• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.7
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• pp.3172-3193
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• 2018

Recently, several studies have shown that linear representation based approaches are very effective and efficient for image classification. One of these linear-representation-based approaches is the Collaborative representation (CR) method. The existing algorithms based on CR have two major problems that degrade their classification performance. First problem arises due to the limited number of available training samples. The large variations, caused by illumintion and expression changes, among query and training samples leads to poor classification performance. Second problem occurs when an image is partially noised (contiguous occlusion), as some part of the given image become corrupt the classification performance also degrades. We aim to extend the collaborative representation framework under limited training samples face recognition problem. Our proposed solution will generate virtual samples and intra-class variations from training data to model the variations effectively between query and training samples. For robust classification, the image patches have been utilized to compute representation to address partial occlusion as it leads to more accurate classification results. The proposed method computes representation based on local regions in the images as opposed to CR, which computes representation based on global solution involving entire images. Furthermore, the proposed solution also integrates the locality structure into CR, using Euclidian distance between the query and training samples. Intuitively, if the query sample can be represented by selecting its nearest neighbours, lie on a same linear subspace then the resulting representation will be more discriminate and accurately classify the query sample. Hence our proposed framework model the limited sample face recognition problem into sufficient training samples problem using virtual samples and intra-class variations, generated from training samples that will result in improved classification accuracy as evident from experimental results. Moreover, it compute representation based on local image patches for robust classification and is expected to greatly increase the classification performance for face recognition task.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.823-828
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• 2008

This study set out investigate what kind of changes would be caused to the craving of alcohol dependent patients by stimuli through virtual reality in the preparation stage for drinking refusal training. With regard to stimulation, it included alcohol exposure, a positive situation, and a negative situation on the basis of drinking encouraging situations(social pressure) known as drinking stimulating situations for alcoholics. The purpose of the study is to provide fundamental materials for the development of new training programs to refuse alcohol and for the examination of the possibility of utilizing virtual reality technology as a new treatment and training tool for alcoholics. To this end, a virtual reality program was conducted involving 12 alcoholics admitted to Severance Mental Health Hospital in Gyeonggi province from December 2006 to September 2007. The data was dealt with various statistic analyses such as frequency analysis, Wilcoxon Matched-pairs Signed-Ranks Test using SPSS/WIN 11.5 The analysis results indicate that avatars encouraging drinking caused more craving than just the background, that the scenes with alcohol exposed caused more craving than the scenes with no alcohol exposed, and that there were no significant changes to craving according to a positive or negative situation. The results confirmed that training sessions using virtual reality presented a situation and environment of drinking pressure that's similar to the actual social pressure and that the virtual reality approach had enormous potential as an effective treatment tool when combined with the existing treatment techniques.

• The KIPS Transactions:PartD
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• v.11D no.6
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• pp.1259-1268
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• 2004

To organize training in limited training area for an actuai combat, realistic training simulation plugged in by various battle conditions is essential. In this paper, we propose a virtual target overlay technique which does not use a virtual image, but Projects a virtual target on ground-based CCD image by appointed scenario for a realistic training simulation. In the proposed method, we create a realistic 3D model (for an instructor) by using high resolution Geographic Tag Image File Format(GeoTIFF) satellite image and Digital Terrain Elevation Data (DTED), and extract the road area from a given CCD image (for both an instructor and a trainee). Satellite images and ground-based sensor images have many differences in observation position, resolution, and scale, thus yielding many difficulties in feature-based matching. Hence, we propose a moving synchronization technique that projects the target on the sensor image according to the marked moving path on 3D satellite image by applying Thin-Plate Spline(TPS) interpolation function, which is an image warping function, on the two given sets of corresponding control point pair. To show the experimental result of the proposed method, we employed two Pentium4 1.8MHz personal computer systems equipped with 512MBs of RAM, and the satellite and sensor images of Daejoen area are also been utilized. The experimental result revealed the effective-ness of proposed algorithm.

• Journal of the Korea Computer Graphics Society
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• v.26 no.5
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• pp.15-24
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• 2020

We present a supervised learning method that estimates the simulation parameters required to simulate the fabric from the static drape shape of a given fabric sample. The static drape shape was inspired by Cusick's drape, which is used in the apparel industry to classify fabrics according to their mechanical properties. The input vector of the training model consists of the feature vector extracted from the static drape and the density value of a fabric specimen. The output vector consists of six simulation parameters that have a significant influence on deriving the corresponding drape result. To generate a plausible and unbiased training data set, we first collect simulation parameters for 400 knit fabrics and generate a Gaussian Mixed Model (GMM) generation model from them. Next, a large number of simulation parameters are randomly sampled from the GMM model, and cloth simulation is performed for each sampled simulation parameter to create a virtual static drape. The generated training data is fitted with a log-linear regression model. To evaluate our method, we check the accuracy of the training results with a test data set and compare the visual similarity of the simulated drapes.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.12
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• pp.131-141
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• 2020

