• Journal of the Korean Solar Energy Society
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• v.26 no.2
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• pp.27-34
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• 2006

In this paper, a forecasting model of wind speed at Walryong Site, Jeju Island is presented, which has been developed and evaluated as a first step toward establishing Korea Forecasting Model of Wind Power Generation. The forecasting model is constructed based on neural network and is trained with wind speed data observed at Cosan Weather Station located near by Walryong Site. Due to short period of measurements at Walryong Site for training statistical model Gosan Weather Station's long-term data are substituted and then transplanted to Walryong Site by using Measure-Correlate-Predict technique. One to three-hour advance forecasting of wind speed show good agreements with the monitoring data of Walryong site with the correlation factors 0.96 and 0.88, respectively.

• Korean Journal of Remote Sensing
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• v.8 no.2
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• pp.131-145
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• 1992

A low cost personal computer and image processing S/W were empolyed to derive Digtal Elevation Model(DEM) of tidal flat from multitemporal LANDSAT TM images, and to create three-dimensional(3D) perspective views of the tidel flat on Komso bay in west coasts of Korea. The method for generation of Digital Elevation Model(DEM) in tidal flat was considered by overlapping techniques of multitemporal LANDSAT TM images and interpolations. The boundary maps of tidal flat extracted from multitemporal images with different water high were digitally combined in x, y, z space with tide in formation and used as an inputcontour data to obtain an elevation model by interpolation using spline function. Elevation errors of less than $\pm$0.1m were achived using overlapping techniques and a spline interpolation approach, respectively. The derived DEM allows for the generation of a perspective grid and drape on the satellite image values to create a realistic terrain visualization model so that the tidal flat may be viewed from and desired direction. As the result of this study, we obtained elevation model of tidal flats which contribute to characterize of topography and monitoring of morphological evolution of tidal flats. Moreover, the modal generated here can be used for simulation of innudation according to tide and support other studies as a supplementary data set.

• Journal of the Korean Data and Information Science Society
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• v.28 no.4
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• pp.889-898
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• 2017

In this research multiple change-points estimation for South Korean electricity generation data is considered. We analyze the South Korean electricity data via deterministically trending dynamic time series model with multiple structural changes in trends in a Bayesian approach. The number of change-points and the timing are unknown. The goal is to find the best model with the appropriate number of change-points and the length of the segments. A genetic algorithm is implemented to solve this optimization problem with a variable dimension of parameters. We estimate the structural change-points for South Korean electricity generation data and Nile River flow data additionally.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.4
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• pp.222-227
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• 2022

In this paper, we implemented a Korean text generation and classification model based on a deep learning algorithm that can be applied to various industries. It consists of two implemented GAN-based Korean handwriting generation models and CNN-based Korean handwriting classification models. The GAN model consists of a generator model for generating fake Korean handwriting data and a discriminator model for discriminating fake handwritten data. In the case of the CNN model, the model was trained using the 'PHD08' dataset, and the learning result was 92.45. It was confirmed that Korean handwriting was classified with % accuracy. As a result of evaluating the performance of the classification model by integrating the Korean cursive data generated through the implemented GAN model and the training dataset of the existing CNN model, it was confirmed that the classification performance was 96.86%, which was superior to the existing classification performance.

• Journal of the Korea Institute of Military Science and Technology
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• v.23 no.4
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• pp.389-398
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• 2020

Test cases in the existing weapon system software were created manually by the tester analyzing the test items defined in the software integration test procedure. However, existing test case generation method has two limitations. First, the quality of test cases can vary depending on the tester's ability to analyze the test items. Second, excessive time and cost may be incurred in writing test cases. This paper proposes a method to automatically generate test cases based on the requirements model and specifications to overcome the limitations of the existing weapon system software test case generation. Generate test sequences and test data based on the use case event model, a model representing the requirements of the weapon system software, and the use case specification specifying the requirements. The proposed method was applied to 8 target models constituting the avionics control system, producing 30 test sequences and 8 test data.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.2
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• pp.383-388
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• 2024

This paper is a study to derive the predicted value of power generation based on the photovoltaic power generation data measured in Jeollanam-do, South Korea. Multivariate variables such as direct current, alternating current, and environmental data were measured in the inverter to measure the amount of power generation, and pre-processing was performed to ensure the stability and reliability of the measured values. Correlation analysis used only data with high correlation with power generation in time series data for prediction using partial autocorrelation function (PACF). Deep learning models were used to measure the amount of power generation to predict the amount of photovoltaic power generation, and the results of correlation analysis of each multivariate variable were used to increase the prediction accuracy. Learning using refined data was more stable than when existing data were used as it was, and the solar power generation prediction algorithm was improved by using only highly correlated variables among multivariate variables by reflecting the correlation analysis results.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.1
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• pp.1-8
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• 2018

Software testing is important to determine the reliability of the system, a task that requires a lot of effort and cost. Model-based testing has been proposed as a way to reduce these costs by automating test designs from models that regularly represent system requirements. For each path of model to generate an input value to perform a test, meta-heuristic technique is used to find the test data. In this paper, we propose an automatic test data generation method using a slicing method and a priority policy, and suppress unnecessary computation by excluding variables not related to target path. And then, experimental results show that the proposed method generates test data more effectively than conventional method.

• Water for future
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• v.19 no.2
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• pp.167-180
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• 1986

Disaggregation model has recently become a major technique in the field of synthetic generation and the model is possibly one of the most widely acepted tools in stochastic hydrology. The application of disaggregation model is evaluated with the streamflow data at the Waegwan and Hyunpung stage gaugin station on the main stem of the Nakdong River. The disaggregation process of annual streamflow data and the method of parameter estimation for the model is reviewed and the statistical analysis of the generated monthly streamflows such as a computation of moment estimation of covariance and correlogram analysis is made. The results, disaggregated monthly streamflow, obtained by Disaggregation Basic Model for single site are compared with the historical streamflow data and also with the other model, Thomas-Fiering Model. The generated monthly streamflow data by two models have been investigated and verified by comparision of mean and standard deviation between the historical and generated data.

• Plant Journal
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• v.16 no.4
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• pp.26-32
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• 2020

The performance monitoring system in the power plant should have the capability to estimate power generation efficiency accurately. Several power generation efficiency models have been proposed for the combined heat and power (CHP) plant which produces both electricity and process steam(or heating energy, hereinafter expressed by process steam only). However, most of the models are not sufficiently accurate due to the wrong evaluation of the process steam value. The study suggests Electricity Conversion Efficiency (ECE) model with determination of the heat rate of process steam using operational data. The suggested method is applied to the design data and the resulted trajectory curve of power generation efficiency meets the data closely with R2 99.91%. This result confirms that ECE model with determination of the model coefficient using the operational data estimate the efficiency so accurately that can be used for performance monitoring of CHP plant.

• Journal of the Korea Institute of Military Science and Technology
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• v.26 no.5
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• pp.392-399
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• 2023

This research paper investigates the effectiveness of using computer graphics(CG) based synthetic data for deep learning in military vehicle detection. In particular, we explore the use of synthetic image generation techniques to train deep neural networks for object detection tasks. Our approach involves the generation of a large dataset of synthetic images of military vehicles, which is then used to train a deep learning model. The resulting model is then evaluated on real-world images to measure its effectiveness. Our experimental results show that synthetic training data alone can achieve effective results in object detection. Our findings demonstrate the potential of CG-based synthetic data for deep learning and suggest its value as a tool for training models in a variety of applications, including military vehicle detection.