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Estimation model and future projections for leaf longevity in alpine grasslands based on XGBoost-SHAP algorithm:A case study of the Three-Rivers-Source Region,China 认领 引用
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作者 Yuxi Wang Lin Zhang 《Geography and Sustainability》 CSCD 2026年第3期121-133,共13页
Alpine grasslands are highly sensitive to environmental changes,with leaf longevity crucially modulating carbon cycling.Addressing uncertainties in long-term leaf longevity dynamics,driving mechanisms and its interpla... Alpine grasslands are highly sensitive to environmental changes,with leaf longevity crucially modulating carbon cycling.Addressing uncertainties in long-term leaf longevity dynamics,driving mechanisms and its interplay with net primary productivity(NPP),we analyzed the spatiotemporal changes in leaf longevity and NPP of the Three-Rivers-Source Region(TRSR)from 2003 to 2022 using multi-source remote sensing data.Key drivers of leaf longevity were identified using XGBoost-SHAP algorithm and lasso regression,while a causality-based model projected future trajectories.Results showed that over 81%of the study area exhibited a significant leaf longevity extension(9.32 days decade-1),mainly due to delayed leaf senescence date.Concurrently,regional NPP increases were dominated by summer gains.There was a non-linear positive correlation between leaf longevity and NPP,confirming that longer leaf longevity enhanced carbon uptake by prolonging photosynthesis.However,this marginal gain declined once leaf longevity surpassed the ecological threshold(about 150 days),indicating that after summer vegetation activity peaks,relying solely on extending the growing season does not lead to substantial net carbon gains,and the carbon sink becomes saturated.Temperature consistently drove leaf longevity variation,while enhanced solar radiation exerted increasing influence,highlighting the greater importance of photothermal resources for foliar phenology.Projections suggested continued leaf longevity extension under SSP245 and SSP585 climate scenarios,with short-term NPP increasing but long-term stagnating or declining.These findings emphasize that alpine grassland management should prioritize ecosystem sustainability and adaptive resilience over maximizing leaf longevity,especially under extreme climate stresses,offering key insights for carbon sequestration optimization and restoration strategies in global alpine ecosystems. 展开更多
关键词 Leaf longevity Net primary productivity Phenology XGBoost-SHAP Future predictions The Three-Rivers-Source Region(TRSR)
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基于InVEST模型和XGBoost-SHAP的重庆市生态系统服务供需时空演变及驱动因素分析 认领 引用 被引量：10
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作者 刘东岳 董文卓 +1 位作者 勾容 苏维词 《环境科学》 EI CAS CSCD 北大核心 2026年第5期3312-3323,共12页
探索复杂山地丘陵下垫面生态系统服务供需时空分异规律与驱动因素,对于优化区域生态安全格局和可持续发展具有重要意义.以重庆市为研究对象,利用InVEST模型、空间自相关分析方法和XGBoost-SHAP模型,系统揭示重庆市2000~2020年生态系统... 探索复杂山地丘陵下垫面生态系统服务供需时空分异规律与驱动因素,对于优化区域生态安全格局和可持续发展具有重要意义.以重庆市为研究对象,利用InVEST模型、空间自相关分析方法和XGBoost-SHAP模型,系统揭示重庆市2000~2020年生态系统服务供需的时空演变特征及其驱动因素.结果表明:①渝东北三峡库区城镇群和渝东南武陵山区城镇群呈现“高供给-低需求”格局,主城都市区则表现为“低供给-高需求”格局;2000~2020年,固碳、生境质量和休憩娱乐供给下降,产水和土壤保持供给先降后增.固碳、产水、生境质量和休憩娱乐需求增长,土壤保持需求先降后增.②重庆市生态系统服务供需关系总体保持动态平衡,但主城都市区出现持续扩大的供需赤字,渝东南和渝东北地区则维持稳定盈余.③2000年、2010年和2020年生态系统服务供需比的Moran's I分别为0.674、0.666和0.679,在空间上呈显著的正相关关系;分布特征以高-高聚集和低-低聚集为主,高-高聚集集中在城口县和南川区等生态屏障区,低-低聚集集中在渝中区和沙坪坝区等城镇化核心区.④XGBoost-SHAP模型表明:降水量、城镇化率、工业产值及农业产值占比是影响供需关系的关键驱动因子.研究结果可以为区域生态保护、生态分区管控与可持续发展提供科学参考. 展开更多
关键词 生态系统服务供需 XGBoost-SHAP模型 驱动因素 InVEST模型 重庆市
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基于XGBoost-SHAP模型的闽三角城市群生态环境质量时空演变及驱动因素分析 认领 引用 被引量：3
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作者 姚雄 陈笑 王肖文 《环境科学》 EI CAS CSCD 北大核心 2026年第6期3758-3769,共12页
探究闽三角城市群生态环境质量的时空演变规律及其驱动因素,对于保障区域生态安全及高质量发展具有重要意义.基于谷歌地球引擎平台的2000~2024年Landsat遥感数据集,采用主成分分析法构建了遥感生态指数(RSEI),结合变异系数分析了闽三角... 探究闽三角城市群生态环境质量的时空演变规律及其驱动因素,对于保障区域生态安全及高质量发展具有重要意义.基于谷歌地球引擎平台的2000~2024年Landsat遥感数据集,采用主成分分析法构建了遥感生态指数(RSEI),结合变异系数分析了闽三角城市群RSEI的时空演变规律,并运用可解释极端梯度提升(XGBoost-SHAP)模型揭示了自然和人类活动因子对生态环境质量的关键驱动因子、非线性效应及交互效应.结果表明:(1)时间尺度上,闽三角城市群RSEI呈先下降后上升的趋势,多年均值为0.62,最低值和最高值分别出现在2010年(0.53)和2024年(0.68),生态环境质量整体良好;(2)空间尺度上,研究区RSEI呈现“西北高、东南低”的分布格局,生态环境质量变好区域面积大于变差区域,两者占比相差4.51%;(3)研究区RSEI变异系数范围为0.58%~84.63%,整体处于稳定状态,但仍有9.10%的区域表现出不稳定状态;(4)生态环境质量变化的主导因素具有时间异质性:2000~2010年期间,该变化主要受植被覆盖度、年均气温和年降水量等自然因素驱动;而2015~2024年期间,则转变为植被覆盖度、夜间灯光强度和人口分布等自然和人类活动因素协同主导.此外,自然与人类活动因素间的交互作用呈现高度复杂性,表现出阈值依赖性和效应方向可逆性.研究结果可为闽三角城市群的生态文明建设和可持续发展提供科学参考. 展开更多
关键词 生态环境质量 遥感生态指数(RSEI) 时空演变 XGBoost-SHAP模型 谷歌地球引擎(GEE) 闽三角城市群
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基于XGboost-SHAP模型汉江流域生态系统服务权衡与协同及驱动力分析 认领 引用 被引量：1
