Considering the uncertainty of grid connection of electric vehicle charging stations and the uncertainty of new energy and residential electricity load,a spatio-temporal decoupling strategy of dynamic reactive power o...Considering the uncertainty of grid connection of electric vehicle charging stations and the uncertainty of new energy and residential electricity load,a spatio-temporal decoupling strategy of dynamic reactive power optimization based on clustering-local relaxation-correction is proposed.Firstly,the k-medoids clustering algorithm is used to divide the reduced power scene into periods.Then,the discrete variables and continuous variables are optimized in the same period of time.Finally,the number of input groups of parallel capacitor banks(CB)in multiple periods is fixed,and then the secondary static reactive power optimization correction is carried out by using the continuous reactive power output device based on the static reactive power compensation device(SVC),the new energy grid-connected inverter,and the electric vehicle charging station.According to the characteristics of the model,a hybrid optimization algorithm with a cross-feedback mechanism is used to solve different types of variables,and an improved artificial hummingbird algorithm based on tent chaotic mapping and adaptive mutation is proposed to improve the solution efficiency.The simulation results show that the proposed decoupling strategy can obtain satisfactory optimization resultswhile strictly guaranteeing the dynamic constraints of discrete variables,and the hybrid algorithm can effectively solve the mixed integer nonlinear optimization problem.展开更多
The high proportion of uncertain distributed power sources and the access to large-scale random electric vehicle(EV)charging resources further aggravate the voltage fluctuation of the distribution network,and the exis...The high proportion of uncertain distributed power sources and the access to large-scale random electric vehicle(EV)charging resources further aggravate the voltage fluctuation of the distribution network,and the existing research has not deeply explored the EV active-reactive synergistic regulating characteristics,and failed to realize themulti-timescale synergistic control with other regulatingmeans,For this reason,this paper proposes amultilevel linkage coordinated optimization strategy to reduce the voltage deviation of the distribution network.Firstly,a capacitor bank reactive power compensation voltage control model and a distributed photovoltaic(PV)activereactive power regulationmodel are established.Additionally,an external characteristicmodel of EVactive-reactive power regulation is developed considering the four-quadrant operational characteristics of the EVcharger.Amultiobjective optimization model of the distribution network is then constructed considering the time-series coupling constraints of multiple types of voltage regulators.A multi-timescale control strategy is proposed by considering the impact of voltage regulators on active-reactive EV energy consumption and PV energy consumption.Then,a four-stage voltage control optimization strategy is proposed for various types of voltage regulators with multiple time scales.Themulti-objective optimization is solved with the improvedDrosophila algorithmto realize the power fluctuation control of the distribution network and themulti-stage voltage control optimization.Simulation results validate that the proposed voltage control optimization strategy achieves the coordinated control of decentralized voltage control resources in the distribution network.It effectively reduces the voltage deviation of the distribution network while ensuring the energy demand of EV users and enhancing the stability and economic efficiency of the distribution network.展开更多
Wireless sensor network deployment optimization is a classic NP-hard problem and a popular topic in academic research.However,the current research on wireless sensor network deployment problems uses overly simplistic ...Wireless sensor network deployment optimization is a classic NP-hard problem and a popular topic in academic research.However,the current research on wireless sensor network deployment problems uses overly simplistic models,and there is a significant gap between the research results and actual wireless sensor networks.Some scholars have now modeled data fusion networks to make them more suitable for practical applications.This paper will explore the deployment problem of a stochastic data fusion wireless sensor network(SDFWSN),a model that reflects the randomness of environmental monitoring and uses data fusion techniques widely used in actual sensor networks for information collection.The deployment problem of SDFWSN is modeled as a multi-objective optimization problem.The network life cycle,spatiotemporal coverage,detection rate,and false alarm rate of SDFWSN are used as optimization objectives to optimize the deployment of network nodes.This paper proposes an enhanced multi-objective mongoose optimization algorithm(EMODMOA)to solve the deployment problem of SDFWSN.First,to overcome the shortcomings of the DMOA algorithm,such as its low convergence and tendency to get stuck in a local optimum,an encircling and hunting strategy is introduced into the original algorithm to propose the EDMOA algorithm.The EDMOA algorithm is designed as the EMODMOA algorithm by selecting reference points using the K-Nearest Neighbor(KNN)algorithm.To verify the effectiveness of the proposed algorithm,the EMODMOA algorithm was tested at CEC 2020 and achieved good results.In the SDFWSN deployment problem,the algorithm was compared with the Non-dominated Sorting Genetic Algorithm II(NSGAII),Multiple Objective Particle Swarm Optimization(MOPSO),Multi-Objective Evolutionary Algorithm based on Decomposition(MOEA/D),and Multi-Objective Grey Wolf Optimizer(MOGWO).By comparing and analyzing the performance evaluation metrics and optimization results of the objective functions of the multi-objective algorithms,the algorithm outperforms the other algorithms in the SDFWSN deployment results.To better demonstrate the superiority of the algorithm,simulations of diverse test cases were also performed,and good results were obtained.展开更多
Project construction and development are an impor-tant part of future army designs.In today’s world,intelligent war-fare and joint operations have become the dominant develop-ments in warfare,so the construction and ...Project construction and development are an impor-tant part of future army designs.In today’s world,intelligent war-fare and joint operations have become the dominant develop-ments in warfare,so the construction and development of the army need top-down,top-level design,and comprehensive plan-ning.The traditional project development model is no longer suf-ficient to meet the army’s complex capability requirements.Projects in various fields need to be developed and coordinated to form a joint force and improve the army’s combat effective-ness.At the same time,when a program consists of large-scale project data,the effectiveness of the traditional,precise mathe-matical planning method is greatly reduced because it is time-consuming,costly,and impractical.To solve above problems,this paper proposes a multi-stage program optimization model based on a heterogeneous network and hybrid genetic algo-rithm and verifies the effectiveness and feasibility of the model and algorithm through an example.The results show that the hybrid algorithm proposed in this paper is better than the exist-ing meta-heuristic algorithm.展开更多
With the continuous growth of power demand and the diversification of power consumption structure,the loss of distribution network has gradually become the focus of attention.Given the problems of single loss reductio...With the continuous growth of power demand and the diversification of power consumption structure,the loss of distribution network has gradually become the focus of attention.Given the problems of single loss reduction measure,lack of economy,and practicality in existing research,this paper proposes an optimization method of distribution network loss reduction based on tabu search algorithm and optimizes the combination and parameter configuration of loss reduction measure.The optimization model is developed with the goal of maximizing comprehensive benefits,incorporating both economic and environmental factors,and accounting for investment costs,including the loss of power reduction.Additionally,the model ensures that constraint conditions such as power flow equations,voltage deviations,and line transmission capacities are satisfied.The solution is obtained through a tabu search algorithm,which is well-suited for solving nonlinear problems with multiple constraints.Combined with the example of 10kV25 node construction,the simulation results show that the method can significantly reduce the network loss on the basis of ensuring the economy and environmental protection of the system,which provides a theoretical basis for distribution network planning.展开更多
