• Journal of the Korean Society for Railway
• /
• v.20 no.3
• /
• pp.311-319
• /
• 2017

The primary suspension system of a railway vehicle restrains the wheelset and the bogie, which greatly affects the dynamic characteristics of the vehicle depending on the stiffness in each direction. In order to improve the dynamic characteristics, different stiffness in each direction is required. However, designing different stiffness in each direction is difficult in the case of a general suspension device. To address this, in this paper, an optimization technique is applied to design different stiffness in each direction by using a conical rubber spring. The optimization is performed by using target and analysis RMS values. Lastly, the final model is proposed by complementing the shape of the weak part of the model. An actual model is developed and the reliability of the optimization model is proved on the basis of a deviation average of about 7.7% compared to the target stiffness through a static load test. In addition, the stiffness value is applied to a multibody dynamics model to analyze the stability and curve performance. The critical speed of the improved model was 190km/h, which was faster than the maximum speed of 110km/h. In addition, the steering performance is improved by 34% compared with the conventional model.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.27 no.5
• /
• pp.574-583
• /
• 2021

Predicting shipping markets is an important issue. Such predictions form the basis for decisions on investment methods, fleet formation methods, freight rates, etc., which greatly affect the profits and survival of a company. To this end, in this study, we propose a shipping freight rate prediction model for container ships using gated recurrent units (GRUs) and long short-term memory structure. The target of our freight rate prediction is the China Container Freight Index (CCFI), and CCFI data from March 2003 to May 2020 were used for training. The CCFI after June 2020 was first predicted according to each model and then compared and analyzed with the actual CCFI. For the experimental model, a total of six models were designed according to the hyperparameter settings. Additionally, the ARIMA model was included in the experiment for performance comparison with the traditional analysis method. The optimal model was selected based on two evaluation methods. The first evaluation method selects the model with the smallest average value of the root mean square error (RMSE) obtained by repeating each model 10 times. The second method selects the model with the lowest RMSE in all experiments. The experimental results revealed not only the improved accuracy of the deep learning model compared to the traditional time series prediction model, ARIMA, but also the contribution in enhancing the risk management ability of freight fluctuations through deep learning models. On the contrary, in the event of sudden changes in freight owing to the effects of external factors such as the Covid-19 pandemic, the accuracy of the forecasting model reduced. The GRU1 model recorded the lowest RMSE (69.55, 49.35) in both evaluation methods, and it was selected as the optimal model.

• Korean Journal of Remote Sensing
• /
• v.37 no.6_1
• /
• pp.1719-1729
• /
• 2021

Leaf area index (LAI) provides valuable information necessary for sustainable and effective management of forests. Although global high resolution LAI data are provided by European Space Agency using Sentinel-2 satellite images, they have not considered forest characteristics in model development and have not been evaluated for various forest ecosystems in South Korea. In this study, we proposed a LAI estimation model combining machine learning and the PROSAIL radiative transfer model using Sentinel-2 satellite data over a local forest area in South Korea. LAI-2200C was used to measure in situ LAI data. The proposed LAI estimation model was compared to the existing Sentinel-2 LAI product. The results showed that the proposed model outperformed the existing Sentinel-2 LAI product, yielding a difference of bias ~ 0.97 and a difference of root-mean-square-error ~ 0.81 on average, respectively, which improved the underestimation of the existing product. The proposed LAI estimation model provided promising results, implying its use for effective LAI estimation over forests in South Korea.

• Journal of Bio-Environment Control
• /
• v.30 no.4
• /
• pp.393-400
• /
• 2021

Body weight of livestock is a crucial indicator for assessing feed requirements and nutritional status. This study was performed to estimate the body weight of Korean cattle (Hanwoo) using body volume determined from three-dimensional (3-D) image. A TOF camera with a resolution of 640×480 pixels, a frame rate of 44 fps and a field of view of 47°(H)×37°(V) was used to capture the 3-D images for Hanwoo. A grid image of the body was obtained through preprocessing such as separating the body from background and removing outliers from the obtained 3-D image. The body volume was determined by numerical integration using depth information to individual grid. The coefficient of determination for a linear regression model of body weight and body volume for calibration dataset was 0.8725. On the other hand, the coefficient of determination was 0.9083 in a multiple regression model for estimating body weight, in which the age of Hanwoo was added to the body volume as an explanatory variable. Mean absolute percentage error and root mean square error in the multiple regression model to estimate the body weight for validation dataset were 8.2% and 24.5kg, respectively. The performance of the regression model for weight estimation was improved and the effort required for estimating body weight could be reduced as the body volume of Hanwoo was used. From these results obtained, it was concluded that the body volume determined from 3-D of Hanwoo could be used as an effective variable for estimating body weight.