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作者 任万杰 司振江 +2 位作者 吕凯 赵梓添 李治军 《中国农村水利水电》 北大核心 2026年第1期97-105,共9页
旨在探讨汉江流域生态系统服务的空间分布、生态服务之间的权衡与协同关系,以及驱动这些服务变化的关键因素。选取了生境质量(HQ)、产水量(WY)、碳储存(CF)、土壤保持(SC)、净初级生产力(NPP)5项生态系统服务作为分析对象,并利用Spearma... 旨在探讨汉江流域生态系统服务的空间分布、生态服务之间的权衡与协同关系,以及驱动这些服务变化的关键因素。选取了生境质量(HQ)、产水量(WY)、碳储存(CF)、土壤保持(SC)、净初级生产力(NPP)5项生态系统服务作为分析对象,并利用Spearman方法评估了它们之间的协同效应和权衡关系,采用了XGBoost-SHAP模型进行驱动因素分析。结果显示:(1)各生态系统服务在时空尺度上表现出显著的不均衡性,在时间上,产水量和土壤保持呈现出先减少后增加的趋势,生境质量呈现出先增加后减少再增加的趋势,净初级生产力和碳储存呈现出持续增加的趋势;在空间上,产水量受降雨影响较大呈现东部地区产量较大,而其他4项生态系统服务总体呈现出东部数值较小。(2)产水量与其他四项服务呈现出明显的权衡关系,尤其是与碳储存的权衡关系最强,而其他4项服务之间则主要表现为协同效应。(3)降雨量是影响产水量的主要自然因素,而高程对净初级生产力、生境质量和碳储存有着重要影响,坡度则是土壤保持的关键决定因素。 展开更多
关键词 生态系统服务 汉江流域 权衡与协同 驱动因素 XGBoost-SHAP模型
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An improved Alpha-shape algorithm for extracting section contours of the super-high steel bridge tower using point clouds 认领 引用 被引量：2
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作者 ZHANG Yiming ZHAO Tianhao +2 位作者 LIAO Ruixuan LI Haoqing WANG Hao 《Journal of Southeast University(English Edition)》 EI CAS 2026年第1期26-35,共10页
The virtual preassembly of super-high steel bridge towers faces a challenge in the efficient and precise extraction of complex cross-sectional features.Factors such as fabrication errors,gravity-induced deformations,a... The virtual preassembly of super-high steel bridge towers faces a challenge in the efficient and precise extraction of complex cross-sectional features.Factors such as fabrication errors,gravity-induced deformations,and temperature fluctuations can compromise the accuracy of contour extraction.To address these limitations,an improved Alpha-shape-based point cloud contour extraction method is proposed.The proposed approach uses a hierarchical strategy to process three-dimensional laser scanning point clouds.The processed data are then subjected to curvatureadaptive voxel filtering to reduce acquisition noise.In addition,an enhanced iterative closest point(ICP)variant with correspondence validation accurately aligns the discrete point cloud segments.The proposed curvature-responsive Alpha-shape framework enables multiscale contour delineation through topology-adaptive threshold modulation,which resolves boundary ambiguities in geometrically complex cross-sections.The method was experimentally validated using field-acquired measurement datasets from the Zhangjinggao Yangtze River Bridge tower segments,confirming its capability to reconstruct noncanonical cross-sectional geometries.Three contour extraction methods,including Poisson reconstruction,the conventional Alpha-shape algorithm,and random sample consensus with ICP(RANSAC-ICP),were compared to evaluate the performance of the proposed Alpha-shape algorithm.The results demonstrate that the proposed method achieves superior contour extraction accuracy and data reduction efficiency,highlighting its effectiveness in contour extraction tasks. 展开更多
关键词 super-high steel bridge tower point cloud contour extraction improved Alpha-shape algorithm
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Novel Sea Otter Optimization Algorithm for WSN Coverage Intelligence Optimization 认领 引用 被引量：2
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作者 WU Jin GAO Yaqiong +2 位作者 SU Zhengdong CHONG Gege XIONG Hao 《Journal of Shanghai Jiaotong university(Science)》 EI 2026年第4期828-842,I0002,共15页
A novel intelligent optimization algorithm inspired by nature,called sea otter optimization algorithm(SOOA),is proposed.The SOOA simulates the natural behaviors of sea otters,such as using tactile senses to search for... A novel intelligent optimization algorithm inspired by nature,called sea otter optimization algorithm(SOOA),is proposed.The SOOA simulates the natural behaviors of sea otters,such as using tactile senses to search for food in seawater,grooming their fur,feeding with the aid of stones,and escaping from danger.In the exploration stage,a wetness factor is introduced to control the behavior of sea otters in foraging and grooming;a danger factor is introduced to control the behavior of sea otters in feeding and avoiding dangers in the exploitation stage,and the behaviors of sea otters in responding to different dangers are mathematically modeled.The proposed algorithm is compared with 9 well-known intelligent optimization algorithms,and evaluated in 13 benchmark functions as well as wireless sensor network coverage optimization problems to verify the effectiveness of the proposed algorithm.The experimental results show that the node coverage after SOOA optimization reaches 91.2%in 2D environment and 90.47%in 3D environment.Compared with other algorithms,SOOA is superior and possesses the ability to solve complex optimization problems. 展开更多
关键词 sea otter optimization algorithm(SOOA) swarm intelligence optimization wireless sensor network coverage optimization
