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A game theoretic model and a double oracle algorithm for the heterogeneous weapon target assignment problem 认领 引用
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作者 MA Yingying LUO He +2 位作者 WANG Guoqiang ZHU Waiming HU Xiaoxuan 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2026年第2期548-566,共19页
Weapon target assignment(WTA)problem is a critical problem in multiplatform confrontation.This paper studies a static WTA problem with heterogeneous weapons in multi-platform air combat scenarios,called heterogeneous ... Weapon target assignment(WTA)problem is a critical problem in multiplatform confrontation.This paper studies a static WTA problem with heterogeneous weapons in multi-platform air combat scenarios,called heterogeneous WTA(HWTA)problem.Heterogeneous indicates that the engagement platforms carry multiple kinds of weapons for different tactical purposes.The targets assigned and the weapons used by one side’s platforms will affect the survival probability and capability of the other side’s platforms.The goal of each side in HWTA is to find a solution to determine the kind of weapon used and the target assigned for each platform,so as to maximize their combat effectiveness.The problem is formulated as a two-player noncooperative game model with considering the conflicts between the engaged sides.The Nash equilibrium is an effective solution to the game in which no player has an incentive to deviate.However,the number of pure strategies in HWTA increases exponentially with the engagement platforms.To improve computing efficiency,a double oracle algorithm with constructive heuristic(DOCH)is developed,within which the constructive heuristic is embedded to solve the oracle subproblems efficiently.Numerical experiments are conducted to verify the effectiveness of the DOCH.The results show that the DOCH can find effective strategies for platforms to improve combat effectiveness.Moreover,the DOCH can find high-quality solutions in seconds,significantly outperforming the state-of-the-art algorithms in terms of computational efficiency,especially for large-scale problems. 展开更多
关键词 weapon target assignment noncooperative game double oracle algorithm constructive heuristic
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Intelligent identification for discrete memristive neuron map:An adaptive chaos game optimization algorithm studied from the perspectives of different sample sizes and objective functions 认领 引用
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作者 Yuexi Peng Xinyi Luo +2 位作者 Zhijun Li Mengjiao Wang Minglin Ma 《Chinese Physics B》 SCIE EI CAS CSCD 2026年第6期276-291,共16页
Discrete memristive neuron systems have attracted considerable attention due to their nonlinear dynamical properties,low computational overhead,and ease of hardware implementation.For the practical engineering applica... Discrete memristive neuron systems have attracted considerable attention due to their nonlinear dynamical properties,low computational overhead,and ease of hardware implementation.For the practical engineering applications of discrete memristive neuron systems,effective control remains a key issue.Parameter identification using intelligent optimization algorithms is an important approach for controlling complex nonlinear systems.However,classical algorithms are prone to falling into local optima and often exhibit high computational complexity,resulting in slow convergence.Therefore,a new algorithm named adaptive chaos game optimization(ACGO)is proposed to address these issues.By introducing a differential evolution mutation strategy and a Cauchy adaptive parameter mechanism,the ACGO algorithm can effectively balance global exploration and local exploitation capabilities.To verify the effectiveness of the proposed algorithm,it is applied to parameter identification in five discrete memristive neuron maps(DMNMs)and compared with seven intelligent optimization algorithms.Simulation results demonstrate that the ACGO algorithm achieves higher accuracy and faster convergence.In addition,an in-depth investigation is conducted into the effects of sample size and objective function on identification performance.The results indicate that setting the sample size to 4 and selecting the mean squared error(MSE)as the objective function can achieve better identification performance and a high level of robustness. 展开更多
关键词 discrete memristive neuron map parameter identification chaos game optimization algorithm sample size
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Propagation Alongside Crossover:An Evolutionary Algorithm for Continuous Optimization and Feature Selection 认领 引用
