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Entropy-MIMR-LSTM framework for rain gauge network optimization in mountainous small watersheds:A case study of Fuhuxi Watershed,China 认领 引用
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作者 CUI Yulong WU Hao +3 位作者 ZHENG Jun MA Xianchun HUANG Yongwei LI Qiang 《Journal of Mountain Science》 SCIE CSCD 2026年第5期2210-2227,共18页
Global climate change has intensified the frequency and severity of extreme rainfall events,thereby exacerbating flood disasters.To mitigate such risks,timely and accurate rainfall measurements are essential,yet cost-... Global climate change has intensified the frequency and severity of extreme rainfall events,thereby exacerbating flood disasters.To mitigate such risks,timely and accurate rainfall measurements are essential,yet cost-effectiveness must also be considered.However,many river basins—particularly mountainous small watersheds—suffer from poorly designed rain gauge networks,limiting real-time data acquisition.Existing optimization methods are largely developed for large river basins or plains and are not directly applicable to mountainous small watersheds,where rainfall exhibits strong spatial heterogeneity and gauge networks are sparse.To address this gap,this study takes the Fuhuxi Watershed of Mount Emei in Sichuan Province,Southwest China,as a case study and develops a collaborative optimization framework integrating information entropy,the Maximum Information Minimum Redundancy(MIMR)criterion,and Long Short-Term Memory(LSTM)networks.Specifically,we quantified the information entropy matrix of seven existing rain gauge stations and applied the MIMR criterion,resulting in the retention of five key stations.The optimized network preserves 99%of the effective rainfall information from the original seven stations while significantly reducing operational and maintenance costs.Using data from nine rainfall-induced flood events between 2018 and 2023,we developed an LSTM-based runoff simulation model.The optimized network,which removes stations with low information content and high redundancy,achieved excellent flood simulation accuracy.The study demonstrates that:(1)information entropy theory effectively interprets the spatial correlation and information redundancy of rain gauge stations in mountainous small watersheds;and(2)the LSTM model validates the feasibility of using an optimized rain gauge network to support highprecision flood simulations.Finally,we propose suggestions for future research,particularly regarding the optimization of rain gauge networks to improve the understanding of optimal network design and thereby enhance the accuracy of rainfall-runoff simulations. 展开更多
关键词 Mountainous small watershed Rain gauge network optimization Information entropy MIMR criterion LSTM Flood simulation
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Planning hierarchical hospital service areas for maternal care using a network optimization approach:A case study in Hubei,China 认领 引用 被引量：1
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作者 TAO Zhuolin CHENG Yang +2 位作者 BAl Lingyao FENG Ling WANG Shaoshuai 《Journal of Geographical Sciences》 SCIE CSCD 2022年第12期2577-2598,共22页
Improving maternal health is one of the Sustainable Development Goals.Hospital service areas(HSAs),which contain most hospitalization behaviors at the local scale,are crucial for health care planning.However,little at... Improving maternal health is one of the Sustainable Development Goals.Hospital service areas(HSAs),which contain most hospitalization behaviors at the local scale,are crucial for health care planning.However,little attention has been given to HSAs for maternal care and the hierarchy structure.Considering Hubei,central China,as a case study,this study aims to fill these gaps by developing a method for delineating hierarchical HSAs for maternal care using a network optimization approach.The approach is driven by actual patient flow data and has an explicit objective to maximize the modularity.It also establishes the hierarchical structure of maternal care HSAs,which is fundamental for the planning of hierarchical maternal care and referral systems.In our case study,45 secondary HSAs and 22tertiary HSAs are delineated to achieve maximal modularity.The HSAs perform well in terms of indices such as the Localization Index and Market Share Index.Furthermore,there is a complementary relationship between secondary and tertiary hospitals,which suggests the need for referral system planning.This study can provide evidence for the validity of the HSA and the planning of maternal care HSAs in China.It also provides transferable methods for planning hierarchical HSAs in other developing countries. 展开更多
关键词 hospital service areas hierarchical structure network optimization modularity maternal care
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Air route network optimization in fragmented airspace based on cellular automata 认领 引用 被引量：25
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作者 Shijin WANG Xi CAO +3 位作者 Haiyun LI Qingyun LI Xu HANG Yanjun WANG 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2017年第3期1184-1195,共12页