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Study on the destabilizing damage precursors of cemented tailings backfill based on critical slowing down theory combined with multiple denoising algorithms under consideration of initial defect conditions 认领 引用 被引量：1
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作者 ZHAO Kang ZHONG Jun-cheng +3 位作者 YAN Ya-jing LIU Yang WEN Dao-tan XIAO Wei-ling 《Journal of Central South University》 SCIE EI CAS CSCD 2026年第1期375-399,共25页
The cemented tailings backfill(CTB)with initial defects is more prone to destabilization damage under the influence of various unfavorable factors during the mining process.In order to investigate its influence on the... The cemented tailings backfill(CTB)with initial defects is more prone to destabilization damage under the influence of various unfavorable factors during the mining process.In order to investigate its influence on the stability of underground mining engineering,this paper simulates the generation of different degrees of initial defects inside the CTB by adding different contents of air-entraining agent(AEA),investigates the acoustic emission RA/AF eigenvalues of CTB with different contents of AEA under uniaxial compression,and adopts various denoising algorithms(e.g.,moving average smoothing,median filtering,and outlier detection)to improve the accuracy of the data.The variance and autocorrelation coefficients of RA/AF parameters were analyzed in conjunction with the critical slowing down(CSD)theory.The results show that the acoustic emission RA/AF values can be used to characterize the progressive damage evolution of CTB.The denoising algorithm processed the AE signals to reduce the effects of extraneous noise and anomalous spikes.Changes in the variance curves provide clear precursor information,while abrupt changes in the autocorrelation coefficient can be used as an auxiliary localization warning signal.The phenomenon of dramatic increase in the variance and autocorrelation coefficient curves during the compression-tightening stage,which is influenced by the initial defects,can lead to false warnings.As the initial defects of the CTB increase,its instability precursor time and instability time are prolonged,the peak stress decreases,and the time difference between the CTB and the instability damage is smaller.The results provide a new method for real-time monitoring and early warning of CTB instability damage. 展开更多
关键词 initial defects cemented tailings backfill critical slowing down acoustic emission RA/AF values denoising algorithms
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Optimization of the frequency offset increment of FDA-MIMO based on cuckoo search algorithm 认领 引用 被引量：2
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作者 WANG Bo ZHAO Yu +2 位作者 LI Yonglin YANG Rennong XUE Junjie 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2026年第1期157-170,共14页
Frequency diverse array multiple-input multiple-output(FDA-MIMO)radar has gained considerable research attention due to its ability to effectively counter active repeater deception jamming in complex electromagnetic e... Frequency diverse array multiple-input multiple-output(FDA-MIMO)radar has gained considerable research attention due to its ability to effectively counter active repeater deception jamming in complex electromagnetic environments.The effectiveness of interference suppression by FDA-MIMO is limited by the inherent range-angle coupling issue in the FDA beampattern.Existing literature primarily focuses on control methods for FDA-MIMO radar beam direction under the assumption of static beampatterns,with insufficient exploration of techniques for managing nonstationary beam directions.To address this gap,this paper initially introduces the FDA-MIMO signal model and the calculation formula for the FDA-MIMO array output using the minimum variance distortionless response(MVDR)beamformer.Building on this,the problem of determining the optimal frequency offset for the FDA is rephrased as a convex optimization problem,which is then resolved using the cuckoo search(CS)algorithm.Simulations confirm the effectiveness of the proposed approach,showing that the frequency offsets obtained through the CS algorithm can create a dot-shaped beam direction at the target location while effectively suppressing interference signals within the mainlobe. 展开更多
关键词 frequency diverse array multiple-input multiple-output(FDA-MIMO) convex optimization cuckoo search algorithm beampattern
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Low-complexity APSK demodulation algorithm based on K-means clustering in LEO satellite communication systems 认领 引用