Accurate identification of unknown internal parameters in photovoltaic(PV)cells is crucial and significantly affects the subsequent system-performance analysis and control.However,noise,insufficient data acquisition,a...Accurate identification of unknown internal parameters in photovoltaic(PV)cells is crucial and significantly affects the subsequent system-performance analysis and control.However,noise,insufficient data acquisition,and loss of recorded data can deteriorate the extraction accuracy of unknown parameters.Hence,this study proposes an intelligent parameter-identification strategy that integrates artificial ecosystem optimization(AEO)and a Bayesian neural network(BNN)for PV cell parameter extraction.A BNN is used for data preprocessing,including data denoising and prediction.Furthermore,the AEO algorithm is utilized to identify unknown parameters in the single-diode model(SDM),double-diode model(DDM),and three-diode model(TDM).Nine other metaheuristic algorithms(MhAs)are adopted for an unbiased and comprehensive validation.Simulation results show that BNN-based data preprocessing com-bined with effective MhAs significantly improve the parameter-extraction accuracy and stability compared with methods without data preprocessing.For instance,under denoised data,the accuracies of the SDM,DDM,and TDM increase by 99.69%,99.70%,and 99.69%,respectively,whereas their accuracy improvements increase by 66.71%,59.65%,and 70.36%,respectively.展开更多
Rapid and reliable onboard optimization of bank angle profiles is crucial for mitigating uncertainties during Mars atmospheric entry.This paper presents a neural-network-accelerated methodology for optimizing parametr...Rapid and reliable onboard optimization of bank angle profiles is crucial for mitigating uncertainties during Mars atmospheric entry.This paper presents a neural-network-accelerated methodology for optimizing parametric bank angle profiles in Mars atmospheric entry missions.The methodology includes a universal approach to handling path constraints and a reliable solution method based on the Particle Swarm Optimization(PSO)algorithm.For illustrative purposes,a mission with the objective of maximizing terminal altitude is considered.The original entry optimization problem is converted into optimizing three coefficients for the bank angle profiles with terminal constraints by formulating a parametric Mars entry bank angle profile and constraint handling methods.The parameter optimization problem is addressed using the PSO algorithm,with reliability enhanced by increasing the PSO swarm size.To improve computational efficiency,an enhanced Deep Operator Network(Deep ONet)is used as a dynamics solver to predict terminal states under various bank angle profiles rapidly.Numerical simulations demonstrate that the proposed methodology ensures reliable convergence with a sufficiently large PSO swarm while maintaining high computational efficiency facilitated by the neural-network-based dynamics solver.Compared to the existing methodologies,this methodology offers a streamlined process,the reduced sensitivity to initial guesses,and the improved computational efficiency.展开更多
With the widespread application of com-munication technology in the non-terrestrial network(NTN),the issue of the insecure communication due to the inherent openness of the NTN is increasingly being recognized.Consequ...With the widespread application of com-munication technology in the non-terrestrial network(NTN),the issue of the insecure communication due to the inherent openness of the NTN is increasingly being recognized.Consequently,safeguarding com-munication information in the NTN has emerged as a critical challenge.To address this issue,we pro-pose a beamforming and horizontal trajectory joint op-timization method for unmanned aerial vehicle(UAV)covert communications in the NTN.First,we formu-late an optimization problem that considers constraints such as the transmitting power and the distance.More-over,we employ the integrated communication and jamming(ICAJ)signal as Alice’s transmitting signal,further protecting the content of communication in-formation.Next,we construct two subproblems,and we propose an alternate optimization(AO)algorithm based on quadratic transform and penalty term method to solve the proposed two subproblems.Simulation re-sults demonstrate that the proposed method is effective and has better performance than benchmarks.展开更多
Wireless Sensor Networks(WSNs)are one of the best technologies of the 21st century and have seen tremendous growth over the past decade.Much work has been put into its development in various aspects such as architectu...Wireless Sensor Networks(WSNs)are one of the best technologies of the 21st century and have seen tremendous growth over the past decade.Much work has been put into its development in various aspects such as architectural attention,routing protocols,location exploration,time exploration,etc.This research aims to optimize routing protocols and address the challenges arising from conflicting objectives in WSN environments,such as balancing energy consumption,ensuring routing reliability,distributing network load,and selecting the shortest path.Many optimization techniques have shown success in achieving one or two objectives but struggle to achieve the right balance between multiple conflicting objectives.To address this gap,this paper proposes an innovative approach that integrates Particle Swarm Optimization(PSO)with a fuzzy multi-objective framework.The proposed method uses fuzzy logic to effectively control multiple competing objectives to represent its major development beyond existing methods that only deal with one or two objectives.The search efficiency is improved by particle swarm optimization(PSO)which overcomes the large computational requirements that serve as a major drawback of existing methods.The PSO algorithm is adapted for WSNs to optimize routing paths based on fuzzy multi-objective fitness.The fuzzy logic framework uses predefined membership functions and rule-based reasoning to adjust routing decisions.These adjustments influence PSO’s velocity updates,ensuring continuous adaptation under varying network conditions.The proposed multi-objective PSO-fuzzy model is evaluated using NS-3 simulation.The results show that the proposed model is capable of improving the network lifetime by 15.2%–22.4%,increasing the stabilization time by 18.7%–25.5%,and increasing the residual energy by 8.9%–16.2% compared to the state-of-the-art techniques.The proposed model also achieves a 15%–24% reduction in load variance,demonstrating balanced routing and extended network lifetime.Furthermore,analysis using p-values obtained from multiple performance measures(p-values<0.05)showed that the proposed approach outperforms with a high level of confidence.The proposed multi-objective PSO-fuzzy model provides a robust and scalable solution to improve the performance of WSNs.It allows stable performance in networks with 100 to 300 nodes,under varying node densities,and across different base station placements.Computational complexity analysis has shown that the method fits well into large-scale WSNs and that the addition of fuzzy logic controls the power usage to make the system practical for real-world use.展开更多
The categorization of brain tumors is a significant issue for healthcare applications.Perfect and timely identification of brain tumors is important for employing an effective treatment of this disease.Brain tumors po...The categorization of brain tumors is a significant issue for healthcare applications.Perfect and timely identification of brain tumors is important for employing an effective treatment of this disease.Brain tumors possess high changes in terms of size,shape,and amount,and hence the classification process acts as a more difficult research problem.This paper suggests a deep learning model using the magnetic resonance imaging technique that overcomes the limitations associated with the existing classification methods.The effectiveness of the suggested method depends on the coyote optimization algorithm,also known as the LOBO algorithm,which optimizes the weights of the deep-convolutional neural network classifier.The accuracy,sensitivity,and specificity indices,which are obtained to be 92.40%,94.15%,and 91.92%,respectively,are used to validate the effectiveness of the suggested method.The result suggests that the suggested strategy is superior for effectively classifying brain tumors.展开更多
