• 한국멀티미디어학회:학술대회논문집
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• 한국멀티미디어학회 2002년도 춘계학술발표논문집(하)
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• pp.971-976
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• 2002

최근 대부분의 정보시스템은 웹기반 정보시스템으로 이주하고 있으며 이의 개발과 유지보수시에 '웹 위기' 현상이 발생하고 있다. 이를 해결하기 위한 웹엔지니어링 기술 중 웹기반 비즈니스 프로세스 명세 기술이 필요하다. 따라서 본 논문에서는 웹기반 비즈니스 프로세스 명세를 위한 BPN(Business Process Net) 모형을 제시한다. BPN 모형은 베타분포형 확률 패트리넷이며 수행가능형 Activity Diagram이라 할 수 있다. BPN을 모형화할 때 Use Case 분석을 이용하며, 비즈니스 프로세스의 수행 시간 및 비용적 불확실성은 베타분포를 이용하고 있다. BPN 모형은 XML 기반 비즈니스 프로세스 명세언어를 위한 공통 명세모형으로 이용될 수 있다.

• 정보처리학회논문지D
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• 제10D권3호
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• pp.471-488
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• 2003

최근의 대부분의 정보시스템은 웹 기반 정보시스템이며 이의 개발과 유지보수시에 “웹 위기” 현상이 발생하고 있다 이를 해결하기 위해, 웹 엔지니어링 기술 중 웹 기반 비즈니스 프로세스로의 리엔지니어링 기술이 필요하다. 본 논문에서는 웹 기반 정보시스템으로의 이주와 웹 기반 비즈니스 프로세스 모형화를 위한, BPN(Business Process Net) 모형과 이를 이용한 리엔지니어링 휴리스틱스를 적용 예와 함께 제시한다. BPN모형은 베타분포형 확률 패트리넷이며 수행가능형 Activity diagram이라 할 수 있다. BPN을 모형화 할 때, Use Case 분석을 이용하며 비즈니스 프로세스의 수행시간 및 비용적 불확실성은 베타분포를 이용하고 있다. BPN 모형은 XML 기반의 비즈니스 프로세스의 명세언어를 위한 공통 명세모형으로 이용될 수 있으며, 제시한 리엔지니어링 방법 및 지침은 웹 기반 정보시스템의 분석 및 설계단계에서 적용할 수 있다.

• 대한약학회:학술대회논문집
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• 대한약학회 2003년도 Proceedings of the Convention of the Pharmaceutical Society of Korea Vol.2-2
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• pp.81.1-81.1
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• 2003

Buprenorphine(BPN) is used to treat withdrawal syndromes in narcotic addictions. When narcotics are stopped, withdrawal syndromes such as pupil dilation and blood pressure increment are appeared. And BPN is often prescribed concomitantly with antihypertensives. We researched whether combined medicines of BPN and antihypertensives affected on the metabolism of BPN. After BPN was incubated with antihypertensives such as nifedipine, verapamil, captopril and propranolol in rat or human microsomes, amounts of BPN and its metabolite, norbuprenorphine (NBPN), were measured. (omitted)

• 한국항공우주학회지
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• 제35권7호
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• pp.633-639
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• 2007

본 논문에서는 비행시간과 충돌각을 동시에 제어하기 위한 새로운 호밍 유도법칙을 제안한다. 제안한 유도법칙은 기존의 충돌각 제어 유도법칙인 BPN을 근간으로 비행시간 제어를 위한 제어명령을 부가한다. 부가되는 제어명령은 요구되는 비행시간과 추정된 잔여 비행시간과의 오차로 구성되는 시변이득을 이용하여 산출한다. 따라서 비행시간 오차가 없는 경우 제안한 유도법칙은 BPN으로 수렴되어 안정된 호밍 루프를 구성하게 된다. 시뮬레이션을 통해 제안한 유도법칙의 성능을 확인한다.