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作者 Najibeh Farzi-Veijouyeh Vahideh Sahargahi Neda Matin 《Journal of Bionic Engineering》 SCIE EI CSCD 2026年第2期1112-1175,共64页
Researchers continuously advance optimization algorithms,recognizing that no meta-heuristic can solve all problem types,as stated by the No Free Lunch theorem.This paper introduces the Propagation alongside Crossover(... Researchers continuously advance optimization algorithms,recognizing that no meta-heuristic can solve all problem types,as stated by the No Free Lunch theorem.This paper introduces the Propagation alongside Crossover(PAC)algorithm to address continuous optimization challenges.The primary goal of PAC is to structure the algorithmic phases in a manner that achieves a robust balance between exploration and exploitation through appropriately designed mechanisms at each stage.PAC simultaneously leverages the benefits of propagation,crossover,and mutation.Three independent operators are defined to generate new candidate solutions separately,and a novel selection strategy allows individuals produced by each operator,along with members of the current population,to independently enter the next generation.This design preserves population diversity,prevents all individuals from converging toward a single point,and enhances the algorithm’s ability to explore the solution space effectively.A key innovation of PAC is its three-mode propagation mechanism,which comprises local search,linear propagation toward the target point,and tear-drop shaped propagation toward the target point.Tear-drop propagation provides a precise and adaptive search around promising solutions,increasing diversity and preventing entrapment in local optima.The target point is typically set as the global optimum;however,when propagating the global optimum itself,a random point is used as the target to further enhance exploration and escape from local optima.The initial population is generated using chaotic mapping to ensure broad coverage of the search space.PAC was rigorously evaluated on 51 benchmark functions and three engineering problems,considering scalability,convergence,sensitivity,and computational efficiency.Comparative analyses with established optimization algorithms demonstrate PAC’s superior performance,as confirmed by Wilcoxon signed-rank and Friedman statistical tests.Furthermore,PAC was applied as a feature selection method on four diverse datasets,achieving substantial dimensionality reduction while outperforming comparative methods in classification accuracy.These results highlight PAC’s versatility,robustness,and practical effectiveness. 展开更多
关键词 Optimization algorithms Meta-heuristic algorithms Continuous optimization Propagation alongside crossover algorithm Intrusion detection
Machine learning supervised algorithms for gas hydrate identification and saturation estimation in marine reservoirs using well log data:A case study of NGHP-01-19B 认领 引用
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作者 Yi-fan Wu Zheng Su +5 位作者 Takeshi Tsuji Dai-dai Wu Guang-rong Jin Chao Yang Chuang-ji Feng Neng-you Wu 《China Geology》 CAS CSCD 2026年第3期519-534,I0023-I0027,共16页
Gas hydrates are increasingly recognized as a significant unconventional energy resource and a key factor in marine geohazards and the global carbon cycle.However,accurately identifying and quantifying hydrate-bearing... Gas hydrates are increasingly recognized as a significant unconventional energy resource and a key factor in marine geohazards and the global carbon cycle.However,accurately identifying and quantifying hydrate-bearing formations remains challenging due to complex geophysical signatures and heterogeneous distribution.This study evaluates twelve supervised machine learning(ML)algorithms for two key tasks:Classification of hydrate-bearing layers and regression-based estimation of hydrate saturation,using well log and pore-water geochemical data from Site NGHP-01-19B.Two physically independent labeling frameworks are employed:One based on Archie's law using resistivity(1350 samples,29%hydratebearing),and another based on a three-phase velocity model(890 samples,25%hydrate-bearing).A diverse set of models,including tree-based ensembles(Decision Tree,Random Forest,GBDT,XGBoost,Light GBM,Cat Boost,Bagging,Ada Boost),kernel methods(SVM,SVR),instance-based learning(KNN),neural networks(MLP),and Gaussian Process models(GPR,GPC),are systematically compared using cross-validation and grid search.Ensemble methods consistently performed best in classification,with Ada Boost and GBDT,achieving test accuracies above 0.94(Archie)and 0.98(velocity-based).For regression,GPR delivered the most accurate hydrate saturation estimates(R2>0.99),while GBDT and Random Forest provided a strong balance of accuracy and computational efficiency.Notably,depth below seafloor(TDEP),though not a direct geophysical input,significantly enhanced model performance by acting as a proxy for stratigraphic and thermodynamic conditions.Group-based validation confirmed that random-sample splitting overestimates performance due to depth-wise autocorrelation,highlighting the importance of geologically informed model assessment.Overall,the consistent performance of ML models across both labeling schemes and input feature sets underscores their robustness and transferability,supporting their use as a reliable toolset for offshore gas hydrate reservoir characterization. 展开更多
