• KIPS Transactions on Computer and Communication Systems
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• v.10 no.10
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• pp.261-268
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• 2021

Cloud computing is a computing paradigm in which users can utilize computing resources in a pay-as-you-go manner. In a cloud system, resources can be dynamically scaled up and down to the user's on-demand so that the total cost of ownership can be reduced. The Modeling and Simulation (M&S) technology is a renowned simulation-based method to obtain engineering analysis and results through CAE software without actual experimental action. In general, M&S technology is utilized in Finite Element Analysis (FEA), Computational Fluid Dynamics (CFD), Multibody dynamics (MBD), and optimization fields. The work procedure through M&S is divided into pre-processing, analysis, and post-processing steps. The pre/post-processing are GPU-intensive job that consists of 3D modeling jobs via CAE software, whereas analysis is CPU or GPU intensive. Because a general-purpose desktop needs plenty of time to analyze complicated 3D models, CAE software requires a high-end CPU and GPU-based workstation that can work fluently. In other words, for executing M&S, it is absolutely required to utilize high-performance computing resources. To mitigate the cost issue from equipping such tremendous computing resources, we propose HEMOS-Cloud service, an integrated cloud and cluster computing environment. The HEMOS-Cloud service provides CAE software and computing resources to users who want to experience M&S in business sectors or academics. In this paper, the economic ripple effect of HEMOS-Cloud service was analyzed by using industry-related analysis. The estimated results of using the experts-guided coefficients are the production inducement effect of KRW 7.4 billion, the value-added effect of KRW 4.1 billion, and the employment-inducing effect of 50 persons per KRW 1 billion.

• The Journal of Korean Society for Radiation Therapy
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• v.30 no.1_2
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• pp.153-160
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• 2018

Purpose : We retrospectively analyzed doses of each radiation therapy technique used in the treatment for left breast cancer patients after partial mastectomy through dose results for normalorgans and tumor volume to use this as a clinical reference for radiation therapy of domestic left breast cancer patients. Materials and Methods : 40 patients who underwent partial mastectomy on left breast cancer were classified in 3 treatment methods. The treatment plan was evaluated by HI(homogeneity index), $D_{95%}$, and CI(conformity index), and the $V_{hot}$ for gross tumor volume and clinical target volume of each treatment method. In Cyberknife treatment, tumor volume was the same as high dose volume in the other techniques, so no consideration was given to clinical target volume. Treatment plan evaluation for normal organs were evaluated by mean dose on ipsilateral lung, heart, left anterior descending artery, opposite breast and lung, and non-target tissue. Result : Treatment with volumetric arc radiotherapy(VMAT) showed $95.84{\pm}0.75%$ of $D_{95%}$ on the clinical target volume, significantly higher than that of 3D-CRT. The $D_{95%}$ value of the total tumor volume was slightly higher than the other treatments. In Cyberknife treatment, the dose to the normal organs was significantly lower than other treatments. Overall, the maximum dose and mean dose to the heart were $26.2{\pm}6.12Gy$ and $1.88{\pm}0.2Gy$ in VMAT treatment and $20.25{\pm}9.35Gy$ and $1.04{\pm}0.19Gy$ in 3D-CRT therapy, respectively. Conclusion : In comparison on 3D-CRT and VMAT, most of the dosimetric parameters for the evaluation of the treatment plan showed similar values, so that there is no significant difference in treatment plan evaluation. It is possible to select the treatment method according to the patient's anatomical structure or possibility of breath control. Cyberknife treatment is very useful treatment for normal organs because of its accurate dose exposure to the tumor volume However, it has restrictions to treat the local area, to have relatively long treatment time and to involve invasive procedure.

