• Journal of Institute of Control, Robotics and Systems
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• v.18 no.4
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• pp.328-336
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• 2012

In this paper, we propose a robust fingertip extraction and extended Continuously Adaptive Mean Shift (CAMSHIFT) based robust hand gesture recognition for natural human-like HRI (Human-Robot Interaction). Firstly, for efficient and rapid hand detection, the hand candidate regions are segmented by the combination with robust $YC_bC_r$ skin color model and haar-like features based adaboost. Using the extracted hand candidate regions, we estimate the palm region and fingertip position from distance transformation based voting and geometrical feature of hands. From the hand orientation and palm center position, we find the optimal fingertip position and its orientation. Then using extended CAMSHIFT, we reliably track the 2D hand gesture trajectory with extracted fingertip. Finally, we applied the conditional density propagation (CONDENSATION) to recognize the pre-defined temporal motion trajectories. Experimental results show that the proposed algorithm not only rapidly extracts the hand region with accurately extracted fingertip and its angle but also robustly tracks the hand under different illumination, size and rotation conditions. Using these results, we successfully recognize the multiple hand gestures.

• Korean Journal of Computational Design and Engineering
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• v.17 no.5
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• pp.375-386
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• 2012

Most engineering design problems require analyses or simulations to evaluate objective functions. However, a single simulation can take many hours or even days to finish for many real world problems. As a result, design optimization becomes impossible since they require hundreds or thousands of simulation evaluations. The surrogate-based optimization (SBO) strategy became a remedy for such computationally expensive analyses and simulations. A surrogate-based optimization strategy has been developed in this study in order to improve global optimization performance. The strategy is a heuristic algorithm and it exploits not only multiple surrogates, but also multiple optimizers. Multiple optimizations of multiple surrogate models yield multiple candidate design points of optima. During the sequential sampling process, the algorithm ranks candidate design points, selects the points as many as specified, and builds the improved surrogate model. Various mathematical functions with different numbers of design variables are chosen to compare the proposed method with the other most recent algorithm, MSEGO. The proposed method shows superior performance to the other method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.3
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• pp.1670-1683
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• 2017

Mitotic event recognition is a crucial and challenging task in biomedical applications. In this paper, we introduce the slow feature analysis and propose a fully-automated mitotic event recognition method for cell populations imaged with time-lapse phase contrast microscopy. The method includes three steps. First, a candidate sequence extraction method is utilized to exclude most of the sequences not containing mitosis. Next, slow feature is learned from the candidate sequences using slow feature analysis. Finally, a hidden conditional random field (HCRF) model is applied for the classification of the sequences. We use a supervised SFA learning strategy to learn the slow feature function because the strategy brings image content and discriminative information together to get a better encoding. Besides, the HCRF model is more suitable to describe the temporal structure of image sequences than nonsequential SVM approaches. In our experiment, the proposed recognition method achieved 0.93 area under curve (AUC) and 91% accuracy on a very challenging phase contrast microscopy dataset named C2C12.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.9
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• pp.977-982
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• 2014

In this paper, we propose a system which can detect the flame in real time from the high-quality IP camera. First, open directly the RTSP streams transmitted from the IP camera using the library FFmpeg as opening a video file. The second thing is to extract the background images from video signal using Gaussian mixture model. Then the foreground images are obtained through subtracting operation between the input image and the background image. Separated foreground image through a mathematical morphology operation are considered as candidate area. By analysing colour information and dynamic characteristics of the candidate area, flame is determined finally. Through the experiments with input videos from IP camera, the proposed algorithms were useful to detect flames.

• Water for future
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• v.24 no.1
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• pp.63-71
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• 1991

This study represents the methodology for feasibility analysis of small hydro power plants. Cumulative density function of Weibull distribution and Thi-essen method were adopted to beside flow duration curve at candidate sites. The performance prediction model and construction cost estimation model for tunnel-type small hydro power plants were developed. Eight candidate sites existing on Han river selected and surveyed for actual sites reconnaissance. The performance characteristics and economical feasibility for these sites were analyzed by using developed models. As a result, it was found that the optimum design flowrates with the lowest unit generation cost for tunnel-type small hydro power plants were the flowrate concerning with between 20 % and 30 % of time ratio on the flow duration curve. Additionally, primary design specifications such as design flowrate, effective head, capacity, annual average load factor, annual electricity production were estimated and discussed for surveyed sites.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.7 no.1
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• pp.55-60
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• 2014

