• Journal of The Korean Society of Agricultural Engineers
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• v.66 no.4
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• pp.51-57
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• 2024

This study was conducted on how to simulate runoff, which was done using existing physical models, using an LSTM (Long Short-Term Memory) model based on deep learning. Tancheon, the first tributary of the Han River, was selected as the target area for the model application. To apply the model, one water level observatory and four rainfall observatories were selected, and hourly data from 2020 to 2023 were collected to apply the model. River water level of the outlet of the Tancheon basin was simulated by inputting precipitation data from four rainfall observation stations in the basin and average preceding 72-hour precipitation data for each hour. As a result of water level simulation using 2021 to 2023 data for learning and testing with 2020 data, it was confirmed that reliable simulation results were produced through appropriate learning steps, reaching a certain mean absolute error in a short period time. Despite the short data period, it was found that the mean absolute percentage error was 0.5544~0.6226%, showing an accuracy of over 99.4%. As a result of comparing the simulated and observed values of the rapidly changing river water level during a specific heavy rain period, the coefficient of determination was found to be 0.9754 and 0.9884. It was determined that the performance of LSTM, which aims to simulate river water levels, could be improved by including preceding precipitation in the input data and using precipitation data from various rainfall observation stations within the basin.

• Journal of radiological science and technology
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• v.23 no.1
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• pp.39-47
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• 2000

In this study, we showed a comparison and analysis making use of DWI(diffusion weighted image) using early diagnosis of cerebral Infarction and with the classified T2 weighted image, FLAIR images signal intensity for brain infarction period. period of cerebral infarction after the condition of a disease by ischemic stroke. To compare 3 types of image, we performed polynomial warping and affined transform for image matching. Using proposed algorithm, calculated signal intensity difference between T2WI, DWI, FLAIR and DWI. The quantification values between hand made and calculated data are almost the same. We quantified the each period and performed pseudo color mapping by comparing signal intensity each other according to previously obtained hand made data, and compared the result of this paper according to obtained quantified data to that of doctors decision. The examined mean and standard deviation for each brain infarction stage are as follows ; the means and standard deviations of signal intensity difference between DWI and T2WI for each period are $197.7{\pm}6.9$ in hyperacute, $110.2{\pm}5.4$ in acute, and $67.8{\pm}7.2$ in subacute. And the means and standard deviations of signal intensity difference between DWI and FLAIR for each period are $199.8{\pm}7.5$ in hyperacute, $115.3{\pm}8.0$ in acute, and $70.9{\pm}5.8$ in subacute. We can quantificate and decide cerebral infarction period objectively. According to this study, DWI is very exact for early diagnosis. We classified the period of infarction occurrence to analyze the region of disease and normal region in DW, T2WI, FLAIR images.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.113-118
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• 2021

The guided weapon system should ensure economical operation and user safety. In particular, in the case of guided weapon systems developed in the form of a guaranteed bomb, the standards for maintaining reliability considering the long-term storage environment are presented during the development stage, and an optimized inspection cycle is required to maintain this. This study calculated the reliability through a trend test, fitness test, and distribution analysis using a mathematical model based on the maintenance status and shooting results during the inspection period for OO missiles currently in operation for a long time in the military. Through this, it was applied to the inspection period model (Martinez) set during the development stage to determine if the improved inspection period can be utilized. Finally, by synthesizing the data from these studies, a policy management plan was developed according to the extension of the inspection period. The One-Shot system was operated at the inspection period set when it was developed. The study analyzed the actual failure and maintenance data to reset the efficient inspection period.

• The Journal of Information Systems
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• v.31 no.3
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• pp.197-220
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• 2022

Purpose The purpose of this study is to identifies the demand pattern categorization of repair parts of Automotive After-sales Service(A/S) and proposes a demand prediction model for Auto repair parts using Long Short-Term Memory (LSTM) of artificial neural networks (ANN). The optimal parts inventory quantity prediction model is implemented by applying daily, weekly, and monthly the parts demand data to the LSTM model for the Lumpy demand which is irregularly in a specific period among repair parts of the Automotive A/S service. Design/methodology/approach This study classified the four demand pattern categorization with 2 years demand time-series data of repair parts according to the Average demand interval(ADI) and coefficient of variation (CV²) of demand size. Of the 16,295 parts in the A/S service shop studied, 96.5% had a Lumpy demand pattern that large quantities occurred at a specific period. lumpy demand pattern's repair parts in the last three years is predicted by applying them to the LSTM for daily, weekly, and monthly time-series data. as the model prediction performance evaluation index, MAPE, RMSE, and RMSLE that can measure the error between the predicted value and the actual value were used. Findings As a result of this study, Daily time-series data were excellently predicted as indicators with the lowest MAPE, RMSE, and RMSLE values, followed by Weekly and Monthly time-series data. This is due to the decrease in training data for Weekly and Monthly. even if the demand period is extended to get the training data, the prediction performance is still low due to the discontinuation of current vehicle models and the use of alternative parts that they are contributed to no more demand. Therefore, sufficient training data is important, but the selection of the prediction demand period is also a critical factor.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.15 no.4
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• pp.287-293
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• 2003

This study examined the temperature-dependent regression model of energy consumption based on various measuring period. The methodology employed was to construct temperature-dependent linear regression model of daily energy consumption from one day to three months data-sets and to compare the annual heating energy consumption predicted by these models with actual annual heating energy consumption. Heating energy consumption from a building in Daejon was examined experimentally. From the results, predicted value based on one day experimental data can have error over 100%. But predicted value based on one week experimental data showed error over 30%. And predicted value based on over three months experimental data provides accurate prediction within 6% but it will be required very expensive.

