• Journal of the Korea Institute of Building Construction
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• v.14 no.2
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• pp.170-176
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• 2014

This study examined the effect of chemical attack on the stress-strain relationship of alkali-activated Hwangtoh concrete. Water-to-binder ratio and air content were selected as mixture parameters. The stress-strain relationship of concrete was measured at chemical immersion times of 0, 7, 28, 56, and 91 days from an age of 28 days. Based on the test results, the reduction in compressive strength of alkali-activated hwangtoh concrete owing to chemical attack was formulated. In sddition the present study demonstrated that the stress-strain behavior of concrete under chemical attack is significantly dependent on the air content and chemical immersion time, indicating the rate of decrease of modulus of elasticity was greater than that of compressive strength at the same immersion time. As a result, the stress-strain behavior of concrete under chemical attack was significantly inconsistent with the conventional models specified in the CEB-FIP provision.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.8
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• pp.3307-3314
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• 2012

The annual energy demand of the standard rural house models was analyzed using the DesignBuilder. Indoor temperature set-point, U-value of outer wall, type of window, and degree of ventilation were selected as simulation parameters. In all the simulation cases, heating energy demand was higher than cooling energy demand regardless of the building size. When the lower U-value of the outer wall was applied to account for the thicker insulation layer, heating energy demand was decreased while cooling energy demand was increased. However, it is better to reduce the area of outer wall which is directly exposed to outdoor air because reducing the U-value of the outer wall is not effective in decreasing heating energy demand. Among the four different window types, the double skin window is most favorable because heating energy demand is the lowest. For a fixed infiltration rate, higher ventilation rate resulted in an increased heating energy demand and had minor impact on cooling energy demand. As long as the indoor air quality is acceptable, lower ventilation rate is favorable to reduce the annual energy demand.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.9
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• pp.688-693
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• 2016

This paper describes the parachute dynamics modeling and simulation results for the development of training simulator of a HALO (High Altitude Low Opening) parachute, which is currently in use for military purposes. The target parachute is a rectangular shaped parafoil and its dynamic model is derived based on the real geometry data as the 9-DOF nonlinear equations of dynamics. The simulation was conducted through the moment of inertia and its aerodynamic derivatives to reflect the real characteristics based on the MATLAB/Simulink. In particular, its modeling includes the typical characteristics of the added mass and moment of inertia, which is shown in the strong effects in Lighter-Than-Air(LTA) flight vehicle. The proposed dynamic modeling was evaluated through the simulation under the spiral turning flight conditions of the asymmetric control inputs and compared with the performance index in the target parachute manual.

• Journal of Advanced Navigation Technology
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• v.23 no.5
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• pp.473-480
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• 2019

The smart home is a technology that can monitor and control by connecting everything to a communication network in various fields such as home appliances, energy consumers, and security devices. The Smart home is developing not only automatic control but also learning situation and user's taste and providing the result accordingly. This paper proposes a model that can provide a comfortable indoor environment control service for the user's characteristics by detecting the user's behavior as well as the automatic remote control service. The whole system consists of ESP 8266 with sensor and Wi-Fi, Firebase as a real-time database, and a smartphone application. This model is divided into functions such as learning mode when the home appliance is operated, learning control through learning results, and automatic ventilation using indoor and outdoor sensor values. The study used moving averages for temperature and humidity in the control of home appliances such as air conditioners, humidifiers and air purifiers. This system can provide higher quality service by analyzing and predicting user's characteristics through various machine learning and deep learning.

• Journal of the Korea Convergence Society
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• v.11 no.12
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• pp.23-30
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• 2020

In this study, the relationship between natural gas (NG) data and gas-related environmental elements was performed using machine learning algorithms to predict the level of gas leakage risk without directly measuring gas leakage data. The study was based on open data provided by the server using the IoT-based remote control Picarro gas sensor specification. The naturel gas leaks into the air, it is a big problem for air pollution, environment and the health. The proposed method is multivariate outlier removing method based Random Forest (RF) classification for predicting risk of NG leak. After, unsupervised k-means clustering, the experimental dataset has done imbalanced data. Therefore, we focusing our proposed models can predict medium and high risk so best. In this case, we compared the receiver operating characteristic (ROC) curve, accuracy, area under the ROC curve (AUC), and mean standard error (MSE) for each classification model. As a result of our experiments, the evaluation measurements include accuracy, area under the ROC curve (AUC), and MSE; 99.71%, 99.57%, and 0.0016 for MOL_RF respectively.