In this paper, we proposed a method for face region extraction by skin-color hue, saturation and facial feature extraction in natural images. The proposed algorithm is composed of lighting correction and face detection process. In the lighting correction step, performing correction function for a lighting change. The face detection process extracts the area of skin color by calculating Euclidian distances to the input images using as characteristic vectors color and chroma in 20 skin color sample images. Eye detection using C element in the CMY color model and mouth detection using Q element in the YIQ color model for extracted candidate areas. Face area detected based on human face knowledge for extracted candidate areas. When an experiment was conducted with 10 natural images of face as input images, the method showed a face detection rate of 100%.

• Survey Research
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• v.12 no.1
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• pp.31-63
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• 2011

This study is to make an effective forecasting model for turn-out rates of the candidates with their visibilities, which are measured in their names on the media during the election period. I make a regressive model, with the data of 2010 gubernatorial election in Korea, where turn-out rate is dependent variable and each candidate's visibility, incumbency effect, local control party effect, corruption effect, strategy voting effect, restrain effect as a mid-term evaluation, and policy effect are independent variables. I got the model, T = -4.65 + 1.02V + 16.90 I + 16.78L - 9.12 R, where T is turn-out rate, V is candidate's visibility, I is incumbent effect, L is local control party effect, and R is restrain effect. This function can be used to predict turn-out rates of the candidates in the forthcoming gubernatorial election in Korea at a small outlay.

• The Magazine of the IEIE
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• v.37 no.8
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• pp.50-59
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• 2010

$0.18-{\mu}m$ high voltage technology 13.5V high voltage well-based symmetric EDMOS isolated by MTI was designed and fabricated. Using calibrated process and device model parameters, the characteristics of the symmetric and asymmetric EDMOS have been simulated. The asymmetric EDMOS has higher performance, better $R_{sp}$ / BVDSS figure-of-merit, short-channel immunity and smaller pitch size than the symmetric EDMOS. The asymmetric EDMOST is a good candidate for low-power and smaller source driver chips. The low voltage logic well-based EDMOS process has advantages over high voltage well-based EDMOS in process cost by eliminating the process steps of high-voltage well/drift implant, high-temperature long-time thermal steps, etc. The specific on-resistance of our well-designed logic well-based EDMOSTs is compatible with the smallest one published. TCAD simulation and measurement results show that the improved logic well-based nEDMOS has better electrical characteristics than those of the conventional one. The improved EDMOS proposed in this paper is an excellent candidate to be integrated with low voltage logic devices for high-performance low-power low-cost chips.

• Genomics & Informatics
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• v.14 no.4
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• pp.149-159
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• 2016

With the success of the genome-wide association studies (GWASs), many candidate loci for complex human diseases have been reported in the GWAS catalog. Recently, many disease prediction models based on penalized regression or statistical learning methods were proposed using candidate causal variants from significant single-nucleotide polymorphisms of GWASs. However, there have been only a few systematic studies comparing existing methods. In this study, we first constructed risk prediction models, such as stepwise linear regression (SLR), least absolute shrinkage and selection operator (LASSO), and Elastic-Net (EN), using a GWAS chip and GWAS catalog. We then compared the prediction accuracy by calculating the mean square error (MSE) value on data from the Korea Association Resource (KARE) with body mass index. Our results show that SLR provides a smaller MSE value than the other methods, while the numbers of selected variables in each model were similar.

• The Journal of the Acoustical Society of Korea
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• v.15 no.3E
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• pp.31-36
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• 1996

In this paper, a two-stage recognition approach based on error pattern hypotheses is proposed to reduce errors of a connected digit recognizer. In the approach, a conventional recognizer is first used to produce N-best candidate strings, and then error patterns are hypothesized by examining the candidate strings. For substitution error pattern hypotheses, error-pattern-dependent classifiers having more discriminative power than the first-stage classifier are used ; and for insertion and deletion errors, word duration and energy contour information are exploited are exploited to discriminated confusing pairs. Simulation results showed that the proposed approach achieves 15% decrease in word error rate for speaker-independent Korean connected digit recognition when a hidden Markov model-based recognizer is used for the first-stage classifier.