Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity.These challenges hinder traditional IDS from...Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity.These challenges hinder traditional IDS from effectively extracting features while maintaining detection accuracy.This paper proposes an industrial Internet ofThings intrusion detection feature selection algorithm based on an improved whale optimization algorithm(GSLDWOA).The aim is to address the problems that feature selection algorithms under high-dimensional data are prone to,such as local optimality,long detection time,and reduced accuracy.First,the initial population’s diversity is increased using the Gaussian Mutation mechanism.Then,Non-linear Shrinking Factor balances global exploration and local development,avoiding premature convergence.Lastly,Variable-step Levy Flight operator and Dynamic Differential Evolution strategy are introduced to improve the algorithm’s search efficiency and convergence accuracy in highdimensional feature space.Experiments on the NSL-KDD and WUSTL-IIoT-2021 datasets demonstrate that the feature subset selected by GSLDWOA significantly improves detection performance.Compared to the traditional WOA algorithm,the detection rate and F1-score increased by 3.68%and 4.12%.On the WUSTL-IIoT-2021 dataset,accuracy,recall,and F1-score all exceed 99.9%.展开更多
The output feedback model predictive control(MPC),for a linear parameter varying(LPV) process system including unmeasurable model parameters and disturbance(all lying in known polytopes),is considered.Some previously ...The output feedback model predictive control(MPC),for a linear parameter varying(LPV) process system including unmeasurable model parameters and disturbance(all lying in known polytopes),is considered.Some previously developed tools,including the norm-bounding technique for relaxing the disturbance-related constraint handling,the dynamic output feedback law,the notion of quadratic boundedness for specifying the closed-loop stability,and the ellipsoidal state estimation error bound for guaranteeing the recursive feasibility,are merged in the control design.Some previous approaches are shown to be the special cases.An example of continuous stirred tank reactor(CSTR) is given to show the effectiveness of the proposed approaches.展开更多
The energy market plays a fundamental role in the global economy,shaping energy prices,inflation,and financial stability across nations.As the world transitions toward low-carbon energy solutions,optimizing trading st...The energy market plays a fundamental role in the global economy,shaping energy prices,inflation,and financial stability across nations.As the world transitions toward low-carbon energy solutions,optimizing trading strategies in this complex and dynamic market has become increasingly critical for investors,policymakers,and energy brokers.Traditional data-driven models often struggle to capture the multifaceted and interconnected factors influencing energy markets,such as macroeconomic conditions,investor sentiment,and the accelerating shift toward decarbonization.To address these challenges,a novel framework is proposed that combines reinforcement learning with methods for analyzing disagreement and connectedness,alongside advanced natural language processing techniques,to develop trading strategies for energy markets.The proposed method integrates structured time-series data with unstructured textual data to incorporate diverse factors,including the interplay between economic influences,green energy transitions,and investor sentiment.The proposed framework also employs a chain-of-reasoning technique to classify investor types,distinguishing between sentiment-driven disagreement and cross-disagreement,and utilizes a connectedness-based method to model the interrelationships among market variables,providing a comprehensive understanding of market dynamics.As a showcase,this framework is applied to the West Texas Intermediate crude oil market,demonstrating its ability to outperform traditional price-prediction-based trading strategies.Experimental results highlight that the proposed framework delivers superior investment returns while addressing key limitations of existing models in terms of data integration and flexibility.This study underscores the potential of the proposed framework as a robust and adaptable solution for optimizing trading strategies across the broader energy market,with particular relevance to the global transition toward sustainable energy systems.展开更多
基金supported by the Major Science and Technology Programs in Henan Province(No.241100210100)Henan Provincial Science and Technology Research Project(No.252102211085,No.252102211105)+3 种基金Endogenous Security Cloud Network Convergence R&D Center(No.602431011PQ1)The Special Project for Research and Development in Key Areas of Guangdong Province(No.2021ZDZX1098)The Stabilization Support Program of Science,Technology and Innovation Commission of Shenzhen Municipality(No.20231128083944001)The Key scientific research projects of Henan higher education institutions(No.24A520042).
文摘Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity.These challenges hinder traditional IDS from effectively extracting features while maintaining detection accuracy.This paper proposes an industrial Internet ofThings intrusion detection feature selection algorithm based on an improved whale optimization algorithm(GSLDWOA).The aim is to address the problems that feature selection algorithms under high-dimensional data are prone to,such as local optimality,long detection time,and reduced accuracy.First,the initial population’s diversity is increased using the Gaussian Mutation mechanism.Then,Non-linear Shrinking Factor balances global exploration and local development,avoiding premature convergence.Lastly,Variable-step Levy Flight operator and Dynamic Differential Evolution strategy are introduced to improve the algorithm’s search efficiency and convergence accuracy in highdimensional feature space.Experiments on the NSL-KDD and WUSTL-IIoT-2021 datasets demonstrate that the feature subset selected by GSLDWOA significantly improves detection performance.Compared to the traditional WOA algorithm,the detection rate and F1-score increased by 3.68%and 4.12%.On the WUSTL-IIoT-2021 dataset,accuracy,recall,and F1-score all exceed 99.9%.
基金Supported by the National High Technology Research and Development Program of China(2014AA041802)the National Natural Science Foundation of China(61573269)
文摘The output feedback model predictive control(MPC),for a linear parameter varying(LPV) process system including unmeasurable model parameters and disturbance(all lying in known polytopes),is considered.Some previously developed tools,including the norm-bounding technique for relaxing the disturbance-related constraint handling,the dynamic output feedback law,the notion of quadratic boundedness for specifying the closed-loop stability,and the ellipsoidal state estimation error bound for guaranteeing the recursive feasibility,are merged in the control design.Some previous approaches are shown to be the special cases.An example of continuous stirred tank reactor(CSTR) is given to show the effectiveness of the proposed approaches.
基金supported by the National Natural Science Foun-dation of China(52477083,52207082)Natural Science Foundation of Jiangsu Province,China(BK20220842)+2 种基金Ningbo Natural Science Foundation,China(2023J194)University of Nottingham Ningbo China Education Foundation(LDS202303)Basic and Commonweal Pro-gramme of Zhejiang Natural Science Foundation,China(LY24F020006).
文摘The energy market plays a fundamental role in the global economy,shaping energy prices,inflation,and financial stability across nations.As the world transitions toward low-carbon energy solutions,optimizing trading strategies in this complex and dynamic market has become increasingly critical for investors,policymakers,and energy brokers.Traditional data-driven models often struggle to capture the multifaceted and interconnected factors influencing energy markets,such as macroeconomic conditions,investor sentiment,and the accelerating shift toward decarbonization.To address these challenges,a novel framework is proposed that combines reinforcement learning with methods for analyzing disagreement and connectedness,alongside advanced natural language processing techniques,to develop trading strategies for energy markets.The proposed method integrates structured time-series data with unstructured textual data to incorporate diverse factors,including the interplay between economic influences,green energy transitions,and investor sentiment.The proposed framework also employs a chain-of-reasoning technique to classify investor types,distinguishing between sentiment-driven disagreement and cross-disagreement,and utilizes a connectedness-based method to model the interrelationships among market variables,providing a comprehensive understanding of market dynamics.As a showcase,this framework is applied to the West Texas Intermediate crude oil market,demonstrating its ability to outperform traditional price-prediction-based trading strategies.Experimental results highlight that the proposed framework delivers superior investment returns while addressing key limitations of existing models in terms of data integration and flexibility.This study underscores the potential of the proposed framework as a robust and adaptable solution for optimizing trading strategies across the broader energy market,with particular relevance to the global transition toward sustainable energy systems.