With the increasing integration of large-scale distributed energy resources into the grid,traditional distribution network optimization and dispatch methods struggle to address the challenges posed by both generation ...With the increasing integration of large-scale distributed energy resources into the grid,traditional distribution network optimization and dispatch methods struggle to address the challenges posed by both generation and load.Accounting for these issues,this paper proposes a multi-timescale coordinated optimization dispatch method for distribution networks.First,the probability box theory was employed to determine the uncertainty intervals of generation and load forecasts,based on which,the requirements for flexibility dispatch and capacity constraints of the grid were calculated and analyzed.Subsequently,a multi-timescale optimization framework was constructed,incorporating the generation and load forecast uncertainties.This framework included optimization models for dayahead scheduling,intra-day optimization,and real-time adjustments,aiming to meet flexibility needs across different timescales and improve the economic efficiency of the grid.Furthermore,an improved soft actor-critic algorithm was introduced to enhance the uncertainty exploration capability.Utilizing a centralized training and decentralized execution framework,a multi-agent SAC network model was developed to improve the decision-making efficiency of the agents.Finally,the effectiveness and superiority of the proposed method were validated using a modified IEEE-33 bus test system.展开更多
Refill friction stir spot welding process is difficultly optimized by accurate modeling because of the high-order functional relationship between welding parameters and joint strength.A database of the welding process...Refill friction stir spot welding process is difficultly optimized by accurate modeling because of the high-order functional relationship between welding parameters and joint strength.A database of the welding process was first established with 6061-T6 aluminum alloy and DP780 galvanized steel as base materials.This dataset was then optimized using a backpropagation neural network.Analyses and mining of the experimental data confirmed the multidimensional mapping relationship between welding parameters and joint strength.Subsequently,intelligent optimization of the welding process and prediction of joint strength were achieved.At the predicted welding parameter(plunging rotation speedω1=1733 r/min,refilling rotation speedω_(2)=1266 r/min,plunging depth p=1.9 mm,and welding speed v=0.5 mm/s),the tensile shear fracture load of the joint reached a maximum value of 10,172 N,while the experimental result was 9980 N,with an error of 1.92%.Furthermore,the correlation of welding parameters-microstructure-joint strength was established.展开更多
Developing an accurate and efficient comprehensive water quality prediction model and its assessment method is crucial for the prevention and control of water pollution.Deep learning(DL),as one of the most promising t...Developing an accurate and efficient comprehensive water quality prediction model and its assessment method is crucial for the prevention and control of water pollution.Deep learning(DL),as one of the most promising technologies today,plays a crucial role in the effective assessment of water body health,which is essential for water resource management.This study models using both the original dataset and a dataset augmented with Generative Adversarial Networks(GAN).It integrates optimization algorithms(OA)with Convolutional Neural Networks(CNN)to propose a comprehensive water quality model evaluation method aiming at identifying the optimal models for different pollutants.Specifically,after preprocessing the spectral dataset,data augmentation was conducted to obtain two datasets.Then,six new models were developed on these datasets using particle swarm optimization(PSO),genetic algorithm(GA),and simulated annealing(SA)combined with CNN to simulate and forecast the concentrations of three water pollutants:Chemical Oxygen Demand(COD),Total Nitrogen(TN),and Total Phosphorus(TP).Finally,seven model evaluation methods,including uncertainty analysis,were used to evaluate the constructed models and select the optimal models for the three pollutants.The evaluation results indicate that the GPSCNN model performed best in predicting COD and TP concentrations,while the GGACNN model excelled in TN concentration prediction.Compared to existing technologies,the proposed models and evaluation methods provide a more comprehensive and rapid approach to water body prediction and assessment,offering new insights and methods for water pollution prevention and control.展开更多
This paper proposes a cost-optimal energy management strategy for reconfigurable distribution networks with high penetration of renewable generation.The proposed strategy accounts for renewable generation costs,mainte...This paper proposes a cost-optimal energy management strategy for reconfigurable distribution networks with high penetration of renewable generation.The proposed strategy accounts for renewable generation costs,maintenance and operating expenses of energy storage systems,diesel generator operational costs,typical daily load profiles,and power balance constraints.A penalty term for power backflow is incorporated into the objective function to discourage undesirable reverse flows.The Bald Eagle Search(BES)meta-heuristic is adopted to solve the resulting constrained optimization problem.Numerical simulations under multiple load scenarios demonstrate that the proposed method effectively reduces operating cost while preventing power backflow and maintaining secure operation of the distribution network.展开更多
Traffic congestion plays a significant role in intelligent transportation systems(ITS)due to rapid urbanization and increased vehicle concentration.The congestion is dependent on multiple factors,such as limited road ...Traffic congestion plays a significant role in intelligent transportation systems(ITS)due to rapid urbanization and increased vehicle concentration.The congestion is dependent on multiple factors,such as limited road occupancy and vehicle density.Therefore,the transportation system requires an effective prediction model to reduce congestion issues in a dynamic environment.Conventional prediction systems face difficulties in identifying highly congested areas,which leads to reduced prediction accuracy.The problem is addressed by integrating Graph Neural Networks(GNN)with the Lion Swarm Optimization(LSO)framework to tackle the congestion prediction problem.Initially,the traffic information is collected and processed through a normalization process to scale the data and mitigate issues of overfitting and high dimensionality.Then,the traffic flow and temporal characteristic features are extracted to identify the connectivity of the road segment.From the connectivity and node relationship graph,modeling improves the overall prediction accuracy.During the analysis,the lion swarm optimization process utilizes the concepts of exploration and exploitation to understand the complex traffic dependencies,which helps predict high congestion on roads with minimal deviation errors.There are three core optimization phases:roaming,hunting,and migration,which enable the framework to make dynamic adjustments to enhance the predictions.The framework’s efficacy is evaluated using benchmark datasets,where the proposed work achieves 99.2%accuracy and minimizes the prediction deviation value by up to 2.5%compared to other methods.With the new framework,there was a more accurate prediction of realtime congestion,lower computational cost,and improved regulation of traffic flow.This system is easily implemented in intelligent transportation systems,smart cities,and self-driving cars,providing a robust and scalable solution for future traffic management.展开更多
In the RSSI-based positioning algorithm,regarding the problem of a great conflict between precision and cost,a low-power and low-cost synergic localization algorithm is proposed,where effective methods are adopted in ...In the RSSI-based positioning algorithm,regarding the problem of a great conflict between precision and cost,a low-power and low-cost synergic localization algorithm is proposed,where effective methods are adopted in each phase of the localization process and fully use the detective information in the network to improve the positioning precision and robustness.In the ranging period,the power attenuation factor is obtained through the wireless channel modeling,and the RSSI value is transformed into distance.In the positioning period,the preferred reference nodes are used to calculate coordinates.In the position optimization period,Taylor expansion and least-squared iterative update algorithms are used to further improve the location precision.In the positioning,the notion of cooperative localization is introduced,in which the located node satisfying certain demands will be upgraded to a reference node so that it can participate in the positioning of other nodes,and improve the coverage and positioning precision.The results show that on the same network conditions,the proposed algorithm in this paper is similar to the Taylor series expansion algorithm based on the actual coordinates,but much higher than the basic least square algorithm,and the positioning precision is improved rapidly with the reduce of the range error.展开更多
