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

• 한국데이터정보과학회:학술대회논문집
• /
• 한국데이터정보과학회 2003년도 추계학술대회
• /
• pp.57-72
• /
• 2003

This article provides integrated neural network models for the interest rate forecasting using change-point detection. The model is composed of three phases. The first phase is to detect successive structural changes in interest rate dataset. The second phase is to forecast change-point group with data mining classifiers. The final phase is to forecast the interest rate 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 network model alone and, in addition, determine which of three classifiers (MDA, CBR and BPN) can perform better. For interest rate forecasting, this study then examines the predictability of integrated neural network models to represent the structural change.

• 자원ㆍ환경경제연구
• /
• 제25권1호
• /
• pp.27-60
• /
• 2016

기후경제통합평가모형(Integrated assessment model of climate and the economy)은 기후변화에 관한 경제 분석과 정책제안을 위한 필수적인 도구가 되어왔다. 최근에는 기후변화에 대응하기 위한 정책적 노력들이 대부분 국가 또는 지역 수준에서 일어난다는 인식 하에 국가 또는 지역에서의 기후변화 영향과 정책수단의 효과를 평가할 수 있는 기후경제통합-지역평가모형(Regional integrated assessment model of climate and the economy)의 중요성이 더욱 커지고 있다. 이 논문에서는 국내에서 기후경제통합-지역평가모형을 개발하기 위한 첫 번째 단계로서 사회후생함수를 중심으로 기후경제통합-지역평가모형을 이론적으로 유형화했으며, RICE(Regional integrated climate-economy model) 모형을 통해 기후변화 대응전략에 따른 국가별 기후변화정책의 변화를 수치적으로 살펴보았다. 변화하는 국제 상황, 새롭게 드러나는 과학적 증거, 국내 여건 등을 모두 반영한 기후변화정책을 수립하기 위해서는 이를 분석할 수 있는 효과적인 도구를 갖추고 있어야 한다. 이 논문에서 살펴본 기후경제통합-지역평가모형은 이를 위한 유용한 도구가 될 수 있다.

• Communications for Statistical Applications and Methods
• /
• 제8권2호
• /
• pp.543-556
• /
• 2001

This study suggests integrated neural network modes for he stock price index forecasting using change-point detection. The basic concept of this proposed model is to obtain significant intervals occurred by change points, identify them as change-point groups, and reflect them in stock price index forecasting. The model is composed of three phases. The first phase is to detect successive structural changes in stock price index dataset. The second phase is to forecast change-point group with various data mining classifiers. The final phase is to forecast the stock price index with backpropagation neural networks. The proposed model is applied to the stock price index forecasting. This study then examines the predictability of integrated neural network models and compares the performance of data mining classifiers.

• 농업과학연구
• /
• 제48권3호
• /
• pp.629-642
• /
• 2021

Land-use change has an important role in the hydrologic characteristics of watersheds because it alters various hydrologic components such as interception, infiltration, and evapotranspiration. For example, rapid urbanization in a watershed reduces infiltration rates and increases peak flow which lead to changes in the hydrologic responses. In this study, a physical hydrologic model the soil and water assessment tool (SWAT) was used to assess long-term continuous daily streamflow corresponding to land-use changes that occurred in the Naesungchun river watershed. For a 30-year model simulation, 3 different land-use maps of the 1990s, 2000s, and 2010s were used to identify the impacts of the land-use changes. Using SWAT-CUP (calibration and uncertainty program), an automated parameter calibration tool, 23 parameters were selected, optimized and compared with the daily streamflow data observed at the upstream, midstream and downstream locations of the watershed. The statistical indexes used for the model calibration and validation show that the model performance is improved at the downstream location of the Naesungchun river. The simulated streamflow in the mainstream considering land-use change increases up to -2 - 30 cm compared with the results simulated with the single land-use map. However, the difference was not significant in the tributaries with or without the impact of land-use change.

