• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.04a
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• pp.733-736
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• 1996

Neural Network(NN) is known to be suitable for forecasting corporate bankruptcy because of discriminant capability. Bankruptcy prediciton on NN by now has mostly been studied based on financial indices at specific point of time. However, the financial profile of corporates fluctuates within a certain range with the elapse of time. Besides, we need a lot of data of different bankrupt types in order to apply NN for better bankruptcy prediciton. Therefore, we have decided to focus on textiles and clothing industries for bankruptcy prediction with limited data. One part of the collected data was used for training and calibration, and the other was used for verification. The model makes a learning with extended data from financial indices at specific point of time. The trained model has been tested and we could get a high hitting ratio relatively.

• Korean Management Science Review
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• v.14 no.2
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• pp.91-111
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• 1997

Neural Network(NN) is known to be suitable for forecasting corporate bankruptcy because of discriminant capability. Bandkruptcy prediction on NN by now has mostly been studied based on financial indices at specific point of time. However, the financial profile of corporates fluctuates within a certain range with the elapse of time. Besides, we need a lot of data of different bankrupt types in order to apply NN for better bankruptcy prediction. Therefore, We have decided to focus on textile and clothing industries for bankruptcy prediction with limited data. One part of the collected data was used for training and calibration, and the other was used for verification. The model makes a learning with extended data from financial indices at specific point of time. The trained model has been tested and we could get a high hitting ratio relatively.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.1002-1005
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• 1991

It is important to express the specific growth rate of a fermentation reaction as a function of substrate and product concentration in developing bioprocess automation techniques such as modeling of the reactor and controlling it via an advanced control scheme. Typical methods of identification utilize graphical representation of the rate constant data or nonlinear regression with an appropriate noise filter. But the former method fails when the data are erroneous and the latter are mathematically complicated to apply in the field. Neural network is another candidate for the identification from time series data since it is insensitive to the random data error and easy to implement. In this study, we will develop a neural network method of specific growth rate estimation from the time series state variable data and test the performance.

• Journal of the Korean Operations Research and Management Science Society
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• v.14 no.2
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• pp.91-91
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• 1989

Neural Network(NN) is known to be suitable for forecasting corporate bankruptcy because of discriminant capability. Bandkruptcy prediction on NN by now has mostly been studied based on financial indices at specific point of time. However, the financial profile of corporates fluctuates within a certain range with the elapse of time. Besides, we need a lot of data of different bankrupt types in order to apply NN for better bankruptcy prediction. Therefore, We have decided to focus on textile and clothing industries for bankruptcy prediction with limited data. One part of the collected data was used for training and calibration, and the other was used for verification. The model makes a learning with extended data from financial indices at specific point of time. The trained model has been tested and we could get a high hitting ratio relatively.

• Nuclear Engineering and Technology
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• v.15 no.1
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• pp.41-49
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• 1983

A computer code, BERD (Bayesian Estimation of Reliability Data), has been developed and tested in order to update the data for the reliability analysis of safety related systems in a specific nuclear power plant. The code has been used to derive the plant-specific data for reliability analysis of the auxiliary feedwater system of a pressurized water reactor. The prior information for components selected was taken from the U.S. Reactor Safety Study, WASH-1400, and the operating experiences from published licensee event reports. The results show that the updated data are well fitted to log-normal distribution curves and the error factors are reduced significantly.

• Journal of Information Processing Systems
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• v.16 no.6
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• pp.1447-1458
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• 2020

This paper describes a structured overlay network scheme based on multiple different time intervals. Many types of data (e.g., sensor data) can be requested at specific time intervals that depend on the user and the system. These queries are referred to as "interval queries." A method for constructing an overlay network that efficiently processes interval queries based on multiple different time intervals is proposed herein. The proposed method assumes a ring topology and assigns nodes to a keyspace based on one-dimensional time information. To reduce the number of forwarded messages for queries, each node constructs shortcut links for each interval that users tend to request. This study confirmed that the proposed method reduces the number of messages needed to process interval queries. The contributions of this study include the clarification of interval queries with specific time intervals; establishment of a structured overlay network scheme based on multiple different time intervals; and experimental verification of the scheme in terms of communication load, delay, and maintenance cost.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.12
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• pp.547-554
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• 2021

