• Proceedings of the Korean Society of Computer Information Conference
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• 2019.07a
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• pp.67-68
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• 2019

소규모 인원으로 구성한 게임 개발 프로젝트에서 품질 관리를 수행할 때 폭포수 개발과 같은 전통적인 개발 방법론을 적용하는데 많은 어려움이 있다. 이에 대한 대안으로 상황에 따라 적용할 수 있도록 일부 과정을 수정하여 스크럼 방식의 애자일방법론을 제시한다. 본 논문에서는 소프트웨어 개발에 적용하는 애자일 기법을 게임 개발에 맞춘 테스팅 방식을 적용하여 실제 개발 과정에서 효과적으로 사용할 수 있는 방식인지를 검증하고 제안한다.

• Journal of Korea Game Society
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• v.15 no.6
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• pp.17-28
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• 2015

This study attempted to identify differences in user experience and app game fun, satisfaction, use intention between fitness app games at different level of interaction design. The playtesting method was conducted on 100(male 50, female 50) university students using 'Enjoy Your Fitness', a fitness app game developed by The Center for Health Communication Studies. The results found that more interactive app game is higher in the level of user experience such as fitness accomplishment, flow, and interest. Fitness app game's fun, satisfaction, use intention is also higher on interactive fitness app game. As the study verified that interactive fitness app game had stronger effects in user fitness experience and user experience than general fitness app games through experimental studies, it may contribute to design fitness mobile app game for health improvement.

• Korea Real Estate Review
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• v.28 no.3
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• pp.21-35
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• 2018

The recent performance of residential mortgages demonstrated how default risk operated separately from prepayment risk. In this study, we investigated the determinants of the borrowers' decisions pertaining to early termination through default from the mortgage performance data released by Freddie Mac, involving securitized mortgage loans from January 2011 to September 2013. We estimated a Cox-type, proportional hazard model with a single risk on fundamental factors associated with default options for individual mortgages. We proposed a mortgage default model that included two specifications of delinquency: one using a delinquency binary variable, while the other using a delinquency probability. We also compared the results obtained from two specifications with respect to goodness-of-fit proposed in the spirit of Vuong (1989) in both overlapping and nested models' cases. We found that a model with our proposed delinquency probability variable showed a statistically significant advantage compared to a benchmark model with delinquency dummy variables. We performed a default prediction power test based on the method proposed in Shumway (2001), and found a much stronger performance from the proposed model.

• Journal of the Korea Convergence Society
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• v.12 no.1
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• pp.29-35
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• 2021

License plate recognition plays a key role in intelligent transportation systems. Therefore, it is a very important process to efficiently detect the number and character areas. In this paper, we propose a method to effectively detect license plate number area by applying deep learning and semantic image segmentation algorithm. The proposed method is an algorithm that detects number and text areas directly from the license plate without preprocessing such as pixel projection. The license plate image was acquired from a fixed camera installed on the road, and was used in various real situations taking into account both weather and lighting changes. The input images was normalized to reduce the color change, and the deep learning neural networks used in the experiment were Vgg16, Vgg19, ResNet18, and ResNet50. To examine the performance of the proposed method, we experimented with 500 license plate images. 300 sheets were used for learning and 200 sheets were used for testing. As a result of computer simulation, it was the best when using ResNet50, and 95.77% accuracy was obtained.

• Journal of Korean Society of Transportation
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• v.33 no.3
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• pp.284-293
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• 2015

The objective of this paper is to determine the conditions of road surface by utilizing the images collected from closed-circuit television (CCTV) cameras installed on roadside. First, a technique was examined to detect wet surfaces at nighttime. From the literature reviews, it was revealed that image processing using polarization is one of the preferred options. However, it is hard to use the polarization characteristics of road surface images at nighttime because of irregular or no light situations. In this study, we proposes a new discriminant for detecting wet and dry road surfaces using CCTV image data at night. To detect the road surface conditions with night vision, we applied the wavelet packet transform for analyzing road surface textures. Additionally, to apply the luminance feature of night CCTV images, we set the intensity histogram based on HSI(Hue Saturation Intensity) color model. With a set of 200 images taken from the field, we constructed a detection criteria hyperplane with SVM (Support Vector Machine). We conducted field tests to verify the detection ability of the wet road surfaces and obtained reliable results. The outcome of this study is also expected to be used for monitoring road surfaces to improve safety.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.5
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• pp.82-96
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• 2020