The Wuding River Basin,situated in the Loess Plateau of northern China,is an ecologically fragile region facing severe soil erosion and imbalanced ecosystem service(ES)functions.However,the mechanisms driving the spat...The Wuding River Basin,situated in the Loess Plateau of northern China,is an ecologically fragile region facing severe soil erosion and imbalanced ecosystem service(ES)functions.However,the mechanisms driving the spatiotemporal evolution of ES functions,as well as the trade-offs and synergies among these functions,remain poorly understood,constraining effective watershed-scale management.To address this challenge,this study quantified four ES functions,i.e.,water yield(WY),carbon storage(CS),habitat quality(HQ),and soil conservation(SC)in the Wuding River Basin from 1990 to 2020 using the Integrated Valuation of Ecosystem Services and Tradeoff(InVEST)model,and proposed an innovative integration of InVEST with a Bayesian Belief Network(BBN)to nonlinearly identify trade-off and synergy relationships among ES functions through probabilistic inference.A trade-off and synergy index(TSI)was developed to assess the spatial interaction intensity among ES functions,while sensitivity and scenario analyses were employed to determine key driving factors,followed by spatial optimization to delineate functional zones.Results revealed distinct spatiotemporal variations:WY increased from 98.69 to 120.52 mm;SC rose to an average of 3.05×104 t/hm2;CS remained relatively stable(about 15.50 t/km2);and HQ averaged 0.51 with localized declines.The BBN achieved a high accuracy of 81.9%and effectively identified strong synergies between WY and SC,as well as between CS and HQ,while clear trade-offs were observed between WY and SC versus CS and HQ.Sensitivity analysis indicated precipitation(variance reduction of 9.4%),land use(9.8%),and vegetation cover(9.1%)as key driving factors.Spatial optimization further showed that core supply and ecological regulation zones are concentrated in the central-southern and southeastern basin,while ecological strengthening and optimization core zones dominate the central-northern and southeastern margins,highlighting strong spatial heterogeneity.Overall,this study advances ES research by combining process-based quantification with probabilistic modeling,offering a robust framework for studying nonlinear interactions,driving mechanisms,and optimization strategies,and providing a transferable paradigm for watershed-scale ES management and ecological planning in arid and semi-arid areas.展开更多
The Report of the 20th National Congress of the Communist Party of China explicitly emphasized the promotion of educational digitalization.The rapid development of new media in the era of network information has not o...The Report of the 20th National Congress of the Communist Party of China explicitly emphasized the promotion of educational digitalization.The rapid development of new media in the era of network information has not only broadened the horizons of college students but also profoundly transformed the content and methods of ideological and political education.As the frontline of ideological work,colleges and universities in Xinjiang are guided by the Party’s strategy for governing Xinjiang in the new era to advance network ideological and political education.This is of great significance in guiding students to develop correct network literacy and promoting ideological and political education to keep pace with the times.Through methods such as text analysis,questionnaire surveys,and interviews,this paper outlines the concept,characteristics,and value of network ideological and political education in colleges and universities in Xinjiang,analyzes its current development status and existing issues,and proposes optimization paths such as adhering to correct political guidance,highlighting regional characteristics,innovating educational methods,and strengthening subject construction.These efforts aim to fulfill the fundamental task of“cultivating talents with moral integrity”and serve the overall goal of social stability and long-term peace in Xinjiang.展开更多
With the birth of Software-Defined Networking(SDN),integration of both SDN and traditional architectures becomes the development trend of computer networks.Network intrusion detection faces challenges in dealing with ...With the birth of Software-Defined Networking(SDN),integration of both SDN and traditional architectures becomes the development trend of computer networks.Network intrusion detection faces challenges in dealing with complex attacks in SDN environments,thus to address the network security issues from the viewpoint of Artificial Intelligence(AI),this paper introduces the Crayfish Optimization Algorithm(COA)to the field of intrusion detection for both SDN and traditional network architectures,and based on the characteristics of the original COA,an Improved Crayfish Optimization Algorithm(ICOA)is proposed by integrating strategies of elite reverse learning,Levy flight,crowding factor and parameter modification.The ICOA is then utilized for AI-integrated feature selection of intrusion detection for both SDN and traditional network architectures,to reduce the dimensionality of the data and improve the performance of network intrusion detection.Finally,the performance evaluation is performed by testing not only the NSL-KDD dataset and the UNSW-NB 15 dataset for traditional networks but also the InSDN dataset for SDN-based networks.Experimental results show that ICOA improves the accuracy by 0.532%and 2.928%respectively compared with GWO and COA in traditional networks.In SDN networks,the accuracy of ICOA is 0.25%and 0.3%higher than COA and PSO.These findings collectively indicate that AI-integrated feature selection based on the proposed ICOA can promote network intrusion detection for both SDN and traditional architectures.展开更多
This study introduces the Smart Exponential-Threshold-Linear with Double Deep Q-learning Network(SETL-DDQN)and an extended Gumbel distribution method,designed to optimize the Contention Window(CW)in IEEE 802.11 networ...This study introduces the Smart Exponential-Threshold-Linear with Double Deep Q-learning Network(SETL-DDQN)and an extended Gumbel distribution method,designed to optimize the Contention Window(CW)in IEEE 802.11 networks.Unlike conventional Deep Reinforcement Learning(DRL)-based approaches for CW size adjustment,which often suffer from overestimation bias and limited exploration diversity,leading to suboptimal throughput and collision performance.Our framework integrates the Gumbel distribution and extreme value theory to systematically enhance action selection under varying network conditions.First,SETL adopts a DDQN architecture(SETL-DDQN)to improve Q-value estimation accuracy and enhance training stability.Second,we incorporate a Gumbel distribution-driven exploration mechanism,forming SETL-DDQN(Gumbel),which employs the extreme value theory to promote diverse action selection,replacing the conventional-greedy exploration that undergoes early convergence to suboptimal solutions.Both models are evaluated through extensive simulations in static and time-varying IEEE 802.11 network scenarios.The results demonstrate that our approach consistently achieves higher throughput,lower collision rates,and improved adaptability,even under abrupt fluctuations in traffic load and network conditions.In particular,the Gumbel-based mechanism enhances the balance between exploration and exploitation,facilitating faster adaptation to varying congestion levels.These findings position Gumbel-enhanced DRL as an effective and robust solution for CW optimization in wireless networks,offering notable gains in efficiency and reliability over existing methods.展开更多
基金funded by the“Research and Application Project of Collaborative Optimization Control Technology for Distribution Station Area for High Proportion Distributed PV Consumption(4000-202318079A-1-1-ZN)”of the Headquarters of the State Grid Corporation.