• The Journal of Korean Society for Radiation Therapy
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• v.33
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• pp.99-108
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• 2021

Purpose: The goal of this study is to evaluate usefulness of noninvasive method instead of previous inserting Fiducial Marker Method when performing Stereotactic Partial Breast Irradiation in CyberKnife. Material and methods: For consistency of Imaging Center, we evaluated both oblique images at angle 45 and 315 acquired from 2D Simulator and CyberKnife quantitatively through dice similarity coefficient. Also, location reproducibility of Surface Fiducial Marker was analyzed from 2D Simulator, treatment plans and CyberKinfe images by using 8 Fiducial Markers made of gold attached to ATOM Phantom based on our institution's protocols. Results: The results of the estimated consistency were 0.87 and 0.9 at the oblique angle 45 and 315, respectively. For location consistency of Surface Fiducial Markers, values of horizontal vertical direction of left breast were Superior/Inferior 0.3 mm, Left/Right -0.3 mm, Anterior/Posterior 0.4 mm, and the values of rotational direction were Roll 0.3 °, Pitch 0.2 °, Yaw 0.4 °. The values of horizontal vertical direction of right breast were Superior/Inferior -0.1 mm, Left/Right -0.1 mm, Anterior/Posterior -0.1 mm, and the values of rotational direction were Roll 0.2°, Pitch 0.1°, Yaw 0.1°. Conclusions: We expect that the protocols used by Surface Fiducial Markers when performing Stereotactic Partial Breast Irradiation in CyberKnife will provide protection from pain and cut expenses for treatment and reduce treatment errors and make treatment more accurate by suggesting treatment protocols based on high consistency of Imaging Center and reproducibility of Fiducial Markers.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.41-59
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• 2022

The timely procurement of military supplies is essential to maintain the military's operational capabilities, and contract work is the first step toward timely procurement. In addition, rapid signing of a contract enables consumers to set a leisurely delivery date and increases the possibility of budget execution, so it is essential to improve the contract process to prevent early execution of the budget and transfer or disuse. Recently, research using big data has been actively conducted in various fields, and process analysis using big data and process mining, an improvement technique, are also widely used in the private sector. However, the analysis of contract work in the military is limited to the level of individual analysis such as identifying the cause of each problem case of budget transfer and disuse contracts using the experience and fragmentary information of the person in charge. In order to improve the contract process, this study analyzed using the process mining technique with data on a total of 560 contract tasks directly contracted by the Department of Finance of the Air Force Logistics Command for about one year from November 2019. Process maps were derived by synthesizing distributed data, and process flow, execution time analysis, bottleneck analysis, and additional detailed analysis were conducted. As a result of the analysis, it was found that review/modification occurred repeatedly after request in a number of contracts. Repeated reviews/modifications have a significant impact on the delay in the number of days to complete the cost calculation, which has also been clearly revealed through bottleneck visualization. Review/modification occurs in more than 60% of the top 5 departments with many contract requests, and it usually occurs in the first half of the year when requests are concentrated, which means that a thorough review is required before requesting contracts from the required departments. In addition, the contract work of the Department of Finance was carried out in accordance with the procedures according to laws and regulations, but it was found that it was necessary to adjust the order of some tasks. This study is the first case of using process mining for the analysis of contract work in the military. Based on this, if further research is conducted to apply process mining to various tasks in the military, it is expected that the efficiency of various tasks can be derived.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.40 no.2
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• pp.260-267
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• 1995

In temperate zone planting rice at different date subjects the Crop to different climatic condition. The present study aimed at comparison of the change in source-sink relationship of the Japonica(J) and that of IndicaxJaponica(I$\times$J) type rice cultivars caused by shift of heading date. Two J- and two I$\times$J-type cultivars were made to head on August 16, August 26, and September 5. Sink capacity was changed by shift of heading date in different mode between the types of cultivars. In both types major determinant of sink capacity was number of effective tillers, and the number of spikelets per panicle was the minor. In J-type earlier planting/heading was beneficial to increased panicle numbers and this was due mainly to a larger diurnal difference in temperature. I$\times$J-type cultivars favored a higher daily mean temperature to increase the sink capacity. The ability of source at heading, in terms of leaf area per panicle, chlorophyll content per spiklet, photosynthetic ability of leaves per unit area at 25$\^{\circ}C$, carbohydrate and N contents of leaves, was not so different among different heading dates in both types. However, the source activity was governed principally by temperature during grain filling. The J-type cultivars headed on Sept. 5 and I$\times$J-type cultivars headed later than August 16 could not have had sufficient source activity in grain filling due to lower temperature.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.121-142
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• 2023

