations could effectively reduce the reconstruction cost,and the quality of potential locations mined in phase 1 surpassed those generated in random or grid form.Case studies showed that cost and carbon emissions for a new block were reduced by up to 10.45% and 7.21 %,respectively.These reductions were because the potential locations mined in 1P reduced the number of metering stations,and 3P adjusted the locations of metering stations in continuous space to shorten the pipeline length.For an old oilfield,the load and connection ratios of the old metering station increased to 89.7% and 94.9%,respectively,enhancing operation efficiency.Meanwhile,recycling facilitated the diversification of reconstruction measures and yielded a profit of 582,573 ¥,constituting 5.56% of the total cost.This study adopted comprehensive reconstruction measures and tapped into potential reductions in cost and carbon emissions for oilfield reconstruction,offering valuable insights for future oilfield design and construction. 展开更多
关键词 Oilfield reconstruction Sustainable development Optimization model Potential location3-phase heuristic model
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Simultaneous hybrid modeling of a nosiheptide fermentation process using particle swarm optimization 认领 引用 被引量：2
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作者 Qiangda Yang Hongbo Gao +1 位作者 Weijun Zhang Huimin Li 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2016年第11期1631-1639,共9页
Hybrid modeling approaches have recently been investigated as an attractive alternative to model fermentation processes. Normally, these approaches require estimation data to train the empirical model part of a hybrid... Hybrid modeling approaches have recently been investigated as an attractive alternative to model fermentation processes. Normally, these approaches require estimation data to train the empirical model part of a hybrid model. This may result in decreasing the generalization ability of the derived hybrid model. Therefore, a simultaneous hybrid modeling approach is presented in this paper. It transforms the training of the empirical model part into a dynamic system parameter identification problem, and thus allows training the empirical model part with only measured data. An adaptive escaping particle swarm optimization(AEPSO) algorithm with escaping and adaptive inertia weight adjustment strategies is constructed to solve the resulting parameter identification problem, and thereby accomplish the training of the empirical model part. The uniform design method is used to determine the empirical model structure. The proposed simultaneous hybrid modeling approach has been used in a lab-scale nosiheptide batch fermentation process. The results show that it is effective and leads to a more consistent model with better generalization ability when compared to existing ones. The performance of AEPSO is also demonstrated. 展开更多
关键词 Bioprocess Dynamic modeling Neural networks Optimization
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Modeling and Optimization of Solar Collector Design for the Improvement of Solar-Air Source Heat Pump Building Heating System 认领 引用
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作者 Jiarui Wu Yuzhen Kang Junxiao Feng 《Energy Engineering》 EI 2023年第12期2783-2802,共20页
To enhance system stability,solar collectors have been integrated with air-source heat pumps.This integration facilitates the concurrent utilization of solar and air as energy sources for the system,leading to an impr... To enhance system stability,solar collectors have been integrated with air-source heat pumps.This integration facilitates the concurrent utilization of solar and air as energy sources for the system,leading to an improvement in the system’s heat generation coefficient,overall efficiency,and stability.In this study,we focus on a residential building located in Lhasa as the target for heating purposes.Initially,we simulate and analyze a solar-air source heat pump combined heating system.Subsequently,while ensuring the system meets user requirements,we examine the influence of solar collector installation angles and collector area on the performance of the solar-air source heat pump dual heating system.Through this analysis,we determine the optimal installation angle and collector area to optimize system performance. 展开更多
关键词 Solar energy air source heat pump optimization model solar-air heat pump heating system
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Optimization and Scheduling of Green Power System Consumption Based on Multi-Device Coordination and Multi-Objective Optimization 认领 引用
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作者 Liang Tang Hongwei Wang +2 位作者 Xinyuan Zhu Jiying Liu Kaiyue Li 《Energy Engineering》 EI 2025年第6期2257-2289,共33页
The intermittency and volatility of wind and photovoltaic power generation exacerbate issues such as wind and solar curtailment,hindering the efficient utilization of renewable energy and the low-carbon development of... The intermittency and volatility of wind and photovoltaic power generation exacerbate issues such as wind and solar curtailment,hindering the efficient utilization of renewable energy and the low-carbon development of energy systems.To enhance the consumption capacity of green power,the green power system consumption optimization scheduling model(GPS-COSM)is proposed,which comprehensively integrates green power system,electric boiler,combined heat and power unit,thermal energy storage,and electrical energy storage.The optimization objectives are to minimize operating cost,minimize carbon emission,and maximize the consumption of wind and solar curtailment.The multi-objective particle swarm optimization algorithm is employed to solve the model,and a fuzzy membership function is introduced to evaluate the satisfaction level of the Pareto optimal solution set,thereby selecting the optimal compromise solution to achieve a dynamic balance among economic efficiency,environmental friendliness,and energy utilization efficiency.Three typical operating modes are designed for comparative analysis.The results demonstrate that the mode involving the coordinated operation of electric boiler,thermal energy storage,and electrical energy storage performs the best in terms of economic efficiency,environmental friendliness,and renewable energy utilization efficiency,achieving the wind and solar curtailment consumption rate of 99.58%.The application of electric boiler significantly enhances the direct accommodation capacity of the green power system.Thermal energy storage optimizes intertemporal regulation,while electrical energy storage strengthens the system’s dynamic regulation capability.The coordinated optimization of multiple devices significantly reduces reliance on fossil fuels. 展开更多
关键词 Multi-objective optimization scheduling model multi-objective particle swarm optimization algorithm consumption capacity of green power wind and solar curtailment coordinated optimization of multiple devices
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Research on the Multidimensional Constraint Stability of Bridge Structure Modeling based on Optimization Model 认领 引用
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作者 Cheng kang 《International Journal of Technology Management》 2016年第9期35-37,共3页
In this paper, we conduct research on the multidimensional constraint stability of bridge structure modeling based on the optimization model. The current internal and the external research results to the truss web str... In this paper, we conduct research on the multidimensional constraint stability of bridge structure modeling based on the optimization model. The current internal and the external research results to the truss web structure, the high internode the aspect ratio and the stiffness of the middle truss brace of the truss web, deffection of composite beams of the impact of stress is a very important problem in the design of the bridge. Structural health monitoring is the use of the field of the non-destructive sensing technology, including the structural response, including structural system characteristics analysis, to achieve the purpose of monitoring structural damage or degradation. Under this basis, this paper proposes the new idea on the modelling and simulates the performance. 展开更多
关键词 Multidimensional Constraint Stability Bridge Structure Optimization Model.