文摘Considering the uncertainty of grid connection of electric vehicle charging stations and the uncertainty of new energy and residential electricity load,a spatio-temporal decoupling strategy of dynamic reactive power optimization based on clustering-local relaxation-correction is proposed.Firstly,the k-medoids clustering algorithm is used to divide the reduced power scene into periods.Then,the discrete variables and continuous variables are optimized in the same period of time.Finally,the number of input groups of parallel capacitor banks(CB)in multiple periods is fixed,and then the secondary static reactive power optimization correction is carried out by using the continuous reactive power output device based on the static reactive power compensation device(SVC),the new energy grid-connected inverter,and the electric vehicle charging station.According to the characteristics of the model,a hybrid optimization algorithm with a cross-feedback mechanism is used to solve different types of variables,and an improved artificial hummingbird algorithm based on tent chaotic mapping and adaptive mutation is proposed to improve the solution efficiency.The simulation results show that the proposed decoupling strategy can obtain satisfactory optimization resultswhile strictly guaranteeing the dynamic constraints of discrete variables,and the hybrid algorithm can effectively solve the mixed integer nonlinear optimization problem.
基金funded by the State Grid Corporation Science and Technology Project(5108-202218280A-2-391-XG).
文摘The high proportion of uncertain distributed power sources and the access to large-scale random electric vehicle(EV)charging resources further aggravate the voltage fluctuation of the distribution network,and the existing research has not deeply explored the EV active-reactive synergistic regulating characteristics,and failed to realize themulti-timescale synergistic control with other regulatingmeans,For this reason,this paper proposes amultilevel linkage coordinated optimization strategy to reduce the voltage deviation of the distribution network.Firstly,a capacitor bank reactive power compensation voltage control model and a distributed photovoltaic(PV)activereactive power regulationmodel are established.Additionally,an external characteristicmodel of EVactive-reactive power regulation is developed considering the four-quadrant operational characteristics of the EVcharger.Amultiobjective optimization model of the distribution network is then constructed considering the time-series coupling constraints of multiple types of voltage regulators.A multi-timescale control strategy is proposed by considering the impact of voltage regulators on active-reactive EV energy consumption and PV energy consumption.Then,a four-stage voltage control optimization strategy is proposed for various types of voltage regulators with multiple time scales.Themulti-objective optimization is solved with the improvedDrosophila algorithmto realize the power fluctuation control of the distribution network and themulti-stage voltage control optimization.Simulation results validate that the proposed voltage control optimization strategy achieves the coordinated control of decentralized voltage control resources in the distribution network.It effectively reduces the voltage deviation of the distribution network while ensuring the energy demand of EV users and enhancing the stability and economic efficiency of the distribution network.
基金supported by the National Natural Science Foundation of China under Grant Nos.U21A20464,62066005Innovation Project of Guangxi Graduate Education under Grant No.YCSW2024313.
文摘Wireless sensor network deployment optimization is a classic NP-hard problem and a popular topic in academic research.However,the current research on wireless sensor network deployment problems uses overly simplistic models,and there is a significant gap between the research results and actual wireless sensor networks.Some scholars have now modeled data fusion networks to make them more suitable for practical applications.This paper will explore the deployment problem of a stochastic data fusion wireless sensor network(SDFWSN),a model that reflects the randomness of environmental monitoring and uses data fusion techniques widely used in actual sensor networks for information collection.The deployment problem of SDFWSN is modeled as a multi-objective optimization problem.The network life cycle,spatiotemporal coverage,detection rate,and false alarm rate of SDFWSN are used as optimization objectives to optimize the deployment of network nodes.This paper proposes an enhanced multi-objective mongoose optimization algorithm(EMODMOA)to solve the deployment problem of SDFWSN.First,to overcome the shortcomings of the DMOA algorithm,such as its low convergence and tendency to get stuck in a local optimum,an encircling and hunting strategy is introduced into the original algorithm to propose the EDMOA algorithm.The EDMOA algorithm is designed as the EMODMOA algorithm by selecting reference points using the K-Nearest Neighbor(KNN)algorithm.To verify the effectiveness of the proposed algorithm,the EMODMOA algorithm was tested at CEC 2020 and achieved good results.In the SDFWSN deployment problem,the algorithm was compared with the Non-dominated Sorting Genetic Algorithm II(NSGAII),Multiple Objective Particle Swarm Optimization(MOPSO),Multi-Objective Evolutionary Algorithm based on Decomposition(MOEA/D),and Multi-Objective Grey Wolf Optimizer(MOGWO).By comparing and analyzing the performance evaluation metrics and optimization results of the objective functions of the multi-objective algorithms,the algorithm outperforms the other algorithms in the SDFWSN deployment results.To better demonstrate the superiority of the algorithm,simulations of diverse test cases were also performed,and good results were obtained.
基金supported by the National Natural Science Foundation of China(724701189072431011).
文摘Project construction and development are an impor-tant part of future army designs.In today’s world,intelligent war-fare and joint operations have become the dominant develop-ments in warfare,so the construction and development of the army need top-down,top-level design,and comprehensive plan-ning.The traditional project development model is no longer suf-ficient to meet the army’s complex capability requirements.Projects in various fields need to be developed and coordinated to form a joint force and improve the army’s combat effective-ness.At the same time,when a program consists of large-scale project data,the effectiveness of the traditional,precise mathe-matical planning method is greatly reduced because it is time-consuming,costly,and impractical.To solve above problems,this paper proposes a multi-stage program optimization model based on a heterogeneous network and hybrid genetic algo-rithm and verifies the effectiveness and feasibility of the model and algorithm through an example.The results show that the hybrid algorithm proposed in this paper is better than the exist-ing meta-heuristic algorithm.
文摘With the continuous growth of power demand and the diversification of power consumption structure,the loss of distribution network has gradually become the focus of attention.Given the problems of single loss reduction measure,lack of economy,and practicality in existing research,this paper proposes an optimization method of distribution network loss reduction based on tabu search algorithm and optimizes the combination and parameter configuration of loss reduction measure.The optimization model is developed with the goal of maximizing comprehensive benefits,incorporating both economic and environmental factors,and accounting for investment costs,including the loss of power reduction.Additionally,the model ensures that constraint conditions such as power flow equations,voltage deviations,and line transmission capacities are satisfied.The solution is obtained through a tabu search algorithm,which is well-suited for solving nonlinear problems with multiple constraints.Combined with the example of 10kV25 node construction,the simulation results show that the method can significantly reduce the network loss on the basis of ensuring the economy and environmental protection of the system,which provides a theoretical basis for distribution network planning.
基金supported by the National Natural Science Foundation of China(62263014)the Yunnan Provincial Basic Research Project(202301AT070443,202401AT070344).