关键词 Gas hydrate Machine learning algorithm Classification Regression Well log data Archie’s law Marine geohazards Global carbon cycle
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Hybrid adaptive machine learning approach for detection and mitigation of GNSS spoofing through enhanced osprey optimization algorithm 认领 引用
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作者 KOTI Sushmitha SANDHYA Rachamalla 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2026年第3期1059-1080,共22页
Global Navigation Satellite Systems(GNSSs)are the specific term utilized with satellite constellation to acquire regional or global services.GNSS sensors use pseudo-distance measurement to estimate the position,veloci... Global Navigation Satellite Systems(GNSSs)are the specific term utilized with satellite constellation to acquire regional or global services.GNSS sensors use pseudo-distance measurement to estimate the position,velocity,and time(PVT).Several GNSS devices are exposed to detect spoofing attacks due to the use of unsafe locations.In addition,misleading signals are intentionally used to generate timing and position,and GNSS signal spoofing provides a constant risk to consumers.In past works,the implementation of the Global Positioning System(GPS)in autonomous vehicle navigation might be endangered by spoofing.To mitigate these issues,this task develops a hybrid machine-learning method for mitigating and detecting GNSS spoofing attacks.The developed model is processed with three phases:data collection,feature extraction,and detection.Initially,the required data is taken from the standard resource.Then,the data is given to the feature extraction phase.The features of the data are retrieved using the principal component analysis(PCA)and t-distributed stochastic neighbor embedding(t-SNE)model.The features obtained from the collected data are transferred to the detection phase.In the final phase,the GNSS spoofing detection and mitigation is executed using a machine learning method called as hybridized adaptive Bayesian learning and multi-layer perceptron(HABMLP).Enhanced osprey optimization algorithm(EOOA)is utilized for optimizing the variables to enhance the efficacy of models and achieves greater performance than other standard models. 展开更多
关键词 Global Navigation Satellite System(GNSS) detection and mitigation of GNSS t-distributed stochastic neighbor embedding(t-SNE) enhanced osprey optimization algorithm principal component analysis(PCA) hybridized adaptive Bayesian learning and multi-layer perceptron(HABMLP)
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A Survey of Distributed Algorithms for Aggregative Games 认领 引用 被引量：1
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作者 Huaqing Li Jun Li +2 位作者 Liang Ran Lifeng Zheng Tingwen Huang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第5期859-871,共13页
Game theory-based models and design tools have gained substantial prominence for controlling and optimizing behavior within distributed engineering systems due to the inherent distribution of decisions among individua... Game theory-based models and design tools have gained substantial prominence for controlling and optimizing behavior within distributed engineering systems due to the inherent distribution of decisions among individuals.In non-cooperative settings,aggregative games serve as a mathematical framework model for the interdependent optimal decision-making problem among a group of non-cooperative players.In such scenarios,each player's decision is influenced by an aggregation of all players'decisions.Nash equilibrium(NE)seeking in aggregative games has emerged as a vibrant topic driven by applications that harness the aggregation property.This paper presents a comprehensive overview of the current research on aggregative games with a focus on communication topology.A systematic classification is conducted on distributed algorithm research based on communication topologies such as undirected networks,directed networks,and time-varying networks.Furthermore,it sorts out the challenges and compares the algorithms'convergence performance.It also delves into real-world applications of distributed optimization techniques grounded in aggregative games.Finally,it proposes several challenges that can guide future research directions. 展开更多
