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Dynamic decision-making of UAV swarm based on constrained multi-objective optimization under incomplete interference information 认领 引用 被引量：1
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作者 Kuixian LI Jinjie LIU +5 位作者 Xin GU Yandie YANG Cheng CHANG Haipeng CHEN Liangtian WAN Yun LIN 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第7期64-77,共14页
The decision-making and resource allocation of UAV swarms play a crucial role in dynamic,uncertain environments.In such complex scenarios,UAV swarms need to effectively collaborate and communicate in frequently changi... The decision-making and resource allocation of UAV swarms play a crucial role in dynamic,uncertain environments.In such complex scenarios,UAV swarms need to effectively collaborate and communicate in frequently changing interference conditions.However,existing resource allocation methods typically assume complete interference information or are suitable only for static environments,leading to significant performance degradation in the face of external uncertainties and incomplete information.To address these challenges,this paper employs fuzzy set theory to dynamically model the uncertainty of external interference and defuzzify its impact on the available frequency bands during iterative diagnostics.Additionally,a dynamic constrained multi-objective optimization model is developed,and a novel Dynamic Constrained MultiObjective Evolutionary Algorithm based on Transfer Search(TrS-DCMOEA)is proposed.By integrating transfer learning and dynamic adjustment strategies,the algorithm quickly adapts to environmental changes,ensuring communication performance while maintaining the security of UAV swarm communications.Simulation results show that the proposed algorithm achieves superior decision-making and resource allocation efficiency in most time slots,with TrS-DCMOEA particularly excelling in tracking the Pareto front in dynamic environments. 展开更多
关键词 UAV swarm Resource allocation Dynamic constraints Incomplete information Dynamic decision-making Multi-objective optimization
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Cost-Optimal Building Energy System Scheduling Integrating Solar Irradiance Forecasting via LSTM-Attention-TCN Model 认领 引用 被引量：1
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作者 Zhengtian Wu Jianyu Li +7 位作者 Yang Gao Chuangyin Dang Chao Tang Yuansheng Li Xinmiao Wang Jinpeng Chen Hongbo Gao Xinyin Xu 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2026年第3期695-708,共14页
Building energy systems integrating multiple energy sources can effectively reduce energy consumption and facilitate renewable energy integration.Integrating electrical energy storage(EES)into these systems helps acco... Building energy systems integrating multiple energy sources can effectively reduce energy consumption and facilitate renewable energy integration.Integrating electrical energy storage(EES)into these systems helps accommodate the increasing share of renewables;however,the stochastic and intermittent nature of solar power still poses challenges to supply reliability.This study proposes a photovoltaic(PV)‐oriented storage scheduling strategy,in which short‐term PV generation forecasts are applied to guide the operation of a building power supply network consisting of photovoltaic panels,the grid,and energy storage systems.The forecasting approach employs a hybrid framework combining a Long Short‐Term Memory(LSTM)network to capture temporal dependencies,an attention mechanism to emphasise critical time steps,and a Temporal Convolutional Network(TCN)to map the enhanced features to PV outputs.Experimental evaluation using historical datasets under multiple weather conditions and time periods shows that the proposed LSTM‐Attention‐TCN model achieves a mean absolute error(MAE)of 20.45 W/m2 and a Nash–Sutcliffe efficiency(NSE)of 0.94,outperforming both standalone LSTM and TCN models as well as their hybrid variants in terms of accuracy and robustness.By providing high‐accuracy solar irradiance forecasts to guide energy storage operation and grid interaction,the proposed model enables more efficient and economical scheduling of building energy systems.Compared with an uncontrolled scenario,the LSTM‐Attention‐TCN‐based scheduling reduces the total operating cost by approximately 52.1%,and achieves an additional 16.5%reduction compared to a conventional strategy without predictive coordination.In addition,compared to other hybrid forecasting models such as LSTM‐TCN and TCN‐Attention,the proposed model achieves the lowest total cost of CNY 14.83 and demonstrates superior scheduling efficiency,thereby enhancing the stability and flexibility of building energy utilization. 展开更多
关键词 cost optimization deep learning energy storage systems optimal scheduling solar irradiance forecasting
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A novel deep learning-based framework for forecasting 认领 引用 被引量：1
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作者 Congqi Cao Ze Sun +2 位作者 Lanshu Hu Liujie Pan Yanning Zhang 《Atmospheric and Oceanic Science Letters》 CAS CSCD 2026年第1期22-26,共5页