• 아동학회지
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• 제37권4호
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• pp.83-99
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• 2016

Objective: The purpose of this study was to analyze correlation effect size between Basic Psychological Needs (BPN) and factors affecting BPN. Methods: This research was reviewed and synthesized systematically by meta-analyse. Fifty-eight published studies between 2008 and 2016 were sampled and the effect sizes were acquired. Results: The major findings were as follows. In general, medium correlation effect sizes were found. Competency among sub-factors of BPN showed highest effects. Parental factor was more related to BPN than school and individual factors. Parenting behavior and family psychological backgrounds were the most crucial factors in the parental factors. School adjustment was the most critical factor in the school factors. In school grade, the elementary school was more related to BPN than junior high school and high school. Conclusion: Based on these results, we suggest a number of components for parent-education programs, and information for future research.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2007년도 정기 학술대회 논문집
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• pp.141-144
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• 2007

Given a number of training data, a traditional BPN is normally trained by minimizing the absolute difference between target outputs and approximate outputs. When BPN is used as a meta-model for inequality constraint function, approximate optimal solutions are sometimes actually infeasible in a case where they are active at the constraint boundary. The paper describes the development of the efficient BPN based meta-model that enhances the constraint feasibility of approximate optimal solution. The modified BPN based meta-model is obtained by including the decision condition between lower/upper bounds of a constraint and an approximate value. The proposed approach is verified through a simple mathematical function and a ten-bar planar truss problem.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2012년도 추계학술발표대회
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• pp.1732-1735
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• 2012

인간은 일주일 단위로 유사한 행동 패턴을 가진다고 한다. 이런 점에서 일주일 단위의 시간-공간 기록의 형태인 인간 이동 데이터를 이용하면, 인간의 행동 패턴을 유추해 낼 수 있다. 본 논문에서 인간의 행동을 유추하기 위해 BPN알고리즘을 사용하였다. BPN알고리즘에 대해 설명하고, 인간 이동의 예측에 관한 적용에 관한 BPN알고리즘의 설계 과정을 논의한다. 그리고 해당 실험의 결과와 분석을 제시한다.

• Journal of the Korean Data and Information Science Society
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• 제16권4호
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• pp.815-821
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• 2005

Among the various artificial neural networks the backpropagation network (BPN) has become a standard one. One of the components in a neural network is an activating function or a transfer function of which a representative function is a sigmoid. We have discovered that by updating the slope parameter of a sigmoid function simultaneous with the weights could improve performance of a BPN.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2000년도 춘계정기학술대회 e-Business를 위한 지능형 정보기술 / 한국지능정보시스템학회
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• pp.235-241
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• 2000

This article suggests integrated neural network models for the interest rate forecasting using change point detection. The basic concept of proposed model is to obtain intervals divided by change point, to identify them as change-point groups, and to involve them in interest rate forecasting. the proposed models consist of three stages. The first stage is to detect successive change points in interest rate dataset. The second stage is to forecast change-point group with data mining classifiers. The final stage is to forecast the desired output with BPN. Based on this structure, we propose three integrated neural network models in terms of data mining classifier: (1) multivariate discriminant analysis (MDA)-supported neural network model, (2) case based reasoning (CBR)-supported neural network model and (3) backpropagation neural networks (BPN)-supported neural network model. Subsequently, we compare these models with a neural networks (BPN)-supported neural network model. Subsequently, we compare these models with a neural network model alone and, in addition, determine which of three classifiers (MDA, CBR and BPN) can perform better. This article is then to examine the predictability of integrated neural network models for interest rate forecasting using change-point detection.

• Computers and Concrete
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• 제20권1호
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• pp.111-118
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• 2017

The present study established prediction models based on multiple nonlinear regressions (MNLRs) and backpropagation neural networks (BPNs) for the expansion of cement mortar caused by oxidization slag that was used as a replacement of the aggregate. The data used for the models were obtained from actual laboratory tests on specimens that were produced with water/cement ratios of 0.485 or 1.5, within which 0%, 10%, 20%, 30%, 40%, or 50% of the cement had been replaced by oxidization slag from electric-arc furnaces; the samples underwent high-temperature curing at either $80^{\circ}C$ or $100^{\circ}C$ for 1-4 days. The varied mixing ratios, curing conditions, and water/cement ratios were all used as input parameters for the expansion prediction models, which were subsequently evaluated based on their performance levels. Models of both the MNLR and BPN groups exhibited $R^2$ values greater than 0.8, indicating the effectiveness of both models. However, the BPN models were found to be the most accurate models.