关键词 Aggregative game distributed algorithm Nash equilibrium(NE) networked control
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A Feature Selection Method for Software Defect Prediction Based on Improved Beluga Whale Optimization Algorithm 认领 引用 被引量：1
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作者 Shaoming Qiu Jingjie He +1 位作者 Yan Wang Bicong E 《Computers, Materials & Continua》 SCIE EI 2025年第6期4879-4898,共20页
Software defect prediction(SDP)aims to find a reliable method to predict defects in specific software projects and help software engineers allocate limited resources to release high-quality software products.Software ... Software defect prediction(SDP)aims to find a reliable method to predict defects in specific software projects and help software engineers allocate limited resources to release high-quality software products.Software defect prediction can be effectively performed using traditional features,but there are some redundant or irrelevant features in them(the presence or absence of this feature has little effect on the prediction results).These problems can be solved using feature selection.However,existing feature selection methods have shortcomings such as insignificant dimensionality reduction effect and low classification accuracy of the selected optimal feature subset.In order to reduce the impact of these shortcomings,this paper proposes a new feature selection method Cubic TraverseMa Beluga whale optimization algorithm(CTMBWO)based on the improved Beluga whale optimization algorithm(BWO).The goal of this study is to determine how well the CTMBWO can extract the features that are most important for correctly predicting software defects,improve the accuracy of fault prediction,reduce the number of the selected feature and mitigate the risk of overfitting,thereby achieving more efficient resource utilization and better distribution of test workload.The CTMBWO comprises three main stages:preprocessing the dataset,selecting relevant features,and evaluating the classification performance of the model.The novel feature selection method can effectively improve the performance of SDP.This study performs experiments on two software defect datasets(PROMISE,NASA)and shows the method’s classification performance using four detailed evaluation metrics,Accuracy,F1-score,MCC,AUC and Recall.The results indicate that the approach presented in this paper achieves outstanding classification performance on both datasets and has significant improvement over the baseline models. 展开更多
关键词 Software defect prediction feature selection beluga optimization algorithm triangular wandering strategy cauchy mutation reverse learning
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基于分层结构和多策略自适应机制的GA-PSO微震震源定位优化算法 认领 引用
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作者 周官群 罗世凌 +4 位作者 高永新 张维鑫 金学良 孟凡彬 王亚飞 《煤炭科学技术》 EI CAS CSCD 北大核心 2026年第6期283-293,共11页
微震震源定位是地质灾害监测与矿区安全预警中的关键环节,但受现场环境噪声强烈等不同类型的影响,传统算法易陷入局部最优,收敛速度慢,难以满足复杂地下条件下的高精度定位需求。为提升定位精度与优化效率,提出一种分层结构与多策略自... 微震震源定位是地质灾害监测与矿区安全预警中的关键环节,但受现场环境噪声强烈等不同类型的影响,传统算法易陷入局部最优,收敛速度慢,难以满足复杂地下条件下的高精度定位需求。为提升定位精度与优化效率,提出一种分层结构与多策略自适应机制的GA-PSO微震震源定位优化算法:首先利用遗传算法(Genetic Algorithm,GA)在全局范围内进行粗搜索,快速获取高质量初始震源位置;随后引入具备“探索群–利用群”结构的自适应粒子群优化(Particle Swarm Optimization,PSO),结合指数衰减的惯性权重、动态学习因子及精英粒子信息交换策略,实现对空间的精细局部优化。该分层混合机制旨在协调全局搜索与局部寻优之间的平衡,提升算法的收敛性能与定位稳定性。通过典型多维复杂函数的优化测试,结果表明所提算法在寻优精度与收敛速度方面均优于传统PSO、GA及优化PSO算法。将该算法应用于实际矿区的校正炮数据中,震源空间定位误差控制在15 m以内,精确度较传统算法提高了12.29%,速度模型反演结果更为准确,适应度函数收敛更快,体现出良好的稳健性与工程适应性。所提出的GA-PSO混合优化算法有效融合了遗传算法的全局搜索优势与粒子群优化算法的高效局部搜索能力,显著提升了微震震源定位在复杂地质环境中的精度与稳定性,为震源精确定位提供了切实可行的优化路径。 展开更多
关键词 遗传算法(GA) 粒子群优化(PSO) 多策略自适应 全局优化 微震定位
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基于GA-SVR的瓦斯抽采纯量预测及自适应调控模型研究 认领 引用
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作者 成小雨 程成 +3 位作者 安靖宇 周爱桃 艾子博 马兴莹 《中国矿业》 CAS 北大核心 2026年第4期125-133,共9页
为了解决瓦斯抽采后期由于含量降低导致瓦斯抽采纯量降低,抽采效果难以预测并且抽采效率低下的问题。基于抽采过程中各因素的非线性耦合特性,选择了支持向量回归机(SVR)和随机森林算法(RF)建立瓦斯抽采纯量预测模型对瓦斯抽采纯量进行预... 为了解决瓦斯抽采后期由于含量降低导致瓦斯抽采纯量降低,抽采效果难以预测并且抽采效率低下的问题。基于抽采过程中各因素的非线性耦合特性,选择了支持向量回归机(SVR)和随机森林算法(RF)建立瓦斯抽采纯量预测模型对瓦斯抽采纯量进行预测,并通过遗传算法(GA)对模型中的超参数进行优选。研究结果表明,GA-SVR模型预测的平均绝对误差为303.62,均方根误差为565.42,预测模型的平均绝对百分比误差为0.023,均高于同类模型。并且在顺层钻孔抽采过程中煤层经过多轮抽采后,抽采负压对抽采浓度和抽采纯量的控制作用被削弱,抽采负压对抽采浓度的控制效果降低,但仍能表现出对单日累计抽采纯量的相关性,而在穿层钻孔抽采条件下,抽采纯量和抽采浓度均对抽采负压的变化较为灵敏。因此,在GA-SVR预测模型的基础上,以管网瓦斯抽采纯量最大为目标,建立了瓦斯抽采负压自适应调控模型,现场对照试验表明以30 d为调控周期的自适应调控模型能在瓦斯抽采后期使试验钻孔组平均瓦斯抽采纯量比对照组提升5.08%,同时在前15 d使瓦斯抽采纯量提升7.15%,研究结果可以为瓦斯抽采后期的纯量预测及负压调控提供指导。 展开更多
关键词 瓦斯抽采 模型预测 支持向量回归机 遗传算法 自适应调控
