Dear Editor,In this letter,a constrained networked predictive control strategy is proposed for the optimal control problem of complex nonlinear highorder fully actuated(HOFA)systems with noises.The method can effectiv...Dear Editor,In this letter,a constrained networked predictive control strategy is proposed for the optimal control problem of complex nonlinear highorder fully actuated(HOFA)systems with noises.The method can effectively deal with nonlinearities,constraints,and noises in the system,optimize the performance metric,and present an upper bound on the stable output of the system.展开更多
A constrained back propagation neural network(C-BPNN)model for standard penetration test based soil liquefaction assessment with global applicability is developed,incorporating existing knowledge for liquefaction trig...A constrained back propagation neural network(C-BPNN)model for standard penetration test based soil liquefaction assessment with global applicability is developed,incorporating existing knowledge for liquefaction triggering mechanism and empirical relationships.For its development and validation,a comprehensive liquefaction data set is compiled,covering more than 600 liquefaction sites from 36 earthquakes in 10 countries over 50 years with 13 complete information entries.The C-BPNN model design procedure for liquefaction assessment is established by considering appropriate constraints,input data selection,and computation and calibration procedures.Existing empirical relationships for overburden correction and fines content adjustment are shown to be able to improve the prediction success rate of the neural network model,and are thus adopted as constraints for the C-BPNN model.The effectiveness of the C-BPNN method is validated using the liquefaction data set and compared with that of several liquefaction assessment methods currently adopted in engineering practice.The C-BPNN liquefaction model is shown to have improved prediction accuracy and high global adaptability.展开更多
With the objective of establishing the necessary conditions for 3-D seismic data from a Permian plutonic oilfield in western China, we compared the technology of several multi-parameter seismic inversion methods in id...With the objective of establishing the necessary conditions for 3-D seismic data from a Permian plutonic oilfield in western China, we compared the technology of several multi-parameter seismic inversion methods in identifying igneous rocks. The most often used inversion methods are Constrained Sparse Spike Inversion (CSSI), Artificial Neural Network Inversion (ANN) and GR Pseudo-impedance Inversion. Through the application of a variety of inversion methods with log curves correction, we obtained relatively high-resolution impedance and velocity sections, effectively identifying the lithology of Permian igneous rocks and inferred lateral variation in the lithology of igneous rocks. By means of a comprehensive comparative study, we arrived at the following conclusions: the CSSI inversion has good waveform continuity, and the ANN inversion has lower resolution than the CSSI inversion. The inversion results show that multi-parameter seismic inversion methods are an effective solution to the identification of igneous rocks.展开更多
This paper presents a survey of image synthesis and editing with Generative Adversarial Networks（GANs）. GANs consist of two deep networks, a generator and a discriminator, which are trained in a competitive way. Due...This paper presents a survey of image synthesis and editing with Generative Adversarial Networks（GANs）. GANs consist of two deep networks, a generator and a discriminator, which are trained in a competitive way. Due to the power of deep networks and the competitive training manner, GANs are capable of producing reasonable and realistic images, and have shown great capability in many image synthesis and editing applications.This paper surveys recent GAN papers regarding topics including, but not limited to, texture synthesis, image inpainting, image-to-image translation, and image editing.展开更多
Smart control techniques have been implemented to address fluctuating power levels within isolated crogrids,mi-mitigating the risk of unstable frequencies and the potential degradation of power supply quality.However,...Smart control techniques have been implemented to address fluctuating power levels within isolated crogrids,mi-mitigating the risk of unstable frequencies and the potential degradation of power supply quality.However,a challenge lies in the fact that employing these computationally complex methods without stability preservation might not suffice to handle the rapid changes of this highly dynamic environment in real-world scenarios over communication delays.This study introduces a flexible real-time approach for the frequency control problem using an artificial neural network(ANN)constrained to stabilized regions.Our solution integrates stabilizing PID controllers,computed through small-signal analysis and tuned via an automated search for optimal ANN weights and reinforcement learning(RL)-based selected constraints.First,we design stabilizing PID controllers by applying the stability boundary locus method and the Mikhailov criterion,specifically addressing communication delays.Next,we refine the controller parameters online through an automated process that identifies optimal coefficient combinations,leveraging a constrained ANN to manage frequency deviations within a restricted parameter range.Our approach is further enhanced by employing the RL technique,which trains the tuning system using an interpolated stability boundary curve to ensure both stability and performance.This one-of-a-kind combination of ANN,RL,and advanced PID tuning methods is a big step forward in how we handle frequency control problems in isolated AC microgrids.The experiments show that our solution outperforms traditional methods due to its reduced parameter search space.In particular,the proposed method reduces transient and steady-state frequency deviations more than semi-and unconstrained methods.The improved metrics and stability analysis show that the method improves system performance and stability under changing conditions.展开更多
基金supported in part by the National Natural Science Foundation of China(62173255,62188101)Shenzhen Key Laboratory of Control Theory and Intelligent Systems(ZDSYS20220330161800001)
文摘Dear Editor,In this letter,a constrained networked predictive control strategy is proposed for the optimal control problem of complex nonlinear highorder fully actuated(HOFA)systems with noises.The method can effectively deal with nonlinearities,constraints,and noises in the system,optimize the performance metric,and present an upper bound on the stable output of the system.
