Northeastern University (China)

About: Northeastern University (China) is a education organization based out in Shenyang, China. It is known for research contribution in the topics: Microstructure & Control theory. The organization has 36087 authors who have published 36125 publications receiving 426807 citations. The organization is also known as: Dōngběi Dàxué & Northeastern University (东北大学).

Dynamic driver fatigue detection using hidden Markov model in real driving condition

Abstract: Quantification and objective estimation of driver fatigue in real prolonged driving.Simultaneous recording of physiological parameters in wireless and nonintrusive way.Develop a dynamic fatigue detection model by multiple features and contexts. Driver's states in successive time slices are not independent, especially, fatigue is one of a cognitive state that is developing over time. Meanwhile, driver fatigue is also influenced by some corresponding contextual information at a certain time. In such case, classifying driving state at each time slice separately from it in before and after time slices obviously has less meaning. Therefore, a dynamic fatigue detection model based on Hidden Markov Model (HMM) is proposed in this paper. Driver fatigue can be estimated by this model in a probabilistic way using various physiological and contextual information. Electroencephalogram (EEG), Electromyogram (EMG), and respiration signals were simultaneously recorded by wearable sensors and sent to computer by Bluetooth during the real driving. From these physiological information, fatigue likelihood can be achieved using kernel distribution estimate at different time sections. Contextual information offered by specific environmental factors were used as prior of fatigue. As time proceeds, the posterior of fatigue can be gotten dynamically by this HMM-based fatigue recognition method. Based on the results of the method in this paper, it shows that it provides an effective way in detecting driver fatigue.

A memetic algorithm with adaptive hill climbing strategy for dynamic optimization problems

Abstract: Dynamic optimization problems challenge traditional evolutionary algorithms seriously since they, once converged, cannot adapt quickly to environmental changes. This paper investigates the application of memetic algorithms, a class of hybrid evolutionary algorithms, for dynamic optimization problems. An adaptive hill climbing method is proposed as the local search technique in the framework of memetic algorithms, which combines the features of greedy crossover-based hill climbing and steepest mutation-based hill climbing. In order to address the convergence problem, two diversity maintaining methods, called adaptive dual mapping and triggered random immigrants, respectively, are also introduced into the proposed memetic algorithm for dynamic optimization problems. Based on a series of dynamic problems generated from several stationary benchmark problems, experiments are carried out to investigate the performance of the proposed memetic algorithm in comparison with some peer evolutionary algorithms. The experimental results show the efficiency of the proposed memetic algorithm in dynamic environments.

Switched Adaptive Fuzzy Tracking Control for a Class of Switched Nonlinear Systems Under Arbitrary Switching

Abstract: This paper deals with the problem of adaptive tracking control for a class of switched uncertain nonlinear systems under arbitrary switching. First, combing fuzzy approximation and dynamic surface control (DSC), a switched adaptive state-feedback control scheme is proposed based on directly tuning the estimation of the switching ideal weight vectors in fuzzy logic systems. Switched adaptive laws and switched first-order filters in DSC are designed at each step in the backstepping to reduce the conservativeness caused by adoption of common adaptive laws and filters for each subsystem. By constructing a novel common Lyapunov function, the boundedness of the closed-loop system is ensured, while the tracking error converges to a small neighborhood of the origin. Next, based on the estimation of ideal weight vectors, the proposed adaptive control scheme is extended to the output-feedback case where a switched fuzzy observer is first proposed to estimate the unmeasured states. The main advantage of the developed adaptive control schemes is that the changes of plant can be considered explicitly due to switching, which contributes to less conservativeness of the designed controllers. Finally, two simulation results illustrate the effectiveness of the proposed schemes.