• 한국CDE학회논문집
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• 제5권4호
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• pp.293-300
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• 2000

The non-manifold geometric modeling technique is to improve design process and to Integrate design, analysis, and manufacturing by handling mixture of wireframe model, surface model, and solid model in a single data structure. For the non-manifold geometric modeling, Euler operators and other high level modeling methods are necessary. Boolean operation is one of the representative modeling method for the non-manifold geometric modeling. This thesis studies Boolean operations of non-manifold model with the data structure of selective storage. The data structure of selective storage is improved non-manifold data structure in that existing non-manifold data structures using ordered topological representation method always store non-manifold information even if edges and vortices are in the manifold situation. To implement Boolean operations for non-manifold model, intersection algorithm for topological cells of three different dimensions, merging and selection algorithm for three dimensional model, and Open Inventor(tm), a 3D toolkit from SGI, are used.

• Journal of the Korean Data and Information Science Society
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• 제10권2호
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• pp.289-298
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• 1999

We consider a minimax approach to make a design robust to many types or uncertainty arising in reality when dealing with non-normal linear models. We try to build a design to protect against the worst case, i.e. to improve the "efficiency" of the worst situation that can happen. In this paper, we especially deal with the generalized linear model. It is a known fact that the generalized linear model is a universal approach, an extension of the normal linear regression model to cover other distributions. Therefore, the optimal design for the generalized linear model has very similar properties as the normal linear model except that it has some special characteristics. Uncertainties regarding the unknown parameters, link function, and the model structure are discussed. We show that the suggested approach is proven to be highly efficient and useful in practice. In the meantime, a computer algorithm is discussed and a conclusion follows.

• 한국시뮬레이션학회:학술대회논문집
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• 한국시뮬레이션학회 1992년도 제2회 정기총회 및 추계학술 발표회 발표논문 초록
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• pp.8-8
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• 1992

Through a simulation experiment, often an experimenter is concerned with estimating the system parameters of the linear model consisting of m design points from the outputs oft the simulation model. To improve the estimation of the system parameters and reliability of these estimators, appropriate simulation techniques have been developed. For the first order linear model, Schruben and Margolin (1978) exploited the random number assignment rules which uses a combination of common random numbers and antithetic streams in a simulation experiment designed to estimate the system parameters when the design matrix of simulation model admits orthogonal blocking into two blocks. Nozari, Arnold and Pegden (1984) developed a method for appliying the method of control variates to the situation of the linear model having multiple design points. This talk deals with a different way of utilizing controls under the correlation induction strategy of Schruben and Margolin's to improve the simulation efficiency, and presents a procedure for obtaining the estimators of the system parameters analytically. Simulation results on a selected simulation model indicate a promising evidence that a proposed method may yield better results than Schruben and Margolin's method.

• International Journal of Computer Science & Network Security
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• 제21권4호
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• pp.272-276
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• 2021

Presently, Online / Offline Users are facing cyber attacks every day. These cyber attacks affect user's performance, resources and various daily activities. Due to this critical situation, attention must be given to prevent such users through cyber attacks. The objective of this research paper is to improve the IDS systems by using machine learning approach to develop a hybrid model which controls the cyber attacks. This Hybrid model uses the available KDD 1999 intrusion detection dataset. In first step, Hybrid Model performs feature optimization by reducing the unimportant features of the dataset through decision tree, support vector machine, genetic algorithm, particle swarm optimization and principal component analysis techniques. In second step, Hybrid Model will find out the minimum number of features to point out accurate detection of cyber attacks. This hybrid model was developed by using machine learning algorithms like PSO, GA and ELM, which trained the system with available data to perform the predictions. The Hybrid Model had an accuracy of 99.94%, which states that it may be highly useful to prevent the users from cyber attacks.

• 스마트미디어저널
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• 제12권5호
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• pp.9-16
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• 2023

In the recommendation system of the credit card company, it is necessary to understand the customer patterns to predict a customer's next merchant based on their histories. The data we want to model is much more complex and there are various patterns that customers choose. In such a situation, it is necessary to use an effective model that not only shows the relevance of the merchants, but also the relevance of the customers relative to these merchants. The proposed model aims to predict the next merchant for the customer. To improve prediction performance, we propose a novel model, called Customer-based Recommendation Model (CRM), to produce a more efficient representation of customers. For the next merchant recommendation system, we use a synthetic credit card usage dataset, BC'17. To demonstrate the applicability of the proposed model, we also apply it to the next item recommendation with another real-world transaction dataset, IJCAI'16.