As the number and weight of imported food are steadily increasing, safety management of imported food to prevent food safety accidents is becoming more important. The Ministry of Food and Drug Safety conducts on-site inspections of foreign food facilities before customs clearance as well as import inspection at the customs clearance stage. However, a data-based safety management plan for imported food is needed due to time, cost, and limited resources. In this study, we tried to increase the efficiency of the on-site inspection by preparing a machine learning prediction model that pre-selects the companies that are expected to fail before the on-site inspection. Basic information of 303,272 foreign food facilities and processing businesses collected in the Integrated Food Safety Information Network and 1,689 cases of on-site inspection information data collected from 2019 to April 2022 were collected. After preprocessing the data of foreign food facilities, only the data subject to on-site inspection were extracted using the foreign food facility_code. As a result, it consisted of a total of 1,689 data and 103 variables. For 103 variables, variables that were '0' were removed based on the Theil-U index, and after reducing by applying Multiple Correspondence Analysis, 49 characteristic variables were finally derived. We build eight different models and perform hyperparameter tuning through 5-fold cross validation. Then, the performance of the generated models are evaluated. The research purpose of selecting companies subject to on-site inspection is to maximize the recall, which is the probability of judging nonconforming companies as nonconforming. As a result of applying various algorithms of machine learning, the Random Forest model with the highest Recall_macro, AUROC, Average PR, F1-score, and Balanced Accuracy was evaluated as the best model. Finally, we apply Kernal SHAP (SHapley Additive exPlanations) to present the selection reason for nonconforming facilities of individual instances, and discuss applicability to the on-site inspection facility selection system. Based on the results of this study, it is expected that it will contribute to the efficient operation of limited resources such as manpower and budget by establishing an imported food management system through a data-based scientific risk management model.

• Korean Journal of Environmental Biology
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• v.40 no.3
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• pp.267-274
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• 2022

Excess nitrogen (N) flowing from livestock manure to water systems poses a serious threat to the natural environment. Thus, livestock wastewater management has recently drawn attention to this related field. This study first attempted to obtain the optimal conditions for the further volatilization of NH₃ gas generated from pig wastewater by adjusting the amount of injected magnesia (MgO). At 0.8 wt.% of MgO (by pig wastewater weight), the volatility rate of NH₃ increased to 75.5% after a day of aeration compared to untreated samples (pig wastewater itself). This phenomenon was attributed to increases in the pH of pig wastewater as MgO dissolved in it, increasing the volatilization efficiency of NH₃. The initial pH of pig wastewater was 8.4, and the pH was 9.2 when MgO was added up to 0.8 wt.%. Second, the residual ammonia nitrogen (NH₄⁺-N) in pig wastewater was removed by precipitation in the form of struvite (NH₄MgPO₄·6H₂O) by adjusting the pH after adding MgO and H₃PO₄. Struvite produced in the pig wastewater was identified by field emission scanning electron microscopy (FE-SEM) and X-ray diffraction (XRD) analysis. White precipitates began to form at pH 6, and the higher the pH, the lower the concentration of NH₄⁺-N in pig wastewater. Of the total 86.1% of NH₄⁺-N removed, 62.4% was achieved at pH 6, which was the highest removal rate. Furthermore, how struvite changes with pH was investigated. Under conditions of pH 11 or higher, the synthesized struvite was completely decomposed. The yield of struvite in the precipitate was determined to be between 68% and 84% through a variety of analyses.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.2
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• pp.80-98
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• 2023