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作者 Guangfu Wu Xiangrui Meng +1 位作者 Changlin Chen Biqun Xiang 《Digital Communications and Networks》 SCIE EI CSCD 2026年第2期343-353,共11页
Amplitude Phase Shift Keying(APSK)is more suitable for the nonlinear channels of Low Earth Orbit(LEO)satellite communication systems compared to Quadrature Amplitude Modulation(QAM).To tackle challenges posed by Direc... Amplitude Phase Shift Keying(APSK)is more suitable for the nonlinear channels of Low Earth Orbit(LEO)satellite communication systems compared to Quadrature Amplitude Modulation(QAM).To tackle challenges posed by Direct Current(DC)interference and high demodulation complexity,we propose an APSK demodulation algorithm based on K-means clustering.Initially,static DC components are calculated and removed from the received APSK signals.Subsequently,the estimated APSK constellation points serve as initial centers for K-means clustering.These centers are refined through the K-means process and act as theoretical APSK constellation points for the Max-Log-MAP demodulation algorithm,effectively eliminating residual DC.We then introduce a low-complexity APSK demodulation algorithm that utilizes the symmetry of constellation points along with the Euclidean distance between DC-eliminated signals and these constellation points to minimize the set of constellation points.Simulation results indicate that for 32-APSK,our proposed demodulation submodule reduces computational complexity to approximately one-third that of the Max-Log-MAP algorithm while improving Bit Error Rate(BER)performance by about 0.23 dB.Furthermore,end-to-end simulation experiments conducted within LEO satellite communication systems demonstrate that our approach not only maintains this complexity advantage but also enhances BER performance by approximately 1.1 dB. 展开更多
关键词 DC elimination APSK demodulation LEO satellite communication K-means algorithm Max-Log-MAP algorithm
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A Metaheuristic Football Optimization Algorithm Integrated with Large Language Models for Automated Seismic Time-Series Modeling 认领 引用
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作者 Amal H.Alharbi Marwa M.Eid +2 位作者 Nima Khodadadi Ebrahim A.Mattar Sayed Elkenawy 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第5期947-987,共41页
Seismic time series forecasting remains challenging due to the nonlinearity,non-stationarity,and noise of earthquake data,and because deep learning models are sensitive to preprocessing and hyperparameter settings.Alt... Seismic time series forecasting remains challenging due to the nonlinearity,non-stationarity,and noise of earthquake data,and because deep learning models are sensitive to preprocessing and hyperparameter settings.Although recent studies have improved neural architectures and optimization techniques,preprocessing is often treated as a fixed or manually designed stage,with limited integration into model optimization.To address this,this paper proposes an integrated,data-driven modelling framework that combines guided preprocessing with systematic hyperparameter optimization for seismic prediction,specifically forecasting earthquake magnitude from seismic catalog time-series data,with experiments conducted on Canadian seismic records.The method uses a Large Language Model to guide data preparation and feature engineering,rather than fully automate them,and applies deep learning-based forecasting with the N-HITS architecture,optimized via metaheuristic-assisted feature selection and hyperparameter tuning.The Football Optimization Algorithm(FbOA),employed as a metaheuristic optimization strategy in this study,is evaluated and compared with several well-known optimizers under identical conditions.The results show significant performance gains,with FbOA achieving superior accuracy,robustness,and convergence compared to baseline and competing methods.Notably,error metrics are reduced(MSE 3.10×10-7,RMSE 5.57×103),with high performance indicators(r=0.982,R2=0.979,NSE=0.981,WI=0.985).These results highlight the value of integrating guided preprocessing with optimization and demonstrate a scalable framework for high-precision time-series prediction in geophysical and related domains. 展开更多
关键词 Seismic time-series forecasting large language models metaheuristic algorithms football optimization algorithm earthquake modeling
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A Quantum-Inspired Algorithm for Clustering and Intrusion Detection 认领 引用
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作者 Gang Xu Lefeng Wang +5 位作者 Yuwei Huang Yong Lu Xin Liu Weijie Tan Zongpeng Li Xiu-Bo Chen 《Computers, Materials & Continua》 SCIE EI 2026年第4期1180-1215,共36页