基金The authors would like to thank the National Natural Science Foundation of China(Grant Nos.51678346 and 51879141)Tsinghua University Initiative Scientific Research Program(2019Z08-QCX 01)for funding this work.
文摘A constrained back propagation neural network(C-BPNN)model for standard penetration test based soil liquefaction assessment with global applicability is developed,incorporating existing knowledge for liquefaction triggering mechanism and empirical relationships.For its development and validation,a comprehensive liquefaction data set is compiled,covering more than 600 liquefaction sites from 36 earthquakes in 10 countries over 50 years with 13 complete information entries.The C-BPNN model design procedure for liquefaction assessment is established by considering appropriate constraints,input data selection,and computation and calibration procedures.Existing empirical relationships for overburden correction and fines content adjustment are shown to be able to improve the prediction success rate of the neural network model,and are thus adopted as constraints for the C-BPNN model.The effectiveness of the C-BPNN method is validated using the liquefaction data set and compared with that of several liquefaction assessment methods currently adopted in engineering practice.The C-BPNN liquefaction model is shown to have improved prediction accuracy and high global adaptability.
文摘With the objective of establishing the necessary conditions for 3-D seismic data from a Permian plutonic oilfield in western China, we compared the technology of several multi-parameter seismic inversion methods in identifying igneous rocks. The most often used inversion methods are Constrained Sparse Spike Inversion (CSSI), Artificial Neural Network Inversion (ANN) and GR Pseudo-impedance Inversion. Through the application of a variety of inversion methods with log curves correction, we obtained relatively high-resolution impedance and velocity sections, effectively identifying the lithology of Permian igneous rocks and inferred lateral variation in the lithology of igneous rocks. By means of a comprehensive comparative study, we arrived at the following conclusions: the CSSI inversion has good waveform continuity, and the ANN inversion has lower resolution than the CSSI inversion. The inversion results show that multi-parameter seismic inversion methods are an effective solution to the identification of igneous rocks.
基金supported by the National Key Technology R&D Program(No.2016YFB1001402)the National Natural Science Foundation of China(No.61521002)+2 种基金the Joint NSFC-ISF Research Program(No.61561146393)Research Grant of Beijing Higher Institution Engineering Research Center and Tsinghua-Tencent Joint Laboratory for Internet Innovation Technologysupported by the EPSRC CDE(No.EP/L016540/1)
文摘This paper presents a survey of image synthesis and editing with Generative Adversarial Networks（GANs）. GANs consist of two deep networks, a generator and a discriminator, which are trained in a competitive way. Due to the power of deep networks and the competitive training manner, GANs are capable of producing reasonable and realistic images, and have shown great capability in many image synthesis and editing applications.This paper surveys recent GAN papers regarding topics including, but not limited to, texture synthesis, image inpainting, image-to-image translation, and image editing.
基金supported by the European Union’s Horizon Europe research and innovation programme under the grant agreement No 101120657project ENFIELD(European Lighthouse to Manifest Trust-worthy and Green AI)+1 种基金by the Estonian Research Council through the grants PRG658 and PRG1463and by the Estonian Centre of Excellence in Energy Efficiency,ENER(grant TK230)funded by the Estonian Ministry of Education and Research.
文摘Smart control techniques have been implemented to address fluctuating power levels within isolated crogrids,mi-mitigating the risk of unstable frequencies and the potential degradation of power supply quality.However,a challenge lies in the fact that employing these computationally complex methods without stability preservation might not suffice to handle the rapid changes of this highly dynamic environment in real-world scenarios over communication delays.This study introduces a flexible real-time approach for the frequency control problem using an artificial neural network(ANN)constrained to stabilized regions.Our solution integrates stabilizing PID controllers,computed through small-signal analysis and tuned via an automated search for optimal ANN weights and reinforcement learning(RL)-based selected constraints.First,we design stabilizing PID controllers by applying the stability boundary locus method and the Mikhailov criterion,specifically addressing communication delays.Next,we refine the controller parameters online through an automated process that identifies optimal coefficient combinations,leveraging a constrained ANN to manage frequency deviations within a restricted parameter range.Our approach is further enhanced by employing the RL technique,which trains the tuning system using an interpolated stability boundary curve to ensure both stability and performance.This one-of-a-kind combination of ANN,RL,and advanced PID tuning methods is a big step forward in how we handle frequency control problems in isolated AC microgrids.The experiments show that our solution outperforms traditional methods due to its reduced parameter search space.In particular,the proposed method reduces transient and steady-state frequency deviations more than semi-and unconstrained methods.The improved metrics and stability analysis show that the method improves system performance and stability under changing conditions.