Deep learning-based methods have become alternatives to traditional numerical weather prediction systems,offering faster computation and the ability to utilize large historical datasets.However,the application of deep... Deep learning-based methods have become alternatives to traditional numerical weather prediction systems,offering faster computation and the ability to utilize large historical datasets.However,the application of deep learning to medium-range regional weather forecasting with limited data remains a significant challenge.In this work,three key solutions are proposed:(1)motivated by the need to improve model performance in data-scarce regional forecasting scenarios,the authors innovatively apply semantic segmentation models,to better capture spatiotemporal features and improve prediction accuracy;(2)recognizing the challenge of overfitting and the inability of traditional noise-based data augmentation methods to effectively enhance model robustness,a novel learnable Gaussian noise mechanism is introduced that allows the model to adaptively optimize perturbations for different locations,ensuring more effective learning;and(3)to address the issue of error accumulation in autoregressive prediction,as well as the challenge of learning difficulty and the lack of intermediate data utilization in one-shot prediction,the authors propose a cascade prediction approach that effectively resolves these problems while significantly improving model forecasting performance.The method achieves a competitive result in The East China Regional AI Medium Range Weather Forecasting Competition.Ablation experiments further validate the effectiveness of each component,highlighting their contributions to enhancing prediction performance. 展开更多
关键词 Weather forecasting Deep learning Semantic segmentation models Learnable Gaussian noise Cascade prediction
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Strategic plan for earthquake forecasting in China (2025−2035): A brief summary 认领 引用 被引量：1
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作者 Zhigang Shao Rui Yan +6 位作者 Wuxing Wang Qi Liu Lingyuan Meng Zhengyang Pan Zhenyu Wang Wei Yan Chong Yue 《Earthquake Science》 CAS CSCD 2026年第2期214-219,共6页
The basic condition of earthquake disasters in China is featured by high frequency,strong intensity,wide distribution,and heavy losses.Earthquake forecasting plays a critical role in reducing seismic risks.To better a... The basic condition of earthquake disasters in China is featured by high frequency,strong intensity,wide distribution,and heavy losses.Earthquake forecasting plays a critical role in reducing seismic risks.To better advance earthquake predicting efforts,the China Earthquake Administration released the Strategic Plan for Earthquake Forecasting in China(2025−2035)on the occasion of the 50th anniversary of the Haicheng earthquake.Here we briefly introduce the main contents of the Strategic Plan,including the main progress,strategic objectives,and development directions of earthquake forecasting in China. 展开更多
关键词 strategic plan earthquake forecasting China Haicheng earthquake earthquake disasters seismic risks strategic planincluding seismic losses
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TransCarbonNet:Multi-Day Grid Carbon Intensity Forecasting Using Hybrid Self-Attention and Bi-LSTM Temporal Fusion for Sustainable Energy Management 认领 引用 被引量：1
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作者 Amel Ksibi Hatoon Albadah +1 位作者 Ghadah Aldehim Manel Ayadi 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第1期812-847,共36页
Sustainable energy systems will entail a change in the carbon intensity projections,which should be carried out in a proper manner to facilitate the smooth running of the grid and reduce greenhouse emissions.The prese... Sustainable energy systems will entail a change in the carbon intensity projections,which should be carried out in a proper manner to facilitate the smooth running of the grid and reduce greenhouse emissions.The present article outlines the TransCarbonNet,a novel hybrid deep learning framework with self-attention characteristics added to the bidirectional Long Short-Term Memory(Bi-LSTM)network to forecast the carbon intensity of the grid several days.The proposed temporal fusion model not only learns the local temporal interactions but also the long-term patterns of the carbon emission data;hence,it is able to give suitable forecasts over a period of seven days.TransCarbonNet takes advantage of a multi-head self-attention element to identify significant temporal connections,which means the Bi-LSTM element calculates sequential dependencies in both directions.Massive tests on two actual data sets indicate much improved results in comparison with the existing results,with mean relative errors of 15.3 percent and 12.7 percent,respectively.The framework has given explicable weights of attention that reveal critical periods that influence carbon intensity alterations,and informed decisions on the management of carbon sustainability.The effectiveness of the proposed solution has been validated in numerous cases of operations,and TransCarbonNet is established to be an effective tool when it comes to carbon-friendly optimization of the grid. 展开更多
关键词 Carbon intensity forecasting self-attention mechanism bidirectional LSTM temporal fusion sustainable energy management smart grid optimization deep learning
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SDformer:Fusing Series Decomposition for Superior Long-Term Time Series Forecasting 认领 引用
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作者 Jiayi Li Zihang Zhang +2 位作者 Chao Zhang Jun Tang Shangce Gao 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第7期1626-1641,共16页