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Dynamic Multi-Objective Gannet Optimization(DMGO):An Adaptive Algorithm for Efficient Data Replication in Cloud Systems 认领 引用
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作者 P.William Ved Prakash Mishra +3 位作者 Osamah Ibrahim Khalaf Arvind Mukundan Yogeesh N Riya Karmakar 《Computers, Materials & Continua》 SCIE EI 2025年第9期5133-5156,共24页
Cloud computing has become an essential technology for the management and processing of large datasets,offering scalability,high availability,and fault tolerance.However,optimizing data replication across multiple dat... Cloud computing has become an essential technology for the management and processing of large datasets,offering scalability,high availability,and fault tolerance.However,optimizing data replication across multiple data centers poses a significant challenge,especially when balancing opposing goals such as latency,storage costs,energy consumption,and network efficiency.This study introduces a novel Dynamic Optimization Algorithm called Dynamic Multi-Objective Gannet Optimization(DMGO),designed to enhance data replication efficiency in cloud environments.Unlike traditional static replication systems,DMGO adapts dynamically to variations in network conditions,system demand,and resource availability.The approach utilizes multi-objective optimization approaches to efficiently balance data access latency,storage efficiency,and operational costs.DMGO consistently evaluates data center performance and adjusts replication algorithms in real time to guarantee optimal system efficiency.Experimental evaluations conducted in a simulated cloud environment demonstrate that DMGO significantly outperforms conventional static algorithms,achieving faster data access,lower storage overhead,reduced energy consumption,and improved scalability.The proposed methodology offers a robust and adaptable solution for modern cloud systems,ensuring efficient resource consumption while maintaining high performance. 展开更多
关键词 Cloud computing data replication dynamic optimization multi-objective optimization gannet optimization algorithm adaptive algorithms resource efficiency scalability latency reduction energy-efficient computing
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‘741杨’赤霉素氧化酶基因PthGA2ox19调节植株生长发育 认领 引用 被引量：1
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作者 张晓宁 王志安 +5 位作者 唐叶 许梓腾 孙大智 许云娇 杨江伟 吴家和 《生物工程学报》 EI CAS CSCD 北大核心 2026年第1期303-318,共16页
赤霉素2-氧化酶(gibberellin 2-oxidase,GA2ox)是植物体内调控赤霉素(gibberellic acid,GAs)代谢的关键酶,鉴定杨树GA2ox基因并解析其在调控植株生长发育中的功能,能够为选育杨树新品种提供技术支持。本研究通过生物信息学方法对‘741杨... 赤霉素2-氧化酶(gibberellin 2-oxidase,GA2ox)是植物体内调控赤霉素(gibberellic acid,GAs)代谢的关键酶,鉴定杨树GA2ox基因并解析其在调控植株生长发育中的功能,能够为选育杨树新品种提供技术支持。本研究通过生物信息学方法对‘741杨’GA2ox基因进行鉴定和分析,共鉴定出34个GA2ox基因,分布在‘741杨’的7对染色体上。利用实时荧光定量PCR(quantitative real-time PCR,qRT-PCR)技术分析PthGA2ox19的组织表达模式和GA3诱导的表达模式,发现PthGA2ox19在茎中高表达并且在GA3诱导下表达水平显著提高。构建PthGA2ox19过表达载体,并使用农杆菌转化法转化杨树,发现转基因株系PthGA2ox19的表达水平相较于野生型植株显著提高,表型显示为株高变矮、茎秆变细、节间缩短、叶片变小等。通过石蜡切片及显微镜观察分析转基因植株维管组织发育情况,维管组织发育分析表明转基因植株的维管束发育畸形、导管口径减小、木质部和韧皮部厚度变薄。本研究鉴定出了34个‘741杨’GA2ox基因,其中PthGA2ox19的过表达抑制了杨树的生长和维管组织的发育,表明杨树GA2ox参与了植株生长发育的调控,本研究结果为杨树株型育种提供了新的途径。 展开更多
关键词 杨树 赤霉素 GA2ox 转基因植株 生长发育
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基于GA优化BP神经网络预测开关柜内部设备温度 认领 引用
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作者 桑仲庆 袁会生 +4 位作者 游一民 戴冬云 周顺雄 廖权昌 姜维云 《高压电器》 CAS CSCD 北大核心 2026年第8期34-42,共9页
由于开关柜通入电流后,内部设备会产生热量,当温度长期超出阈值,会造成设备损坏无法保证安全,因此需要对开关柜内部温度进行监测,提前对柜内设备的温度进行预测,方便对设备进行维护。为了能够准确预测开关柜内部设备温度,基于BP神经网络... 由于开关柜通入电流后,内部设备会产生热量,当温度长期超出阈值,会造成设备损坏无法保证安全,因此需要对开关柜内部温度进行监测,提前对柜内设备的温度进行预测,方便对设备进行维护。为了能够准确预测开关柜内部设备温度,基于BP神经网络,采用GA算法对BP神经网络优化,提出GA-BP神经网络开关柜内部设备温度预测模型。首先分析影响开关柜温度上升的影响因素,并将其作为预测模型的输入数据;再通过预测模型的训练与测试;最后通过衡量指标来评价网络模型的优劣。测试结果表明,该方法能够有效预测开关柜内部设备的温度值,为变电站内设备进行维护提供了便利。 展开更多
关键词 开关柜 预测 温度 BP神经网络 GA算法
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Evolutionary Algorithm Based on Surrogate and Inverse Surrogate Models for Expensive Multiobjective Optimization 认领 引用
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作者 Qi Deng Qi Kang +4 位作者 MengChu Zhou Xiaoling Wang Shibing Zhao Siqi Wu Mohammadhossein Ghahramani 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第5期961-973,共13页