The Demand Responsive Transit (DRT) system is the flexible public transport service that determines the route and schedule of the service vehicles according to users' requests. With increasing importance of public transport systems in urban areas, the development of stable and fast routing algorithms for DRT has become the goal of many researches over the past decades. In this study, a new heuristic method is proposed to generate fast and efficient routes for multiple vehicles using demand clustering and destination demand priority searching method considering the imbalance of users' origin and destination demands. The proposed algorithm is tested in various demand distribution scenarios including random, concentration and directed cases. The result shows that the proposed method reduce the drop of service ratio due to an increase in demand density and save computation time compared to other algorithms. In addition, compared to other clustering-based algorithms, the walking cost of the passengers is significantly reduced, but the detour time and in-vehicle travel time of the passenger is increased due to the detour burden.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.5
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• pp.999-1008
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• 2023

Early prediction of chronic diseases such as diabetes is an important issue, and improving the accuracy of diabetes prediction is especially important. Various machine learning and deep learning-based methodologies are being introduced for diabetes prediction, but these technologies require large amounts of data for better performance than other methodologies, and the learning cost is high due to complex data models. In this study, we aim to verify the claim that DNN using the pima dataset and k-fold cross-validation reduces the efficiency of diabetes diagnosis models. Machine learning classification methods such as decision trees, SVM, random forests, logistic regression, KNN, and various ensemble techniques were used to determine which algorithm produces the best prediction results. After training and testing all classification models, the proposed system provided the best results on XGBoost classifier with ADASYN method, with accuracy of 81%, F1 coefficient of 0.81, and AUC of 0.84. Additionally, a domain adaptation method was implemented to demonstrate the versatility of the proposed system. An explainable AI approach using the LIME and SHAP frameworks was implemented to understand how the model predicts the final outcome.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.669-671
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• 1998

Due to the serial nature of scan chains, the use of the boundary scan chain leads the high application costs. And with 3-state net, it is important to avoid enabling the two drivers in a net. In this paper, the new test method for 3-state nets in the multiple boundary scan chains is presented. This method configures the boundary scan cells as multiple scan chains and the test application time can be reduced. Also three efficient algorithms are proposed for testing the interconnects in a board without the collision of the test data in 3-state nets.

• Annual Conference on Human and Language Technology
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• 2002.10e
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• pp.285-291
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• 2002

본 논문은 자연어 검색엔진에서의 검색결과에 대한 HIT LIST[6]와 검색 문서의 요약을 위하여 질의 기반의 3단계 문서요약을 제안한다. 첫째단계로 IR에 주어지는 질의를 유의어 DB를 통해 질의확장을 거친다. 둘째로 질의와 검색문서상의 문장의 유사도 계산을 통해 문장의 중요도 점수를 구한다. 좀더 정확한 요약을 위해 4가지 방법론을 적용하여 각 문장의 중요도를 ranking한다. 셋째로 MMR (Maximal Marginal Relevance)방식을 적용하여 요약 시 중복이 되는 부분을 줄인다. 이때 요약 압축률을 임의로 조절할 수 있다. 실험은 KORDIC의 신문기사로 구성된 문서요약 테스트 집합을 사용하여 좋은 요약결과를 얻었다.

• Proceedings of the Korea Information Processing Society Conference
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• 2004.11a
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• pp.279-282
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• 2004

In this paper, we present the methodology of performance testing for web based university resource planning systems. The methodology contains an adequate procedure and workload assessment method. We execute real performance testing to the university resource system during system test stage. To verify the adequacy of the methodology, We compare the result between testing system's hardware utilization and operating system's. The result is nearly the same. Empirical experiments are also shown.