Long-term time series forecasting is crucial in numerous real-world dynamic systems and has garnered extensive research attention.In the context of time series forecasting,time series decomposition serves as an effect... Long-term time series forecasting is crucial in numerous real-world dynamic systems and has garnered extensive research attention.In the context of time series forecasting,time series decomposition serves as an effective tool for analyzing time series data,enabling the extraction of underlying patterns and trends that enhance predictive accuracy.Despite its potential,time series decomposition has been underutilized in existing models that incorporate decomposition architectures,particularly in feature extraction.To address this gap,we propose the series decomposition encoder(SDE)block,which separates time series data into seasonal and trend components.By leveraging these decomposed elements,the SDE block enhances the model's ability to capture essential temporal features.We substitute the initial layer of the conventional Transformer architecture with SDE,thereby introducing the series decomposition transformer(SDformer).Empirical assessments on nine benchmark datasets substantiate that our proposed SDformer attains state-of-the-art performance in long-term forecasting.The code implementation is available at the provided repository:http://gffzz188fe103f8f1460asn5n0oo9c5bwf6o6k.ffgz.tsg.suse.edu.cn/Rirock/SDformer. 展开更多
关键词 Long time series forecasting time series decomposition time series forecasting transformer
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Physics-informed deep learning for data-efficient and robust photovoltaic power forecasting 认领 引用
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作者 Chang HUANG Xuanbin HUANG +3 位作者 Jinmin GUO Zhuo CAO Ting HE Wentao SHANG 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS CSCD 2026年第8期885-896,I0038,共12页
Photovoltaic(PV)power forecasting is challenged by its inherent variability.Pure data-driven models struggle with generalization under data scarcity and complex weather conditions.In this paper,we introduce a physics-... Photovoltaic(PV)power forecasting is challenged by its inherent variability.Pure data-driven models struggle with generalization under data scarcity and complex weather conditions.In this paper,we introduce a physics-informed deep learning hybrid model(PIDL-HM)that systematically generates physically grounded input features(e.g.,plane-of-array irradiance and module temperature)for a convolutional neural network-long short-term memory(CNN-LSTM),establishing a principled integration framework beyond simple ensemble methods.Rigorously validated across multiple PV power plants in China and Australia for 15-min,4-h,and 24-h forecasting,our approach demonstrates superior performance,with a reduction of up to 8.93%in root mean square error compared to a purely data-driven baseline.Crucially,the model shows remarkable data efficiency,maintaining high accuracy with only three months of training data,and exceptional robustness,providing a 6.87%improvement in performance under strong cross-seasonal data distribution shifts.This work provides a reliable and data-efficient forecasting solution,establishing the PIDL-HM as a foundational element for next-generation forecasting systems. 展开更多
关键词 Photovoltaic(PV)power forecasting Physics-informed deep learning Various forecasting horizons Data efficiency Physically grounded features
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A new chapter in the odyssey towards earthquake forecasting: The International Symposium on Earthquake Forecasting to Commemorate the 50th Anniversary of the 1975 Haicheng M7.3 Earthquake, China 认领 引用
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作者 Jing Huang Wenjun Tian Zhongliang Wu 《Earthquake Science》 CAS CSCD 2026年第2期235-240,共6页
Symposium overview The International Symposium on Earthquake Forecasting to Commemorate the 50th Anniversary of the 1975 Haicheng M7.3 Earthquake,Liaoning,China,was held in Shenyang,China,from 8 to 11 July 2025.The sy... Symposium overview The International Symposium on Earthquake Forecasting to Commemorate the 50th Anniversary of the 1975 Haicheng M7.3 Earthquake,Liaoning,China,was held in Shenyang,China,from 8 to 11 July 2025.The symposium was organized by the Institute of Earthquake Forecasting,China Earthquake Administration(CEA),the State Key Laboratory of Earthquake Dynamics and Forecasting,and the China Seismic Experimental Site(CSES),in collaboration with the International Association of Seismology and Physics of the Earth’s Interior(IASPEI),the APEC Cooperation for Earthquake Science(ACES). 展开更多
关键词 earthquake forecasting international symposium earthquake commemoration seismic forecasting China Haicheng earthquake
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Environment-adaptive design of multilayer optical structures for surface polariton excitation via dynamic multi-objective optimization 认领 引用
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作者 Ru Lei Lin Li +4 位作者 Yiqi Feng Ziyue Ren Kun Zhang Lixun Sun Ting Mei 《Defence Technology（防务技术）》 SCIE EI CAS CSCD 2026年第5期267-279,共13页
