• KIPS Transactions on Computer and Communication Systems
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• v.11 no.11
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• pp.411-418
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• 2022

Incorrectly loaded containers can easily knock down by strong winds. Container collapse accidents can lead to material damage and paralysis of the port system. In this paper, We propose a deep learning-based container loading state and accident risk detection technique. Using Darknet-based YOLO, the container load status identifies in real-time through corner casting on the top and bottom of the container, and the risk of accidents notifies the manager. We present criteria for classifying container alignment states and select efficient learning algorithms based on inference speed, classification accuracy, detection accuracy, and FPS in real embedded devices in the same environment. The study found that YOLOv4 had a weaker inference speed and performance of FPS than YOLOv3, but showed strong performance in classification accuracy and detection accuracy.

• Journal of Korean Society of Disaster and Security
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• v.15 no.2
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• pp.45-56
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• 2022

The countermeasure for the shortage of water during dry season and drought period has not been considered with return flowrate in detail. In this study, the outflow of STP was predicted through a data-based machine learning model, LSTM. As the first step, outflow, inflow, precipitation and water elevation were utilized as input data, and the distribution of variance was additionally considered to improve the accuracy of the prediction. When considering the variability of the outflow data, the residual between the observed value and the distribution was assumed to be in the form of a complex trigonometric function and presented in the form of the optimal distribution of the outflow along with the theoretical probability distribution. It was apparently found that the degree of error was reduced when compared to the case not considering where the variance distribution. Therefore, it is expected that the outflow prediction model constructed in this study can be used as basic data for establishing an efficient river management system as more accurate prediction is possible.

• Journal of Software Assessment and Valuation
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• v.17 no.2
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• pp.117-124
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• 2021

Livestock behavioral analysis is a factor that has a great influence on livestock health management and agricultural productivity increase. However, most digital devices introduced for behavioral analysis of livestock do not provide raw data and also provide limited analysis results. Such a closed system makes it more difficult to integrate data and build big data, which are essential for the introduction of advanced IT technologies. Therefore, it is necessary to supply farm-scale data collection devices that can be easily used at low cost. This study presents a data collection system for analyzing the behavior of livestock. The system consists of a number of miniature computing units that operate wirelessly, and collects livestock body temperature and acceleration data, location information, and livestock environment data. In addition, this study presents an algorithm for estimating the behavior of livestock based on the collected acceleration data. For the experiment, a system was built in a Korean cattle farm in Icheon, Gyeonggi-do, and data were collected for 20 Korean cattle, and based on this, the empirical and analysis results were presented.

• Journal of the Korean Geotechnical Society
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• v.38 no.9
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• pp.45-52
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• 2022

Because discontinuity in the rock mass and contact of soil-structure interaction exhibits coupled thermal-hydromechanical (THM) behavior, it is necessary to develop an interface element based on the full governing equations. In this study, we derive force equilibrium, fluid continuity, and energy equilibrium equations for the interface element. Additionally, we present a stiffness matrix of the elastoplastic mechanical model for the interface element. The developed interface element uses six nodes for displacement and four nodes for water pressure and temperature in a two-dimensional analysis. The fully coupled THM analysis for fluid injection into a fault can model the complicated evolution of injection pressure due to decreasing effective stress in the fault and thermal contraction of the surrounding rock mass. However, the result of hydromechanical analysis ignoring thermal phenomena overestimates hydromechanical variables.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.5
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• pp.889-896
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• 2022

Data required in a wireless sensor network environment requires more accurate figures as technology advances and its complexity increases. However, in the case of operating a large number of sensor nodes in a large area, the balance between the power consumed and the data quality that can be acquired accordingly should be considered for that purpose. In particular, in complex, densely populated urban areas or military operations with specific goals, location data requires increasingly detailed and high accuracy over a wide range. In this paper, we propose a method of mounting a Global Positioning System(: GPS) only on some of the sensor nodes deployed in the wireless sensor network and improving the error of GPS location data measured on that sensor node through Angle of Arrival(: AoA) and Received Signal Strength Indicator(: RSSI).

• Journal of the Korea Society of Computer and Information
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• v.27 no.11
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• pp.19-27
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• 2022

In this study, we propose a language and platform to describe and manage the MLOps(Machine Learning Operations) workflow for time series data anomaly detection. Time series data is collected in many fields, such as IoT sensors, system performance indicators, and user access. In addition, it is used in many applications such as system monitoring and anomaly detection. In order to perform prediction and anomaly detection of time series data, the MLOps platform that can quickly and flexibly apply the analyzed model to the production environment is required. Thus, we developed Python-based AI/ML Modeling Language (AMML) to easily configure and execute MLOps workflows. Python is widely used in data analysis. The proposed MLOps platform can extract and preprocess time series data from various data sources (R-DB, NoSql DB, Log File, etc.) using AMML and predict it through a deep learning model. To verify the applicability of AMML, the workflow for generating a transformer oil temperature prediction deep learning model was configured with AMML and it was confirmed that the training was performed normally.