When dealing with expensive multiobjective optimization problems,majority of existing surrogate-assisted evolutionary algorithms(SAEAs)generate solutions in decision space and screen candidate solutions mostly by usin... When dealing with expensive multiobjective optimization problems,majority of existing surrogate-assisted evolutionary algorithms(SAEAs)generate solutions in decision space and screen candidate solutions mostly by using designed surrogate models.The generated solutions exhibit excessive randomness,which tends to reduce the likelihood of generating good-quality solutions and cause a long evolution to the optima.To improve SAEAs greatly,this work proposes an evolutionary algorithm based on surrogate and inverse surrogate models by 1)Employing a surrogate model in lieu of expensive(true)function evaluations;and 2)Proposing and using an inverse surrogate model to generate new solutions.By using the same training data but with its inputs and outputs being reversed,the latter is simple to train.It is then used to generate new vectors in objective space,which are mapped into decision space to obtain their corresponding solutions.Using a particular example,this work shows its advantages over existing SAEAs.The results of comparing it with state-of-the-art algorithms on expensive optimization problems show that it is highly competitive in both solution performance and efficiency. 展开更多
关键词 Expensives multi-objective optimization reverse model surrogate-assisted evolutionary algorithms(SAEAs)
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Temperature control for liquid-cooled fuel cells based on fuzzy logic and variable-gain generalized supertwisting algorithm 认领 引用
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作者 CHEN Lin JIA Zhi-huan +1 位作者 DING Tian-wei GAO Jin-wu 《控制理论与应用》 EI CAS CSCD 北大核心 2025年第8期1596-1605,共10页
The liquid cooling system(LCS)of fuel cells is challenged by significant time delays,model uncertainties,pump and fan coupling,and frequent disturbances,leading to overshoot and control oscillations that degrade tempe... The liquid cooling system(LCS)of fuel cells is challenged by significant time delays,model uncertainties,pump and fan coupling,and frequent disturbances,leading to overshoot and control oscillations that degrade temperature regulation performance.To address these challenges,we propose a composite control scheme combining fuzzy logic and a variable-gain generalized supertwisting algorithm(VG-GSTA).Firstly,a one-dimensional(1D)fuzzy logic controler(FLC)for the pump ensures stable coolant flow,while a two-dimensional(2D)FLC for the fan regulates the stack temperature near the reference value.The VG-GSTA is then introduced to eliminate steady-state errors,offering resistance to disturbances and minimizing control oscillations.The equilibrium optimizer is used to fine-tune VG-GSTA parameters.Co-simulation verifies the effectiveness of our method,demonstrating its advantages in terms of disturbance immunity,overshoot suppression,tracking accuracy and response speed. 展开更多
关键词 liquid-cooled fuel cell temperature control generalized supertwisting algorithm fuzzy control equilibrium optimizer
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Functional cartography of heterogeneous combat networks using operational chain-based label propagation algorithm 认领 引用
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作者 CHEN Kebin JIANG Xuping +2 位作者 ZENG Guangjun YANG Wenjing ZHENG Xue 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2025年第5期1202-1215,共14页
To extract and display the significant information of combat systems,this paper introduces the methodology of functional cartography into combat networks and proposes an integrated framework named“functional cartogra... To extract and display the significant information of combat systems,this paper introduces the methodology of functional cartography into combat networks and proposes an integrated framework named“functional cartography of heterogeneous combat networks based on the operational chain”(FCBOC).In this framework,a functional module detection algorithm named operational chain-based label propagation algorithm(OCLPA),which considers the cooperation and interactions among combat entities and can thus naturally tackle network heterogeneity,is proposed to identify the functional modules of the network.Then,the nodes and their modules are classified into different roles according to their properties.A case study shows that FCBOC can provide a simplified description of disorderly information of combat networks and enable us to identify their functional and structural network characteristics.The results provide useful information to help commanders make precise and accurate decisions regarding the protection,disintegration or optimization of combat networks.Three algorithms are also compared with OCLPA to show that FCBOC can most effectively find functional modules with practical meaning. 展开更多
关键词 functional cartography heterogeneous combat network functional module label propagation algorithm operational chain
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Study on the destabilizing damage precursors of cemented tailings backfill based on critical slowing down theory combined with multiple denoising algorithms under consideration of initial defect conditions 认领 引用 被引量：1
