• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.04a
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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.

• 한국데이터정보과학회:학술대회논문집
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• 2003.10a
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• pp.57-72
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• 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.

• Environmental and Resource Economics Review
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• v.25 no.1
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• pp.27-60
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• 2016

An integrated assessment model of climate and the economy (IAM) has been a standard tool for the economic analysis of climate change and policy recommendations. Since policy measures to address climate change take places at a national level, a regional integrated assessment model of climate and the economy (RIAM) is gaining more importance. A RIAM is a useful tool for the assessment of regional (or national) impacts of climate change. This paper investigates the main features of the currently available RIAMs. The focus is social welfare functions and the regional aspects of climate change. The comparative analysis shows that there is a huge gap between the economics of climate change and its applications to RIAMs. As an application, this paper examines the effect of social welfare functions on optimal solutions of the RICE (Regional Integrated model of Climate and the Economy) model. It is found that optimal climate policy such as carbon tax or emissions control rate is very sensitive to the assumptions on social welfare functions of RIAMs. It is better for each country to have their own RIAM as a basic tool for national climate policy-making and for international bargaining in greenhouse-gas mitigation. This is because a country's own preferences such as efficiency, equity, and sustainable development as well as national circumstances can be reflected in RIAM. The Republic of Korea has not developed its own RIAM yet. The comparative analysis and the numerical model in this paper can be a stepping stone for the development of such a national model.

• Communications for Statistical Applications and Methods
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• v.8 no.2
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• pp.543-556
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• 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.

• Korean Journal of Agricultural Science
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• v.48 no.3
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• pp.629-642
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• 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.

• Korean Journal of Cognitive Science
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• v.35 no.1
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• pp.1-21
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• 2024

This study investigates the characteristics of visual working memory (VWM) representations by examining two theoretical models: the integrated-object and the parallel-independent feature storage models. Experiment I involved a change detection task where participants memorized arrays of either orientation bars, colored squares, or both. In the one-feature condition, the memory array consisted of one feature (either orientations or colors), whereas the two-feature condition included both. We found no differences in change detection performance between the conditions, favoring the integrated object model over the parallel-independent feature storage model. Experiment II employed a recall task with memory arrays of isosceles triangles' orientations, colored squares, or both, and one-feature and two-feature conditions were compared for their recall performance. We found again no clear difference in recall accuracy between the conditions, but the results of analyses for memory precision and guessing responses indicated the weak object model over the strong object model. For ongoing debates surrounding VWM's representational characteristics, these findings highlight the dominance of the integrated object model over the parallel independent feature storage model.

• Journal of Information Technology Applications and Management
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• v.12 no.4
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• pp.173-195
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• 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.

• Journal of KIBIM
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• v.8 no.2
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• pp.10-18
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• 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
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• v.14 no.6
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• pp.728-732
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• 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
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• v.11 no.4
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• pp.99-111
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• 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.