• 한국보건간호학회지
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• 제18권2호
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• pp.205-217
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• 2004

This study conducted a survey with the 67 students who are in their 2nd year at 3 vocational high schools. The researcher of this study developed and operated the smoking cessation program based on the Transtheoretical model. To evaluate the effects, experiments were repeatedly done. The data were collected from June 21, 2004 till July 9, 2004. The survey was done based on the questionnaire-interview before and after education. The collected date was computerized by using SPSS-WIN 10.0 program. The results are as follows: Firstly. before education, the smoking cessation stage of change shows precontemplation stage $37.3\%$, preparation stage $37.3\%$, and contemplation stage $25.4\%$ respectively. However, after education it shows preparation stage $44.8\%$, action stage $25.4\%$, contemplation stage $19.4\%$, precontemplation stage $10.4\%$. The positive result was $62.7\%$ and smoking cessation stage of change made a 0.9 step increase. Secondly, process of change rose in all the areas except helping relationship. It also shows statistical improvement in all the fields except the dramatic relief of the experiential process, the helping relationship of behavioral process and stimulus control Thirdly, negative affective situation temptation, positive social situation temptation, and habitual strength temptation's average points decreased but showed statistical differences. Weight control temptation's average points decreased after education but didn't show statistical differences. Self efficiency increased measurably after education. Fourthly, concerning the social pros and coping pros caused by smoking, the average points were low after education and showed statistic decrease. As for the cons due to smoking, the average points increased but didn't show any statistical differences. In conclusion, thanks to the smoking cessation program applied by the Transtheoretical model, the smoking rate of vocational high school students became low and their smoking temptation also dropped considerably. In conclusion, while the pros for smoking were lessened, its corns were heightened through the educational awareness offered by this program.

• 한국축산식품학회지
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• 제39권2호
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• pp.222-228
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• 2019

This research was motivated by our encounter with the situation where an optimization was done based on statistically non-significant models having poor fits. Such a situation took place in a research to optimize manufacturing conditions for improving storage stability of coffee-supplemented milk beverage by using response surface methodology, where two responses are $Y_1$=particle size and $Y_2$=zeta-potential, two factors are $F_1$=speed of primary homogenization (rpm) and $F_2$=concentration of emulsifier (%), and the optimization objective is to simultaneously minimize $Y_1$ and maximize $Y_2$. For response surface analysis, practically, the second-order polynomial model is almost solely used. But, there exists the cases in which the second-order model fails to provide a good fit, to which remedies are seldom known to researchers. Thus, as an alternative to a failed second-order model, we present the heterogeneous third-order model, which can be used when the experimental plan is a two-factor central composite design having -1, 0, and 1 as the coded levels of factors. And, for multi-response optimization, we suggest a modified desirability function technique. Using these two methods, we have obtained statistical models with improved fits and multi-response optimization results with the predictions better than those in the previous research. Our predicted optimum combination of conditions is ($F_1$, $F_2$)=(5,000, 0.295), which is different from the previous combination. This research is expected to help improve the quality of response surface analysis in experimental sciences including food science of animal resources.

• 한국재난정보학회 논문집
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• 제19권4호
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• pp.968-975
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• 2023

연구목적: 화재 탐지시 불꽃/연기의 오탐지율이 높은 것을 확인하고 오탐지율을 낮추기 위해 화재 상황을 인식하여 분류하는 방법과 데이터셋을 제안하고자 한다. 연구방법: 동영상을 학습데이터로 활용하여 화재 상황의 특징을 추출하여 분류모델에 적용하고, 평가는 한국정보화진흥원(NIA)에서 진행하는 화재 데이터셋을 이용하여 Yolov8, Slowfast의 모델 성능을 비교 및 분석하였다. 연구결과: YOLO는 배경의 영향에 따라 탐지 성능이 민감하게 변화하며, 화재의 규모가 너무 크거나 작을 때에도 화재를 제대로 감지하지 못했다. SlowFast는 동영상의 시간 축을 같이 학습하기 때문에 비정형 객체에 대해 주변이 흐리거나 밝아 형상을 명확하게 유추할 수 없는 상황에서도 우수하게 화재를 탐지하는 것을 확인했다. 결론: 화재 탐지율은 이미지 데이터 방식보다는 동영상 기반의 인공지능 인식(Detection) 모델을 활용했을 때 더 적절했음을 확인했다.