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作者 ZHAO Kang ZHONG Jun-cheng +3 位作者 YAN Ya-jing LIU Yang WEN Dao-tan XIAO Wei-ling 《Journal of Central South University》 SCIE EI CAS CSCD 2026年第1期375-399,共25页
The cemented tailings backfill(CTB)with initial defects is more prone to destabilization damage under the influence of various unfavorable factors during the mining process.In order to investigate its influence on the... The cemented tailings backfill(CTB)with initial defects is more prone to destabilization damage under the influence of various unfavorable factors during the mining process.In order to investigate its influence on the stability of underground mining engineering,this paper simulates the generation of different degrees of initial defects inside the CTB by adding different contents of air-entraining agent(AEA),investigates the acoustic emission RA/AF eigenvalues of CTB with different contents of AEA under uniaxial compression,and adopts various denoising algorithms(e.g.,moving average smoothing,median filtering,and outlier detection)to improve the accuracy of the data.The variance and autocorrelation coefficients of RA/AF parameters were analyzed in conjunction with the critical slowing down(CSD)theory.The results show that the acoustic emission RA/AF values can be used to characterize the progressive damage evolution of CTB.The denoising algorithm processed the AE signals to reduce the effects of extraneous noise and anomalous spikes.Changes in the variance curves provide clear precursor information,while abrupt changes in the autocorrelation coefficient can be used as an auxiliary localization warning signal.The phenomenon of dramatic increase in the variance and autocorrelation coefficient curves during the compression-tightening stage,which is influenced by the initial defects,can lead to false warnings.As the initial defects of the CTB increase,its instability precursor time and instability time are prolonged,the peak stress decreases,and the time difference between the CTB and the instability damage is smaller.The results provide a new method for real-time monitoring and early warning of CTB instability damage. 展开更多
关键词 initial defects cemented tailings backfill critical slowing down acoustic emission RA/AF values denoising algorithms
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Hybrid genetic algorithm for parametric optimization of surface pipeline networks in underground natural gas storage harmonized injection and production conditions 认领 引用
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作者 Jun Zhou Zichen Li +4 位作者 Shitao Liu Chengyu Li Yunxiang Zhao Zonghang Zhou Guangchuan Liang 《Natural Gas Industry B》 2025年第2期234-250,共17页
The surface injection and production system(SIPS)is a critical component for effective injection and production processes in underground natural gas storage.As a vital channel,the rational design of the surface inject... The surface injection and production system(SIPS)is a critical component for effective injection and production processes in underground natural gas storage.As a vital channel,the rational design of the surface injection and production(SIP)pipeline significantly impacts efficiency.This paper focuses on the SIP pipeline and aims to minimize the investment costs of surface projects.An optimization model under harmonized injection and production conditions was constructed to transform the optimization problem of the SIP pipeline design parameters into a detailed analysis of the injection condition model and the production condition model.This paper proposes a hybrid genetic algorithm generalized reduced gradient(HGA-GRG)method,and compares it with the traditional genetic algorithm(GA)in a practical case study.The HGA-GRG demonstrated significant advantages in optimization outcomes,reducing the initial cost by 345.371×104 CNY compared to the GA,validating the effectiveness of the model.By adjusting algorithm parameters,the optimal iterative results of the HGA-GRG were obtained,providing new research insights for the optimal design of a SIPS. 展开更多
关键词 Underground natural gas storage Surface injection and production pipeline Parameter optimization Hybrid genetic algorithm
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华北克拉通南缘少华山-崤山-熊耳山地区2.9~1.7Ga多期次花岗质岩浆作用成因与陆壳演化 认领 引用 被引量：1
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作者 周艳艳 郑亚莉 +5 位作者 笪永发 张儒诚 祝禧艳 赵磊 赵太平 翟明国 《岩石学报》 SCIE EI CAS CSCD 北大核心 2026年第1期38-70,共33页
