• Journal of The Geomorphological Association of Korea
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• v.26 no.2
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• pp.1-14
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• 2019

This study analyzed the relativity between block stream and talus distributions by employing a likelihood ratio approach. Possible distribution sites for each debris slope landform were extracted by applying a spatial integration model, in which we combined fuzzy set model, Bayesian predictive model, and logistic regression model. Moreover, to verify model performance, a success rate curve was prepared by cross-validation. The results showed that elevation, slope, curvature, topographic wetness index, geology, soil drainage, and soil depth were closely related to the debris slope landform sites. In addition, all spatial integration models displayed an accuracy of over 90%. The accuracy of the distribution potential area map of the block stream was highest in the logistic regression model (93.79%). Eventually, the accuracy of the distribution potential area map of the talus was also highest in the logistic regression model (97.02%). We expect that the present results will provide essential data and propose methodologies to improve the performance of efficient and systematic micro-landform studies. Moreover, our research will potentially help to enhance field research and topographic resource management.

• Journal of the Korean Geosynthetics Society
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• v.12 no.1
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• pp.11-19
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• 2013

Coarse-grained soils are typical engineering materials commonly used in many civil engineering applications such as structural fills, subgrade and drainage fills for dam, railway and bridge. Various researches have been performed with related to constitutive laws for numerical analysis of such structures. This paper presents a parametric study for a constitutive model for coarse grained materials. The model is a kind of the bounding surface models based on critical state theory. A distinct feature of the model is to capture the response of coarse-grained materials with different void ratios and confining pressures using a single set of model parameters. The model behavior is defined with a set of elastic parameters, critical state parameters, and model-specific parameters. The parametric study was performed for the model-specific parameters. The result of parametric study shows that the model is capable to capture stress-dilatancy behavior and kinematic-hardening under non-associative plastic flow.

• Korean Journal of Agricultural and Forest Meteorology
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• v.18 no.4
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• pp.253-263
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• 2016

Chrysanthemum production would benefit from crop growth simulations, which would support decision-making in crop management. Chrysanthemum is a typical short day plant of which floral initiation and development is sensitive to photoperiod. We developed a model to predict phenological development and leaf appearance of chrysanthemum (cv. Baekseon) using daylength (including civil twilight period), air temperature, and management options like light interruption and ethylene treatment as predictor variables. Chrysanthemum development stage (DVS) was divided into juvenile (DVS=1.0), juvenile to budding (DVS=1.33), and budding to flowering (DVS=2.0) phases for which different strategies and variables were used to predict the development toward the end of each phenophase. The juvenile phase was assumed to be completed at a certain leaf number which was estimated as 15.5 and increased by ethylene application to the mother plant before cutting and the transplanted plant after cutting. After juvenile phase, development rate (DVR) before budding and flowering were calculated from temperature and day length response functions, and budding and flowering were completed when the integrated DVR reached 1.33 and 2.0, respectively. In addition the model assumed that leaf appearance terminates just before budding. This model predicted budding date, flowering date, and leaf appearance with acceptable accuracy and precision not only for the calibration data set but also for the validation data set which are independent of the calibration data set.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.27-40
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• 2023

With the increase in the spread of smart devices and the impact of COVID-19, the consumption of media contents through smart devices has significantly increased. Along with this trend, the amount of media contents viewed through OTT platforms is increasing, that makes contents recommendations on these platforms more important. Previous contents-based recommendation researches have mostly utilized metadata that describes the characteristics of the contents, with a shortage of researches that utilize the contents' own descriptive metadata. In this paper, various text data including titles and synopses that describe the contents were used to recommend similar contents. KLUE-RoBERTa-large, a Korean language model with excellent performance, was used to train the model on the text data. A dataset of over 20,000 contents metadata including titles, synopses, composite genres, directors, actors, and hash tags information was used as training data. To enter the various text features into the language model, the features were concatenated using special tokens that indicate each feature. The test set was designed to promote the relative and objective nature of the model's similarity classification ability by using the three contents comparison method and applying multiple inspections to label the test set. Genres classification and hash tag classification prediction tasks were used to fine-tune the embeddings for the contents meta text data. As a result, the hash tag classification model showed an accuracy of over 90% based on the similarity test set, which was more than 9% better than the baseline language model. Through hash tag classification training, it was found that the language model's ability to classify similar contents was improved, which demonstrated the value of using a language model for the contents-based filtering.

• Proceedings of the Korea Institutes of Information Security and Cryptology Conference
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• 2003.07a
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• pp.208-211
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• 2003

최근 인터넷의 급속한 발달은 개인의 생활에 많은 변화를 주었다. 새로운 환경에서 개인의 전자거래는 시장의 요구사항과 산업적 경향에 따라서 변화와 발전을 거듭하게 되었고 그 결과 전자지갑, 전자화폐, 신용카드와 같은 안전한 전자지불시스템이 필연적으로 등장하게 되었다. 하지만 국내에 도입 또는 국내에서 개발된 많은 전자지불시스템이 국산 암호알고리즘을 사용하지 않는 것이 대부분이다. 따라서, 본 논문에서는 전자지 불프로토콜인 SET의 암호알고리즘 중 DES를 국내 암호알고리즘인 SEED로 대체 적용하여 실험실 모델을 설계·구현하였다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.11C
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• pp.1592-1599
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• 2004

In this paper, we proposed the 3D mesh watermarking using projection onto convex sets (POCS). 3D mesh is projected iteratively onto two constraint convex sets until it satisfy the convergence condition. These sets consist of the robustness set and the invisibility set that designed to embed watermark Watermark is extracted without original mesh by using the decision values and the index that watermark is embedded. Experimental results verified that the watermarked mesh have the robustness against mesh simplification, cropping, affine transformation, and vertex randomization as well as the invisibility.