The graphene±dielectric multilayer architecture constitutes a fundamental and widely utilized platform for sustaining surface polariton(SP)propagation.Owing to their extraordinary prospects in defence critical te... The graphene±dielectric multilayer architecture constitutes a fundamental and widely utilized platform for sustaining surface polariton(SP)propagation.Owing to their extraordinary prospects in defence critical technologies,including radar-absorbing stealth coatings,high-power microwave shielding,and ultrafast optical switching,SPs have attracted intense and sustained interest.In this study,we develop an environment-adaptive design framework that models wavelength variation as a dynamic environmental change and automatically adjusts the design parameters in response.Our method employs a dynamic multi-objective optimization algorithm augmented with a predictive transfer strategy,optimizing SP coupling efficiency,structural compactness,and fabrication feasibility.Using a population history prediction mechanism,the framework not only adaptively generates multilayer designs across the full visible spectrum without full re-initialization,but also retains and exploits knowledge of how environmental variations influence the distribution of optimal solutions.This enables rapid adjustment of the optimization direction when parameters such as wavelength,angle,or doping change,thus avoiding the need to restart the search from scratch.Comprehensive comparisons demonstrate outstanding robustness under continuous wavelength shifts.The optimized graphene-coated distributed Bragg reflector(DBR)stacks achieve near-perfect absorption(>98%)at each individual wavelength across the visible spectrum.This work not only provides theoretical guidance for SP excitation experiments,but also contributes to the optimization of polariton device design,which is crucial for enhancing the performance of defence-related optical systems. 展开更多
关键词 Distributed bragg reflectors Surface polariton Dynamic multi-objective optimization
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Unconventional oil production forecasting based on PiAM meta-learning 认领 引用
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作者 Yu-Long Zhao Qing-Yu Xiao +4 位作者 Xing-Jie Zeng Bin Xiong Shuai Wang Song Zhao Yun-Sheng Wei 《Petroleum Science》 SCIE EI CAS CSCD 2026年第3期1335-1347,共13页
Accurate production forecasting serves as a critical determinant for optimizing extraction strategies,guiding long-term field management in reservoir development.Both conventional methods and deep learning techniques ... Accurate production forecasting serves as a critical determinant for optimizing extraction strategies,guiding long-term field management in reservoir development.Both conventional methods and deep learning techniques face significant challenges in production forecasting due to the increasing complexities of reservoir extraction.Firstly,traditional production forecasting methods often fail to fully captu re the complex reservoir behavior.Finally,these approaches demonstrate suboptimal perfo rmance in wells with limited data.These problems can lead to a decrease in prediction accuracy.To address these challenges,this paper introduces the Patching-iTransformer method and applies meta learning.The method improves prediction accuracy and overcomes the problem of few samples in production forecasting.Specifically,we implement a patching mechanism that segments the input time series,thereby converting the univariate time series into a two-dimensional representation.This architectural enhancement significantly strengthens the model's capability to capture latent interdependencies among variables.Currently,we develop a PiAM meta-learning algorithm with domain-specific adaptation for oil field applications by quantitatively assessing individual well contributions to reservoir exploitation.We use time series data from real wells to evaluate the accuracy of multiple wells under the PiAM model.The experimental results demonstrate that Patching-iTransformer achieved better performance improvements than the iTransformer method.R2 increased by 0.297,RMSE decreased by11.64% and MAE decreased by 3.49%.PiAM meta-learning method demonstrated superior performance over the Patching-iTransformer model,showing a 0.535-point improvement in the R2 coefficient along with a reduction of 27.54% in RMSE and a decrease of 28.22% in MAE. 展开更多
关键词 Production forecasting Meta learning Transformer Time series
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Multi-objective topology optimization for cutout design in deployable composite thin-walled structures 认领 引用
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作者 Hao JIN Ning AN +3 位作者 Qilong JIA Chun SHAO Xiaofei MA Jinxiong ZHOU 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第1期674-694,共21页
Deployable Composite Thin-Walled Structures(DCTWS)are widely used in space applications due to their ability to compactly fold and self-deploy in orbit,enabled by cutouts.Cutout design is crucial for balancing structu... Deployable Composite Thin-Walled Structures(DCTWS)are widely used in space applications due to their ability to compactly fold and self-deploy in orbit,enabled by cutouts.Cutout design is crucial for balancing structural rigidity and flexibility,ensuring material integrity during large deformations,and providing adequate load-bearing capacity and stability once deployed.Most research has focused on optimizing cutout size and shape,while topology optimization offers a broader design space.However,the anisotropic properties of woven composite laminates,complex failure criteria,and multi-performance optimization needs have limited the exploration of topology optimization in this field.This work derives the sensitivities of bending stiffness,critical buckling load,and the failure index of woven composite materials with respect to element density,and formulates both single-objective and multi-objective topology optimization models using a linear weighted aggregation approach.The developed method was integrated with the commercial finite element software ABAQUS via a Python script,allowing efficient application to cutout design in various DCTWS configurations to maximize bending stiffness and critical buckling load under material failure constraints.Optimization of a classical tubular hinge resulted in improvements of 107.7%in bending stiffness and 420.5%in critical buckling load compared to level-set topology optimization results reported in the literature,validating the effectiveness of the approach.To facilitate future research and encourage the broader adoption of topology optimization techniques in DCTWS design,the source code for this work is made publicly available via a Git Hub link:http://gffzz188fe103f8f1460asn5n0oo9c5bwf6o6k.ffgz.tsg.suse.edu.cn/jinhao-ok1/Topo-for-DCTWS.git. 展开更多
