• The Transactions of the Korean Institute of Electrical Engineers P
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• v.64 no.3
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• pp.187-192
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• 2015

Detecting human in images is a challenging task owing to their variable appearance and the wide range of poses the they can adopt. The first need is a robust feature set that allows the human form to be discriminated cleanly, even in cluttered background under difficult illumination. A large number of vision application rely on matching keypoints across images. These days, the deployment of vision algorithms on smart phones and embedded device with low memory and computation complexity has even upped the ante: the goal is to make descriptors faster compute, more compact while remaining robust scale, rotation and noise. In this paper we focus on improving the speed of pedestrian(walking person) detection using Histogram of Oriented Gradient(HOG) descriptors provide excellent performance and tracking using kalman filter.

• Journal of KSNVE
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• v.6 no.5
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• pp.595-605
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• 1996

If the control force designed on the basis of the mathematical model with parameter errors is applied to control the actual system, the closed-loop performance of the actual system will be degraded depending on the degree of the errors, In this study, the effect of parameter errors on the robustness of several natural controls has been analyzed and compared. Every asymptoic stability condition for the natural controls has been derived using Lyapunov approach, and the characteristics of the stability conditions has also been compared. The extent of deviation of the closed-loop performance from the designed one for the natural controls is derived using operator techniques, and evaluated by numerical method. It has been found that the optimal control, acceleration feedback control, and acceleration-position feedback control among the considered natural controls would be robust one with respect to the parameter errors.

• Industrial Engineering and Management Systems
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• v.6 no.2
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• pp.119-124
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• 2007

The most difficult and time-intensive issue in the successful implementation of genetic algorithms is to find good parameter setting, one of the most popular subjects of current research in genetic algorithms. In this study, we present a new efficient experimental design method for parameter optimization in a genetic algorithm for general job shop scheduling problem using the Taguchi method. Four genetic parameters including the population size, the crossover rate, the mutation rate, and the stopping condition are treated as design factors. For the performance characteristic, makespan is adopted. The number of jobs, the number of operations required to be processed in each job, and the number of machines are considered as noise factors in generating various job shop environments. A robust design experiment with inner and outer orthogonal arrays is conducted by computer simulation, and the optimal parameter setting is presented which consists of a combination of the level of each design factor. The validity of the optimal parameter setting is investigated by comparing its SN ratios with those obtained by an experiment with full factorial designs.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.11a
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• pp.373-376
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• 2006

In general, there are three kinds of methods in analyzing dynamic robust design experiment: loss model approach, response function approach, and response model approach. In this talk, we review the three modeling approaches in terms of several criteria in comparison. This talk also generalizes the response model approach based on a generalized linear model. We develop a generalized two-step optimization procedure to substantially reduce the process variance by dampening the effect of both explicit and hidden noise variables. The proposed method provides more reliable results through iterative modeling of the residuals from the fitted response model. The method is compared with three existing approaches in practical examples.

• Journal of the Korea Institute of Military Science and Technology
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• v.4 no.2
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• pp.30-41
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• 2001

In Underwater Flight Vehicle depth control system, the followings must be required. First, It needs a robust performance which can get over the nonlinear characteristics due to hull shape. Second, It needs an accurate performance which has the small overshoot phenomenon and steady state error to avoid colliding with ground surface and obstacles. Third, It needs a continuous control input to reduce the acoustic noise. Finally, It needs an effective interpolation method which can reduce the dependency of control parameters on speed. To solve these problems, we propose a Intelligence depth control method using Fuzzy Sliding Mode Controller and Neural Network Interpolator. Simulation results show the proposed control scheme has robust and accurate performance by continuous control input and has no speed dependency problem.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.713-716
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• 2007

In this paper, we consider one of robust control system, fuzzy PI+fuzzy D controller dealing with noise, load, changed parameters of plant. We apply PI+D controller with a design for output of differential function and, we plan fuzzy controller with input for PID parameter of PI+D controller so We design control system meet with the change of environment with robust in relation to change of parameter. Fuzzy control is possessed of easy 4 rules and membership function and We design fuzzy PI+fuzzy D controller. Plant of this paper make a choice of 3 phase induction motor.

• Proceedings of the KIEE Conference
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• 1999.11c
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• pp.552-554
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• 1999

In this paper, a robust control system with the disturbance observer is proposed for BLDC servo system. The overall control system consists of the speed controller which is implemented with PI controller and the disturbance observer with free parameters. The proposed control system is designed the command input response and the closed loop characteristics independently by using two-degrees-of-freedom concept, so it can improve the closed loop characteristics with no influence on the command input response. The effective suppression of disturbance with the observer improves the characteristics of the closed loop of the system. And also, by fluting the bandwidth of free parameters, measurement noise is considered. To verify the better performance of the proposed control system than that of the conventional PI controllers, the performance of the controller is analyzed theoretically and some simulation results are presented.

• Proceedings of the KIEE Conference
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• 1989.11a
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• pp.426-429
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• 1989

The robust self-tuning controller is designed which is guaranteed the asymptotic regulation and tracking in the presence of a bounded parameter perturbation. The global stability in the presence of a finite noise and disturbance is ensured. The controller has a error driven structure, and involves the common model of a disturbance and reference input in the internal model. The adaptive system tunes the controller parameters such that the quadratic performance index which involves a weighting factor is optimized.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.909-911
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• 1999

This paper proposes a robust control system with the disturbance observer for BLDC servo system. The overall control system is composed of the speed controller which is implemented with PI controller and the disturbance observer with free parameters. The BLDC servo system can improve the closed loop characteristics without affecting the command input response. The characteristics of the closed loop system is improved by suppressing disturbance effectively with the disturbance observer. Measurement noise is also considered by adjusting bandwidth of free parameters. We can overcome the drawbacks of the conventional PI controller. Finally, the performance of the controller is analyzed theoretically and some simulation results are presented to demonstrate the better performance than the conventional PI controllers.

• Speech Sciences
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• v.13 no.1
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• pp.129-139
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• 2006

In this paper, we propose two effective energy feature normalization methods for robust speech recognition in noisy environments. In the first method, we estimate the noise energy and remove it from the noisy speech energy. In the second method, we propose a modified algorithm for the Log-energy Dynamic Range Normalization (ERN) method. In the ERN method, the log energy of the training data in a clean environment is transformed into the log energy in noisy environments. If the minimum log energy of the test data is outside of a pre-defined range, the log energy of the test data is also transformed. Since the ERN method has several weaknesses, we propose a modified transform scheme designed to reduce the residual mismatch that it produces. In the evaluation conducted on the Aurora2.0 database, we obtained a significant performance improvement.