• 한국지구과학회지
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• 제25권4호
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• pp.205-213
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• 2004

본 연구에서는 문제중심학습(Problem Based Learning)을 과학 수업에 적용하기 위한 지도전략과 '문제상황'을 설계하고 이를 적용한 후 학습자의 학습과정과 문제중심학습에 대한 인식을 밝히고자 하였다. 본 연구에서 적용한 문제중심학습의 단계는 문제상황 제시-문제 만나기-알고 있는 것과 알아야 할 것에 대한 확인(Know/Need to Know)-문제의 진술-정보의 수집과 공유-가능한 문제해결책 형성-최적 문제해결책 결정-문제해결책 발표이고, 소집단 토론 전략과 학습자 주도적 문제해결 전략 등의 단계별 지도전략을 설계하였다. '온실효과를 줄이기 위한 미래의 에너지로 무엇이 좋을까?'라는 문제상황으로, 과학고등학교 2학년 지구과학 수업시간에 문제중심학습을 실시하였고, 학습과정과 문제중심학습에 대한 인식을 밝혔다. 연구결과, 문제의 진술 단계에서는 초기에는 광범위한 문제 진술이 많았으나, 토론을 거듭할수록 문제해결책을 찾기 용이한 좁은 범위의 문제 진술로 정교화되었다. 그리고 K/NK단계에서 알고 있는 것과 알아야할 것에 대하여 명료하게 진술하는 활동이 해결책을 찾는 과정에서 학습자에게 가장 도움이 되는 활동으로 분석되었다. 소감문 분석결과 학습자들은 문제상황에서 문제를 찾아내는 과정을 가장 어려워하면서도 또한 흥미있는 활동으로 인식하는 것으로 나타났다. 이러한 연구 결과로부터, 내실있는 문제중심학습의 적용을 위하여, 다각도의 문제인식이 가능한 간학문적이고 현실적인 문제 상황의 개발이 우선되어야함을 알 수 있다.

• 해양환경안전학회지
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• 제17권3호
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• pp.227-233
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• 2011

우리나라 연안 해역은 빈번한 선박조우로 인한 해양사고 발생 잠재 위험이 높은 해역이며 현재 각 항만의 VTS 관제실에서 VHF통달거리를 넘어서 통항하는 선박에 대하여는 정보 파악 자체가 불가능하고, VTS 관제범위 바깥해역에서는 효율적인 교통관리가 어려워 관련사고 예방이 어려운 실정이다. 최근 우리나라는 항만의 확장 및 신항만 개발에 따른 해상교통안전성 평가는 있지만 실시간으로 변화하는 해상교통환경 변화에 따른 위험도 정보를 제공할 수 있는 시스템은 없는 실정이다. 따라서 대상해역에서의 해상교통환경과 관련된 일반적인 정보 및 위험도 정보를 제공하고, 해상교통환경 평가를 통한 해역 위험도 여부를 평가할 수 있는 평가지표 개발이 필요하다. 본 연구에서는 선박운항자의 위험의식을 바탕으로 선박의 전장별, 선박의 조우 형태 중 횡단(Crossing situation) 상황에 따른 마주치는 각도($045^{\circ}$, $090^{\circ}$, $135^{\circ}$), 추월(Overtaking) 및 정면으로 마주치는 경우(Head-on situation)와 좌현, 우현에서 선박을 조우하였을 경우와 항계 내 및 항계 밖에서의 경우, 타선과의 속력 중 비슷하거나 또는 느리거나 빠를 경우, 타선과의 속력차에 따른 경우, 타선과의 거리에 따른 선박운항자의 주관적 위험도를 조사 분석하여 해상교통안전성 평가모델 개발을 위한 기초 식을 제안하였다. 제안한 기초 식은 국내 해상교통환경 평가에 적합한 것임을 확인하였다.