关键词 Composite laminates Deployable structures Multi-objective optimization Thin-walled structures Topology optimization
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Multi-Objective Optimization of a Tapered Cathode Flow Channel in a Proton Exchange Membrane Fuel Cell 认领 引用
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作者 Wei Dong Baoqi Guo +2 位作者 Weiwei Zhao Hui Jian Zhenzong He 《Frontiers in Heat and Mass Transfer》 EI CAS 2026年第2期115-133,共19页
This study explores the design of a tapered cathode flow channel in a proton exchange membrane fuel cell(PEMFC),leveraging artificial intelligence and multi-objective optimization techniques to attain an optimal confi... This study explores the design of a tapered cathode flow channel in a proton exchange membrane fuel cell(PEMFC),leveraging artificial intelligence and multi-objective optimization techniques to attain an optimal configuration.First,the influence of the channel height ratio and mass flow rate on PEMFC performance was systematically examined.The results reveal that decreasing the height ratio and increasing the mass flow rate lead to reduction in the standard deviation of current density,accompanied by a monotonic rise in pressure drop.The average current density initially rises before exhibiting a slight decline.Subsequently,a surrogate model based on a Backpropagation(BP)neural network was constructed,with height ratio and mass flow rate as input variables,to accurately predict the average current density,its standard deviation,and the channel pressure drop.The findings demonstrate that the BP-based surrogate model can reliably predict current density,its standard deviation,and channel pressure drop.The Mean Relative Errors(MREs)for current density,standard deviation,and pressure drop are 0.84%,1.44%,and 1.77%,respectively,with all coefficients of determination(R2)exceeding 0.999.Finally,Pareto optimal solutions for current density,standard deviation,and pressure drop of the tapered PEMFC were obtained through integration a multi-objective genetic algorithm.Results show that the optimized tapered PEMFC achieves the current density of 3141.41 A/m2,the standard deviation of 53.58 A/m2,and the channel pressure drop of 5.49 Pa.Compared with the conventional channel,the optimized PEMFC exhibits an 7.02%increase in current density and an 3.7%reduction in standard deviation,while maintaining the pressure drop within an acceptable range. 展开更多
关键词 PEMFC tapered flow channel current density multi-objective optimization
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Prolonged High-skill Windows for Subseasonal Forecasting of Surface Air Temperature over East Asia in Summer 1993 and 1994 认领 引用
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作者 Xiaolei LIU Jingzhi SU +2 位作者 Yihao PENG Xinli LIU Huijun WANG 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2026年第6期1132-1143,共12页
Although there is an increasing demand for subseasonal prediction,the skill of subseasonal forecasting is currently limited.Under specific oceanic and atmospheric conditions,the subseasonal forecast skill can reach a ... Although there is an increasing demand for subseasonal prediction,the skill of subseasonal forecasting is currently limited.Under specific oceanic and atmospheric conditions,the subseasonal forecast skill can reach a high level intermittently during a long period.Based on the S2S(subseasonal-to-seasonal)database,the forecast skill windows for surface air temperature(SAT)over East Asia are identified using the PCC(pattern correlation coefficient).Two longlasting windows stand out over the past 30 years—namely,the cold summer of 1993 and the hot summer of 1994 in central and northeastern East Asia.The two windows lasted about two months with high forecast skill in week-3 SAT,and even in week-4 and week-5 SAT.The persistent large-scale oceanic and atmospheric climate anomalies were generally reinforcing in these two summers,providing windows of opportunity for high forecast skill.The combination of the tropical western Pacific sea surface temperature anomaly(SSTA),the Japan Sea-Kuroshio-Kuroshio Extension(K-KE)SSTA,and the North Atlantic SSTA,favored the atmospheric teleconnection,resulting in the cold event in 1993 and the hot event in 1994.The NAO(North Atlantic Oscillation)stayed in its negative or positive phase persistently,contributing to the climate anomalies over East Asia.Climate model experiments with prescribed SST variations demonstrated that the SSTA in the three regions influences East Asian SAT.An index based on the preceding SSTA in the three regions can be used to help identify whether the current forecast case is within the real-time forecast window.This enhances the practical application of the study and has a positive impact on real-time operational forecasting. 展开更多
