• Journal of the Korea Society of Computer and Information
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• v.15 no.3
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• pp.27-35
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• 2010

Accurately extracting skin color regions is very important in various areas such as face recognition and tracking, facial expression recognition, adult image identification, health-care, and so forth. In this paper, we evaluate the performances of several skin color detection algorithms in indoor environments by changing the distance between the camera and the object as well as the background colors of the object. The distance is from 60cm to 120cm and the background colors are white, black, orange, pink, and yellow, respectively. The algorithms that we use for the performance evaluation are Peer algorithm, NNYUV, NNHSV, LutYUV, and Kimset algorithm. The experimental results show that NNHSV, NNYUV and LutYUV algorithm are stable, but the other algorithms are somewhat sensitive to the changes of backgrounds. As a result, we expect that the comparative experimental results of this paper will be used very effectively when developing a new skin color extraction algorithm which are very robust to dynamic real environments.

• Journal of KIISE:Computing Practices and Letters
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• v.11 no.3
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• pp.284-291
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• 2005

I propose a framework for detecting, recognizing and classifying facial features based on learned expression patterns. The framework recognizes facial expressions by using PCA and expression HMM(EHMM) which is Hidden Markov Model (HMM) approach to represent the spatial information and the temporal dynamics of the time varying visual expression patterns. Because the low level spatial feature extraction is fused with the temporal analysis, a unified spatio-temporal approach of HMM to common detection, tracking and classification problems is effective. The proposed recognition framework is accomplished by applying posterior probability between current visual observations and previous visual evidences. Consequently, the framework shows accurate and robust results of recognition on as well simple expressions as basic 6 facial feature patterns. The method allows us to perform a set of important tasks such as facial-expression recognition, HCI and key-frame extraction.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.6
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• pp.746-753
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• 2008

In this paper, we introduce the improved control method are communicated between a master and a slave robot in the teleoperation systems. When the master and slave robots are located in different places, time delay is unavoidable under the network environment and it is well known that the system can become unstable when even a small time delay exists in the communication channel. The time delay may cause instability in teleoperation systems especially if those systems include haptic feedback. This paper presents a control scheme based on the estimator with virtual master model in teleoperation systems over the network. As the behavior of virtual model is tracking the one of master model, the operator can control real master robot by manipulating the virtual robot. And LQG/LTR scheme was adopted for the compensation of un-modeled dynamics. The approach is based on virtual master model, which has been implemented on a robot over the network. Its performance is verified by the computer simulation and the experiment.

• Journal of Advanced Marine Engineering and Technology
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• v.37 no.2
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• pp.213-219
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• 2013

Recently, with the rapid advancement in information and communication technology, indoor location-based services(LBSs) that require precise position tracking have been actively studied with outdoor-LBS using GPS. However, in case of a ship which consists of steel structure, it is difficult to measure a precise localization due to significant ranging error by the diffraction and refraction of radio waves. In order to reduce location measurement errors that occur in these indoor environments, this paper presents distance compensation algorithms that are suitable for a narrow passage such as ship corridors without any additional sensors by using UWB(Ultra-wide-band), which is robust to multi-path and has an error in the range of a few centimeters in free space. These improvement methods are that Pythagorean theory and adjacent node technique are used to solve the distance error due to the node deployment and distance variation analysis technique is applied to reduce the ranging errors which are significantly fluctuated in the corner section. The experimental results show that the number of nodes and the distance error are reduced to 66% and 57.41%, respectively, compared with conventional CSS(Chirp spread spectrum) method.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.3
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• pp.319-327
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• 2012

In this paper, we present a sign language recognition model which does not use any wearable devices for object tracking. The system design issues and implementation issues such as data representation, feature extraction and pattern classification methods are discussed. The proposed data representation method for sign language patterns is robust for spatio-temporal variances of feature points. We present a feature extraction technique which can improve the computation speed by reducing the amount of feature data. A neural network model which is capable of incremental learning is described and the behaviors and learning algorithm of the model are introduced. We have defined a measure which reflects the relevance between the feature values and the pattern classes. The measure makes it possible to select more effective features without any degradation of performance. Through the experiments using six types of sign language patterns, the proposed model is evaluated empirically.

• The Transactions of the Korean Institute of Power Electronics
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• v.10 no.4
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• pp.411-419
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• 2005

In this paper, a sliding mode observer for solar array current estimation in the photovoltaic power generation system is presented. The solar array current estimation Information is obtained from the sliding mode observer and fed into the maximum power point tracker to update the reference voltage. The parameter values such as inverter dc link capacitances cm be changed up to 50$\%$ from their nominal values and the linear observer can't estimate the correct state values under the parameter variations and noisy environments. The configuration of sliding mode observer is simple, but it shows the robust tracking performance against parameter variations and modeling uncertainties. In this paper, the method for constructing the sliding mode observer using equivalent control input is presented and the convergence of the proposed observer is verified by the Lyapunov method. The mathematical modeling and the experimental results verify the validity of the proposed method.