The Intrusion Detection System(IDS)is a security mechanism developed to observe network traffic and recognize suspicious or malicious activities.Clustering algorithms are often incorporated into IDS;however,convention... The Intrusion Detection System(IDS)is a security mechanism developed to observe network traffic and recognize suspicious or malicious activities.Clustering algorithms are often incorporated into IDS;however,conventional clustering-based methods face notable drawbacks,including poor scalability in handling high-dimensional datasets and a strong dependence of outcomes on initial conditions.To overcome the performance limitations of existing methods,this study proposes a novel quantum-inspired clustering algorithm that relies on a similarity coefficient-based quantum genetic algorithm(SC-QGA)and an improved quantum artificial bee colony algorithm hybrid K-means(IQABC-K).First,the SC-QGA algorithmis constructed based on quantum computing and integrates similarity coefficient theory to strengthen genetic diversity and feature extraction capabilities.For the subsequent clustering phase,the process based on the IQABC-K algorithm is enhanced with the core improvement of adaptive rotation gate and movement exploitation strategies to balance the exploration capabilities of global search and the exploitation capabilities of local search.Simultaneously,the acceleration of convergence toward the global optimum and a reduction in computational complexity are facilitated by means of the global optimum bootstrap strategy and a linear population reduction strategy.Through experimental evaluation with multiple algorithms and diverse performance metrics,the proposed algorithm confirms reliable accuracy on three datasets:KDD CUP99,NSL_KDD,and UNSW_NB15,achieving accuracy of 98.57%,98.81%,and 98.32%,respectively.These results affirm its potential as an effective solution for practical clustering applications. 展开更多
关键词 Intrusion detection clustering quantum artificial bee colony algorithm K-means quantum genetic algorithm
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长江经济带新型城镇化与碳排放时空耦合及影响因素:基于XGBoost-SHAP模型 认领 引用
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作者 张少杰 周灿 +1 位作者 肖铁桥 纪明波 《环境科学》 EI CAS CSCD 北大核心 2026年第8期5154-5167,共14页
随着全球气候变化加剧,碳排放问题对可持续发展的影响日益加深.为探究长江经济带新型城镇化与碳排放的协调关系,选取2010~2022年长江经济带108个城市为研究对象,通过熵权TOPSIS法和降尺度模型等方法测度新型城镇化水平与碳排放强度,借... 随着全球气候变化加剧,碳排放问题对可持续发展的影响日益加深.为探究长江经济带新型城镇化与碳排放的协调关系,选取2010~2022年长江经济带108个城市为研究对象,通过熵权TOPSIS法和降尺度模型等方法测度新型城镇化水平与碳排放强度,借助修正耦合协调模型分析两者的时空耦合演变.并引入XGBoost-SHAP模型,探究耦合协调度的关键影响因素及其非线性作用机制.结果表明:①新型城镇化与碳排放强度在空间和时间上呈现显著差异,两者表现出明显的负相关关系.就两者水平而言,下游优于中游、中游优于上游.②耦合协调度整体不断提升,但区域间差异与内部两极分化依然突出,空间格局由最初的“东高西低”演变为“多核心”分布形态.下游地区核心地带的优势地位逐步扩大.③各因素作用机制呈现非线性与区域异质性,人口密度与收入水平对协调度贡献最大,而产业结构与政府支出等变量在不同区域出现作用机制和阈值差异.最后,根据研究发现提出基于区域差异的对策建议,以期为长江经济带实现绿色、高质量发展提供可行路径与决策参考. 展开更多
关键词 长江经济带 新型城镇化 碳排放强度 耦合协调 XGBoost-SHAP模型 非线性机制
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Optimization of a self-tuning force control system for the milling process using a dynamic enhanced genetic algorithm 认领 引用
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作者 Yao Li Zhengcai Zhao +3 位作者 Ning Qian Lei Zhang Wenfeng Ding Yucan Fu 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2026年第2期33-43,共11页
When milling structural components with varying axial depths and widths,cutting forces tend to fluctuate,negatively impacting tool life and machining accuracy.To mitigate the force fluctuations and enhance tool longev... When milling structural components with varying axial depths and widths,cutting forces tend to fluctuate,negatively impacting tool life and machining accuracy.To mitigate the force fluctuations and enhance tool longevity,developing a simple,reliable,and easy-to-implement force control system for milling is essential,which is an important step toward advancing intelligent manufacturing.This paper explores the use of genetic algorithms(GA) for powerful optimization capabilities in developing self-tuning milling force controllers.A comprehensive framework for optimizing a fuzzy logic controller using an enhanced GA is specifically designed for the milling process.The optimization integrates the GA with a simulation model,fine-tuning membership functions and optimizing fuzzy rule selection.The enhanced GA incorporates the Integral of Time-weighted Absolute Error(ITAE) as the fitness criterion to improve the robustness and responsiveness of the controller.The optimized fuzzy logic controller is implemented within a computer numerical control system,adjusting feed rates in real-time to control milling forces.The performance of the proposed controller is validated through step and slope milling tests,demonstrating an average control accuracy of 95.52%.Comparative evaluations with other controllers show that the proposed system offers a significant improvement,achieving up to 4.58% better control accuracy in step milling tests. 展开更多