关键词 subseasonal prediction surface air temperature forecast skill window
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Toward efficient multi-objective seismic design optimization of self-centering bridges using machine learning 认领 引用
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作者 Xueqi ZHONG Lintao TANG +1 位作者 Xiangnan LI Liuyang LI 《ENGINEERING Structure and Civil Engineering》 SCIE EI CAS CSCD 2026年第3期461-481,共21页
Self-centering rocking bridge piers,characterized by their minimal residual deformation and rapid postseismic recovery,have emerged as a promising solution for enhancing the seismic resilience of bridge systems.Howeve... Self-centering rocking bridge piers,characterized by their minimal residual deformation and rapid postseismic recovery,have emerged as a promising solution for enhancing the seismic resilience of bridge systems.However,their inherently nonlinear behavior and pronounced sensitivity to multiple interdependent design parameters make it challenging to achieve balanced seismic performance among all piers within an integrated bridge system.This work develops a system-oriented optimization framework for self-centering rocking bridges to address this issue.The proposed framework integrates machine learning-based surrogate modeling to markedly accelerate the optimization process.A detailed case study of a four-span self-centering rocking bridge is conducted to demonstrate the framework’s applicability and effectiveness.Results show that substituting traditional finite element model with an XGBoost-based surrogate model reduces computational time by 92%while preserving high predictive accuracy.Furthermore,the optimized design significantly enhances system-level performance uniformity,achieving a 52.3%reduction in inter-pier shear force variability and a 19.0%decrease in displacement disparity compared with the baseline configuration. 展开更多
关键词 self-centering rocking bridge machine learning seismic design multi-objective optimization nonlinear dynamics multi-criteria decision-making
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Multi-Objective Approach for Optimizing Production Parameters of Low-Permeability Oil Well to Enhance Energy Efficiency 认领 引用
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作者 LIU Peijin DING Haojian +3 位作者 YAN Dongyang SUN Haofeng HUANG Tao LI Jie 《Journal of Shanghai Jiaotong university(Science)》 EI 2026年第2期486-498,共13页
Aiming at the practical problems of high energy consumption and low energy efficiency during the exploitation of low-permeability oil wells because of insufficient traceability and poor matching performance of product... Aiming at the practical problems of high energy consumption and low energy efficiency during the exploitation of low-permeability oil wells because of insufficient traceability and poor matching performance of production parameters,this paper proposes a multi-objective approach for optimizing production parameters of low-permeability oil well to enhance its energy efficiency.First,a sub-model of daily liquid production yield and a sub-model of unit production energy consumption cost for single low-permeability oil well were established,and the Gaussian mixture model method was employed to compensate for the errors in the sub-model of unit production energy consumption cost,to solve the problem of the influence of uncertain facts during the oil well exploitation and to improve the precision of the model.Second,a multi-objective optimization model was established by taking into account the decision variables and constraints of the model,to maximize the daily liquid production yield while minimizing the unit production energy consumption cost.Subsequently,the non-dominated sorting genetic algorithm was employed to solve the multi-objective optimization model and obtain the production parameters.Finally,the solution set with obvious features was taken as the production parameters and applied to the actual production verification of low-permeability oil wells in a certain oil production plant of the ChangQing Oilfield.The results showed an increase in oil well production yield,and a significant energy-saving effect,thereby verifying the effectiveness of the proposed model and optimization algorithm in this paper. 展开更多
关键词 low-permeability oil well production parameter model error compensation multi-objective optimization non-dominated genetic algorithm
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Multi-objective optimization design of biomimetic porous scaffolds based on cancellous bone structure 认领 引用
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作者 Sujing Tian He Gong +2 位作者 Xiang Zhang Jiazi Gao Liming Zhou 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第6期546-564,共19页