• Journal of the Institute of Convergence Signal Processing
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• v.11 no.4
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• pp.320-325
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• 2010

This paper is about the method and procedure to register the candidate sequentially in the face recognition system using the PCA(Principal Components Analysis). We use the method to update the principal components sequentially with the SKL algorithm which is improved R-SVD algorithm. This algorithm enable us to solve the re-training problem of the increase the candidates number sequentially in the face recognition using the PCA. Also this algorithm can use in robust tracking system with the bright change based to the principal components. This paper proposes the procedure in the face recognition system which sequentially updates the principal components using the SKL algorithm. Then we compared the face recognition performance with the batch procedure for calculating the principal components using the standard KL algorithm and confirms the effects of the forgetting factor in the SKL algorithm experimentally.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.5
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• pp.937-941
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• 2008

People who have hearing difficulties use sign language as the most important communication method, and they can broaden personal relations and manage their everyday lives without inconvenience through sign language. But they suffer from absence of interpolation between normal people and people who have hearing difficulties in increasing video chatting or video communication services by recent growth of internet communication. In this paper, we proposed a sign language recognition method in order to solve such a problem. In the proposed method, regions of two hands are extracted by tracking of two hands using RGB, YUV and HSI color information from a sign language image acquired from a video camera and by removing noise in the segmented images. The extracted regions of two hands are teamed and recognized by ART2 algorithm that is robust for noise and damage. In the experiment by the proposed method and images of finger number from 1 to 10, we verified the proposed method recognize the numbers efficiently.

• Smart Structures and Systems
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• v.24 no.5
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• pp.567-585
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• 2019

In the past two decades, structural health monitoring (SHM) systems have been widely installed on various civil infrastructures for the tracking of the state of their structural health and the detection of structural damage or abnormality, through long-term monitoring of environmental conditions as well as structural loadings and responses. In an SHM system, there are plenty of sensors to acquire a huge number of monitoring data, which can factually reflect the in-service condition of the target structure. In order to bridge the gap between SHM and structural maintenance and management (SMM), it is necessary to employ advanced data processing methods to convert the original multi-source heterogeneous field monitoring data into different types of specific physical indicators in order to make effective decisions regarding inspection, maintenance and management. Conventional approaches to data analysis are confronted with challenges from environmental noise, the volume of measurement data, the complexity of computation, etc., and they severely constrain the pervasive application of SHM technology. In recent years, with the rapid progress of computing hardware and image acquisition equipment, the deep learning-based data processing approach offers a new channel for excavating the massive data from an SHM system, towards autonomous, accurate and robust processing of the monitoring data. Many researchers from the SHM community have made efforts to explore the applications of deep learning-based approaches for structural damage detection and structural condition assessment. This paper gives a review on the deep learning-based SHM of civil infrastructures with the main content, including a brief summary of the history of the development of deep learning, the applications of deep learning-based data processing approaches in the SHM of many kinds of civil infrastructures, and the key challenges and future trends of the strategy of deep learning-based SHM.

• Journal of Power Electronics
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• v.19 no.4
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• pp.881-893
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• 2019

This paper proposes a practical sliding-mode controller design for shaping the impedances of cascaded boost-converter power decoupling circuits for reducing the second order harmonic ripple in photovoltaic (PV) current. The cascaded double-boost converter, when used as power decoupling circuit, has some advantages in terms of a high step-up voltage-ratio, a small number of switches and a better efficiency when compared to conventional topologies. From these features, it can be seen that this topology is suitable for residential (PV) rooftop systems. However, a robust controller design capable of rejecting double frequency inverter ripple from passing to the (PV) source is a challenge. The design constraints are related to the principle of the impedance-shaping technique to maximize the output impedance of the input-side boost converter, to block the double frequency PV current ripple component, and to prevent it from passing to the source without degrading the system dynamic responses. The design has a small recovery time in the presence of transients with a low overshoot or undershoot. Moreover, the proposed controller ensures that the ripple component swings freely within a voltage-gap between the (PV) and the DC-link voltages by the small capacitance of the auxiliary DC-link for electrolytic-capacitor elimination. The second boost controls the main DC-link voltage tightly within a satisfactory ripple range. The inverter controller performs maximum power point tracking (MPPT) for the input voltage source using ripple correlation control (RCC). The robustness of the proposed control was verified by varying system parameters under different load conditions. Finally, the proposed controller was verified by simulation and experimental results.