The linear systems affected by additive external sinusoidal disturbances is studied. The problem is to damp this forced oscillation in an optimal fashion. The main result of this paper is a new design approach is prop...The linear systems affected by additive external sinusoidal disturbances is studied. The problem is to damp this forced oscillation in an optimal fashion. The main result of this paper is a new design approach is proposed of realizable feedforward and feedback optimal control law for a linear time invariant system with sinusoidal disturbances. The algorithm of solving the optimal control law is given. It is shown that the control law is easily realized and is robust with respect to errors produced by the external sinusoidal disturbances through simulation results.展开更多
The optimal control is investigated for linear systems affected by external harmonic disturbance and applied to vibration control systems of offshore steel jacket platforms. The wave-induced force is the dominant load...The optimal control is investigated for linear systems affected by external harmonic disturbance and applied to vibration control systems of offshore steel jacket platforms. The wave-induced force is the dominant load that offshore structures are subjected to, and it can be taken as harmonic excitation for the system. The linearized Morison equation is employed to estimate the wave loading. The main result concerns the existence and design of a realizable optimal regulator, which is proposed to damp the forced oscillation in an optimal fashion. For demonstration of the effectiveness of the control scheme, the platform performance is investigated for different wave states. The simulations are based on the tuned mass damper and the active mass damper control devices. It is demonstrated that the control scheme is useful in reducing the displacement response of jacket-type offshore platforms.展开更多
There have been various studies on approximation ability of feedforward neural networks (FNNs). Most of the existing studies are, however, only concerned with density or upper bound estimation on how a multivariate fu...There have been various studies on approximation ability of feedforward neural networks (FNNs). Most of the existing studies are, however, only concerned with density or upper bound estimation on how a multivariate function can be approximated by an FNN, and consequently, the essential approximation ability of an FNN cannot be revealed. In this paper, by establishing both upper and lower bound estimations on approximation order, the essential approximation ability (namely, the essential approximation order) of a class of FNNs is clarified in terms of the modulus of smoothness of functions to be approximated. The involved FNNs can not only approximate any continuous or integrable functions defined on a compact set arbitrarily well, but also provide an explicit lower bound on the number of hidden units required. By making use of multivariate approximation tools, it is shown that when the functions to be approximated are Lipschitzian with order up to 2, the approximation speed of the FNNs is uniquely determined by modulus of smoothness of the functions.展开更多
With vast amounts of data being generated daily and the ever increasing interconnectivity of the world’s internet infrastructures,a machine learning based Intrusion Detection Systems(IDS)has become a vital component ...With vast amounts of data being generated daily and the ever increasing interconnectivity of the world’s internet infrastructures,a machine learning based Intrusion Detection Systems(IDS)has become a vital component to protect our economic and national security.Previous shallow learning and deep learning strategies adopt the single learning model approach for intrusion detection.The single learning model approach may experience problems to understand increasingly complicated data distribution of intrusion patterns.Particularly,the single deep learning model may not be effective to capture unique patterns from intrusive attacks having a small number of samples.In order to further enhance the performance of machine learning based IDS,we propose the Big Data based Hierarchical Deep Learning System(BDHDLS).BDHDLS utilizes behavioral features and content features to understand both network traffic characteristics and information stored in the payload.Each deep learning model in the BDHDLS concentrates its efforts to learn the unique data distribution in one cluster.This strategy can increase the detection rate of intrusive attacks as compared to the previous single learning model approaches.Based on parallel training strategy and big data techniques,the model construction time of BDHDLS is reduced substantially when multiple machines are deployed.展开更多
文摘The linear systems affected by additive external sinusoidal disturbances is studied. The problem is to damp this forced oscillation in an optimal fashion. The main result of this paper is a new design approach is proposed of realizable feedforward and feedback optimal control law for a linear time invariant system with sinusoidal disturbances. The algorithm of solving the optimal control law is given. It is shown that the control law is easily realized and is robust with respect to errors produced by the external sinusoidal disturbances through simulation results.
文摘The optimal control is investigated for linear systems affected by external harmonic disturbance and applied to vibration control systems of offshore steel jacket platforms. The wave-induced force is the dominant load that offshore structures are subjected to, and it can be taken as harmonic excitation for the system. The linearized Morison equation is employed to estimate the wave loading. The main result concerns the existence and design of a realizable optimal regulator, which is proposed to damp the forced oscillation in an optimal fashion. For demonstration of the effectiveness of the control scheme, the platform performance is investigated for different wave states. The simulations are based on the tuned mass damper and the active mass damper control devices. It is demonstrated that the control scheme is useful in reducing the displacement response of jacket-type offshore platforms.
文摘There have been various studies on approximation ability of feedforward neural networks (FNNs). Most of the existing studies are, however, only concerned with density or upper bound estimation on how a multivariate function can be approximated by an FNN, and consequently, the essential approximation ability of an FNN cannot be revealed. In this paper, by establishing both upper and lower bound estimations on approximation order, the essential approximation ability (namely, the essential approximation order) of a class of FNNs is clarified in terms of the modulus of smoothness of functions to be approximated. The involved FNNs can not only approximate any continuous or integrable functions defined on a compact set arbitrarily well, but also provide an explicit lower bound on the number of hidden units required. By making use of multivariate approximation tools, it is shown that when the functions to be approximated are Lipschitzian with order up to 2, the approximation speed of the FNNs is uniquely determined by modulus of smoothness of the functions.
基金partially supported by Research Initiative for Summer Engagement(RISE)from the Office of the Vice President for Research at University of South Carolina
文摘With vast amounts of data being generated daily and the ever increasing interconnectivity of the world’s internet infrastructures,a machine learning based Intrusion Detection Systems(IDS)has become a vital component to protect our economic and national security.Previous shallow learning and deep learning strategies adopt the single learning model approach for intrusion detection.The single learning model approach may experience problems to understand increasingly complicated data distribution of intrusion patterns.Particularly,the single deep learning model may not be effective to capture unique patterns from intrusive attacks having a small number of samples.In order to further enhance the performance of machine learning based IDS,we propose the Big Data based Hierarchical Deep Learning System(BDHDLS).BDHDLS utilizes behavioral features and content features to understand both network traffic characteristics and information stored in the payload.Each deep learning model in the BDHDLS concentrates its efforts to learn the unique data distribution in one cluster.This strategy can increase the detection rate of intrusive attacks as compared to the previous single learning model approaches.Based on parallel training strategy and big data techniques,the model construction time of BDHDLS is reduced substantially when multiple machines are deployed.