• Journal of Internet Computing and Services
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• v.13 no.4
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• pp.53-62
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• 2012

Object segmentation is a demanding research area and not a trivial problem of image processing and computer vision. Tremendous segmentation algorithms were addressed on gray-scale (or biomedical) images that rely on numerous image features as well as their strategies. These works in practice cannot apply to natural color images because of their negative effects to color values due to the use of gray-scale gradient information. In this paper, we proposed a new approach for color object segmentation by modifying a geometric active contour model named distance regularized level set evolution (DRLSE). Its speed function will be designed to exploit as much as possible color gradient information of images. Finally, we provide experiments to show performance of our method with respect to its accuracy and time efficiency using various color images.

• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.321-335
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• 2021

Particulate matter (PM10 and PM2.5 with a diameter less than 10 and 2.5 ㎛, respectively) can be absorbed by the human body and adversely affect human health. Although most of the PM monitoring are based on ground-based observations, they are limited to point-based measurement sites, which leads to uncertainty in PM estimation for regions without observation sites. It is possible to overcome their spatial limitation by using satellite data. In this study, we developed machine learning-based retrieval algorithm for ground-level PM10 and PM2.5 concentrations using aerosol parameters from Geostationary Ocean Color Imager (GOCI) satellite and various meteorological parameters from a numerical weather prediction model during January to December of 2019. Gradient Boosted Regression Trees (GBRT) and Light Gradient Boosting Machine (LightGBM) were used to estimate PM concentrations. The model performances were examined for two types of feature sets-all input parameters (Feature set 1) and a subset of input parameters without meteorological and land-cover parameters (Feature set 2). Both models showed higher accuracy (about 10 % higher in R2) by using the Feature set 1 than the Feature set 2. The GBRT model using Feature set 1 was chosen as the final model for further analysis(PM10: R2 = 0.82, nRMSE = 34.9 %, PM2.5: R2 = 0.75, nRMSE = 35.6 %). The spatial distribution of the seasonal and annual-averaged PM concentrations was similar with in-situ observations, except for the northeastern part of China with bright surface reflectance. Their spatial distribution and seasonal changes were well matched with in-situ measurements.

• 전기의세계
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• v.39 no.8
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• pp.7-12
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• 1990

전문가 시스템에 있어서의 불확실성 정보의 표현 및 처리를 담당하는 주요 추론모델중 Bayesian모델, Certainty Factor 모델 그리고 Dempster-Shafer 모델의 기본이론을 살펴보고자 한다. 이외의 주요 추론 방법으로서 Fuzzy추론 모델이 있는데 이는 판단 지식에 대한 주관적 불확실성과 "매우", "많이" 등의 자연어가 포함하고 있는 불분명성을 체계적이고 효과적으로 다룰 수 있는 Fuzzy Set 이론에 근거한 방법으로서, 불확실성 또는 불명료성을 0에서부터 1 사이의 값을 갖는 membership degree로 표시하며 이를 "MIN"과 "MAX" 함수를 이용한 합성 추론 규칙(Composition Rule of Inference)를 적용하여 처리한다. Fuzzy 추론 모델은 자연어를 포함하는 전문가의 지식 처리에 매우 적합하여 앞으로 그 응용이 높이 기대되는 방법이다. 이외에 Bayesian 모델을 변형 응용한 PROSPECTOR의 Likelyhood Ratio 모델, 정량적 방법인 Theory of Endorsement 모델 등 여러 방법이 있다. 그러나 어느 모델이 더 일반성을 갖고 더 좋은 방법인가 하는 문제에 대하여는 아직 많은 연구가 요구된다. 따라서 이러한 모델들의 전문가 시스템 적용에 있어서는 각 모델의 장단점을 고려하여 주어진 문제 영역에 적합한 모델을 선택하는 것이 바람직하다. 현재 불확실성 처리에 있어서 각 문제에 따른 경험적인 처리에 의존하는 전력 계통 분야의 적용에 있어서도 이러한 실인간 전문가의 추론방법에 근접된 반성을 갖는 불확실성 추론 방버 도입이 요구된다.가의 추론방법에 근접된 반성을 갖는 불확실성 추론 방버 도입이 요구된다.

• Journal of KIISE:Software and Applications
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• v.37 no.6
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• pp.455-463
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• 2010

In this paper, we present an approach to sentence boundary detection for web documents that builds on statistical-based methods and uses rule-based correction. The proposed system uses the classification model learned offline using a training set of human-labeled web documents. The web documents have many word-spacing errors and frequently no punctuation mark that indicates the end of sentence boundary. As sentence boundary candidates, the proposed method considers every Ending Eomis as well as punctuation marks. We optimize engine performance by selecting the best feature, the best training data, and the best classification algorithm. For evaluation, we made two test sets; Set1 consisting of articles and blog documents and Set2 of web community documents. We use F-measure to compare results on a large variety of tasks, Detecting only periods as sentence boundary, our basis engine showed 96.5% in Set1 and 56.7% in Set2. We improved our basis engine by adapting features and the boundary search algorithm. For the final evaluation, we compared our adaptation engine with our basis engine in Set2. As a result, the adaptation engine obtained improvements over the basis engine by 39.6%. We proved the effectiveness of the proposed method in sentence boundary detection.