Air route network optimization,one of the essential parts of the airspace planning,is an effective way to optimize airspace resources,increase airspace capacity,and alleviate air traffic congestion.However,little has ... Air route network optimization,one of the essential parts of the airspace planning,is an effective way to optimize airspace resources,increase airspace capacity,and alleviate air traffic congestion.However,little has been done on the optimization of air route network in the fragmented airspace caused by prohibited,restricted,and dangerous areas（PRDs）.In this paper,an air route network optimization model is developed with the total operational cost as the objective function while airspace restriction,air route network capacity,and non-straight-line factors（NSLF） are taken as major constraints.A square grid cellular space,Moore neighbors,a fixed boundary,together with a set of rules for solving the route network optimization model are designed based on cellular automata.The empirical traffic of airports with the largest traffic volume in each of the 9 flight information regions in China's Mainland is collected as the origin-destination（OD） airport pair demands.Based on traffic patterns,the model generates 35 air routes which successfully avoids 144 PRDs.Compared with the current air route network structure,the number of nodes decreases by 41.67%,while the total length of flight segments and air routes drop by 32.03% and 5.82% respectively.The NSLF decreases by 5.82% with changes in the total length of the air route network.More importantly,the total operational cost of the whole network decreases by 6.22%.The computational results show the potential benefits of the model and the advantage of the algorithm.Optimization of air route network can significantly reduce operational cost while ensuring operation safety. 展开更多
关键词 Air route network planning Airspace restriction Cellular automata Network capacity Optimization of nodes
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Distribution Network Optimization Based on Topology Security-constrained Integrated Reinforcement Learning 认领 引用 被引量：2
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作者 Haixiang Zang Yongkai Zhao +4 位作者 Kang Sun Guoqiang Sun Lilin Cheng Jingxuan Liu Zhinong Wei 《Protection and Control of Modern Power Systems》 SCIE EI CSCD 2026年第2期48-61,共14页
With the increasing penetration of large-scale renewable energy sources into the power grid,distribution networks are facing significant challenges,including intensified voltage fluctuations and increased network loss... With the increasing penetration of large-scale renewable energy sources into the power grid,distribution networks are facing significant challenges,including intensified voltage fluctuations and increased network losses.Although deep reinforcement learning has made considerable advancements in addressing optimi-zation problems compared to traditional algorithms,there has been limited focus on enhancing convergence and safety in cooperative optimization scenarios,particularly those involving topological reconstruction.To overcome these challenges,this paper proposes a distribution network optimization model that incorporates topological security-constrained integrated reinforcement learning.The model improves the encoding of topologies by representing them in a multi-dimensional discrete space and introduces a topological masking mechanism to achieve high safety and computational efficiency.Addi-tionally,an ensemble strategy is utilized to develop an action network group,improving action prediction and screening,thereby achieving better training stability.Experiments conducted on an enhanced IEEE33-node distribution network system indicate that the proposed improvements significantly enhance training stability and support the safe and efficient operation of the system. 展开更多
关键词 Distribution network optimization en-semble learning reinforcement learning topology recon-struction
Utilizing network optimization to mitigate rising greenspace exposure inequalities in Chinese cities from 2000 to 2050 认领 引用
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作者 Rundong Feng Bin Chen +3 位作者 Shenghe Liu Fuyuan Wang Kaiyong Wang Bojie Fu 《Environmental Science and Ecotechnology》 SCIE CAS CSCD 2025年第6期170-180,共11页
Urban greenspaces enhance human well-being and promote sustainable development in rapidly urbanizing regions by delivering vital ecosystem services,including cooling,air purification,and recreation.In China,where citi... Urban greenspaces enhance human well-being and promote sustainable development in rapidly urbanizing regions by delivering vital ecosystem services,including cooling,air purification,and recreation.In China,where cities accommodate a large share of the population amid persistent environmental pressures,disparities in greenspace exposure pose a major obstacle to equitable access;these disparities arise from geographic,climatic,socioeconomic,and landscape factors.Although awareness of such inequalities is growing,their long-term trajectories,demographic and city-scale patterns,and viable spatial optimization approaches remain largely unexplored.Here we show that greenspace exposure inequality across 246 Chinese cities increased by 25%from 2000 to 2020 and is projected to rise further by 12.2-15.7%by 2050 under middle-of-the-road and fossil-fueled development scenarios,disproportionately affecting older,less-educated women and megacity residents.Geodetector and random forest analyses reveal that this rise results from interactions among greenspace coverage,population density,and patch connectivity,which explain 83.9%of the inequality.A network-based optimization approach that improves patch connectivity—without expanding total greenspace—can reduce disparities by 10.3-20.8%,with greater efficacy in high-inequality cities and among vulnerable populations.Our results highlight how precise landscape interventions can advance social equity in greenspace access,supporting Sustainable Development Goal 11 for inclusive,resilient urban environments. 展开更多