This study explores the effectiveness of various data preprocessing algorithms for improving subseasonal to seasonal (S2S) climate predictions from six climate forecast models and their Multi-Model Ensemble (MME) using a deep learning-based postprocessing model. A pipeline of data transformation algorithms was constructed to convert raw S2S prediction data into the training data processed with several statistical distribution. A dimensionality reduction algorithm for selecting features through rankings of correlation coefficients between the observed and the input data. The training model in the study was designed with TimeDistributed wrapper applied to all convolutional layers of U-Net: The TimeDistributed wrapper allows a U-Net convolutional layer to be directly applied to 5-dimensional time series data while maintaining the time axis of data, but every input should be at least 3D in U-Net. We found that Robust and Standard transformation algorithms are most suitable for improving S2S predictions. The dimensionality reduction based on feature selections did not significantly improve predictions of daily precipitation for six climate models and even worsened predictions of daily maximum and minimum temperatures. While deep learning-based postprocessing was also improved MME S2S precipitation predictions, it did not have a significant effect on temperature predictions, particularly for the lead time of weeks 1 and 2. Further research is needed to develop an optimal deep learning model for improving S2S temperature predictions by testing various models and parameters.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.5
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• pp.171-180
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• 2023

The three major sleep disorders in Korea are snoring, sleep apnea, and insomnia. Lack of sleep is the root of all diseases. Some of the most serious potential problems associated with sleep deprivation are cardiovascular problems, cognitive impairment, obesity, diabetes, colitis, prostate cancer, etc. To solve these problems, the Korean government provided low-cost national health insurance benefits for polysomnography tests in July 2018. However, insomnia patients still have problems getting treated in terms of time, space, and economic perspectives. Therefore, it would be better for insomnia patients to be allowed to test at home. The measuring device can measure six biosignals (eye movement, tossing and turning, body temperature, oxygen saturation, heart rate, and audio). A gyroscope sensor (MPU9250, InvenSense, USA) was used for eye movement, tossing, and turning. The input range of the sensor was in 258°/sec to 460°/sec, and the data range was in the input range. Body temperature, oxygen saturation range, and heart rate were measured by a sensor (MAX30102, Analog Devices, USA). The body temperature was measured in 30 ℃ to 45 ℃, and the oxygen saturation range was 0% for the unused state and 20 % to 90 % for the used state. The heart rate measurement range was in 40 bpm to 180 bpm. The measurement of audio signal was performed by an audio sensor (AMM2742-T-R, PUIaudio, USA). The was -42 dB ±1 dB frequency range was 20 Hz to 20 kHz. The measured data was successfully received in wireless network conditions. The system configuration was consisted of a PC and a mobile app for bio-signal measurement and data collection. The measured data was collected by mobile phones and desktops. The data collected can be used as preliminary data to determine the stage of sleep and perform the screening function for sleep induction and sleep disturbances. In the future, this convenient sleep measurement device could be beneficial for treating insomnia.

• Korean Journal of Plant Resources
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• v.36 no.5
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• pp.435-445
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• 2023

The aim of this study was to explore the effects of vinegar extract from seed of common buckwheat (Fagopyrum esculentum Moench) and seed of tartary buckwheat (F. tataricum Gaertner) on acute ethanol-induced hangover in Sprague-Dawley rats. Vinegar extract from buckwheat is rich choline, quercetin and its glycoside, rutin known as flavonoid antioxidants. The test extract containing buckwheat was proven to alleviate hangovers through a significant reduction in the concentration of alcohol and acetaldehyde in the context of an alcohol-induced hangover model. Hepatic alcohol dehydrogenase (ADH) and acetaldehyde dehydrogenase (ALDH) activities were significantly higher in buckwheat vinegar-treated rats than in ethanol-treated rats. Moreover, tartary buckwheat vinegar upregulated antioxidant enzyme such as superoxide dismutase and Catalase activities in liver tissues. These results suggest that buckwheat vinegar extract could alleviate ethanol-induced hangover symptoms by elevating activities related to hepatic ethanol-metabolizing enzymes against ethanol induced metabolites, and in particular, tartary buckwheat should be further developed to be a novel anti-hangover material.