• Journal of rehabilitation welfare engineering & assistive technology
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• v.11 no.2
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• pp.179-186
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• 2017

Because facial asymmetry is caused by various causes, the cause analysis is important and quantitative index is needed to the evaluation. In this study, we applied the Nottingham Grading System that was used as a quantitative index to evaluate the facial paralysis by tracking the markers through the image processing and calculating the distance between the markers with images obtained by using the webcam, to evaluate facial asymmetry. The existing Nottingham Grading System has a problem of causing a measurement error in the specific case because the left and right are compared by summing the distance change between the feature points of the face part according to the change of the facial expression. We compared the case of the facial asymmetry and case of normal subject by using the existing Nottingham Grading System and the improved Nottingham grading system. In the existing Nottingham Grading System, case of facial asymmetry and case of facial symmetry were 99.0% and 95.0% respectively in the normal range, but the improved Nottingham Grading System showed facial asymmetry case was 74.0% and facial symmetrical case was 93.2%. The results of experiment show that the improved Nottingham Grading System allows detailed evaluation of each site and improved the problem of the Nottingham Grading System for specific cases.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.5
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• pp.722-727
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• 2010

This paper is presented to study simplification of face image using cubic spline interpolation for a business card with 2D barcode. People often forget business card's owner because business cards don't be included a face picture generally. To solve such problem, many applications have be developed through mobile devices, Internet and so on. But they couldn't caught up with value of existing business card mad by paper. Hence, some methods which can put information on business card using 1D or 2D barcode had suggested. but they couldn't include information like face image or company logo image which have too much data. Therefore, we study the simplification method of face image to encode from a face image to 2D barcode. The simplification method using spline curves defined by feature points which we dotted on face, ears, hair, eyebrows, nose, lips, neck, etc.. on a face area. for experiment, we see real face image and simplified face image made by proposed method after we automatically extract face watch through camera. In experimental results, data of simplified face image was reduced as small as it can be expressed by 2D barcode, and confirmed that it can effectively express features.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.5
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• pp.95-101
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• 2008

Modeling of stock prices forecast has been considered as one of the most difficult problem to develop accurately since stock prices are highly correlated with various environmental conditions including economics and political situation. In this paper, we propose a agent system approach to predict Korea Composite Stock Price Index (KOSPI) using neural network and statistical methods. To minimize mean of prediction error and variation of prediction error, agent system includes sub-agent modules for feature extraction, variables selection, forecast engine selection, and forecasting results analysis. As a first step to develop agent system for KOSPI forecasting, twelve economic indices are selected from twenty two basic standard economic indices using principal component analysis. From selected twelve economic indices, prediction model input variables are chosen again using best-subsets regression method. Two different types data are tested for KOSPI forecasting and the Prediction results showed 11.92 points of root mean squared error for consecutive thirty days of prediction. Also, it is shown that proposed agent system approach for KOSPI forecast is effective since required types and numbers of prediction variables are time-varying, so adaptable selection of modeling inputs and prediction engine are essential for reliable and accurate forecast model.

• Journal of Animal Science and Technology
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• v.46 no.2
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• pp.251-260
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• 2004

The feasibility and stability of ORP, pH(mV) and DO as a real-time control parameter for SBR process were evaluated in this study. During operation, NBP(nitrogen break point) and NKP(nitrate knee point), which reveal the biological and chemical changes of pollutants, were clearly observed on ORP and pH(mV)-time profiles, and those control points were easily detected by tracking the moving slope changes(MSC). However, when balance of aeration rate to loading rate, or to OUR(oxygen uptake rate), was not optimally maintained, either false NBP was occurred on ORP and DO curves before the appearance of real NBP or specific NBP feature was disappeared on ORP curve. Under that condition, however, very distinct NBP was found on pH(mV)-time profile, and stable detection of that point was feasible by tracking MSC. These results might mean that pH(mV) is superior real-time control parameter for aerobic process than ORP and DO. Meanwhile, as a real-time control parameter for anoxic process, ORP was very stable and more useful parameter than others. Based on these results, a stable real-time control of process can be achieved by using the ORP and pH(mv) parameters in combination rather than using separately. A complete removal of pollutants could be always ensured with this real-time control technology, despite the variations of wastewater and operation condition, as well as an optimization of treatment time and capacity could be feasible.

• Journal of KIISE:Computer Systems and Theory
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• v.31 no.12
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• pp.726-734
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• 2004

This paper presents a facial animation method that enables the user to select a sequence of facial frames from the facial expression space, whose level of details the user can select hierarchically Our system creates the facial expression space from about 2400 captured facial frames. To represent the state of each expression, we use the distance matrix that represents the distance between pairs of feature points on the face. The shortest trajectories are found by dynamic programming. The space of facial expressions is multidimensional. To navigate this space, we visualize the space of expressions in 2D space by using the multidimensional scaling(MDS). But because there are too many facial expressions to select from, the user faces difficulty in navigating the space. So, we visualize the space hierarchically. To partition the space into a hierarchy of subspaces, we use fuzzy clustering. In the beginning, the system creates about 10 clusters from the space of 2400 facial expressions. Every tine the level increases, the system doubles the number of clusters. The cluster centers are displayed on 2D screen and are used as candidate key frames for key frame animation. The user selects new key frames along the navigation path of the previous level. At the maximum level, the user completes key frame specification. We let animators use the system to create example animations, and evaluate the system based on the results.

