• Journal of the Institute of Electronics Engineers of Korea SD
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• v.44 no.2
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• pp.127-138
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• 2007

In this paper, we propose a new X-masking scheme for utilizing logic built-in self-test The new scheme exploits the phase-shifting network which is based on the shift-and-add property of maximum length pseudorandom binary sequences(m-sequences). The phase-shifting network generates mask-patterns to multiple scan chains by appropriately shifting the m-sequence of an LFSR. The number of shifts required to generate each scan chain mask pattern can be dynamically reconfigured during a test session. An iterative simulation procedure to synthesize the phase-shifting network is proposed. Because the number of candidates for phase-shifting that can generate a scan chain mask pattern are very large, the proposed X-masking scheme reduce the hardware overhead efficiently. Experimental results demonstrate that the proposed X-masking technique requires less storage and hardware overhead with the conventional methods.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.9C
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• pp.537-545
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• 2011

This paper analyzed performance of three dimensional turbo code and turbo ${\Phi}$ codes proposed in the next generation DVB-RCS systems. In the view of turbo ${\Phi}$ codes, we proposed the optimal permutation and puncturing patterns for triple binary input data. We also proposed optimal post-encoder types and interleaving algorithm for three dimensional turbo codes. Based on optimal parameters, we simulated both turbo codes, and we confirmed that the performance of turbo ${\Phi}$ codes are better than that of three dimensional turbo codes. However, the complexity of turbo ${\Phi}$ is more complex than that of three dimensional turbo codes by 18%.

• The Journal of the Korea Contents Association
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• v.12 no.2
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• pp.369-387
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• 2012

Based on the survey data, this study focused venture firms examines how organizational resources and capabilities along with its external environmental conditions have an effect on its strategy and performance. In particular, this article attempts, by performing a binary logistic regression analysis, to identify the venture-specific importance and priority of the factors that may influence firms' strategy patterns, with multiple regression analysis on the relationships between some variables included in the model. The survey was conducted from October 1, 2010 through October 30, 2010. The results of this study are the following. First, the more firms are exposed to high industry growth and low competitive intensity, the higher chance they get to pursuit aggressive strategy. And then a firm seeks aggressive strategy, when it has more technological resources and human resources. Third, environmental uncertainty, industry growth, technological resources, human resources, financial resources and marketing capabilities have positive effects on firm's performance. But, competitive intensity has no direct influence firm's performance. Finally, CEO competence directly influences firm's performance, but the interaction. of CEO competence with other variables is not significant.

• Journal of Korea Game Society
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• v.11 no.6
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• pp.81-93
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• 2011

Identity theft happens frequently in popular MMORPG(Massively Multi-player Online Role Playing Games) where profits can be gained easily. In spite of the importance of security about identity theft in MMORPG, few methods to prevent and detect identity theft in online games have been proposed. In this study, we investigate real identity theft cases of an online game and define the representative patterns of identity theft as the speedy type, cautious type, and bold type. We then propose the automatic identity theft detection model based on the multi-class classification. We verify the system with one of the leading online games in Korea. The multi-class detection model outperforms the existing binary-class one(hacked or not).

• The KIPS Transactions:PartB
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• v.11B no.2
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• pp.149-158
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• 2004

This study proposes a method for segmentation and recognition of traffic signs using shape information and edge image in real image. It first segments traffic sign candidate regions by connected component algorithm from binary images, obtained by utilizing the RGB color ratio of each pixel in the image, and then extracts actual traffic signs based on their symmetries on X- and Y-axes. Histogram equalization is performed for unsegmented candidate regions caused by low contrast in the image. In the recognition stage, it utilizes shape information including projection profiles on X- and Y-axes, moment, and the number of crossings and distance which concentric circular patterns and 8-directional rays from region center intersects with edges of traffic signs. It finally performs recognition by measuring similarity with the templates in the database. It will be shown from several experimental results that the system is robust to environmental factors, such as light and weather condition.

• Journal of Information Technology Services
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• v.11 no.4
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• pp.233-250
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• 2012

In this paper, credit card delinquency means the possibility of occurring bad debt within the certain near future from the normal accounts that have no debt and the problem is to predict, on the monthly basis, the occurrence of delinquency 3 months in advance. This prediction is typical binary classification problem but suffers from the issue of data imbalance that means the instances of target class is very few. For the effective prediction of bad debt occurrence, Support Vector Machine (SVM) with kernel trick is adopted using credit card usage and payment patterns as its inputs. SVM is widely accepted in the data mining society because of its prediction accuracy and no fear of overfitting. However, it is known that SVM has the limitation in its ability to processing the large-scale data. To resolve the difficulties in applying SVM to bad debt occurrence prediction, two stage clustering is suggested as an effective data reduction method and ensembles of SVM models are also adopted to mitigate the difficulty due to data imbalance intrinsic to the target problem of this paper. In the experiments with the real world data from one of the major domestic credit card companies, the suggested approach reveals the superior prediction accuracy to the traditional data mining approaches that use neural networks, decision trees or logistics regressions. SVM ensemble model learned from T2 training set shows the best prediction results among the alternatives considered and it is noteworthy that the performance of neural networks with T2 is better than that of SVM with T1. These results prove that the suggested approach is very effective for both SVM training and the classification problem of data imbalance.

