• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.1
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• pp.91-96
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• 2015

In this study, we introduce robust face recognition system with illumination variation realized with the aid of CT preprocessing method. As preprocessing algorithm, Census Transform(CT) algorithm is used to extract locally facial features under unilluminated condition. The dimension reduction of the preprocessed data is carried out by using $(2D)^2$PCA which is the extended type of PCA. Feature data extracted through dimension algorithm is used as the inputs of proposed radial basis function neural networks. The hidden layer of the radial basis function neural networks(RBFNN) is built up by fuzzy c-means(FCM) clustering algorithm and the connection weights of the networks are described as the coefficients of linear polynomial function. The essential design parameters (including the number of inputs and fuzzification coefficient) of the proposed networks are optimized by means of artificial bee colony(ABC) algorithm. This study is experimented with both Yale Face database B and CMU PIE database to evaluate the performance of the proposed system.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.2
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• pp.127-135
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• 1999

Multivarialbe Boiler-Turbine H_\infty Control System Genetic Algorithm Weighting Functions $W_1$(s), $W_2$(s), and design parameter $\gamma$ that are given by Glover-Doyle algorithm, to optimally follow the output of reference model. The first method to do this is that the gains of weighting functions $W_1$(s), $W_2$(s), and design parameter are optimized simultaneously by genetic algorithm with the tournament method that can search more diversely, in the search domain which guarantees the robust stability of system. And the second method is that not only by genetic algorithm with the roulette-wheel method that can search more fast, in that search domain. The boiler-turbine H_\infty control system designed by theabove second method has not only the robust stability to a modeling error but also the the better command tracking preformance than those of the H_\infty control system designed by trial-and-error method and the above first method. Also, this boiler-turbine H_\infty control system has the better performance than that of the LQG/LTR contro lsystem. The effectiveness of this boiler-turbineH_\infty control system is verified by computer simulation.

• IEMEK Journal of Embedded Systems and Applications
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• v.14 no.5
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• pp.277-286
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• 2019

In this paper, we propose an efficient hyperplane generation technique to classify human activity from combination of events and sequence information obtained from multiple-event sensors. By generating hyperplane efficiently, our machine learning algorithm classify with less memory and run time than the LSVM (Linear Support Vector Machine) for embedded system. Because the fact that light weight and high speed algorithm is one of the most critical issue in the IoT, the study can be applied to smart home to predict human activity and provide related services. Our approach is based on reducing numbers of hyperplanes and utilizing robust string comparing algorithm. The proposed method results in reduction of memory consumption compared to the conventional ML (Machine Learning) algorithms; 252 times to LSVM and 34,033 times to LSTM (Long Short-Term Memory), although accuracy is decreased slightly. Thus our method showed outstanding performance on accuracy per hyperplane; 240 times to LSVM and 30,520 times to LSTM. The binarized image is then divided into groups, where each groups are converted to binary number, in order to reduce the number of comparison done in runtime process. The binary numbers are then converted to string. The test data is evaluated by converting to string and measuring similarity between hyperplanes using Levenshtein algorithm, which is a robust dynamic string comparing algorithm. This technique reduces runtime and enables the proposed algorithm to become 27% faster than LSVM, and 90% faster than LSTM.

• Proceedings of the KIEE Conference
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• 2008.04a
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• pp.213-214
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• 2008

This paper considers design of a robust controller that alleviates disturbance effects and compensates performance degradation of plants with time-delay. Disturbance observer(DOB) approach as a tool of robust control has been widely employed in industry. However, since the time-delay makes the plant non-minimum phase, classical DOB cannot be applied directly to the time-delay system. By using a new DOB structure for non-minimum phase systems together with the Smith Predictor, we propose a new control algorithm for reducing the effects of disturbance and time-delay of the system.