关键词 Greenspace exposure Multi-scenario projections Structural differences Mitigate strategy Network optimization
Artificial Intelligence (AI)-Enabled Unmanned Aerial Vehicle (UAV) Systems for Optimizing User Connectivity in Sixth-Generation (6G) Ubiquitous Networks 认领 引用 被引量：1
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作者 Zeeshan Ali Haider Inam Ullah +2 位作者 Ahmad Abu Shareha Rashid Nasimov Sufyan Ali Memon 《Computers, Materials & Continua》 SCIE EI 2026年第1期534-549,共16页
The advent of sixth-generation(6G)networks introduces unprecedented challenges in achieving seamless connectivity,ultra-low latency,and efficient resource management in highly dynamic environments.Although fifth-gener... The advent of sixth-generation(6G)networks introduces unprecedented challenges in achieving seamless connectivity,ultra-low latency,and efficient resource management in highly dynamic environments.Although fifth-generation(5G)networks transformed mobile broadband and machine-type communications at massive scales,their properties of scaling,interference management,and latency remain a limitation in dense high mobility settings.To overcome these limitations,artificial intelligence(AI)and unmanned aerial vehicles(UAVs)have emerged as potential solutions to develop versatile,dynamic,and energy-efficient communication systems.The study proposes an AI-based UAV architecture that utilizes cooperative reinforcement learning(CoRL)to manage an autonomous network.The UAVs collaborate by sharing local observations and real-time state exchanges to optimize user connectivity,movement directions,allocate power,and resource distribution.Unlike conventional centralized or autonomous methods,CoRL involves joint state sharing and conflict-sensitive reward shaping,which ensures fair coverage,less interference,and enhanced adaptability in a dynamic urban environment.Simulations conducted in smart city scenarios with 10 UAVs and 50 ground users demonstrate that the proposed CoRL-based UAV system increases user coverage by up to 10%,achieves convergence 40%faster,and reduces latency and energy consumption by 30%compared with centralized and decentralized baselines.Furthermore,the distributed nature of the algorithm ensures scalability and flexibility,making it well-suited for future large-scale 6G deployments.The results highlighted that AI-enabled UAV systems enhance connectivity,support ultra-reliable low-latency communications(URLLC),and improve 6G network efficiency.Future work will extend the framework with adaptive modulation,beamforming-aware positioning,and real-world testbed deployment. 展开更多
关键词 6G networks UAV-based communication cooperative reinforcement learning network optimization user connectivity energy efficiency
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Koopman-WNN Based MPC for Hierarchical Optimal Voltage and Network Power Loss Control in ADNs 认领 引用
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作者 Wenfei Yi Mingzhong Zheng +2 位作者 Jiayi Wang Hao Yang Zhenglong Sun 《Energy Engineering》 EI 2026年第4期52-73,共22页
With the growing integration of renewable energy sources(RESs)and smart interconnected devices,conventional distribution networks have turned to active distribution networks(ADNs)with complex system model and power fl... With the growing integration of renewable energy sources(RESs)and smart interconnected devices,conventional distribution networks have turned to active distribution networks(ADNs)with complex system model and power flow dynamics.The rapid fluctuation of RES power may easily result in frequent voltage violation issues.Taking the flexible RES reactive power as control variables,this paper proposes a two-layer control scheme with Koopman wide neural network(WNN)based model predictive control(MPC)method for optimal voltage regulation and network loss reduction.Based on Koopman operator theory,a data-driven WNN method is presented to fit a high-dimensional linear model of power flow.With the model,voltage and network loss sensitivities are computed analytically,and utilized for ADN partition and control model formulation.In the lower level,a dual-mode adaptive switching MPC strategy is put forward for optimal voltage control and network loss optimization in each individual partition to decide the RES reactive power.The upper level is to calculate the adjustment coefficients of the RES reactive power given in the low level by taking the coupling effects of different partitions into account,and then the final reactive power dispatches of RESs are obtained to realize optimal control of voltage and network loss.Simulation results on two ADNs demonstrate that the proposed strategy can reliably maintain the voltage at each node within the secure range,reduce network power losses,and enhance the overall system security and economic efficiency. 展开更多
关键词 Active distribution network voltage violations Koopman operator voltage regulation network loss optimization hierarchical model predictive control