As age advances,accumulation of bone regeneration inhibitors in osteoporotic patients increases,resulting in larger bone defect areas and varying degrees of defects.When studying bone regeneration in osteoporotic bone... As age advances,accumulation of bone regeneration inhibitors in osteoporotic patients increases,resulting in larger bone defect areas and varying degrees of defects.When studying bone regeneration in osteoporotic bone defects,researchers often lack specificity on different bone quality statuses.To design a porous scaffold more similar to cancellous bone to promote bone regeneration,a multi-objective optimization design of biomimetic porous scaffold based on cancellous bone images was carried out in this study.Vertebral cancellous bones from rats with different bone quality statuses caused by various ovariectomy durations served as examples.First,the microstructure,mechanical and biological properties parameters of vertebral cancellous bones were calculated based on images from 20-,30-,and 38-week-old rats without ovariectomy and 30-and 38-week-old rats with ovariectomy(10 weeks and 18 weeks after ovariectomy).Second,the effects of constant value(C),which affects scaffold thickness,scale factor of z-axis(N),influencing stretching and compression of unit cell,and unit cell size(L)on the mechanical and biological properties of Schoen Gyroid and Schoen I-WP were investigated.Third,Schoen Gyroid and Schoen I-WP were optimized and evaluated using non-dominated genetic algorithm-II(NSGA-II)and complex proportional assessment method,with the elastic modulus of cancellous bones from 30-and 38-week-old ovariectomized rats as performance constraint to obtain the best structure tailored to each ovariectomized group.The surface curvature of the scaffold could be changed by stretching or compressing the unit cell,and the pore size could be changed by altering the unit cell thickness and size to obtain scaffolds suitable for different extents of bone defects.The optimized scaffolds met mechanical and biological requirements.Schoen I-WP exhibited superior comprehensive performance compared to Schoen Gyroid.The optimized design framework proposed in this study can be applied to bone defects of any age,bone site,and bone quality status,and has potential application for personalized treatment of bone defects. 展开更多
关键词 Osteoporotic bone defect Biomimetic porous scaffold Comprehensive performance Multi-objective optimization Complex proportional assessment method
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Multi-objective spatial optimization by considering land use suitability in the Yangtze River Delta region 认领 引用
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作者 CHENG Qianwen LI Manchun +4 位作者 LI Feixue LIN Yukun DING Chenyin XIAO Lishan LI Weiyue 《Journal of Geographical Sciences》 SCIE CSCD 2026年第1期45-78,共34页
Rapid urbanization in China has led to spatial antagonism between urban development and farmland protection and ecological security maintenance.Multi-objective spatial collaborative optimization is a powerful method f... Rapid urbanization in China has led to spatial antagonism between urban development and farmland protection and ecological security maintenance.Multi-objective spatial collaborative optimization is a powerful method for achieving sustainable regional development.Previous studies on multi-objective spatial optimization do not involve spatial corrections to simulation results based on the natural endowment of space resources.This study proposes an Ecological Security-Food Security-Urban Sustainable Development(ES-FS-USD)spatial optimization framework.This framework combines the non-dominated sorting genetic algorithm II(NSGA-II)and patch-generating land use simulation(PLUS)model with an ecological protection importance evaluation,comprehensive agricultural productivity evaluation,and urban sustainable development potential assessment and optimizes the territorial space in the Yangtze River Delta(YRD)region in 2035.The proposed sustainable development(SD)scenario can effectively reduce the destruction of landscape patterns of various land-use types while considering both ecological and economic benefits.The simulation results were further revised by evaluating the land-use suitability of the YRD region.According to the revised spatial pattern for the YRD in 2035,the farmland area accounts for 43.59%of the total YRD,which is 5.35%less than that in 2010.Forest,grassland,and water area account for 40.46%of the total YRD—an increase of 1.42%compared with the case in 2010.Construction land accounts for 14.72%of the total YRD—an increase of 2.77%compared with the case in 2010.The ES-FS-USD spatial optimization framework ensures that spatial optimization outcomes are aligned with the natural endowments of land resources,thereby promoting the sustainable use of land resources,improving the ability of spatial management,and providing valuable insights for decision makers. 展开更多
关键词 multi-objective spatial optimization multi-scenario simulation ecological protection importance comprehensive agricultural productivity urban sustainable development land-use suitability
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Coupled Meteorological-Electricity Behavior Analysis and Multi-Energy Load Forecasting Based on a Combined Model 认领 引用
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作者 Nantian Huang Jingyuan Zhang +3 位作者 Shicheng Ren Hao Zhang Bingling Li Yaoyao Wang 《Energy Engineering》 EI 2026年第8期427-453,共27页
User electricity consumption behavior analysis and multi-load forecasting in integrated energy systems are crucial for system operation and scheduling.Traditional user electricity consumption behavior analysis fails t... User electricity consumption behavior analysis and multi-load forecasting in integrated energy systems are crucial for system operation and scheduling.Traditional user electricity consumption behavior analysis fails to adequately incorporate meteorological factors,limiting the accuracy of characterizing user electricity consumption patterns.Traditional multi-load forecasting models do not consider the differentiated coupling relationships with meteorological factors across different seasons,which restricts the improvement of forecasting accuracy.To address the above issues,a method integrating data cleaning and