Electroencephalograph (EEG) recordings during right and left motor imagery can be used to move a cursor to a target on a computer screen. Such an EEG-based brain-computer interface (BCI) can provide a new communicatio...
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Electroencephalograph (EEG) recordings during right and left motor imagery can be used to move a cursor to a target on a computer screen. Such an EEG-based brain-computer interface (BCI) can provide a new communication channel to replace an impaired motor function. It can be used by e.g., handicap users with amyotrophic lateral sclerosis (ALS). In this study, statistical pattern recognition method based on AR model was introduced to discriminate the EEG signals recorded during right and left motor imagery. And learning methods (processing period for parameter estimation, AR order, etc.) were investigated. Finally, the effectiveness of our method was confirmed through the experimental studies.
The paper considers the objective of optimally specifying redundant actuators under constraints, a problem commonly referred to as control allocation. The problem is posed as a mixed /spl lscr//sub 2/-norm optimizatio...
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The paper considers the objective of optimally specifying redundant actuators under constraints, a problem commonly referred to as control allocation. The problem is posed as a mixed /spl lscr//sub 2/-norm optimization objective and converted to a quadratic programming formulation. The implementation of an interior-point algorithm is presented. Alternative methods including fixed-point and active set methods are used to evaluate the reliability, accuracy and efficiency of the primal-dual interior-point method. While the computational load of the interior-point method is found to be greater for problems of small size, convergence to the optimal solution is also more uniform and predictable. In addition, the properties of the algorithm scale favorably with problem size.
This paper proposes the development of a fuzzy predictive control. Genetic algorithms (GA's) are used to automatically tune the controller. A recurrent neural network is used to identify the process, and then prov...
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This paper proposes the development of a fuzzy predictive control. Genetic algorithms (GA's) are used to automatically tune the controller. A recurrent neural network is used to identify the process, and then provides predictions about the process behavior, based on control actions applied to the system. These predictions are used by the fuzzy controller, in order to accomplish a better control of an alcoholic fermentation process from chemical industry. This problem has been chosen due to its non-linearity and large accommodation time, that make it hard to control by standard controllers. Comparison of performance is made with non-predictive approaches(PID and Fuzzy-PD), and also with another predictive approach, GPC(Generalized Predictive control).
In this paper, we explore the possibility of applying Monte Carlo methods (i.e., randomization) to semi-infinite programming problems. Equivalent stochastic optimization problems are derived for a general class of sem...
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In this paper, we explore the possibility of applying Monte Carlo methods (i.e., randomization) to semi-infinite programming problems. Equivalent stochastic optimization problems are derived for a general class of semi-infinite programming problems. For the equivalent stochastic optimization problems, algorithms based on stochastic approximation and Monte Carlo sampling methods are proposed. The asymptotic behavior of the proposed algorithms is analyzed and sufficient conditions for their almost sure convergence are obtained.
A panel discussion with the title Whitherto Robust control was held during the IEEE Conference on Decision and control in Honolulu, HI, in December 1990. Organizers and Chairs were B.R. Barmish and P.P. Khargonekar, w...
A panel discussion with the title Whitherto Robust control was held during the IEEE Conference on Decision and control in Honolulu, HI, in December 1990. Organizers and Chairs were B.R. Barmish and P.P. Khargonekar, while the panelists were D.S. Bernstein, S.P. Bhattacharyya, S.P. Boyd, J.C. Doyle, I.M. Horowitz and R.E. Skelton. Today, the goals of researchers working on robust control are probably quite different than thirteen years ago. This seems true both in the progress of novel theoretic paradigms as well as in the developments of new tools required by emerging applications. This panel discussion addresses classical issues and outlines future directions within robust control. We hope that it will contribute to its growth in the years to come. The panelists’ viewpoint are presented in the next pages.
Using model reduction, a new approach for low-order speech modeling is presented. In this approach, the modeling process starts with a relatively high-order (full-order) autoregressive (AR) model obtained by some clas...
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Using model reduction, a new approach for low-order speech modeling is presented. In this approach, the modeling process starts with a relatively high-order (full-order) autoregressive (AR) model obtained by some classical method. The AR model is then reduced using the a state projection method, operating in the state space. The model reduction yields a reduced-order autoregressive moving-average (ARMA) model which interestingly preserves the key properties of the original full-order model such as causality, stability, minimality, and phase minimality. Line spectral frequencies (LSF) and signal-to-noise ratio (SNR) behavior are also investigated. To illustrate the performance and the effectiveness of the proposed approach, some computer simulations are conducted on some practical speech segments.
作者：
宋红石峰Department of Computer Science and Engineering
Beijing Institute of Technology Beijing 100081 China Department of Computer Science and Engineering
Beijing Institute of Technology Beijing 100081 Chinaecurity access control systems and automatic video surveillance systems are becoming increasingly important recently and detecting human faces is one of the indispensable processes. In this paper an approach is presented to detect faces in video surveillance. Firstly both the skin-color and motion components are applied to extract skin-like regions. The skin-color segmentation algorithm is based on the BPNN (back-error-propagation neural network) and the motion component is obtained with frame difference algorithm. Secondly the image is clustered into separated face candidates by using the region growing technique. Finally the face candidates are further verified by the rule-based algorithm. Experiment results demonstrate that both the accuracy and processing speed are very promising and the approach can be applied for the practical use.
Security access controlsystems and automatic video surveillance systems are becoming increasingly important recently,and detecting human faces is one of the indispensable *** this paper,an approach is presented to de...
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Security access controlsystems and automatic video surveillance systems are becoming increasingly important recently,and detecting human faces is one of the indispensable *** this paper,an approach is presented to detect faces in video ***,both the skin-color and motion components are applied to extract skin-like *** skin-color segmentation algorithm is based on the BPNN (back-error-propagation neural network) and the motion component is obtained with frame difference ***,the image is clustered into separated face candidates by using the region growing ***,the face candidates are further verified by the rule-based *** results demonstrate that both the accuracy and processing speed are very promising and the approach can be applied for the practical use.
We now apply the model predictive control (MPC) of speed limits that we have presented in previous publications to a calibrated METANET model of a 19 km stretch of the real-world freeway A1 in The Netherlands. This fr...
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We now apply the model predictive control (MPC) of speed limits that we have presented in previous publications to a calibrated METANET model of a 19 km stretch of the real-world freeway A1 in The Netherlands. This freeway regularly suffers from shock waves originating mainly from on-ramps, and speed limits are now used to suppress these shock waves. First, we calibrate and validate the extended METANET model with data from the A1 freeway, and we use the Delft OD method to estimate the origin-destination patterns that are needed for the simulation of the destination oriented traffic. Next, we verify from data whether the necessary conditions for applying speed limits against shock waves are satisfied. We show that the MPC controller performs well even under the assumption that the traffic demand is not known on the on-ramps and is known for only a few kilometers upstream and downstream of the controlled stretch. This approach results in an improvement of the total time spent in the network with about 15%.
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