华北克拉通太古宙-古元古代的构造-岩浆-沉积记录丰富、完整,是研究早期陆壳多阶段增生和演化规律的天然实验室。然而,目前关于其早期陆壳增生机制和构造演变过程仍存有争议。华北克拉通南缘太华杂岩发育完整的太古宙-古元古代结晶基底... 华北克拉通太古宙-古元古代的构造-岩浆-沉积记录丰富、完整,是研究早期陆壳多阶段增生和演化规律的天然实验室。然而,目前关于其早期陆壳增生机制和构造演变过程仍存有争议。华北克拉通南缘太华杂岩发育完整的太古宙-古元古代结晶基底,出露丰富的TTG及基性-花岗质岩石组合,是研究早期陆壳生长和演化的理想区域。本文聚焦华北克拉通南缘少华山-崤山-熊耳山地区2.9~1.7Ga的TTG及花岗质岩石,开展系统的岩石学、年代学和地球化学的研究。结果显示,研究区至少发育七期TTG及花岗质岩浆作用,包括~2.9Ga英云闪长岩(TTG)、~2.7Ga花岗闪长岩(TTG)、2.53~2.42Ga英云闪长岩(TTG)和钾长-二长花岗岩、2.33~2.27Ga奥长花岗岩(TTG)、闪长岩和钾长-二长花岗岩、2.22~2.19Ga二长花岗岩及侵入TTG片麻岩中的浅色脉体、1.94~1.81Ga钾长-二长花岗岩-花岗闪长岩,以及1.78~1.76Ga的钾长-二长花岗岩。其中,2.9~2.3Ga TTG以中-低压型为主;~2.5Ga的花岗质岩石显示I-S型花岗岩特征;~2.3Ga、~2.2Ga及~1.7Ga的花岗质岩石类似于A型花岗岩;1.94~1.81Ga同时发育A型花岗岩和I-S型花岗岩。~2.9Ga、~2.7Ga和~2.5Ga的三期TTG和~2.5Ga花岗质岩石记录了早期陆壳多阶段的生长和演化,可能形成于俯冲-碰撞的构造环境。~2.3Ga TTG总体具有低压特征,来自基性下地壳在高地温梯度下的部分熔融,与同期古老富集地幔来源的闪长岩和板内A型花岗岩一起指示板内伸展环境。2.2~2.1Ga A型花岗岩来自古老陆壳物质的重熔,结合已有2.3~2.1Ga双峰式火山岩、A型花岗岩、低δ18 O花岗岩-辉长闪长岩等,指示了伸展-裂解背景下不同深度地壳和地幔的再循环。1.94~1.81Ga I-S型花岗岩和A型花岗岩可能记录了古元古代俯冲-碰撞拼合的历史。1.78~1.76Ga A型花岗岩可能是板内伸展-裂解作用下的陆壳减薄诱发地壳部分熔融的产物。华北克拉通南缘太古宙-古元古代广泛发育的花岗质岩浆作用记录了多阶段的陆壳生长、演化和构造体制转型,为揭示不同阶段地球圈层物质循环规律和动力学过程提供了关键约束。 展开更多
关键词 华北克拉通南缘 2.9~1.7Ga TTG-花岗质岩石 锆石U-Pb定年 锆石Lu-Hf同位素 陆壳生长与再循环
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3D numerical manifold method for crack propagation in rock materials using a local tracking algorithm 认领 引用
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作者 Boyi Su Tao Xu +3 位作者 Genhua Shi Michael J.Heap Xianyang Yu Guanglei Zhou 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2025年第6期3449-3463,共15页
The modeling of crack growth in three-dimensional(3D)space poses significant challenges in rock mechanics due to the complex numerical computation involved in simulating crack propagation and interaction in rock mater... The modeling of crack growth in three-dimensional(3D)space poses significant challenges in rock mechanics due to the complex numerical computation involved in simulating crack propagation and interaction in rock materials.In this study,we present a novel approach that introduces a 3D numerical manifold method(3D-NMM)with a geometric kernel to enhance computational efficiency.Specifically,the maximum tensile stress criterion is adopted as a crack growth criterion to achieve strong discontinuous crack growth,and a local crack tracking algorithm and an angle correction technique are incorporated to address minor limitations of the algorithm in a 3D model.The implementation of the program is carried out in Python,using object-oriented programming in two independent modules:a calculation module and a crack module.Furthermore,we propose feasible improvements to enhance the performance of the algorithm.Finally,we demonstrate the feasibility and effectiveness of the enhanced algorithm in the 3D-NMM using four numerical examples.This study establishes the potential of the 3DNMM,combined with the local tracking algorithm,for accurately modeling 3D crack propagation in brittle rock materials. 展开更多
关键词 3D numerical manifold method(3D NMM) Crack propagation Local tracking algorithm Brittle materials
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Optimization of the frequency offset increment of FDA-MIMO based on cuckoo search algorithm 认领 引用 被引量：1
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作者 WANG Bo ZHAO Yu +2 位作者 LI Yonglin YANG Rennong XUE Junjie 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2026年第1期157-170,共14页
Frequency diverse array multiple-input multiple-output(FDA-MIMO)radar has gained considerable research attention due to its ability to effectively counter active repeater deception jamming in complex electromagnetic e... Frequency diverse array multiple-input multiple-output(FDA-MIMO)radar has gained considerable research attention due to its ability to effectively counter active repeater deception jamming in complex electromagnetic environments.The effectiveness of interference suppression by FDA-MIMO is limited by the inherent range-angle coupling issue in the FDA beampattern.Existing literature primarily focuses on control methods for FDA-MIMO radar beam direction under the assumption of static beampatterns,with insufficient exploration of techniques for managing nonstationary beam directions.To address this gap,this paper initially introduces the FDA-MIMO signal model and the calculation formula for the FDA-MIMO array output using the minimum variance distortionless response(MVDR)beamformer.Building on this,the problem of determining the optimal frequency offset for the FDA is rephrased as a convex optimization problem,which is then resolved using the cuckoo search(CS)algorithm.Simulations confirm the effectiveness of the proposed approach,showing that the frequency offsets obtained through the CS algorithm can create a dot-shaped beam direction at the target location while effectively suppressing interference signals within the mainlobe. 展开更多
关键词 frequency diverse array multiple-input multiple-output(FDA-MIMO) convex optimization cuckoo search algorithm beampattern
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