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Optimal scheduling of active distribution networks based on multi-scenario fuzzy set based charging station resource prediction 认领 引用
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作者 Zhang Maosong Zhang Chunyu +3 位作者 Hao Shi Yang Jie Yang Lingxiao Wang Xiuqin 《High Technology Letters》 EI CAS 2026年第1期97-108,共12页
With the large-scale integration of new energy sources,various resources such as energy storage,electric vehicles(EVs),and photovoltaics(PV) have participated in the scheduling of active distribution networks(ADNs),po... With the large-scale integration of new energy sources,various resources such as energy storage,electric vehicles(EVs),and photovoltaics(PV) have participated in the scheduling of active distribution networks(ADNs),posing new challenges to the operation and scheduling of distribution networks.Aiming at the uncertainty of PV and EV,an optimal scheduling model for ADNs based on multi-scenario fuzzy set based charging station resource forecasting is constructed.To address the scheduling uncertainties caused by PV and load forecasting errors,a day-ahead optimal scheduling model based on conditional value at risk(CVaR) for cost assessment is established,with the optimization objectives of minimizing the operation cost of distribution networks and the risk cost caused by forecasting errors.An improved subtractive optimizer algorithm is proposed to solve the model and formulate day-ahead optimization schemes.Secondly,a forecasting model for dispatchable resources in charging stations is constructed based on event-based fuzzy set theory.On this basis,an intraday scheduling model is built to comprehensively utilize the dispatchable resources of charging stations to coordinate with the output of distributed power sources,achieving optimal scheduling with the goal of minimizing operation costs.Finally,an experimental scenario based on the IEEE-33 node system is designed for simulation verification.The comparison of optimal scheduling results shows that the proposed method can fully exploit the potential scheduling resources of charging stations,improving the operation stability of ADNs and the accommodution capacity of new energy. 展开更多
关键词 charging station resource prediction subtractive optimizer algorithm multi-scenario fuzzy set two-stage optimal scheduling distribution network cost optimization
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Application of digital twin technology in smart warehousing and distribution network optimization 认领 引用
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作者 Jianing Wang 《Journal of Fintech and Business Analysis》 2025年第2期108-114,共7页
With the rapid development of smart logistics,the integration of digital twin technology into warehousing and distribution network optimization has emerged as a promising solution to improve operational efficiency,red... With the rapid development of smart logistics,the integration of digital twin technology into warehousing and distribution network optimization has emerged as a promising solution to improve operational efficiency,reduce costs,and enhance decision-making capabilities.This study explores how a digital twin framework can be constructed for smart warehousing and how it can be leveraged to optimize distribution networks.We review existing literature on digital twin foundations,smart warehousing systems,and distribution optimization methods,highlighting research gaps and practical challenges.A conceptual three-layer architecture is proposed,comprising the perception layer,data layer,and application layer,along with data flow and functional modules including warehousing simulation,dynamic routing optimization,and predictive maintenance.The discussion elaborates on the advantages,limitations,and managerial implications of the proposed framework.Future research directions include empirical validation,integration with blockchain,and applications in green logistics. 展开更多
关键词 digital twin smart logistics smart warehousing distribution network optimization
Enhancing Renewable Energy Integration:A Gaussian-Bare-Bones Levy Cheetah Optimization Approach to Optimal Power Flow in Electrical Networks 认领 引用 被引量：4
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作者 Ali S.Alghamdi Mohamed A.Zohdy Saad Aldoihi 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第8期1339-1370,共32页
In the contemporary era,the global expansion of electrical grids is propelled by various renewable energy sources(RESs).Efficient integration of stochastic RESs and optimal power flow(OPF)management are critical for n... In the contemporary era,the global expansion of electrical grids is propelled by various renewable energy sources(RESs).Efficient integration of stochastic RESs and optimal power flow(OPF)management are critical for network optimization.This study introduces an innovative solution,the Gaussian Bare-Bones Levy Cheetah Optimizer(GBBLCO),addressing OPF challenges in power generation systems with stochastic RESs.The primary objective is to minimize the total operating costs of RESs,considering four functions:overall operating costs,voltage deviation management,emissions reduction,voltage stability index(VSI)and power loss mitigation.Additionally,a carbon tax is included in the objective function to reduce carbon emissions.Thorough scrutiny,using modified