• The KIPS Transactions:PartB
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• v.15B no.1
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• pp.25-36
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• 2008

In this paper, we present a robust hand recognition approach to sudden illumination changes. The proposed approach constructs a background model with respect to hue and hue gradient in HSI color space and extracts a foreground hand region from an input image using the background subtraction method. Eighteen features are defined for a hand pose and multi-class SVM(Support Vector Machine) approach is applied to learn and classify hand poses based on eighteen features. The proposed approach robustly extracts the contour of a hand with variations in illumination by applying the hue gradient into the background subtraction. A hand pose is defined by two Eigen values which are normalized by the size of OBB(Object-Oriented Bounding Box), and sixteen feature values which represent the number of hand contour points included in each subrange of OBB. We compared the RGB-based background subtraction, hue-based background subtraction and the proposed approach with sudden illumination changes and proved the robustness of the proposed approach. In the experiment, we built a hand pose training model from 2,700 sample hand images of six subjects which represent nine numerical numbers from one to nine. Our implementation result shows 92.6% of successful recognition rate for 1,620 hand images with various lighting condition using the training model.

• The Journal of the Convergence on Culture Technology
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• v.7 no.2
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• pp.411-417
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• 2021

The field that studies human-computer interaction is called HCI (Human-computer interaction). This field is an academic field that studies how humans and computers communicate with each other and recognize information. This study is a study on hand gesture recognition for human interaction. This study examines the problems of existing recognition methods and proposes an algorithm to improve the recognition rate. The hand region is extracted based on skin color information for the image containing the shape of the human hand, and the center of gravity profile is calculated using principal component analysis. I proposed a method to increase the recognition rate of hand gestures by comparing the obtained information with predefined shapes. We proposed a method to increase the recognition rate of hand gestures by comparing the obtained information with predefined shapes. The existing center of gravity profile has shown the result of incorrect hand gesture recognition for the deformation of the hand due to rotation, but in this study, the center of gravity profile is used and the point where the distance between the points of all contours and the center of gravity is the longest is the starting point. Thus, a robust algorithm was proposed by re-improving the center of gravity profile. No gloves or special markers attached to the sensor are used for hand gesture recognition, and a separate blue screen is not installed. For this result, find the feature vector at the nearest distance to solve the misrecognition, and obtain an appropriate threshold to distinguish between success and failure.

• Journal of Navigation and Port Research
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• v.42 no.6
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• pp.491-500
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• 2018

Due to changes in the distribution industry, inner harbors have been on a sharp decline in the recent past. However, through the application of the right development plans, such harbors can be revitalized into vibrant urban areas again. The importance of inner harbors has been recognized by the relevant authorities in Korea which are now pushing forward with redevelopment plans for its inner harbors. This study proposes a new approach to redevelopment plans based on the recognition that inner harbors have unique characteristics involving both inland areas and the ocean. In the study, representative inner harbors were selected and analyzed comparatively according to two distinct concepts of location: the Gateway Concept and the Central Place Concept. Based on these concepts, the conditions of the inner harbors were examined. Their location can be typed, and development directions were proposed according to their types and conditions. However, difficult points such as isolation and separation between an urban district and the harbor area, can be obstructions to their potential revival. An inner harbor needs to be considered as an intermediary, connecting place between the ocean and the city, not as another ordinary development area. In addition, a redevelopment plan should be accompanied by a strategic viewpoint to make the most of this feature.

• Korean Journal of Remote Sensing
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• v.38 no.3
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• pp.283-298
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• 2022

As Synthetic Aperture Radar (SAR) image can be acquired regardless of the weather and day or night, it is highly recommended to be used for Automatic Target Recognition (ATR) in the fields of surveillance, reconnaissance, and national security. However, there are some limitations in terms of cost and operation to build various and vast amounts of target images for the SAR-ATR system. Recently, interest in the development of an ATR system based on simulated SAR images using a target model is increasing. Attributed Scattering Center (ASC) matching and template matching mainly used in SAR-ATR are applied to target classification. The method based on ASC matching was developed by World View Vector (WVV) feature reconstruction and Weighted Bipartite Graph Matching (WBGM). The template matching was carried out by calculating the correlation coefficient between two simulated images reconstructed with adjacent points to each other. For the performance analysis of the two proposed methods, the Synthetic and Measured Paired Labeled Experiment (SAMPLE) dataset was used, which has been recently published by the U.S. Defense Advanced Research Projects Agency (DARPA). We conducted experiments under standard operating conditions, partial target occlusion, and random occlusion. The performance of the ASC matching is generally superior to that of the template matching. Under the standard operating condition, the average recognition rate of the ASC matching is 85.1%, and the rate of the template matching is 74.4%. Also, the ASC matching has less performance variation across 10 targets. The ASC matching performed about 10% higher than the template matching according to the amount of target partial occlusion, and even with 60% random occlusion, the recognition rate was 73.4%.

• Korean Journal of Remote Sensing
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• v.38 no.6_4
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• pp.1901-1910
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• 2022

Arriving in the new space age, securing technology for fusion application of KOMPSAT-3·3A and global satellite images is becoming more important. In general, multi-sensor satellite images have relative geometric errors due to various external factors at the time of acquisition, degrading the quality of the satellite image outputs. Therefore, we propose a fine-image registration methodology to minimize the relative geometric error between KOMPSAT-3·3A and global satellite images. After selecting the overlapping area between the KOMPSAT-3·3A and foreign satellite images, the spatial resolution between the two images is unified. Subsequently, tie-points are extracted using a hybrid matching method in which feature- and area-based matching methods are combined. Then, fine-image registration is performed through iterative registration based on pyramid images. To evaluate the performance and accuracy of the proposed method, we used KOMPSAT-3·3A, Sentinel-2A, and PlanetScope satellite images acquired over Daejeon city, South Korea. As a result, the average RMSE of the accuracy of the proposed method was derived as 1.2 and 3.59 pixels in Sentinel-2A and PlanetScope images, respectively. Consequently, it is considered that fine-image registration between multi-sensor satellite images can be effectively performed using the proposed method.