The purpose of this study is to compare the effects on nursing students' knowledge, performance confidence, and skills from combined virtual IV simulator and mannequin arm IV cannulation training against training with a mannequin arm only. A non-equivalent control group pretest-posttest experimental study was carried out. Ninety-three sophomore nursing students who were just beginning their fundamental skills training were recruited. Participants were divided into two groups (46 for the combined group and 47 for the mannequin-only group). Data were collected from March 18-29. For the experimental group, both virtual IV simulator and mannequin-arm training were provided for 30 minutes (15 minutes each). For the control group, training for 30 minutes with a mannequin arm only was provided. After intervention, there was no statistically significant difference in the knowledge score between the two groups (F=2.52, p=.116). However, there was a significant improvement in performance confidence (t=2.14, p=.035) and nursing skills (t=5.34, p<.001) in the experimental group, compared with the control. Overall, this study provides empirical evidence that the combination of virtual IV simulator and mannequin arm training may further enhance nursing students' performance confidence and nursing skills.

• Journal of Korean Society on Water Environment
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• v.27 no.1
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• pp.30-35
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• 2011

2D pollutant transport model was applied to the simulation of contaminant transport in the channel. At first, two kinds of virtual channels having different slopes were designed. The distribution of contaminant, which flows from one of the three drainages to the main channel, was simulated by each 2D model. Concentrations of 745 nodes were converted to input data of neural network model (Multi-perceptron) for training and verification using matrix. The first three cases (Case A-1, A-2, A-3) were used for training Multi-perceptron, the other three cases (Case B-1, B-2, B-3) were used for verification. As a result, Multi-perceptron reasonably divided the cases into the three characteristics which have different contaminant distributions due to the different input point of water pollution source. It can be a useful methodology for the water quality monitoring and backtracking.

• The KIPS Transactions:PartB
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• v.9B no.2
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• pp.173-180
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• 2002

The virtual cell loss rate was introduced for the training pattern of the neural network in the VOB(Virtual Output Buffer) model. The VOB model shows that the neural network can find the connection admission boundary without the real cell loss rate. But the VOB model tends to overestimate the cell loss rate, so the utilization of network is low. In this paper, we uses the reference curve of the cell loss rate, which contains the information about the cell loss rate at the connection admission boundary. We process the patterns of the virtual cell loss rate using the reference curve, We performed the simulation with two major ATM traffic classes. One is On-Off traffic class that has the traffic characteristic of LAN data and other is Auto-Regressive traffic class that has the traffic characteristic of a video image communication.

• The Journal of the Korea Contents Association
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• v.18 no.12
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• pp.218-228
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• 2018

The purpose of this study was to present a educational content developed for training of dental extraction skill in a virtual environment. The development of the content consists of five steps: learning content analysis, draw a design model, development, test of validity, rectification and complete of the content. We developed the virtual reality (VR) simulator with producing an animation of surgical stages on the 3D models of human face for simulating dental extraction procedure. The results of validity tests for the content were mean 4.81 (SD, 0.72) for interface validity and mean 4.66 (SD, 0.71) for content validity, which represents strong evidence for the validity of the content. The data of the study indicates that the educational content developed for training of dental extraction skill using VR technology can be suitable to improve surgical skill of dental extraction in clinical field. We expect that further development of the education contents based on the VR technology to improve various surgical skills in clinical field will be addressed in the future.

• Journal of Information Science Theory and Practice
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• v.4 no.3
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• pp.6-27
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• 2016

There has been a growing interest and enthusiasm for the application of virtual worlds in learning and training. This research proposes a design framework of a virtual world-based learning environment that integrates two unique features of the virtual world technology, immersion and interactivity, with an instructional strategy that promotes self-regulatory learning. We demonstrate the usefulness and assess the effectiveness of our design in the context of information security learning. In particular, the information security learning module implemented in Second Life was incorporated into an Introduction to Information Security course. Data from pre- and post- learning surveys were used to evaluate the effectiveness of the learning module. Overall, the results strongly suggest that the virtual world-based learning environment enhances information security learning, thus supporting the effectiveness of the proposed design framework. Additional results suggest that learner traits have an important influence on learning outcomes through perceived enjoyment. The study offers useful design and implementation guidelines for organizations and universities to develop a virtual world-based learning environment. It also represents an initial step towards the design and explanation theories of virtual world-based learning environments.

• KIPS Transactions on Computer and Communication Systems
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• v.5 no.11
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• pp.393-402
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• 2016

A Military L-V-C system consists of distributed complex systems integrating Live systems working on physical wall-clock time, Virtual systems ruled by virtually pseudo realtime events on a computer, and Constructive systems only depending on the causal relationship between the continuous events. Recently many needs for L-V-C training systems are increasing in order to achieve the maximum training effects with low costs. While theoretical/logical researches or only partially interworking technologies have been proposed, there are few perfect interworking architectures for totally interoperating L-V-C systems in world-wide. In this paper, we design and develop a novel interworking architecture based on data centric middleware for the consistent global time with the same states on the entire L-V-C data and events by means of integrating the heterogeneous distributed middleware standards of each L-V-C system. In addition, simulated L-V-C systems based on real systems will be used for the efficiency and performance of the developed interworking architecture.