IEEE 30-bus and IEEE 118-bus systems,validates GBBLCO’s superior performance in achieving optimal solutions.Simulation results demonstrate GBBLCO’s efficacy in six optimization scenarios:total cost with valve point effects,total cost with emission and carbon tax,total cost with prohibited operating zones,active power loss optimization,voltage deviation optimization and enhancing voltage stability index(VSI).GBBLCO outperforms conventional techniques in each scenario,showcasing rapid convergence and superior solution quality.Notably,GBBLCO navigates complexities introduced by valve point effects,adapts to environmental constraints,optimizes costs while considering prohibited operating zones,minimizes active power losses,and optimizes voltage deviation by enhancing the voltage stability index(VSI)effectively.This research significantly contributes to advancing OPF,emphasizing GBBLCO’s improved global search capabilities and ability to address challenges related to local minima.GBBLCO emerges as a versatile and robust optimization tool for diverse challenges in power systems,offering a promising solution for the evolving needs of renewable energy-integrated power grids. 展开更多
关键词 Renewable energy integration optimal power flow stochastic renewable energy sources gaussian-bare-bones levy cheetah optimizer electrical network optimization carbon tax optimization
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Node deployment strategy optimization for wireless sensor network with mobile base station 认领 引用 被引量：7
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作者 龙军 桂卫华 《Journal of Central South University》 SCIE EI CAS 2012年第2期453-458,共6页
The optimization of network performance in a movement-assisted data gathering scheme was studied by analyzing the energy consumption of wireless sensor network with node uniform distribution. A theoretically analytica... The optimization of network performance in a movement-assisted data gathering scheme was studied by analyzing the energy consumption of wireless sensor network with node uniform distribution. A theoretically analytical method for avoiding energy hole was proposed. It is proved that if the densities of sensor nodes working at the same time are alternate between dormancy and work with non-uniform node distribution. The efficiency of network can increase by several times and the residual energy of network is nearly zero when the network lifetime ends. 展开更多
关键词 wireless sensor network mobile base station network optimization energy consumption balancing density ratio of sensor node network lifetime
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Optimization of PERT Network and Compression of Time 认领 引用 被引量：1
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作者 李平 胡建兵 顾新一 《Journal of Southwest Jiaotong University(English Edition)》 2005年第2期161-166,共6页
In the traditional methods of program evaluation and review technique （PERT） network optimization and compression of time limit for project, the uncertainty of free time difference and total time difference were not... In the traditional methods of program evaluation and review technique （PERT） network optimization and compression of time limit for project, the uncertainty of free time difference and total time difference were not considered as well as its time risk. The authors of this paper use the theory of dependent-chance programming to establish a new model about compression of time for project and multi-objective network optimization, which can overcome the shortages of traditional methods and realize the optimization of PERT network directly. By calculating an example with genetic algorithms, the following conclusions are drawn： （ 1 ） compression of time is restricted by cost ratio and completion probability of project; （2） activities with maximal standard difference of duration and minimal cost will be compressed in order of precedence; （3） there is no optimal solutions but noninferior solutions between chance and cost, and the most optimal node time depends on decision-maker＇s preference. 展开更多
关键词 Time compression for project Network optimization Dependent-chance programming Genetic algorithms PERT network
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A Situational Awareness-Based Framework for Wireless Network Management:Innovations and Applications 认领 引用 被引量：1
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作者 Gao Peng Zhang Dongchen +3 位作者 Jiang Tao Li Xingzheng Tan Youheng Liu Guanghua 《China Communications》 SCIE EI CSCD 2025年第7期95-108,共14页
Wireless networks support numerous terminals,manage large data volumes,and provide diverse services,but the vulnerability to environmental changes leads to increased complexity and costs.Situational awareness has been... Wireless networks support numerous terminals,manage large data volumes,and provide diverse services,but the vulnerability to environmental changes leads to increased complexity and costs.Situational awareness has been widely applied in network management,but existing methods fail to find optimal solutions due to the high heterogeneity of base stations,numerous metrics,and complex intercell dependencies.To address this gap,this paper proposes a specialized framework for wireless networks,integrating an evaluation model and control approach.The framework expands the indicator set into four key areas,introduces an evaluation method,and proposes the indicator perturbation greedy(IPG)algorithm and the adjustment scheme selection method based on damping coefficient(DCSS)for effective network optimization.A case study in an urban area demonstrates the framework’s ability to balance and improve network performance,enhancing situational awareness and operational efficiency under dynamic conditions. 展开更多
