Multivariables, strong coupling, nonlinearity, and large delays characterize the boiler-turbine coordinated control systems for ship power equipment. To better deal with these conditions, a compound control strategy b...Multivariables, strong coupling, nonlinearity, and large delays characterize the boiler-turbine coordinated control systems for ship power equipment. To better deal with these conditions, a compound control strategy based on a support vector machine （SVM） with inverse identification was proposed and applied to research simulating coordinated control systems. This method combines SVM inverse control and fuzzy control, taking advantage of the merits of SVM inverse controls which can be designed easily and have high reliability, and those of fuzzy controls, which respond rapidly and have good anti-jamming capability and robustness. It ensures the controller can be controlled with near instantaneous adjustments to maintain a steady state, even if the SVM is not trained well. The simulation results show that the control quality of this fuzzy-SVM compound control algorithm is high, with good performance in dynamic response speed, static stability, restraint of overshoot, and robustness.展开更多
The traditional inspection methods are mostly based on manual inspection which is very likely to make erroneous judgments due to personal subjectivity or eye fatigue, and can＇t satisfy the accuracy. To overcome these...The traditional inspection methods are mostly based on manual inspection which is very likely to make erroneous judgments due to personal subjectivity or eye fatigue, and can＇t satisfy the accuracy. To overcome these difficulties, we develop a machine vision inspection system. We first compare several kinds of methods for feature extraction and classification, and then present a real-time automated visual inspection system for copper strips surface （CSS） defects based on compound moment invariants and support vector machine （SVM）. The proposed method first processes images collected by hardware system, and then extracts feature characteristics based on grayscale characteristics and morphologic characteristics （Hu and Zernike compound moment invariants）. Finally, we use SVM to classify the CSS defects. Furthermore, performance comparisons among SVM, back propagation （BP） and radial basis function （RBF） neural networks have been involved. Experimental results show that the proposed approach achieves an accuracy of 95.8% in detecting CSS defects.展开更多
The process of estimating the level of water surface in two-stage waterways is a crucial aspect in the design of flood control and diversion structures.Human activities carried out along the course of rivers,such as a...The process of estimating the level of water surface in two-stage waterways is a crucial aspect in the design of flood control and diversion structures.Human activities carried out along the course of rivers,such as agricultural and construction operation,have the potential to modify the geometry of floodplains,leading to the formation of compound channels with non-prismatic floodplains,thus possibly exhibiting convergent,divergent,or skewed characteristics.In the current investigation,the Support Vector Machine(SVM)technique is employed to approximate the water surface profile of compound channels featuring narrowing floodplains.Some models are constructed by utilizing significant experimental data obtained from both contemporary and previous investigations.Water surface profiles in these channels can be estimated through the utilization of non-dimensional geometric and flow parameters,including:converging angle,width ratio,relative depth,aspect ratio,relative distance,and bed slope.The results of this study indicate that the SVM-generated water surface profile exhibits a high degree of concordance with both the empirical data and the findings from previous research,as evidenced by its R^(2) value of 0.99,RMSE value of 0.0199,and MAPE value of 1.263.The findings of this study based on statistical analysis demonstrate that the SVM model developed is dependable and suitable for applications in this particular domain,exhibiting superior performance in forecasting water surface profiles.展开更多
文摘Multivariables, strong coupling, nonlinearity, and large delays characterize the boiler-turbine coordinated control systems for ship power equipment. To better deal with these conditions, a compound control strategy based on a support vector machine （SVM） with inverse identification was proposed and applied to research simulating coordinated control systems. This method combines SVM inverse control and fuzzy control, taking advantage of the merits of SVM inverse controls which can be designed easily and have high reliability, and those of fuzzy controls, which respond rapidly and have good anti-jamming capability and robustness. It ensures the controller can be controlled with near instantaneous adjustments to maintain a steady state, even if the SVM is not trained well. The simulation results show that the control quality of this fuzzy-SVM compound control algorithm is high, with good performance in dynamic response speed, static stability, restraint of overshoot, and robustness.
基金Supported by the National Natural Science Foundation of China （No. 60872096） and the Fundamental Research Funds for the Central Universities （No. 2009B31914）.
文摘The traditional inspection methods are mostly based on manual inspection which is very likely to make erroneous judgments due to personal subjectivity or eye fatigue, and can＇t satisfy the accuracy. To overcome these difficulties, we develop a machine vision inspection system. We first compare several kinds of methods for feature extraction and classification, and then present a real-time automated visual inspection system for copper strips surface （CSS） defects based on compound moment invariants and support vector machine （SVM）. The proposed method first processes images collected by hardware system, and then extracts feature characteristics based on grayscale characteristics and morphologic characteristics （Hu and Zernike compound moment invariants）. Finally, we use SVM to classify the CSS defects. Furthermore, performance comparisons among SVM, back propagation （BP） and radial basis function （RBF） neural networks have been involved. Experimental results show that the proposed approach achieves an accuracy of 95.8% in detecting CSS defects.
文摘The process of estimating the level of water surface in two-stage waterways is a crucial aspect in the design of flood control and diversion structures.Human activities carried out along the course of rivers,such as agricultural and construction operation,have the potential to modify the geometry of floodplains,leading to the formation of compound channels with non-prismatic floodplains,thus possibly exhibiting convergent,divergent,or skewed characteristics.In the current investigation,the Support Vector Machine(SVM)technique is employed to approximate the water surface profile of compound channels featuring narrowing floodplains.Some models are constructed by utilizing significant experimental data obtained from both contemporary and previous investigations.Water surface profiles in these channels can be estimated through the utilization of non-dimensional geometric and flow parameters,including:converging angle,width ratio,relative depth,aspect ratio,relative distance,and bed slope.The results of this study indicate that the SVM-generated water surface profile exhibits a high degree of concordance with both the empirical data and the findings from previous research,as evidenced by its R^(2) value of 0.99,RMSE value of 0.0199,and MAPE value of 1.263.The findings of this study based on statistical analysis demonstrate that the SVM model developed is dependable and suitable for applications in this particular domain,exhibiting superior performance in forecasting water surface profiles.
