High-rise buildings are usually considered as flexible structures with low inherent damping. Therefore, these kinds of buildings are susceptible to wind-induced vibration. Tuned Mass Damper（TMD） can be used as an ef...High-rise buildings are usually considered as flexible structures with low inherent damping. Therefore, these kinds of buildings are susceptible to wind-induced vibration. Tuned Mass Damper（TMD） can be used as an effective device to mitigate excessive vibrations. In this study, Artificial Neural Networks is used to find optimal mechanical properties of TMD for high-rise buildings subjected to wind load. The patterns obtained from structural analysis of different multi degree of freedom（MDF） systems are used for training neural networks. In order to obtain these patterns, structural models of some systems with 10 to 80 degrees-of-freedoms are built in MATLAB/SIMULINK program. Finally, the optimal properties of TMD are determined based on the objective of maximum displacement response reduction. The Auto-Regressive model is used to simulate the wind load. In this way, the uncertainties related to wind loading can be taken into account in neural network’s outputs. After training the neural network, it becomes possible to set the frequency and TMD mass ratio as inputs and get the optimal TMD frequency and damping ratio as outputs. As a case study, a benchmark 76-story office building is considered and the presented procedure is used to obtain optimal characteristics of the TMD for the building.展开更多
PID controllers were used for the hydraulic servo system of sliding gate and the tundish weight control system in continuous caster.These two loops were synthesized in mould level controller based on model reduction a...PID controllers were used for the hydraulic servo system of sliding gate and the tundish weight control system in continuous caster.These two loops were synthesized in mould level controller based on model reduction and internal model control strategy.Satisfactory control performance of this synthetic mould level controller was demonstrated by simulations and on-line experiments.展开更多
A new auto tuning procedure for PI/D controller based on phase and amplitude margin specifications was proposed.The procedure applied a modified relay feedback experiment to identifying the process frequency response ...A new auto tuning procedure for PI/D controller based on phase and amplitude margin specifications was proposed.The procedure applied a modified relay feedback experiment to identifying the process frequency response of the point where the Nyquist curve intersects the negative imaginary axis,and the PI/D controller settings can be obtained based on this single point.The auto tuning method has all the merits of the tuning method that Astrom and Hagglund had proposed,and overcomes its problems.The simulation results show that the proposed tuning method has better performance than Astrom Hagglund’s tuning method.展开更多
This paper presents a control strategy for maglev system based on the sliding mode controller with auto-tuning law. The designed adaptive controller will replace the conventional sliding mode control (SMC) to eliminat...This paper presents a control strategy for maglev system based on the sliding mode controller with auto-tuning law. The designed adaptive controller will replace the conventional sliding mode control (SMC) to eliminate the chattering resulting from the SMC. The stability of maglev system is ensured based on the Lyapunov theory. Simulation results verify the effectiveness of the proposed method. In addition, the advantages of the proposed controller are indicated in comparison with a traditional sliding mode controller.展开更多
CC’s(Cloud Computing)networks are distributed and dynamic as signals appear/disappear or lose significance.MLTs(Machine learning Techniques)train datasets which sometime are inadequate in terms of sample for inferrin...CC’s(Cloud Computing)networks are distributed and dynamic as signals appear/disappear or lose significance.MLTs(Machine learning Techniques)train datasets which sometime are inadequate in terms of sample for inferring information.A dynamic strategy,DevMLOps(Development Machine Learning Operations)used in automatic selections and tunings of MLTs result in significant performance differences.But,the scheme has many disadvantages including continuity in training,more samples and training time in feature selections and increased classification execution times.RFEs(Recursive Feature Eliminations)are computationally very expensive in its operations as it traverses through each feature without considering correlations between them.This problem can be overcome by the use of Wrappers as they select better features by accounting for test and train datasets.The aim of this paper is to use DevQLMLOps for automated tuning and selections based on orchestrations and messaging between containers.The proposed AKFA(Adaptive Kernel Firefly Algorithm)is for selecting features for CNM(Cloud Network Monitoring)operations.AKFA methodology is demonstrated using CNSD(Cloud Network Security Dataset)with satisfactory results in the performance metrics like precision,recall,F-measure and accuracy used.展开更多
文摘High-rise buildings are usually considered as flexible structures with low inherent damping. Therefore, these kinds of buildings are susceptible to wind-induced vibration. Tuned Mass Damper（TMD） can be used as an effective device to mitigate excessive vibrations. In this study, Artificial Neural Networks is used to find optimal mechanical properties of TMD for high-rise buildings subjected to wind load. The patterns obtained from structural analysis of different multi degree of freedom（MDF） systems are used for training neural networks. In order to obtain these patterns, structural models of some systems with 10 to 80 degrees-of-freedoms are built in MATLAB/SIMULINK program. Finally, the optimal properties of TMD are determined based on the objective of maximum displacement response reduction. The Auto-Regressive model is used to simulate the wind load. In this way, the uncertainties related to wind loading can be taken into account in neural network’s outputs. After training the neural network, it becomes possible to set the frequency and TMD mass ratio as inputs and get the optimal TMD frequency and damping ratio as outputs. As a case study, a benchmark 76-story office building is considered and the presented procedure is used to obtain optimal characteristics of the TMD for the building.
文摘PID controllers were used for the hydraulic servo system of sliding gate and the tundish weight control system in continuous caster.These two loops were synthesized in mould level controller based on model reduction and internal model control strategy.Satisfactory control performance of this synthetic mould level controller was demonstrated by simulations and on-line experiments.
文摘A new auto tuning procedure for PI/D controller based on phase and amplitude margin specifications was proposed.The procedure applied a modified relay feedback experiment to identifying the process frequency response of the point where the Nyquist curve intersects the negative imaginary axis,and the PI/D controller settings can be obtained based on this single point.The auto tuning method has all the merits of the tuning method that Astrom and Hagglund had proposed,and overcomes its problems.The simulation results show that the proposed tuning method has better performance than Astrom Hagglund’s tuning method.
文摘This paper presents a control strategy for maglev system based on the sliding mode controller with auto-tuning law. The designed adaptive controller will replace the conventional sliding mode control (SMC) to eliminate the chattering resulting from the SMC. The stability of maglev system is ensured based on the Lyapunov theory. Simulation results verify the effectiveness of the proposed method. In addition, the advantages of the proposed controller are indicated in comparison with a traditional sliding mode controller.
文摘CC’s(Cloud Computing)networks are distributed and dynamic as signals appear/disappear or lose significance.MLTs(Machine learning Techniques)train datasets which sometime are inadequate in terms of sample for inferring information.A dynamic strategy,DevMLOps(Development Machine Learning Operations)used in automatic selections and tunings of MLTs result in significant performance differences.But,the scheme has many disadvantages including continuity in training,more samples and training time in feature selections and increased classification execution times.RFEs(Recursive Feature Eliminations)are computationally very expensive in its operations as it traverses through each feature without considering correlations between them.This problem can be overcome by the use of Wrappers as they select better features by accounting for test and train datasets.The aim of this paper is to use DevQLMLOps for automated tuning and selections based on orchestrations and messaging between containers.The proposed AKFA(Adaptive Kernel Firefly Algorithm)is for selecting features for CNM(Cloud Network Monitoring)operations.AKFA methodology is demonstrated using CNSD(Cloud Network Security Dataset)with satisfactory results in the performance metrics like precision,recall,F-measure and accuracy used.