• ETRI Journal
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• v.35 no.3
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• pp.512-522
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• 2013

In this paper, the radius weight mean (RWM) and the feature-embedding technique are used to present a novel watermarking scheme for color images. Simulations validate that the stego-images generated by the proposed scheme are robust against most common image-processing operations, such as compression, color quantization, bit truncation, noise addition, cropping, blurring, mosaicking, zigzagging, inversion, (edge) sharpening, and so on. The proposed method possesses outstanding performance in resisting high compression ratio attacks: JPEG2000 and JPEG. Further, to provide extra hiding storage, a steganographic method using the RWM with the least significant bit substitution technique is suggested. Experiment results indicate that the resulting perceived quality is desirable, whereas the peak signal-to-noise ratio is high. The payload generated using the proposed method is also superior to that generated by existing approaches.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.368-371
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• 1996

In this paper, a feedforward-plus-feedback control scheme is presented to prevent congestion in store-and-forward packet switching networks. The control scheme consists of two algorithms. Specifically, the input traffic adjustment algorithm employs a fairness policy such that the transmission rate of the input traffic is proportional to its offered rate. The control signal computation algorithms to ensure stability of the overall system in the robust sense and to ensure the desired transient behavior in the adaptive, with respect to variations of input traffic, are designed.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.798-802
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• 2003

The robustness and reliability of vision algorithms is the key issue in robotic research and industrial applications. In this paper, the robust real time visual tracking in complex scene is considered. A common approach to increase robustness of a tracking system is to use different models (CAD model etc.) known a priori. Also fusion of multiple features facilitates robust detection and tracking of objects in scenes of realistic complexity. Because voting is a very simple or no model is needed for fusion, voting-based fusion of cues is applied. The approach for this algorithm is tested in a 3D Cartesian robot which tracks a toy vehicle moving along 3D rail, and the Kalman filter is used to estimate the motion parameters, namely the system state vector of moving object with unknown dynamics. Experimental results show that fusion of cues and motion estimation in a tracking system has a robust performance.

• Transactions of the Korean Society of Mechanical Engineers
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• v.15 no.1
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• pp.78-89
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• 1991

This Paper presents an application example of the Adaptive Robust Servocontrol (ARSC) scheme, which is an explicit (or indirect) pole-assignment adaptive algorithm with the property of "robustness". The ARSC scheme is applied to an end-milling process for cutting force regulation. It is shown that the federate of an end-milling process can be maximized by the adaptive regulation of the peak cutting force through the ARSC scheme. The results of simulation study and real cutting experiment are presented. It has been verified that asymptotic regulation can be achieved with robustness against the slowly time-varying perturbations to the process model parameters, which are caused by nonlinear cutting dynamics. dynamics.

• IE interfaces
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• v.15 no.2
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• pp.189-198
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• 2002

Simulated Annealing(SA) approach has been successfully applied to the combinatorial optimization problems with NP-hard complexity. To apply an SA algorithm to specific problems, generic parameters as well as problem-specific parameters must be determined. To overcome the embedded nature of SA, long computational time, some studies suggested the parameter design methods of determining SA related parameters. In this study, we propose a new parameter design approach based on robust design method. To show the effectiveness of the proposed method, the extensive computation experiments are conducted on the mixed-model sequencing problems.

• Proceedings of the KIPE Conference
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• 2001.10a
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• pp.374-379
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• 2001

In this paper, a novel design algorithm of speed controller for an Induction motor with the inertia variation is proposed. The main contribution of our work is a very robust, reliable and stable procedure for setting of the PI gains against the specified range of the inertia variation of an induction motor using Kharitonovs robust control theory. Therefore, the basic segment of controller design, the variation of induction motor inertia is estimated by the RLS (Recursive least square) method. PI based speed controller is widely used in industrial application for its simple structure and reliable performance. In addition the Kharitonov robust control theory is used for verification stability of closed-loop transfer function. The performance of this proposed design method is proved by digital simulation and experimentation with high performance DSP based induction motor driving system.