• The Transactions of the Korea Information Processing Society
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• v.3 no.3
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• pp.572-580
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• 1996

This paper describes a test generation method for sequential circuits which improves the application limits of the IPMT method by applying the partial scan design to the IPMT method. To solve the problem that the IPMT method requires enormous computation time in image computation, and generates test patterns after the partialscan design is introduced to reduce test complexity. Scan flip-flops are selected for the partial scan design according to the node size of the state functions of a sequential circuit in their binary decision diagram representations. Experimental results on ISCAS'95 benchmark circuits show that a test generator based on our method has achieved 100% fault coverage by use of either 20% scan FFs for s344, s349, and s420 or 80% scan FFs for sl423. However, test gener-ators based on the previous IPM method have not achieved 100% fault coverage for those circuits.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.11
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• pp.137-146
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• 2015

In this paper, we propose a pothole detection method using various features of intensity and motion. Segmentation, decision steps of pothole detection are processed according to the values which are derived from feature characteristics. For segmentation using intensity, we use a binarization method using histogram to separate pothole region from background. For segmentation using motion, we filter using high pass filter and get standard deviation value. This value is divided by regression value according to camera environment such as photographing angle, height, velocity, etc. We get binary image by histogram based binarization. For decision, candidate regions are decided whether pothole or not using comparison of candidate and background's features. Experimental results show that our proposed pothole detection method has better results than existing methods and good performance in discrimination between pothole and similar patterns.

• Current Optics and Photonics
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• v.4 no.4
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• pp.286-292
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• 2020

Imaging through multicore fiber (MCF) is of great significance in the biomedical domain. Although several techniques have been developed to image an object from a signal passing through MCF, these methods are strongly dependent on the surroundings, such as vibration and the temperature fluctuation of the fiber's environment. In this paper, we apply a new, strong technique called deep learning to reconstruct the phase image through a MCF in which each core is multimode. To evaluate the network, we employ the binary cross-entropy as the loss function of a convolutional neural network (CNN) with improved U-net structure. The high-quality reconstruction of input objects upon spatial light modulation (SLM) can be realized from the speckle patterns of intensity that contain the information about the objects. Moreover, we study the effect of MCF length on image recovery. It is shown that the shorter the fiber, the better the imaging quality. Based on our findings, MCF may have applications in fields such as endoscopic imaging and optical communication.

• Journal of dental hygiene science
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• v.21 no.3
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• pp.133-139
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• 2021

Background: Smoking in adolescence leads to an intensified addiction to nicotine when physical and mental growth has not yet been completed. With the advent of e-cigarettes, the rate of e-cigarette use among Korean adolescents has been steadily increasing. To date, studies on e-cigarettes and oral health, especially on the relationship between smoking styles and oral health in adolescents, are limited. Therefore, this study aimed to identify the risk factors for oral health problems caused by the repeated use of conventional cigarettes and e-cigarettes. Methods: This explanatory research study compared the adolescents' experiences of periodontal disease symptoms according to smoking type through a secondary analysis of the original data from the 15th Adolescent Health Behavior Survey (2019). Cross-analysis was performed to compare the smoking patterns according to the adolescents' general characteristics. Finally, a binary logistic regression analysis was performed to determine how smoking characteristics affect the adolescents' experience of periodontal disease symptoms. Results: In terms of patients' general characteristics, significant differences were observed in sex, school level, grades, household economic status, type of residence, and father's education level between adolescents who smoked conventional cigarettes alone and those who smoked both conventional cigarettes and e-cigarettes (p<0.05). After checking the factors affecting the smoking pattern and the experience of periodontal disease symptoms in adolescents, it was found that the duplicate smoking group was more likely to experience periodontal disease symptoms (odds ratio, 1.20) than the group that smoked conventional cigarettes alone (p<0.05). Conclusion: Duplicate smokers experienced more symptoms of periodontal disease than those who smoked cigarettes alone. Based on the findings of this study, smoking cessation counseling according to the smoking type and differentiated education for oral health promotion should be provided.