• 인지과학
• /
• 제35권1호
• /
• pp.1-21
• /
• 2024

본 연구는 두 가지 이론적 모델인 통합된 객체 모형과 특장 병렬-독립 저장 모형을 검증함으로써 시각작업기억 표상의 특성을 조사하였다. 실험 I에서 참가자들은 색상 사각형, 방위 막대 또는 두 가지 모두로 구성된 배열을 기억한 뒤 이를 토대로 변화탐지과제를 수행했다. 단일 특징 조건에서 기억배열은 하나의 특징(방위 또는 색상)으로만 구성된 반면, 두 가지 특징 조건은 둘 모두를 포함했다. 두 조건간 변화탐지 수행의 차이는 없었으며 이는 병렬-독립 저장 모형보다는 통합된 객체 모형을 지지한다. 실험 II에서는 이등변삼각형의 방위, 색상 사각형 또는 두 특징 모두로 구성된 기억배열을 대상으로 회상과제가 실시되었으며, 단일 특징과 두 가지 특징 조건 간 회상 수행이 비교되었다. 두 조건 간 회상 정확도에는 차이가 없었으나 표상 선명도와 추측반응에 대한 분석 결과는 강한 객체 모형보다는 약한 객체 모형을 시사했다. 본 연구의 결과는 시각작업기억의 표상 특성을 둘러싼 현시점의 논쟁에 있어서 병렬-독립 저장 모형이 아닌 통합된 객체 모형의 우세를 지지한다.

• Journal of Information Technology Applications and Management
• /
• 제12권4호
• /
• pp.173-195
• /
• 2005

As the amount of informatization investment is rapidly increasing in many organizations, it becomes more inevitable to manage computing resources (information systems, computing facilities and manpower etc.) effectively. Specially, in public sector It is thought to be very important to achieve the economy of scale by integrating computing resources which are managed individually in many agencies. Also, our government have been recently making much efforts to raise the effectiveness of operating the information systems by promoting joint information use among many public agencies, enhancing the operating systems and the expertise of IS staff and applying the optimal information security systems. This study focuses on presenting the framework to effectively integrate omputing resources and proposing the ways of constructing and operating the integrated computing environment for the institutions and the affiliated groups under the Ministry of Culture & Tourism which are in charge of implementing cultural informatization. The main implications of this study are 1) building the ideal model of the integrated computing environment architecture suitable to cultural informatization area, 2) showing the criteria of deciding whether the organizations use the Integrated computing environment or not and how extensively they commit their computing resources to it, and 3) suggesting the ways of the phased integration and the change management to minimize confusion in the process of adopting the integrated computing environment and behavioral problems such as conflict and resistance of IS-related Personnel Influenced by Implementing the integrated computing environment.

• 한국BIM학회 논문집
• /
• 제8권2호
• /
• pp.10-18
• /
• 2018

BIM technologies store, share and integrate the information produced in each sector of the construction industry. From this point on, it increases the efficiency of the work. Currently, quantity take-off and process schedule are derived separately based on BIM technology. When calculating the quantity by process, relevant information shall be collected, reinterpreted, and reevaluated as required by the practice. The purpose of this study is to develop an integrated process and quantity management system through BIM collaboration and to build prototypes for steel structures. The main research is to build a construction BIM model for steel structures and a process BIM model through BIM collaboration. Furthermore, necessary information was selected and processed according to the user's needs for integrated management. Relevant integration outcomes are visualized graphically to maximize utilization. Through these studies, a system for integrated control of processes and supplies is provided, and the results are expected to contribute to the improvement of working efficiency and are easily and quickly reflected in design change and process change. In this study, we intended to enhance the usability of information by linking process schedules with quantity calculations based on BIM. Thus, the process for integrated control of the quantity of structural components by process unit and the BIM based schedule information was established. In addition, the efficiency of the information link of the integrated management system was considered for design changes and process schedule changes.

• JSTS:Journal of Semiconductor Technology and Science
• /
• 제14권6호
• /
• pp.728-732
• /
• 2014

Macro-model of magnetic tunnel junction (MTJ) for spin transfer torque magnetic random access memory (STT-MRAM) has been developed. The macro-model can describe the dynamic behavior such as the state change of MTJ as a function of the pulse width of driving current and voltage. The statistical behavior has been included in the model to represent the variation of the MTJ characteristic due to process variation. The macro-model has been developed in Verilog-A.

• Asia pacific journal of information systems
• /
• 제11권4호
• /
• pp.99-111
• /
• 2001

The prediction of stock price index is a very difficult problem because of the complexity of stock market data. It has been studied by a number of researchers since they strongly affect other economic and financial parameters. The movement of stock price index has a series of change points due to the strategies of institutional investors. This study presents a two-stage forecasting model of stock price index using change-point detection and artificial neural networks. The basic concept of this proposed model is to obtain intervals divided by change points, to identify them as change-point groups, and to use them in stock price index forecasting. First, the proposed model tries to detect successive change points in stock price index. Then, the model forecasts the change-point group with the backpropagation neural network(BPN). Finally, the model forecasts the output with BPN. This study then examines the predictability of the integrated neural network model for stock price index forecasting using change-point detection.