关键词 communication system control system situation awareness wireless communication system wireless network optimization
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Integration strategies of hydrogen network in a refinery based on operational optimization of hydrotreating units 认领 引用 被引量：4
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作者 Le Wu Xiaoqiang Liang +1 位作者 Lixia Kang Yongzhong Liu 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2017年第8期1061-1068,共8页
Inferior crude oil and fuel oil upgrading lead to escalating increase of hydrogen consumption in refineries.It is imperative to reduce the hydrogen consumption for energy-saving operations of refineries.An integration... Inferior crude oil and fuel oil upgrading lead to escalating increase of hydrogen consumption in refineries.It is imperative to reduce the hydrogen consumption for energy-saving operations of refineries.An integration strategy of hydrogen network and an operational optimization model of hydrotreating(HDT)units are proposed based on the characteristics of reaction kinetics of HDT units.By solving the proposed model,the operating conditions of HDT units are optimized,and the parameters of hydrogen sinks are determined by coupling hydrodesulfurization(HDS),hydrodenitrification(HDN)and aromatic hydrogenation(HDA)kinetics.An example case of a refinery with annual processing capacity of eight million tons is adopted to demonstrate the feasibility of the proposed optimization strategies and the model.Results show that HDS,HDN and HDA reactions are the major source of hydrogen consumption in the refinery.The total hydrogen consumption can be reduced by 18.9%by applying conventional hydrogen network optimization model.When the hydrogen network is optimized after the operational optimization of HDT units is performed,the hydrogen consumption is reduced by28.2%.When the benefit of the fuel gas recovery is further considered,the total annual cost of hydrogen network can be reduced by 3.21×10~7CNY·a-1,decreased by 11.9%.Therefore,the operational optimization of the HDT units in refineries should be imposed to determine the parameters of hydrogen sinks base on the characteristics of reaction kinetics of the hydrogenation processes before the optimization of the hydrogen network is performed through the source-sink matching methods. 展开更多
关键词 Hydrogenation reaction kinetics Hydrogen network Integration strategies Optimization
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Ethanol mediated As(Ⅲ) adsorption onto Zn-loaded pinecone biochar:Experimental investigation,modeling,and optimization using hybrid artificial neural network-genetic algorithm approach 认领 引用 被引量：5
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作者 Mohd.Zafar N.Van Vinh +1 位作者 Shishir Kumar Behera Hung-Suck Park 《Journal of Environmental Sciences》 SCIE EI CAS CSCD 2017年第4期114-125,共12页
Organic matters（OMs） and their oxidization products often influence the fate and transport of heavy metals in the subsurface aqueous systems through interaction with the mineral surfaces. This study investigates the... Organic matters（OMs） and their oxidization products often influence the fate and transport of heavy metals in the subsurface aqueous systems through interaction with the mineral surfaces. This study investigates the ethanol（EtO H）-mediated As（Ⅲ） adsorption onto Zn-loaded pinecone（PC） biochar through batch experiments conducted under Box–Behnken design. The effect of EtO H on As（Ⅲ） adsorption mechanism was quantitatively elucidated by fitting the experimental data using artificial neural network and quadratic modeling approaches. The quadratic model could describe the limiting nature of EtO H and pH on As（Ⅲ） adsorption,whereas neural network revealed the stronger influence of Et OH（64.5%） followed by pH（20.75%）and As（Ⅲ） concentration（14.75%） on the adsorption phenomena. Besides, the interaction among process variables indicated that Et OH enhances As（Ⅲ） adsorption over a pH range of2 to 7, possibly due to facilitation of ligand–metal（Zn） binding complexation mechanism.Eventually, hybrid response surface model–genetic algorithm（RSM–GA） approach predicted a better optimal solution than RSM, i.e., the adsorptive removal of As（Ⅲ）（10.47 μg/g） is facilitated at 30.22 mg C/L of Et OH with initial As（Ⅲ） concentration of 196.77 μg/L at pH 5.8. The implication of this investigation might help in understanding the application of biochar for removal of various As（Ⅲ） species in the presence of OM. 展开更多
关键词 As（Ⅲ） removal Competitive adsorption Ethanol Box–Behnken design Artificial neural network Hybrid RSM–GA optimization
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Efficient Virtual Network Embedding Algorithm Based on Restrictive Selection and Optimization Theory Approach 认领 引用 被引量：2
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作者 Haotong Cao Zhicheng Qu +1 位作者 Yishi Xue Longxiang Yang 《China Communications》 SCIE CSCD 2017年第10期39-60,共22页