• Journal of Applied Reliability
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• v.8 no.1
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• pp.1-13
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• 2008

A methodology for collection and analysis of automotive field reliability data is presented. Automotive warranty system usually covers a pre-determined period of time and/or mileage accumulation. Therefore mileage information for the vehicles that have not experienced any failure or problems during the warranty period is not available. In this paper, a reliability analysis method using the estimated mileage distribution from an additional survey for vehicles that have not any record during the warranty period is proposed. Methods of reliability analysis using the warranty information collected under the EU and US warranty policies are also provided.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.343-346
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• 2002

Toward more accurate determination of the water cycle in association with climate variability and change as well as baseline data on the impacts of this variability on water resources, the Coordinated Enhanced Observing Period (CEOP) was launched on July 1,2001. The preliminary data period, EOP-1, was implemented from July to September in 2001. The first annual enhanced observing period, EOP-3, is going to start on October 1,2002. CEOP is seeking to achieve a database of common measurements from both in situ and satellite remote sensing, model output, and four-dimensional data analyses (4DDA; including global and regional reanalyses) for a specified period. In this context a number of carefully selected reference stations are linked closely with the existing network of observing sites involved in the GEWEX Continental Scale Experiments, which are distributed across the world. The initial step of CEOP is to develop a pilot global hydro-climatological dataset with global consistency under the climate variability that can be used to help validate satellite hydrology products and evaluate, develop and eventually predict water and energy cycle processes in global and regional models. Based on the dataset, we will address the studies on the inter-comparison and inter-connectivity of the monsoon systems and regional water and energy budget, and a path to down-scaling from the global climate to local water resources, as the second step.

• Journal of Korean Critical Care Nursing
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• v.17 no.1
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• pp.1-16
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• 2024

Purpose : This study aimed to explore and describe intensive care unit (ICU) nurses' experience of clinical adaptation. Methods : The participants were 14 ICU nurses with more than two years of working experience in the ICU. Data were collected through in-depth individual interviews conducted between July and October 2021. Theoretical sampling was used to the point of theoretical saturation. Data were analyzed using the Strauss and Corbin method. Results : A total of 79 concepts, 37 subcategories, and 16 categories were identified through open coding. Axial coding based on the paradigm model revealed that the central phenomenon was "The harsh adversity faced in the nursing field where life and death are determined" and the core category was "Enduring the adversity of caring for critically ill patients and achieving self-realization." ICU nurses' clinical adaptation process was explained in five phases: "confrontation period," "turbulent period," "seeking period," "struggling period," and "stabilized period." The five phases that affect interventional conditions were "Support from reliable people," "Recognition of administrative and financial support." Conclusion : This study provided novel insights for a comprehensive understanding of ICU nurses' clinical adaptation processes. Furthermore, the findings are expected to be used as basic data to develop multifaceted strategies to help ICU nurses' adaptation to critical care.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.35 no.1
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• pp.24-31
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• 2012

This study is designed to predict the overall electric power load, to apply the method of time sharing and to reduce simultaneous load factor of electric power when authorized by user entering demand plans and using schedules into the user's interface for a certain period of time. This is about smart grid, which reduces electric power load through simultaneous load factor of electric power reduction system supervision agent. Also, this study has the following characteristics. First, it is the user interface which enables authorized users to enter and send/receive such data as demand plan and using schedule for a certain period of time. Second, it is the database server, which collects, classifies, analyzes, saves and manages demand forecast data for a certain period of time. Third, is the simultaneous load factor of electric power control agent, which controls usage of electric power by getting control signal, which is intended to reduce the simultaneous load factor of electric power by the use of the time sharing control system, form the user interface, which also integrate and compare the data which were gained from the interface and the demand forecast data of the certain period of time.

• The Research Journal of the Costume Culture
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• v.11 no.2
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• pp.253-262
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• 2003

The purposes of the study were (1) to analysis Korean traditional wedding costumes through families's wedding photographs from 1940 to 2000, and (2) to categorize bride and groom's costumes according to the wedding time by means of a time series analysis. (3) to find out functional relationship among changes in garment types, garment details, embellishments and colors. The study was a documentary research and data were collected from 390 family wedding photographs by a convenient sampling. The data were analyzed by qualitative and quantitative method and the statistic used were frequency, content analysis, and cross-tab analysis. The results were as follows; First, the garments of wedding couples were categorized into 5 period according to garment's characteristics. 1. The period between 1940～1959 : Korean traditional wedding costumes and western style wedding costumes were existed together in Korean wedding culture. 2. The period between 1960～early 1970's western wedding costumes were dominated. 3. The period of late 1970's : wedding couple's costumes became more formal and decorative. 4. The period of 1980's : introducing see-through materials for brides and tuxedo suit for grooms. 5. The period of 1990's : extravagance in shapes and exposure. Second, there were significant relationships among brides's dress types and neckline, glove length, embellishments and transparency of materials and among groom's garment types and necktie types, types and color of shirts, vests. Third, the time series analysis of bride and groom's outfit produced 5 schematic expressions of wedding outfits according to the period.