文摘Accurate identification of unknown internal parameters in photovoltaic(PV)cells is crucial and significantly affects the subsequent system-performance analysis and control.However,noise,insufficient data acquisition,and loss of recorded data can deteriorate the extraction accuracy of unknown parameters.Hence,this study proposes an intelligent parameter-identification strategy that integrates artificial ecosystem optimization(AEO)and a Bayesian neural network(BNN)for PV cell parameter extraction.A BNN is used for data preprocessing,including data denoising and prediction.Furthermore,the AEO algorithm is utilized to identify unknown parameters in the single-diode model(SDM),double-diode model(DDM),and three-diode model(TDM).Nine other metaheuristic algorithms(MhAs)are adopted for an unbiased and comprehensive validation.Simulation results show that BNN-based data preprocessing com-bined with effective MhAs significantly improve the parameter-extraction accuracy and stability compared with methods without data preprocessing.For instance,under denoised data,the accuracies of the SDM,DDM,and TDM increase by 99.69%,99.70%,and 99.69%,respectively,whereas their accuracy improvements increase by 66.71%,59.65%,and 70.36%,respectively.
基金supported in part by the National Defense Basic Scientific Research Program of China(No.JCKY2021603B030)the Shenzhen Fundamental Research Program,China(No.JCYJ20220818102601004)the Science Center Program of National Natural Science Foundation of China(No.62188101)。
文摘Rapid and reliable onboard optimization of bank angle profiles is crucial for mitigating uncertainties during Mars atmospheric entry.This paper presents a neural-network-accelerated methodology for optimizing parametric bank angle profiles in Mars atmospheric entry missions.The methodology includes a universal approach to handling path constraints and a reliable solution method based on the Particle Swarm Optimization(PSO)algorithm.For illustrative purposes,a mission with the objective of maximizing terminal altitude is considered.The original entry optimization problem is converted into optimizing three coefficients for the bank angle profiles with terminal constraints by formulating a parametric Mars entry bank angle profile and constraint handling methods.The parameter optimization problem is addressed using the PSO algorithm,with reliability enhanced by increasing the PSO swarm size.To improve computational efficiency,an enhanced Deep Operator Network(Deep ONet)is used as a dynamics solver to predict terminal states under various bank angle profiles rapidly.Numerical simulations demonstrate that the proposed methodology ensures reliable convergence with a sufficiently large PSO swarm while maintaining high computational efficiency facilitated by the neural-network-based dynamics solver.Compared to the existing methodologies,this methodology offers a streamlined process,the reduced sensitivity to initial guesses,and the improved computational efficiency.
基金supported in part by the National Natural Science Foundation of China under Grant U2441250 and 62231027in part by Natural Science Basic Research Programof Shaanxi under Grant 2024JC-JCQN-63+2 种基金in part by InnovationCapability Support Program of Shaanxi under Grant2024RS-CXTD-01in part by New Technology Research University Cooperation Project under Grant SKX242010031in part by the FundamentalResearch Funds for the Central Universities and theInnovation Fund of Xidian University under GrantYJSJ25007.
文摘With the widespread application of com-munication technology in the non-terrestrial network(NTN),the issue of the insecure communication due to the inherent openness of the NTN is increasingly being recognized.Consequently,safeguarding com-munication information in the NTN has emerged as a critical challenge.To address this issue,we pro-pose a beamforming and horizontal trajectory joint op-timization method for unmanned aerial vehicle(UAV)covert communications in the NTN.First,we formu-late an optimization problem that considers constraints such as the transmitting power and the distance.More-over,we employ the integrated communication and jamming(ICAJ)signal as Alice’s transmitting signal,further protecting the content of communication in-formation.Next,we construct two subproblems,and we propose an alternate optimization(AO)algorithm based on quadratic transform and penalty term method to solve the proposed two subproblems.Simulation re-sults demonstrate that the proposed method is effective and has better performance than benchmarks.
基金funded by Deanship of Graduate studies and Scientific Research at Jouf University under grant No.(DGSSR-2023-2-02038).
文摘Wireless Sensor Networks(WSNs)are one of the best technologies of the 21st century and have seen tremendous growth over the past decade.Much work has been put into its development in various aspects such as architectural attention,routing protocols,location exploration,time exploration,etc.This research aims to optimize routing protocols and address the challenges arising from conflicting objectives in WSN environments,such as balancing energy consumption,ensuring routing reliability,distributing network load,and selecting the shortest path.Many optimization techniques have shown success in achieving one or two objectives but struggle to achieve the right balance between multiple conflicting objectives.To address this gap,this paper proposes an innovative approach that integrates Particle Swarm Optimization(PSO)with a fuzzy multi-objective framework.The proposed method uses fuzzy logic to effectively control multiple competing objectives to represent its major development beyond existing methods that only deal with one or two objectives.The search efficiency is improved by particle swarm optimization(PSO)which overcomes the large computational requirements that serve as a major drawback of existing methods.The PSO algorithm is adapted for WSNs to optimize routing paths based on fuzzy multi-objective fitness.The fuzzy logic framework uses predefined membership functions and rule-based reasoning to adjust routing decisions.These adjustments influence PSO’s velocity updates,ensuring continuous adaptation under varying network conditions.The proposed multi-objective PSO-fuzzy model is evaluated using NS-3 simulation.The results show that the proposed model is capable of improving the network lifetime by 15.2%–22.4%,increasing the stabilization time by 18.7%–25.5%,and increasing the residual energy by 8.9%–16.2% compared to the state-of-the-art techniques.The proposed model also achieves a 15%–24% reduction in load variance,demonstrating balanced routing and extended network lifetime.Furthermore,analysis using p-values obtained from multiple performance measures(p-values<0.05)showed that the proposed approach outperforms with a high level of confidence.The proposed multi-objective PSO-fuzzy model provides a robust and scalable solution to improve the performance of WSNs.It allows stable performance in networks with 100 to 300 nodes,under varying node densities,and across different base station placements.Computational complexity analysis has shown that the method fits well into large-scale WSNs and that the addition of fuzzy logic controls the power usage to make the system practical for real-world use.
文摘The categorization of brain tumors is a significant issue for healthcare applications.Perfect and timely identification of brain tumors is important for employing an effective treatment of this disease.Brain tumors possess high changes in terms of size,shape,and amount,and hence the classification process acts as a more difficult research problem.This paper suggests a deep learning model using the magnetic resonance imaging technique that overcomes the limitations associated with the existing classification methods.The effectiveness of the suggested method depends on the coyote optimization algorithm,also known as the LOBO algorithm,which optimizes the weights of the deep-convolutional neural network classifier.The accuracy,sensitivity,and specificity indices,which are obtained to be 92.40%,94.15%,and 91.92%,respectively,are used to validate the effectiveness of the suggested method.The result suggests that the suggested strategy is superior for effectively classifying brain tumors.
基金funded by Jilin Province Science and Technology Development Plan Project,grant number 20220203163SF.