Network virtualization(NV) is widely considered as a key component of the future network and promises to allow multiple virtual networks(VNs) with different protocols to coexist on a shared substrate network(SN). One ... Network virtualization(NV) is widely considered as a key component of the future network and promises to allow multiple virtual networks(VNs) with different protocols to coexist on a shared substrate network(SN). One main challenge in NV is virtual network embedding(VNE). VNE is a NPhard problem. Previous VNE algorithms in the literature are mostly heuristic, while the remaining algorithms are exact. Heuristic algorithms aim to find a feasible embedding of each VN, not optimal or sub-optimal, in polynomial time. Though presenting the optimal or sub-optimal embedding per VN, exact algorithms are too time-consuming in smallscaled networks, not to mention moderately sized networks. To make a trade-off between the heuristic and the exact, this paper presents an effective algorithm, labeled as VNE-RSOT(Restrictive Selection and Optimization Theory), to solve the VNE problem. The VNERSOT can embed virtual nodes and links per VN simultaneously. The restrictive selection contributes to selecting candidate substrate nodes and paths and largely cuts down on the number of integer variables, used in the following optimization theory approach. The VNE-RSOT fights to minimize substrate resource consumption and accommodates more VNs. To highlight the efficiency of VNERSOT, a simulation against typical and stateof-art heuristic algorithms and a pure exact algorithm is made. Numerical results reveal that virtual network request(VNR) acceptance ratio of VNE-RSOT is, at least, 10% higher than the best-behaved heuristic. Other metrics, such as the execution time, are also plotted to emphasize and highlight the efficiency of VNE-RSOT. 展开更多
关键词 network virtualization virtual network embedding NP-hard heuristic exact restrictive selection optimization theory
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Behavior of Spikes in Spiking Neural Network (SNN)Model with Bernoulli for Plant Disease on Leaves 认领 引用
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作者 Urfa Gul M.Junaid Gul +1 位作者 Gyu Sang Choi Chang-Hyeon Park 《Computers, Materials & Continua》 SCIE EI 2025年第8期3811-3834,共24页
Spiking Neural Network(SNN)inspired by the biological triggering mechanism of neurons to provide a novel solution for plant disease detection,offering enhanced performance and efficiency in contrast to Artificial Neur... Spiking Neural Network(SNN)inspired by the biological triggering mechanism of neurons to provide a novel solution for plant disease detection,offering enhanced performance and efficiency in contrast to Artificial Neural Networks(ANN).Unlike conventional ANNs,which process static images without fully capturing the inherent temporal dynamics,our approach represents the first implementation of SNNs tailored explicitly for agricultural disease classification,integrating an encoding method to convert static RGB plant images into temporally encoded spike trains.Additionally,while Bernoulli trials and standard deep learning architectures likeConvolutionalNeuralNetworks(CNNs)and Fully Connected Neural Networks(FCNNs)have been used extensively,our work is the first to integrate these trials within an SNN framework specifically for agricultural applications.This integration not only refines spike regulation and reduces computational overhead by 30%but also delivers superior accuracy(93.4%)in plant disease classification,marking a significant advancement in precision agriculture that has not been previously explored.Our approach uniquely transforms static plant leaf images into time-dependent representations,leveraging SNNs’intrinsic temporal processing capabilities.This approach aligns with the inherent ability of SNNs to capture dynamic,timedependent patterns,making them more suitable for detecting disease activations in plants than conventional ANNs that treat inputs as static entities.Unlike prior works,our hybrid encoding scheme dynamically adapts to pixel intensity variations(via threshold),enabling robust feature extraction under diverse agricultural conditions.The dual-stage preprocessing customizes the SNN’s behavior in two ways:the encoding threshold is derived from pixel distributions in diseased regions,and Bernoulli trials selectively reduce redundant spikes to ensure energy efficiency on low-power devices.We used a comprehensive dataset of 87,000 RGB images of plant leaves,which included 38 distinct classes of healthy and unhealthy leaves.To train and evaluate three distinct neural network architectures,DeepSNN,SimpleCNN,and SimpleFCNN,the dataset was rigorously preprocessed,including stochastic rotation,horizontal flip,resizing,and normalization.Moreover,by integrating Bernoulli trials to regulate spike generation,ourmethod focuses on extracting themost relevant featureswhile reducingcomputational overhead.Using a comprehensivedatasetof87,000RGB images across 38 classes,we rigorously preprocessed the data and evaluated three architectures:DeepSNN,SimpleCNN,and SimpleFCNN.The results demonstrate that DeepSNN outperforms the other models,achieving superior accuracy,efficient feature extraction,and robust spike management,thereby establishing the potential of SNNs for real-time,energy-efficient agricultural applications. 展开更多
关键词 Agriculture image processing machine learning neural network optimization plant disease detection spiking neural networks(SNNs)
