• Proceedings of the KIEE Conference
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• 1990.11a
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• pp.391-395
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• 1990

A new control algorithm is developed to achieve the robust performance of the system during the overall control process. Time-varying sliding manifolds are proposed to remove the reaching phase which is one of common shortcomings of variable structure control scheme. A necessary and sufficient condition for the existence of a sliding mode on the newly proposed time-varying sliding mode on the newly proposed time-varying sliding manifolds is derived by Lyapunov's second method. The digital simulation results show that the newly proposed control algorithm is superior to the typical variable structure control algorithm with respect to the robust performance of the system. The simplicity of the proposed control algorithm encourages control engineers to implement the proposed control algorithm in many control problems.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.05a
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• pp.164-169
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• 2005

Microphone array techniques are being used widely in wind tunnel measurements for identification of the distributed aerodynamic noise sources on the model being tested. Depending on the frequencies and sound levels, conventional beamforming algorithm has limitation in separating two adjacent sources. Several modifications to the classical beamforming have been developed to enhance way resolution and reduce sidelobe levels. In this Paper the robust adaptive beamforming and the CLEAN algorithm are used to compare to the result of conventional beamforming method. It is found that the CLEAN algorithm is capable of pin-pointing locations of multiple sources nearby, while these sources are unidentifiable with robust adaptive or conventional beamforming techniques.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.4
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• pp.167-173
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• 2024

This paper proposes a robust algorithm for heart rate estimation using remote photoplethysmography (rPPG). The algorithm employs a combination of adaptive filtering and frequency tracking to enhance the signal-to-noise ratio (SNR) and accurately estimate heart rates from facial videos. The LGI dataset, comprising videos of six participants performing various activities (resting, rotation, talk, gym), was utilized for evaluation. The ground truth heart rate was obtained using a CMS50E pulse oximeter, and a 10-second data window with FFT-based frequency analysis was applied to derive reference heart rates. The proposed method detects the face using Mediapipe API, selects the forehead region of interest (ROI), and extracts RGB signals. The signals undergo preprocessing, motion noise removal via adaptive filtering, and heart rate estimation using an adaptive notch filter. Experimental results demonstrate that the proposed algorithm outperforms existing methods, especially in challenging conditions such as during gym and talk activities.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.177-180
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• 1996

The purpose of this paper is to design a robust adaptive control algorithm for a class of systems having continuous relay nonlinearity. This continuous relay nonlinearity can be defined as an analytic nonlinear function having unknown parameters and bounded unmodeling part. By this mathematical modeling, the whole system can be considered as a nonlinear system having unknown parameters and bounded perturbation. The control algorithm of this paper, RALC, can be constructed by robust adaptive law, feedback linearization, and indirect robust adaptive control. By this RALC, we can obtain that the output of given system can follow that of a stable reference linear model made by designer and the boundedness of all signals in closed-loop system can be maintained. Therefore, we can confirm a robust adaptive control for a class of systems having continuous relay nonlinearity.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.30 no.4
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• pp.329-340
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• 1994

This paper presents a dynamic compensation methodology for robust trajectory tracking control of uncertain robot manipulators. To improve tracking performance of the system, a full model-based feedforward compensation with continuous VS-type robust control is developed in this paper(i.e,. robust decentralized adaptive control scheme). Since possible bounds of uncertainties are unknown, the adaptive bounds of the robust control is used to directly estimate the uncertainty bounds(instead of estimating manipulator parameters as in centralized adaptive control0. The global stability and robustness issues of the proposed control algorithm have been investigated extensively and rigorously via a Lyapunov method. The presented control algorithm guarantees that all system responses are uniformly ultimately bounded. Thus, it is shown that the control system is evaluated to be highly robust with respect to significant uncertainties.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.6
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• pp.656-666
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• 1999

In this paper, we design the optimal PD control system which has the robust performance. This PD control system is designed by applying genetic algorithm (GA) to the determination of proportional gain KP and derivative gain KD that are given by PD servo controller, to make the output of plant follow the output of reference model optimally. These proportional and derivatibe gains are simultaneously optimized in the search domain guaranteeing the robust performance of system. And, this PD control system is compared with $\mu$ -synthesis control system for the robust performance. The PD control system designed by the proposed method has not only the robust performance but also the better command tracking performance than that of the $\mu$ -synthesis control system. The effectiveness of this control system is verified by computer simulation.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.4
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• pp.362-368
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• 2001

We study on the velocity matching algorithm for transfer alignment of inertial navigation system(INS) using a robust H$_2$ filter. We suggest an uncertainty model and a discrete robust H$_2$filter for INS and apply the suggested robust H$_2$ filter to the uncertainty model. The discrete robust H$_2$filter is shown by simulation to have better performance time and accuracy than Kalman filter.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.9
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• pp.4684-4705
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• 2019

Collaborative filtering recommender systems are vulnerable to shilling attacks in which malicious users may inject biased profiles to promote or demote a particular item being recommended. To tackle this problem, many robust collaborative recommendation methods have been presented. Unfortunately, the robustness of most methods is improved at the expense of prediction accuracy. In this paper, we construct a robust Bayesian probabilistic matrix factorization model for collaborative filtering recommender systems by incorporating the detection of user anomaly rating behaviors. We first detect the anomaly rating behaviors of users by the modified K-means algorithm and target item identification method to generate an indicator matrix of attack users. Then we incorporate the indicator matrix of attack users to construct a robust Bayesian probabilistic matrix factorization model and based on which a robust collaborative recommendation algorithm is devised. The experimental results on the MovieLens and Netflix datasets show that our model can significantly improve the robustness and recommendation accuracy compared with three baseline methods.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.7 no.3
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• pp.65-78
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• 1997

Due to the cryptographic functional structure including the S-box, DES is not robust against differential cryptoanalysis (DC). Therefore, to increase the security against DC, we have to redesign the S-box or modify DES algorithm to decrease the probability for the N-1 round characteristics. However, it has been shown that a new design for the S-box is not secure enough. Rather, if will be more reliable to devise a modified cryptographic algorithm. In this paper, we propose a modified DES algorithm to decrease the probability of N-1 round characteristics to be robust against DC. According to our comparative study, the proposed algorithm is shown to be more robust against the DC than DES.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.632-635
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• 2003

This paper describes a system fur tracking multiple faces in an input video sequence using facial convex hull based facial segmentation and robust hausdorff distance. The algorithm adapts skin color reference map in YCbCr color space and hair color reference map in RGB color space for classifying face region. Then, we obtain an initial face model with preprocessing and convex hull. For tracking, this algorithm computes displacement of the point set between frames using a robust hausdorff distance and the best possible displacement is selected. Finally, the initial face model is updated using the displacement. We provide an example to illustrate the proposed tracking algorithm, which efficiently tracks rotating and zooming faces as well as existing multiple faces in video sequences obtained from CCD camera.