文摘With the increasing integration of large-scale distributed energy resources into the grid,traditional distribution network optimization and dispatch methods struggle to address the challenges posed by both generation and load.Accounting for these issues,this paper proposes a multi-timescale coordinated optimization dispatch method for distribution networks.First,the probability box theory was employed to determine the uncertainty intervals of generation and load forecasts,based on which,the requirements for flexibility dispatch and capacity constraints of the grid were calculated and analyzed.Subsequently,a multi-timescale optimization framework was constructed,incorporating the generation and load forecast uncertainties.This framework included optimization models for dayahead scheduling,intra-day optimization,and real-time adjustments,aiming to meet flexibility needs across different timescales and improve the economic efficiency of the grid.Furthermore,an improved soft actor-critic algorithm was introduced to enhance the uncertainty exploration capability.Utilizing a centralized training and decentralized execution framework,a multi-agent SAC network model was developed to improve the decision-making efficiency of the agents.Finally,the effectiveness and superiority of the proposed method were validated using a modified IEEE-33 bus test system.
基金the financial supports provided by the National Key Research and Development Program of China(2023YFE0201500)the National Natural Science Foundation of China(52375315)+2 种基金the Key Talent Plan Project of Guangdong Province(2023TQ07C702)the Research and Development Program in Key Areas of Dongguan(20201200300122)the GDAS’Project of Science and Technology Development(2022GDASZH-2022010203).
文摘Refill friction stir spot welding process is difficultly optimized by accurate modeling because of the high-order functional relationship between welding parameters and joint strength.A database of the welding process was first established with 6061-T6 aluminum alloy and DP780 galvanized steel as base materials.This dataset was then optimized using a backpropagation neural network.Analyses and mining of the experimental data confirmed the multidimensional mapping relationship between welding parameters and joint strength.Subsequently,intelligent optimization of the welding process and prediction of joint strength were achieved.At the predicted welding parameter(plunging rotation speedω1=1733 r/min,refilling rotation speedω_(2)=1266 r/min,plunging depth p=1.9 mm,and welding speed v=0.5 mm/s),the tensile shear fracture load of the joint reached a maximum value of 10,172 N,while the experimental result was 9980 N,with an error of 1.92%.Furthermore,the correlation of welding parameters-microstructure-joint strength was established.
基金Supported by Natural Science Basic Research Plan in Shaanxi Province of China(Program No.2022JM-396)the Strategic Priority Research Program of the Chinese Academy of Sciences,Grant No.XDA23040101+4 种基金Shaanxi Province Key Research and Development Projects(Program No.2023-YBSF-437)Xi'an Shiyou University Graduate Student Innovation Fund Program(Program No.YCX2412041)State Key Laboratory of Air Traffic Management System and Technology(SKLATM202001)Tianjin Education Commission Research Program Project(2020KJ028)Fundamental Research Funds for the Central Universities(3122019132)。
文摘Developing an accurate and efficient comprehensive water quality prediction model and its assessment method is crucial for the prevention and control of water pollution.Deep learning(DL),as one of the most promising technologies today,plays a crucial role in the effective assessment of water body health,which is essential for water resource management.This study models using both the original dataset and a dataset augmented with Generative Adversarial Networks(GAN).It integrates optimization algorithms(OA)with Convolutional Neural Networks(CNN)to propose a comprehensive water quality model evaluation method aiming at identifying the optimal models for different pollutants.Specifically,after preprocessing the spectral dataset,data augmentation was conducted to obtain two datasets.Then,six new models were developed on these datasets using particle swarm optimization(PSO),genetic algorithm(GA),and simulated annealing(SA)combined with CNN to simulate and forecast the concentrations of three water pollutants:Chemical Oxygen Demand(COD),Total Nitrogen(TN),and Total Phosphorus(TP).Finally,seven model evaluation methods,including uncertainty analysis,were used to evaluate the constructed models and select the optimal models for the three pollutants.The evaluation results indicate that the GPSCNN model performed best in predicting COD and TP concentrations,while the GGACNN model excelled in TN concentration prediction.Compared to existing technologies,the proposed models and evaluation methods provide a more comprehensive and rapid approach to water body prediction and assessment,offering new insights and methods for water pollution prevention and control.
基金the Science and Technology Project of State Grid Jiangsu Electric Power Co.,Ltd.(Project No.J2024066).
文摘This paper proposes a cost-optimal energy management strategy for reconfigurable distribution networks with high penetration of renewable generation.The proposed strategy accounts for renewable generation costs,maintenance and operating expenses of energy storage systems,diesel generator operational costs,typical daily load profiles,and power balance constraints.A penalty term for power backflow is incorporated into the objective function to discourage undesirable reverse flows.The Bald Eagle Search(BES)meta-heuristic is adopted to solve the resulting constrained optimization problem.Numerical simulations under multiple load scenarios demonstrate that the proposed method effectively reduces operating cost while preventing power backflow and maintaining secure operation of the distribution network.
基金Deanship of Graduate Studies and Scientific Research at Jouf University under grant No.(DGSSR-2025-02-01641)。
文摘Traffic congestion plays a significant role in intelligent transportation systems(ITS)due to rapid urbanization and increased vehicle concentration.The congestion is dependent on multiple factors,such as limited road occupancy and vehicle density.Therefore,the transportation system requires an effective prediction model to reduce congestion issues in a dynamic environment.Conventional prediction systems face difficulties in identifying highly congested areas,which leads to reduced prediction accuracy.The problem is addressed by integrating Graph Neural Networks(GNN)with the Lion Swarm Optimization(LSO)framework to tackle the congestion prediction problem.Initially,the traffic information is collected and processed through a normalization process to scale the data and mitigate issues of overfitting and high dimensionality.Then,the traffic flow and temporal characteristic features are extracted to identify the connectivity of the road segment.From the connectivity and node relationship graph,modeling improves the overall prediction accuracy.During the analysis,the lion swarm optimization process utilizes the concepts of exploration and exploitation to understand the complex traffic dependencies,which helps predict high congestion on roads with minimal deviation errors.There are three core optimization phases:roaming,hunting,and migration,which enable the framework to make dynamic adjustments to enhance the predictions.The framework’s efficacy is evaluated using benchmark datasets,where the proposed work achieves 99.2%accuracy and minimizes the prediction deviation value by up to 2.5%compared to other methods.With the new framework,there was a more accurate prediction of realtime congestion,lower computational cost,and improved regulation of traffic flow.This system is easily implemented in intelligent transportation systems,smart cities,and self-driving cars,providing a robust and scalable solution for future traffic management.
基金National Natural Science Foundation of China,grant number 62205120,funded this research.