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Rail profile optimization through balancing of wear and fatigue 认领 引用 被引量：1
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作者 Binjie XU Zhiyong SHI +2 位作者 Yun YANG Jianxi WANG Kaiyun WANG 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS CSCD 2025年第10期967-982,共16页
Rail profile optimization is a critical strategy for mitigating wear and extending service life.However,damage at the wheel-rail contact surface goes beyond simple rail wear,as it also involves fatigue phenomena.Focus... Rail profile optimization is a critical strategy for mitigating wear and extending service life.However,damage at the wheel-rail contact surface goes beyond simple rail wear,as it also involves fatigue phenomena.Focusing solely on wear and not addressing fatigue in profile optimization can lead to the propagation of rail cracks,the peeling of material off the rail,and even rail fractures.Therefore,we propose an optimization approach that balances rail wear and fatigue for heavy-haul railway rails to mitigate rail fatigue damage.Initially,we performed a field investigation to acquire essential data and understand the characteristics of track damage.Based on theory and measured data,a simulation model for wear and fatigue was then established.Subsequently,the control points of the rail profile according to cubic non-uniform rational B-spline(NURBS)theory were set as the research variables.The rail’s wear rate and fatigue crack propagation rate were adopted as the objective functions.A multi-objective,multi-variable,and multi-constraint nonlinear optimization model was then constructed,specifically using a Levenberg Marquardt-back propagation neural network as optimized by the particle swarm optimization algorithm(PSO-LM-BP neural network).Ultimately,optimal solutions from the model were identified using a chaos microvariation adaptive genetic algorithm,and the effectiveness of the optimization was validated using a dynamics model and a rail damage model. 展开更多
关键词 Heavy-haul railway Rail wear Rail fatigue Levenberg Marquardt-back propagation neural network as optimized by the particle swarm optimization algorithm(PSO-LM-BP neural network) Rail profile optimization Multi-objective optimization
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Distributed Chunk-Based Optimization for MultiCarrier Ultra-Dense Networks 认领 引用 被引量：2
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作者 GUO Shaozhen XING Chengwen +2 位作者 FEI Zesong ZHOU Gui YAN Xinge 《China Communications》 SCIE CSCD 2016年第1期80-90,共11页
In this paper,a distributed chunkbased optimization algorithm is proposed for the resource allocation in broadband ultra-dense small cell networks.Based on the proposed algorithm,the power and subcarrier allocation pr... In this paper,a distributed chunkbased optimization algorithm is proposed for the resource allocation in broadband ultra-dense small cell networks.Based on the proposed algorithm,the power and subcarrier allocation problems are jointly optimized.In order to make the resource allocation suitable for large scale networks,the optimization problem is decomposed first based on an effective decomposition algorithm named optimal condition decomposition(OCD) algorithm.Furthermore,aiming at reducing implementation complexity,the subcarriers are divided into chunks and are allocated chunk by chunk.The simulation results show that the proposed algorithm achieves more superior performance than uniform power allocation scheme and Lagrange relaxation method,and then the proposed algorithm can strike a balance between the complexity and performance of the multi-carrier Ultra-Dense Networks. 展开更多
关键词 ultra-dense small cell networks optimization chunk power allocation subcarrier allocation distributed resource allocation
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Self-Organized Optimization of Transport on Complex Networks 认领 引用 被引量：5
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作者 牛瑞吾 潘贵军 《Chinese Physics Letters》 SCIE EI CAS CSCD 2016年第6期153-156,共4页
We propose a self-organized optimization mechanism to improve the transport capacity of complex gradient networks. We find that, regardless of network topology, the congestion pressure can be strongly reduced by the s... We propose a self-organized optimization mechanism to improve the transport capacity of complex gradient networks. We find that, regardless of network topology, the congestion pressure can be strongly reduced by the self-organized optimization mechanism. Furthermore, the random scale-free topology is more efficient to reduce congestion compared with the random Poisson topology under the optimization mechanism. The reason is that the optimization mechanism introduces the correlations between the gradient field and the local topology of the substrate network. Due to the correlations, the cutoff degree of the gradient network is strongly reduced and the number of the nodes exerting their maximal transport capacity consumedly increases. Our work presents evidence supporting the idea that scale-free networks can efficiently improve their transport capacity by self- organized mechanism under gradient-driven transport mode. 展开更多
关键词 of work in that Self-Organized Optimization of Transport on Complex Networks is node on link
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