meteorological correlation for electricity consumption behavior and multi-dimensional forecasting analysis is proposed.First,the Akima interpolation method is used to repair anomalous points in the load data.Second,the BK-Means algorithm is employed to determine the optimal number of clusters and initial centers,achieving a coupled analysis of wind-solar power output,load-meteorology clustering,and user electricity consumption behavior.Subsequently,the Kendall rank correlation coefficient method is applied to analyze the correlation between multi-dimensional loads and meteorological factors,constructing differentiated input feature sets for various loads tailored to different seasons.Finally,a combined model is used to generate multi-load forecasting results.The results demonstrate that compared to single forecasting methods,the proposed method achieves higher forecasting accuracy for electricity,cool,and heating loads across different seasons. 展开更多
关键词 NRBO algorithm BK-means algorithm LSTM algorithm XGBoost algorithm multivariate load forecasting
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Empowering PV system modeling and forecasting:a review of public benchmark datasets 认领 引用
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作者 Bochao Zhao Yaqing Wang +1 位作者 Wenpeng Luan Hanju Cai 《Global Energy Interconnection》 EI CSCD 2026年第2期219-242,共24页
Driven by the high penetration of renewable energy,the inherent intermittency of photovoltaic(PV)generation poses severe challenges to grid stability.To manage this volatility and ensure reliable grid integration,prec... Driven by the high penetration of renewable energy,the inherent intermittency of photovoltaic(PV)generation poses severe challenges to grid stability.To manage this volatility and ensure reliable grid integration,precise PV system modeling and power forecasting have emerged as critical solutions.However,existing research predominantly focuses on algorithmic innovations and model architectures,frequently overlooking the foundational role of dataset selection.Because capturing the complex spatiotemporal dynamics of solar generation increasingly requires the integration of diverse data types,understanding how to select and fuse these multimodal sources is crucial for determining the upper bound of predictive performance.To address the persistent fragmentation of data resources in PV predictive modeling,this paper delivers a comprehensive taxonomy of publicly available benchmark datasets,establishing a roadmap for future data-driven research.We categorize these valuable resources into three core pillars:1)meteorological datasets(encompassing observational,synthetic,hybrid,and reanalysis types);2)PV generation datasets(grouped by temporal resolution);and 3)static system parameters(including plant-level geospatial data and module-level physical properties).Building upon this categorization,this review thoroughly examines multimodal data fusion strategies across various forecasting horizons and elucidates the specific data dependencies of persistence,physical,and data-driven modeling paradigms.Furthermore,we critically analyze key challenges in multi-source data fusion,particularly spatiotemporal misalignment and the lack of standardized quality control flags.Ultimately,this work provides researchers with an authoritative guide for robust data selection and model construction. 展开更多
关键词 PV system modeling PV forecasting Meteorological datasets PV generation datasets Static system parameters
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A Multi-Objective Deep Reinforcement Learning Algorithm for Computation Offloading in Internet of Vehicles 认领 引用
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作者 Junjun Ren Guoqiang Chen +1 位作者 Zheng-Yi Chai Dong Yuan 《Computers, Materials & Continua》 SCIE EI 2026年第1期2111-2136,共26页
Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrain... Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrained onboard devices to nearby Roadside Unit(RSU),thereby achieving lower delay and energy consumption.However,due to the limited storage capacity and energy budget of RSUs,it is challenging to meet the demands of the highly dynamic Internet of Vehicles(IoV)environment.Therefore,determining reasonable service caching and computation offloading strategies is crucial.To address this,this paper proposes a joint service caching scheme for cloud-edge collaborative IoV computation offloading.By modeling the dynamic optimization problem using Markov Decision Processes(MDP),the scheme jointly optimizes task delay,energy consumption,load balancing,and privacy entropy to achieve better quality of service.Additionally,a dynamic adaptive multi-objective deep reinforcement learning algorithm is proposed.Each Double Deep Q-Network(DDQN)agent obtains rewards for different objectives based on distinct reward functions and dynamically updates the objective weights by learning the value changes between objectives using Radial Basis Function Networks(RBFN),thereby efficiently approximating the Pareto-optimal decisions for multiple objectives.Extensive experiments demonstrate that the proposed algorithm can better coordinate the three-tier computing resources of cloud,edge,and vehicles.Compared to existing algorithms,the proposed method reduces task delay and energy consumption by 10.64%and 5.1%,respectively. 展开更多
关键词 Deep reinforcement learning internet of vehicles multi-objective optimization cloud-edge computing computation offloading service caching
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