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Key Optimization Issues for Renewable Energy Systems under Carbon-Peaking and Carbon Neutrality Targets:Current States and Perspectives 认领 引用 被引量：3
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作者 Bo Yang Zhengxun Guo +3 位作者 JingboWang Chao Duan Yaxing Ren Yixuan Chen 《Energy Engineering》 EI 2022年第5期1789-1795,共7页
1 Introduction The United States,Japan,Canada,the European Union,and other developed countries and regions have all formulated climate strategies and pledged to achieve net-zero CO2 emissions by 2050.China,meanwhil... 1 Introduction The United States,Japan,Canada,the European Union,and other developed countries and regions have all formulated climate strategies and pledged to achieve net-zero CO2 emissions by 2050.China,meanwhile,has announced through the“carbon-peaking and carbon neutrality targets”in September 2020 that it aims to achieve“peak carbon use”by 2030 and“carbon neutrality”by 2060[1].According to statistical data from the International Energy Agency(IEA),Fig.1 illustrates the carbon intensity of electricity generation in various regions in the Announced Pledge Scenario(APS)from 2010 to 2040[2].One can easily observe that each region aims to accomplish a sharp decrease in the carbon intensity of electricity generation after 2020. 展开更多
关键词 Carbon-peaking and carbon neutrality targets renewable energy optimization modeling power forecast planning and dispatching reliability and security analysis
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Automated machine learning-based reverse osmosis membrane flux prediction and chemical dosage dynamic adjustment 认领 引用
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作者 Zhiyang Cheng Yang Yu +4 位作者 Xia Meng Yating Wang Fangong Kong Minghua Zhou Jie Wang 《ENGINEERING Environment》 SCIE EI CAS CSCD 2026年第1期35-47,共13页
The complexity of influent water quality and the diversity of predictive models pose significant challenges to the efficient identification of optimal membrane flux prediction models.This study proposes an integrated ... The complexity of influent water quality and the diversity of predictive models pose significant challenges to the efficient identification of optimal membrane flux prediction models.This study proposes an integrated decision tree approach combined with AutoML to classify influent types simultaneously(with an accuracy?96.08%)and rapidly screen the optimal model from 2400 candidates.By classifying the influent types under typical conditions such as groundwater,reservoir water,reclaimed water,and recycled water,the model optimization efficiencies increased by 43.59%,52.35%,51.52%,and 48.05%,respectively,compared with that of the unclassified treatment.The optimal model screened by AutoML demonstrated excellent predictive performance across varying influent parameter fluctuations(R2?94.5%,RMSE?0.061),with a reduction in optimization iterations of up to 60.48%.Parameter importance analysis revealed that accurate matching between influent parameter variations and model characteristics is key to achieving high prediction accuracy.Furthermore,the selected optimal models enabled accurate prediction of the optimal scale of inhibitor dosage under different influent conditions,providing theoretical and technical support for the scientific application of membrane treatment chemicals. 展开更多
关键词 Automated machine learning Optimal model Influent classification Model fast screening Scale inhibitor
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Optimizing import patterns to reduce the environmental effects and risks of China’s grain imports 认领 引用
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作者 Jian Wang Fei Li +2 位作者 Yibin Wang Xinjun Yang Yao Zhao 《Geography and Sustainability》 CSCD 2026年第3期213-225,共13页
Grain imports are an important means of ensuring China’s food security,but they also create dual global challenges in terms of environmental spillover effects and supply risks.It is crucial to balance the environment... Grain imports are an important means of ensuring China’s food security,but they also create dual global challenges in terms of environmental spillover effects and supply risks.It is crucial to balance the environmental sustainability with supply stability in grain import strategies.Therefore,based on environmental footprint,country-specific risks,and market concentration,this study assessed the global environmental impacts and potential supply risks of China’s grain imports,and used a multi-objective optimization model to identify improved import strategies.The results showed that from 2000 to 2020,the global cultivated land savings caused by China’s grain imports increased from 1,859.70 kha to 18,209.11 kha,water saving increased from 3,665.43 Mm3 to 63,572.45 Mm3,and GHG emission reduction increased from 1,282.30 kt to 13,811.94 kt,but these effects varied substantially across crops.Overall,China’s grain import risk(CGIR)followed a rise-then-decline pattern,with periodic certain cyclical fluctuations.Import risks differed significantly across crops,with maize(mainly from the United States and Ukraine)and soybeans(primarily from the United States and Brazil)remaining at elevated risk levels.It was difficult to reconcile environmental impacts and import risk through adjustments to grain import patterns in a single-objective setting.Under dual environmental and risk constraints,adjusting China’s grain import strategy reduced cultivated land consumption by 4.92%,GHG emissions by 5.02%,and import risk by 36.55%,despite increasing water consumption by 5.41%.This study offers policy-relevant recommendations to help alleviate the tension between grain import security and sustainable development. 展开更多
关键词 Environment effect Import risk Multi-objective optimization model Grain import distribution China
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