关键词 Optimization Self-tuning Force control system Milling process Genetic algorithm
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长江经济带新型城镇化与水安全韧性耦合协调的XGBoost-SHAP解析 认领 引用
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作者 袁菊红 黄思宇 +2 位作者 张梦娜 黎浩 胡绵好 《水土保持学报》 CAS CSCD 北大核心 2026年第3期447-465,共19页
[目的]探明长江经济带新型城镇化与水安全韧性间的耦合协调发展水平与作用机理,为韧性城市建设与水安全差异化治理的协同推进提供科学支撑。[方法]以长江经济带为研究区域,以2006—2023年为研究时段,分别构建新型城镇化与水安全韧性的... [目的]探明长江经济带新型城镇化与水安全韧性间的耦合协调发展水平与作用机理,为韧性城市建设与水安全差异化治理的协同推进提供科学支撑。[方法]以长江经济带为研究区域,以2006—2023年为研究时段,分别构建新型城镇化与水安全韧性的综合评价指标体系,并采用博弈论组合赋权-TOPSIS方法、耦合协调度模型、Dagum基尼系数、探索性空间数据分析及XGBoost-SHAP模型,系统分析二者耦合协调发展的时空演变特征、空间关联格局及其内在驱动机理。[结果]1)长江经济带新型城镇化与水安全韧性间的耦合协调度呈现稳步提升与空间收敛趋势,并形成“下游>中游>上游”的梯度格局;2)其空间关联模式从集聚走向均衡,局部尺度上演化为“核心引领、梯度过渡、低值突破”的协同网络结构;3)驱动机理具有非线性与交互性特征,基础设施与城乡收入差距是核心驱动力,各因子普遍存在阈值效应及复杂交互作用,且该机制表现出显著的流域异质性。[结论]长江经济带应通过实施差异化治理策略、推行精准阈值调控与空间引导、构建多因子协同治理体系、健全动态监测与适应管理机制,提升新型城镇化与水安全韧性的耦合协调水平,以推动流域高质量发展。 展开更多
关键词 新型城镇化 水安全韧性 耦合协调 XGBoost-SHAP模型 长江经济带
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An Efficient Evolutionary Algorithm for Few-for-Many Optimization 认领 引用
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作者 Ke Shang Hisao Ishibuchi +1 位作者 Zexuan Zhu Qingfu Zhang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第6期1362-1377,共16页
Few-for-many(F4M)optimization,recently introduced as a novel paradigm in multi-objective optimization,aims to find a small set of solutions that effectively handle a large number of conflicting objectives.Unlike tradi... Few-for-many(F4M)optimization,recently introduced as a novel paradigm in multi-objective optimization,aims to find a small set of solutions that effectively handle a large number of conflicting objectives.Unlike traditional many-objective optimization methods,which typically attempt comprehensive coverage of the Pareto front,F4M optimization emphasizes finding a small representative solution set to efficiently address highdimensional objective spaces.Motivated by the computational complexity and practical relevance of F4M optimization,this paper proposes a new evolutionary algorithm explicitly tailored for efficiently solving F4M optimization problems.Inspired by Smetric selection evolutionary multi-objective optimization algorithm(SMS-EMOA),our proposed approach employs a(μ+1)-evolution strategy guided by the objective of F4M optimization.Furthermore,to facilitate rigorous performance assessment,we propose a novel benchmark test suite specifically designed for F4M optimization by leveraging the similarity betw een the R2indicator and F4M formulations.Our test suite is highly flexible,allowing any existing multi-objective optimization problem to be transformed into a corresponding F4M instance via scalarization using the weighted Tchebycheff function.Comprehensive experimental evaluations on benchmarks demonstrate the superior performance of our algorithm compared to existing state-of-the-art algorithms,especially on instances involving a large number of objectives.The source code of the proposed algorithm will be released publicly.Source code is available at http://gffzz188fe103f8f1460asp9pnxwu5xpwf6n6k.ffgz.tsg.suse.edu.cn/MOL-SZU/SoM-EMOA. 展开更多
关键词 Evolutionary algorithm few-for-many optimization many-objective optimization (MOO) multi-objective optimization
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An algorithm-assisted high-resolution D-TOF imaging system with reconfigurable macropixel-based SPAD image sensor 认领 引用
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作者 Zhe Wang Jia-xing Song +8 位作者 Na Tian Xing-jia Ni Xu Yang Run-jiang Dou Peng Feng Jian Liu Nan-jian Wu Li-yuan Liu Shuang-ming Yu 《Journal of Semiconductors》 EI CAS CSCD 2026年第7期61-71,共11页
Single-photon avalanche diode(SPAD)image sensors are widely used in direct time-of-flight(D-TOF)imaging,but their ranging performance is often constrained by limited laser power.This article presents a SPAD-based D-TO... Single-photon avalanche diode(SPAD)image sensors are widely used in direct time-of-flight(D-TOF)imaging,but their ranging performance is often constrained by limited laser power.This article presents a SPAD-based D-TOF imaging system that combines a reconfigurable macro-pixel sensor architecture with a lightweight depth completion algorithm to achieve long-range depth imaging with enhanced spatial resolution under low optical power.The proposed sensor adopts a back-side illuminated(BSI)3D-stacked architecture with programmable macro-pixels that enhance detection sensitivity and enable flexible sensitivity–resolution trade-offs.An injection-locked ring-oscillator-based time-to-digital converter(RO-TDC)array achieves a time resolution of 152.5 ps,enabling accurate TOF measurement at an optical power of 10 mW.To compensate for macropixel-induced resolution loss,a probabilistic normalized convolutional neural network(pNCNN)is employed for depth completion using sparse depth inputs only.Experimental results demonstrate that up to 30×effective resolution enhancement of the system can be achieved via the depth completion algorithm without changing the physical resolution of the sensor.Additionally,the proposed system achieves a maximum ranging distance of 90 m and a range-to-power figure-of-merit(FOM)of9 m/mW,which validates the effectiveness of the system. 展开更多