• Journal of the Korean Society for Library and Information Science
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• v.57 no.1
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• pp.69-92
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• 2023

The purpose of the present study is to investigate the quality attributes of archival information service affecting user satisfaction. The independent variables in this study were equality, tangibles(appearance), convenience, responsiveness, empathy. And the dependent variable was a user satisfaction. The survey was conducted by online and offline for 78 people that used the archival information service. To solve the research question, we used the descriptive statistics, correlation analysis, multiple regression analysis. As a correlation analysis result, the quality attributes of archival information service had positive correlation with a user satisfaction. And as a multiple regression analysis result, tangibles(appearance) had a significant effect, followed by empathy and convenience. For improving on quality of archival information service, it is required that improving facilities, keeping environment clean, enhancing empathy ability, updating the information of archive, reforming search tools.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.372-372
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• 2023

강우 유출 시 지표면 토양수분포화도는 직접유출량 및 지하수 저류에 영향을 미칠 수 있기 때문에 강우에 의한 유역 유출량 산정에서 토양수분포화도는 보다 실제와 유사한 모의 결과를 도출하는데 중요한 인자가 될 수 있다. 기존의 모형 기반의 유출량 산정 연구에서 토양수분포화도의경우 강수량, 하천수위, 유량, 지하수위 등 타 수문순환 요소에 비하여 관측 지점 및 관측 자료가 부족하기 때문에 유역 내 수문환경 특성에 따라 가정 된 값을 입력하여 유출량을 산정하였다. 최근 IoT, 5G 통신 등 정보 기술의 혁신과 이상 홍수에 의한 피해 저감을 위한 실시간 유출량 해석 모형 개발 등에 적용할 경우 모의 결과가 실제와 매우 다르게 나타나는 경우가 발생할 수 있다. 본 연구에서는 토양수분포화도의 지상 관측 자료와 위성 관측 자료를 동기화 하는 기법을 개발함으로써 중권역 단위의 유출량 산정 정확도를 향상시키고자 하였다. 기존의 지상 관측 자료는 토양수분포화도의 비교적 정확한 데이터를 제공하나 관측 자료를 유역의 대푯값으로 적용할 수 있는 지에 대한 추가 검증이 필요하다. 위성 관측자료는 유역 전반의 토양수분포화도 정보를 관측할 수 있으나 고해상도의 자료를 제공하지 못하기 때문에 유역 전체에 일관된 데이터를 적용할 수밖에 없는 한계가 발생한다. 지상 관측자료와 위성관측자료의 동기화 기법을 개발함으로써 본 연구진은 중권역 단위의 유역 내 비교적 정확한 토양수분포화도 데이터를 적용할 수 있도록 하였다. 본 연구의 결과물은 유출량 해석 결과의 정확도를 높임으로써 급격한 호우 사상 발생에 따른 이상홍수에 대응할 수 있는 유역 물 관리 대책의 기초 자료로 활용 할 수 있을 것으로 기대된다.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.3
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• pp.483-494
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• 2023

Recently, gas turbine generators are widely used for frequency control of power systems. Although the inlet temperature of a gas turbine is a key factor related to the performance and lifespan of the device, the inlet temperature is not measured directly for reasons such as the turbine structure and operating environment. In particular, the inlet temperature of the reheating gas turbine is very important for stable operation management, but field workers are experiencing a lot of difficulties because the manufacturer does not provide information on the calculation formula. Therefore, in this study, we propose a method for estimating the inlet temperature of a gas turbine using a machine learning-based linear regression analysis method based on a polytropic process equation. In addition, by proposing an inlet temperature calculation algorithm through the usefulness analysis and verification of the inlet temperature calculation model obtained through linear regression analysis, it is intended to help to improve the level of reheat gas turbine combustion tuning technology.

• Journal of the Korean Geotechnical Society
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• v.26 no.10
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• pp.5-14
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• 2010

The slope stability method using the soil stabilizer is a way to ensure that the slope stability from reinforcing method is environmentally friendly. However, the reinforcing method does not ensure slope stability for lack of research on the reinforcement effect of the mixture with soil. So the application of this method implies difficult technical issues. In this research, reinforcement effect is investigated according to the different ratio of mixture. And the optimum reinforcement depth is proposed according to the height of slope from numerical analysis. The results show that approximately the soil strength increases from two to three times. From numerical analysis, it is possible to estimate the optimum height according to the height of slope. It is anticipated that the use of soil stabilizer will increase the slope stability.