• Journal of the Architectural Institute of Korea Structure & Construction
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• v.34 no.6
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• pp.63-69
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• 2018

The machine learning model can capture the dynamics of building systems with less inputs than the first principle based simulation model. The training data for developing a machine learning model are usually selected in a heuristic manner. In this study, the authors developed a machine learning model which can describe supply air temperature from an AHU in a real office building. For rational reduction of the training data, the progressive sampling method was used. It is found that even though the progressive sampling requires far less training data (n=60) than the offline regular sampling (n=1,799), the MBEs of both models are similar (2.6% vs. 5.4%). In addition, for the update of the machine learning model, the normalized mutual information (NMI) was applied. If the NMI between the simulation output and the measured data is less than 0.2, the model has to be updated. By the use of the NMI, the model can perform better prediction ($5.4%{\rightarrow}1.3%$).

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.3
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• pp.215-222
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• 2022

The purpose of air traffic flow management is to balance demand and capacity in the national airspace, and its performance relies on an accurate capacity prediction of the airport or airspace. This paper developed a regression model that predicts the number of aircraft actually departing and arriving in a terminal maneuvering area. The regression model is based on a boosting ensemble learning algorithm that learns past aircraft operational data such as time, weather, scheduled demand, and unfulfilled demand at a specific airport in the terminal maneuvering area. The developed model was tested using historical departure and arrival flight data at Incheon International Airport, and the coefficient of determination is greater than 0.95. Also, the capacity of the terminal maneuvering area of interest is implicitly predicted by using the model.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1331-1339
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• 2023

In support of the National ATM Reformation and Enhancement Plan (NARAE), a trajectory-based aviation weather service is under development through the NARAE-Weather project. Specifically, weather data presented in a standardized digital format facilitates the seamless integration of digital weather data with air traffic information. Thus, this paper introduces an approach that entails structuring numerical model data to integrate aviation weather information and flight trajectory data. The extraction results using structurally transformed data showed superior performance compared to the results extracted from the original data in terms of performance, and this research is poised to enhance the safety and efficiency of airline operations.

• Journal of Advanced Navigation Technology
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• v.28 no.1
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• pp.149-154
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• 2024

Research on particulate matter is advancing in real-time, and various methods are being studied to improve the accuracy of prediction models. Furthermore, studies that take into account various factors to understand the precise causes and impacts of particulate matter are actively being pursued. This paper trains an LSTM model using seasonal data and another LSTM model using concentration-based data. It compares and analyzes the PM_2.5 prediction performance of the two models. To train the model, weather data and air pollutant data were collected. The collected data was then used to confirm the correlation with PM_2.5. Based on the results of the correlation analysis, the data was structured for training and evaluation. The seasonal prediction model and the concentration-specific prediction model were designed using the LSTM algorithm. The performance of the prediction model was evaluated using accuracy, RMSE, and MAPE. As a result of the performance evaluation, the prediction model learned by concentration had an accuracy of 91.02% in the "bad" range of AQI. And overall, it performed better than the prediction model trained by season.

• Food Engineering Progress
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• v.21 no.1
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• pp.42-48
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• 2017

The purpose of this study was to verify the drying characteristics of steamed sweet potato and to establish optimal drying conditions for far-infrared drying of steamed sweet potato. 4 kg of steamed sweet potato was sliced to thicknesses of 8 and 10 mm, and dried by a far-infrared dryer until a final moisture content of $25{\pm}0.5%$. The far-infrared dryer conditions were an air velocity of 0.6, 0.8 m/s and drying temperature of 60, 70, and $80^{\circ}C$. The results can be summarized as follows. The drying time tended to be reduced as temperature and air velocity for drying increased. The Lewis and Modified Wang and Singh models were found to be suitable for drying of steamed sweet potato by a far-infrared dryer. The color difference was 35.09 on the following conditions: Thickness of 8 mm, temperature of $80^{\circ}C$, and air velocity of 0.8 m/s. The highest sugar content ($59.11^{\circ}Brix$) was observed on the conditions of a thickness of 8 mm, temperature of 80, and air velocity of 0.8 m/s. Energy consumption decreased on the conditions of higher temperature, slower air velocity, and thinner steamed sweet potato.