关键词 SPAD reconfigurable macro-pixel time-to-digital converter(TDC) depth completion algorithm
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Flood predictions from metrics to classes by multiple machine learning algorithms coupling with clustering-deduced membership degree 认领 引用
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作者 ZHAI Xiaoyan ZHANG Yongyong +5 位作者 XIA Jun ZHANG Yongqiang TANG Qiuhong SHAO Quanxi CHEN Junxu ZHANG Fan 《Journal of Geographical Sciences》 SCIE CSCD 2026年第1期149-176,共28页
Accurate prediction of flood events is important for flood control and risk management.Machine learning techniques contributed greatly to advances in flood predictions,and existing studies mainly focused on predicting... Accurate prediction of flood events is important for flood control and risk management.Machine learning techniques contributed greatly to advances in flood predictions,and existing studies mainly focused on predicting flood resource variables using single or hybrid machine learning techniques.However,class-based flood predictions have rarely been investigated,which can aid in quickly diagnosing comprehensive flood characteristics and proposing targeted management strategies.This study proposed a prediction approach of flood regime metrics and event classes coupling machine learning algorithms with clustering-deduced membership degrees.Five algorithms were adopted for this exploration.Results showed that the class membership degrees accurately determined event classes with class hit rates up to 100%,compared with the four classes clustered from nine regime metrics.The nonlinear algorithms(Multiple Linear Regression,Random Forest,and least squares-Support Vector Machine)outperformed the linear techniques(Multiple Linear Regression and Stepwise Regression)in predicting flood regime metrics.The proposed approach well predicted flood event classes with average class hit rates of 66.0%-85.4%and 47.2%-76.0%in calibration and validation periods,respectively,particularly for the slow and late flood events.The predictive capability of the proposed prediction approach for flood regime metrics and classes was considerably stronger than that of hydrological modeling approach. 展开更多
关键词 flood regime metrics class prediction machine learning algorithms hydrological model
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Phased-Enhancement Marine Predators Algorithm for Global Optimization and Medical Insurance Fraud Detection 认领 引用
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作者 Wen Long Yujia Wang +2 位作者 Qinghua Long Yang Yang Ming Xu 《Journal of Bionic Engineering》 SCIE EI CSCD 2026年第2期1088-1111,共24页
The Marine Predators Algorithm(MPA),while promising for complex optimization,suffers from limited solution precision,imbalanced exploration–exploitation,and premature convergence.To address these shortcomings,this pa... The Marine Predators Algorithm(MPA),while promising for complex optimization,suffers from limited solution precision,imbalanced exploration–exploitation,and premature convergence.To address these shortcomings,this paper proposes a phased-enhancement variant named PEMPA,which integrates three novel strategies into distinct phases of MPA:1)embedding historical best positions in the high-velocity ratio phase to refine solution quality;2)introducing an adaptive inertia weight based on an inverted Sigmoid function in the unit-velocity ratio phase to systematically balance exploration and exploitation;and 3)designing a two-stage opposition-based learning operator in the low-velocity ratio phase to prevent premature convergence.The performance of PEMPA is comprehensively evaluated across 23 classical benchmark functions,the IEEE Congress on Evolutionary Computation(CEC)2017 test suite,21 feature selection tasks,and a real-world medical insurance fraud detection problem.Experimental results confirm that the proposed strategies significantly enhance the efficiency and robustness of MPA.Furthermore,PEMPA demonstrates highly competitive performance compared with several state-of-the-art metaheuristic algorithms,validating its effectiveness and scalability for diverse optimization challenges. 展开更多