文摘In the RSSI-based positioning algorithm,regarding the problem of a great conflict between precision and cost,a low-power and low-cost synergic localization algorithm is proposed,where effective methods are adopted in each phase of the localization process and fully use the detective information in the network to improve the positioning precision and robustness.In the ranging period,the power attenuation factor is obtained through the wireless channel modeling,and the RSSI value is transformed into distance.In the positioning period,the preferred reference nodes are used to calculate coordinates.In the position optimization period,Taylor expansion and least-squared iterative update algorithms are used to further improve the location precision.In the positioning,the notion of cooperative localization is introduced,in which the located node satisfying certain demands will be upgraded to a reference node so that it can participate in the positioning of other nodes,and improve the coverage and positioning precision.The results show that on the same network conditions,the proposed algorithm in this paper is similar to the Taylor series expansion algorithm based on the actual coordinates,but much higher than the basic least square algorithm,and the positioning precision is improved rapidly with the reduce of the range error.
基金supported by the Science and Technology Project of Shaanxi Province Water Conservancy,China(2025slkj-10)the Natural Science Basic Research Program of Shaanxi Province,China(S2025-JC-QN-2416).
文摘The Wuding River Basin,situated in the Loess Plateau of northern China,is an ecologically fragile region facing severe soil erosion and imbalanced ecosystem service(ES)functions.However,the mechanisms driving the spatiotemporal evolution of ES functions,as well as the trade-offs and synergies among these functions,remain poorly understood,constraining effective watershed-scale management.To address this challenge,this study quantified four ES functions,i.e.,water yield(WY),carbon storage(CS),habitat quality(HQ),and soil conservation(SC)in the Wuding River Basin from 1990 to 2020 using the Integrated Valuation of Ecosystem Services and Tradeoff(InVEST)model,and proposed an innovative integration of InVEST with a Bayesian Belief Network(BBN)to nonlinearly identify trade-off and synergy relationships among ES functions through probabilistic inference.A trade-off and synergy index(TSI)was developed to assess the spatial interaction intensity among ES functions,while sensitivity and scenario analyses were employed to determine key driving factors,followed by spatial optimization to delineate functional zones.Results revealed distinct spatiotemporal variations:WY increased from 98.69 to 120.52 mm;SC rose to an average of 3.05×104 t/hm2;CS remained relatively stable(about 15.50 t/km2);and HQ averaged 0.51 with localized declines.The BBN achieved a high accuracy of 81.9%and effectively identified strong synergies between WY and SC,as well as between CS and HQ,while clear trade-offs were observed between WY and SC versus CS and HQ.Sensitivity analysis indicated precipitation(variance reduction of 9.4%),land use(9.8%),and vegetation cover(9.1%)as key driving factors.Spatial optimization further showed that core supply and ecological regulation zones are concentrated in the central-southern and southeastern basin,while ecological strengthening and optimization core zones dominate the central-northern and southeastern margins,highlighting strong spatial heterogeneity.Overall,this study advances ES research by combining process-based quantification with probabilistic modeling,offering a robust framework for studying nonlinear interactions,driving mechanisms,and optimization strategies,and providing a transferable paradigm for watershed-scale ES management and ecological planning in arid and semi-arid areas.
基金Social Science Fund Project of the Xinjiang Uygur Autonomous Region“Research on the Construction of Network Ideological Discourse Power in Colleges and Universities in Xinjiang”(2023BKS010)。
文摘The Report of the 20th National Congress of the Communist Party of China explicitly emphasized the promotion of educational digitalization.The rapid development of new media in the era of network information has not only broadened the horizons of college students but also profoundly transformed the content and methods of ideological and political education.As the frontline of ideological work,colleges and universities in Xinjiang are guided by the Party’s strategy for governing Xinjiang in the new era to advance network ideological and political education.This is of great significance in guiding students to develop correct network literacy and promoting ideological and political education to keep pace with the times.Through methods such as text analysis,questionnaire surveys,and interviews,this paper outlines the concept,characteristics,and value of network ideological and political education in colleges and universities in Xinjiang,analyzes its current development status and existing issues,and proposes optimization paths such as adhering to correct political guidance,highlighting regional characteristics,innovating educational methods,and strengthening subject construction.These efforts aim to fulfill the fundamental task of“cultivating talents with moral integrity”and serve the overall goal of social stability and long-term peace in Xinjiang.
基金supported by the National Natural Science Foundation of China under Grant 61602162the Hubei Provincial Science and Technology Plan Project under Grant 2023BCB041.
文摘With the birth of Software-Defined Networking(SDN),integration of both SDN and traditional architectures becomes the development trend of computer networks.Network intrusion detection faces challenges in dealing with complex attacks in SDN environments,thus to address the network security issues from the viewpoint of Artificial Intelligence(AI),this paper introduces the Crayfish Optimization Algorithm(COA)to the field of intrusion detection for both SDN and traditional network architectures,and based on the characteristics of the original COA,an Improved Crayfish Optimization Algorithm(ICOA)is proposed by integrating strategies of elite reverse learning,Levy flight,crowding factor and parameter modification.The ICOA is then utilized for AI-integrated feature selection of intrusion detection for both SDN and traditional network architectures,to reduce the dimensionality of the data and improve the performance of network intrusion detection.Finally,the performance evaluation is performed by testing not only the NSL-KDD dataset and the UNSW-NB 15 dataset for traditional networks but also the InSDN dataset for SDN-based networks.Experimental results show that ICOA improves the accuracy by 0.532%and 2.928%respectively compared with GWO and COA in traditional networks.In SDN networks,the accuracy of ICOA is 0.25%and 0.3%higher than COA and PSO.These findings collectively indicate that AI-integrated feature selection based on the proposed ICOA can promote network intrusion detection for both SDN and traditional architectures.
文摘This study introduces the Smart Exponential-Threshold-Linear with Double Deep Q-learning Network(SETL-DDQN)and an extended Gumbel distribution method,designed to optimize the Contention Window(CW)in IEEE 802.11 networks.Unlike conventional Deep Reinforcement Learning(DRL)-based approaches for CW size adjustment,which often suffer from overestimation bias and limited exploration diversity,leading to suboptimal throughput and collision performance.Our framework integrates the Gumbel distribution and extreme value theory to systematically enhance action selection under varying network conditions.First,SETL adopts a DDQN architecture(SETL-DDQN)to improve Q-value estimation accuracy and enhance training stability.Second,we incorporate a Gumbel distribution-driven exploration mechanism,forming SETL-DDQN(Gumbel),which employs the extreme value theory to promote diverse action selection,replacing the conventional-greedy exploration that undergoes early convergence to suboptimal solutions.Both models are evaluated through extensive simulations in static and time-varying IEEE 802.11 network scenarios.The results demonstrate that our approach consistently achieves higher throughput,lower collision rates,and improved adaptability,even under abrupt fluctuations in traffic load and network conditions.In particular,the Gumbel-based mechanism enhances the balance between exploration and exploitation,facilitating faster adaptation to varying congestion levels.These findings position Gumbel-enhanced DRL as an effective and robust solution for CW optimization in wireless networks,offering notable gains in efficiency and reliability over existing methods.