Cancer patients can have different kinds of cancer driver genes, and identification of these patient-specific cancer driver genes is an important step in the development of personalized cancer treatment and drug development. Several bioinformatic methods have been proposed for this purpose, but there is room for improvement in terms of accuracy. In this paper, we propose NPD (Network based Patient-specific Driver gene identification) for identifying patient-specific cancer driver genes. NPD consists of three steps, constructing a patient-specific gene network, applying the modified PageRank algorithm to assign scores to genes, and identifying cancer driver genes through a score comparison method. We applied NPD on six cancer types of TCGA data, and found that NPD showed generally higher F1 score compared to existing patient-specific cancer driver gene identification methods.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.5
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• pp.627-637
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• 2022

To know the magnitudes of extreme floods for various sizes of watersheds, massive streamflow data is fundamentally required. However, small/medium-size watersheds missed streamflow data because of the lack of gauge stations. In this study, the Streamflow Propagation Method (SPM) was applied to generate streamflow data for small/medium size watersheds with no measurements. Based on the generated streamflow data for ungauged watersheds at three different locations (i.e., Chungju Dam (CJD), Seomjin Dam (SJD), and Andong Dam (ADD) watersheds), the scale ranges of extreme floods were evaluated for different sizes of ungauged watersheds by using the specific flood distribution analysis. As a general result, a range of specific floods decreases with increasing watershed size. The distribution of the specific flood in the same size of a watershed possibly depends on the size and topography of the watershed area. The delivered equations were compared to show the relations between the specific flood and sizes of watersheds. In the comparisons of equations, the Creager envelope curve has the higher potential to represent the maximum flood distribution for each watershed. For the generalization of the maximum flood distribution for three watersheds, optimized envelop curves are obtained with lower RMSE than that of Creager envelope curve.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.85-85
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• 2020

South Korea experiences few large scale erosion and sedimentation problems, however, there are numerous local sedimentation problems. A reliable and consistent approach to modelling and management for sediment processes are desirable in the country. In this study, field measurements of sediment concentration from 34 alluvial river basins in South Korea were used with the Modified Einstein Procedure (MEP) to determine the total sediment load at the sampling locations. And then the Flow Duration-Sediment Rating Curve (FD-SRC) method was used to estimate the specific degradation for all gauging stations. The specific degradation of most rivers were found to be typically 50-300 tons/㎢·yr. A model tree data mining technique was applied to develop a model for the specific degradation based on various watershed characteristics of each watershed from GIS analysis. The meaningful parameters are: 1) elevation at the middle relative area of the hypsometric curve [m], 2) percentage of wetland and water [%], 3) percentage of urbanized area [%], and 4) Main stream length [km]. The Root Mean Square Error (RMSE) of existing models is in excess of 1,250 tons/㎢·yr and the RMSE of the proposed model with 6 additional validations decreased to 65 tons/㎢·yr. Erosion loss maps from the Revised Universal Soil Loss Equation (RUSLE), satellite images, and aerial photographs were used to delineate the geospatial features affecting erosion and sedimentation. The results of the geospatial analysis clearly shows that the high risk erosion area (hill slopes and construction sites at urbanized area) and sedimentation features (wetlands and agricultural reservoirs). The result of physiographical analysis also indicates that the watershed morphometric characteristic well explain the sediment transport. Sustainable management with the data mining methodologies and geospatial analysis could be helpful to solve various erosion and sedimentation problems under different conditions.

• Proceedings of the Korean Powder Metallurgy Institute Conference
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• 2006.09a
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• pp.58-59
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• 2006

In Spray Forming, specific enthalpy is a key parameter in the deposition process as it influences the thermal condition of the impinging droplets as well as that of the deposit surface. An empirical model for the distribution of specific enthalpy in the spray cone was developed as an easy to handle alternative to numerical models with which the descriptive partial differential equations are solved numerically. The model results were compared with the experimental data to validate its applicability.