关键词 Marine predators algorithm Opposite-based learning Inertia weight Numerical optimization Feature selection
A Deep-Learning-Based Constitutive Method for Geomaterials Using a Neural Cutting Plane Algorithm 认领 引用
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作者 Qingxiang Meng Zijie He +1 位作者 Yajun Cao Weijiang Chu 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第6期158-180,共23页
Constitutive modeling for geomaterials remains challenging because of limited data availability,strong nonlinearity,pressure sensitivity,and the non-smooth characteristics of commonly used yield surfaces.This study pr... Constitutive modeling for geomaterials remains challenging because of limited data availability,strong nonlinearity,pressure sensitivity,and the non-smooth characteristics of commonly used yield surfaces.This study presents a deep-learning-based constitutive method for geomaterials that incorporates a neural stress-integration procedure based on the cutting plane algorithm(CPA).Two compact fully connected networks are trained to learn the yield function and its stress gradient from an augmented stress-state dataset.The trained networks are then incorporated into a cutting plane return-mapping procedure,in which only first-order information is required for the plastic stress return.This avoids explicit analytical yield expressions and second-derivative evaluations and is therefore more naturally compatible with non-smooth Mohr-Coulomb-type yield-surface representations in a first-order returnmapping sense.Numerical results show that the proposed method reproduces the reference Mohr-Coulomb response along the examined monotonic triaxial compression paths.Compared with the finite-difference closest-point projection method(CPPM)implementation considered in this study,the CPA-based neural stress-update procedure requires fewer network calls per update,indicating a more economical implementation for the present learned constitutive framework. 展开更多
关键词 Geomaterials constitutive modeling deep learning cutting plane algorithm stress integration
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RRT*-GSQ:A hybrid sampling path planning algorithm for complex orchard scenarios 认领 引用
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作者 ZHU Qingzhen ZHAO Jiamuyang +1 位作者 DAI Xu YU Yang 《农业工程学报》 EI CAS CSCD 北大核心 2026年第3期13-25,共13页
Traditional sampling-based path planning algorithms,such as the rapidly-exploring random tree star(RRT*),encounter critical limitations in unstructured orchard environments,including low sampling efficiency in narr... Traditional sampling-based path planning algorithms,such as the rapidly-exploring random tree star(RRT*),encounter critical limitations in unstructured orchard environments,including low sampling efficiency in narrow passages,slow convergence,and high computational costs.To address these challenges,this paper proposes a novel hybrid global path planning algorithm integrating Gaussian sampling and quadtree optimization(RRT*-GSQ).This methodology aims to enhance path planning by synergistically combining a Gaussian mixture sampling strategy to improve node generation in critical regions,an adaptive step-size and direction optimization mechanism for enhanced obstacle avoidance,a Quadtree-AABB collision detection framework to lower computational complexity,and a dynamic iteration control strategy for more efficient convergence.In obstacle-free and obstructed scenarios,compared with the conventional RRT*,the proposed algorithm reduced the number of node evaluations by 67.57%and 62.72%,and decreased the search time by 79.72%and 78.52%,respectively.In path tracking tests,the proposed algorithm achieved substantial reductions in RMSE of the final path compared to the conventional RRT*.Specifically,the lateral RMSE was reduced by 41.5%in obstacle-free environments and 59.3%in obstructed environments,while the longitudinal RMSE was reduced by 57.2%and 58.5%,respectively.Furthermore,the maximum absolute errors in both lateral and longitudinal directions were constrained within 0.75 m.Field validation experiments in an operational orchard confirmed the algorithm's practical effectiveness,showing reductions in the mean tracking error of 47.6%(obstacle-free)and 58.3%(with obstructed),alongside a 5.1%and 7.2%shortening of the path length compared to the baseline method.The proposed algorithm effectively enhances path planning efficiency and navigation accuracy for robots,presenting a superior solution for high-precision autonomous navigation of agricultural robots in orchard environments and holding significant value for engineering applications. 展开更多
关键词 robot path planning orchard improved RRT*algorithm Gaussian sampling autonomous navigation
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