• Korean Journal of Remote Sensing
• /
• v.37 no.5_2
• /
• pp.1307-1315
• /
• 2021

The Changjiang Diluted Water (CDW) spreads over the East China Sea every summer and significantly affects the sea surface salinity changes in the seas around Jeju Island and the southern coast of Korea peninsula. Sometimes its effect extends to the eastern coast of Korea peninsula through the Korea Strait. Specifically, the CDW has a significant impact on marine physics and ecology and causes damage to fisheries and aquaculture. However, due to the limited field surveys, continuous observation of the CDW in the East China Sea is practically difficult. Many studies have been conducted using satellite measurements to monitor CDW distribution in near-real time. In this study, an algorithm for estimating Sea Surface Salinity (SSS) in the East China Sea was developed using the Geostationary Ocean Color Imager (GOCI). The Multilayer Perceptron Neural Network (MPNN) method was employed for developing an algorithm, and Soil Moisture Active Passive (SMAP) SSS data was selected for the output. In the previous study, an algorithm for estimating SSS using GOCI was trained by 2016 observation data. By comparison, the train data period was extended from 2015 to 2020 to improve the algorithm performance. The validation results with the National Institute of Fisheries Science (NIFS) serial oceanographic observation data from 2011 to 2019 show 0.61 of coefficient of determination (R²) and 1.08 psu of Root Mean Square Errors (RMSE). This study was carried out to develop an algorithm for monitoring the surface salinity of the East China Sea using GOCI and is expected to contribute to the development of the algorithm for estimating SSS by using GOCI-II.

• Land and Housing Review
• /
• v.12 no.4
• /
• pp.93-101
• /
• 2021

As the number of small households (1 to 2 persons per household) in Korea gradually increases, so does the importance of housing supply policies for small households. In response to the increase in small households, the government has been continuously supplying urban housing for these households. Since housing for small households is a sales and rental business similar to apartments and general business facilities, it is important for the building owner to calculate the project's estimated construction duration during the planning stage. Review of literature found a model for estimating the duration of construction of large-scale buildings but not for small-scale buildings such as urban housing for small households. Therefore this study aimed to develop and verify a model for estimating construction duration for urban housing at the planning stage based on multiple regression analysis. Independent variables inputted into the estimation model were building site area, building gross floor area, number of below ground floors, number of above ground floors, number of buildings, and location. The modified coefficient of determination (R_a²) of the model was 0.547. The developed model resulted in a Root Mean Square Error (RMSE) of 171.26 days and a Mean Absolute Percentage Error (MAPE) of 26.53%. The developed estimation model is expected to provide reliable construction duration calculations for small-scale urban residential buildings during the planning stage of a project.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.22 no.1
• /
• pp.93-102
• /
• 2019

In this study, in order to analyze the possibility of observing a typhoon track based on the Global Navigation Satellite System(GNSS), Typhoon NARI, the 11th typhoon of 2007, was analyzed in terms of the typhoon track as well as the local variation of perceptible water over time. The perceptible water was estimated using data obtained from observatories located on the typhoon track from Jeju to the southern coast of Korea for a total of 18 days from September 7(DOY 250) to September 24(DOY 267), 2007, including the period when the observatories were affected by the typhoon at full-scale, as well as one previous week and one following week. The results show that the trend of the variation of perceptible water was similar between the observatories near the typhoon track. Variation of perceptible water over time depending on the development and landing of the typhoon was distinctively observed. Several hours after the daily maximum of perceptible water was found at the JEJU Observatory, the first struck by the typhoon on the typhoon track, the maximum value was found at other observatories located on the southern coast. In the observation period, the time point at which the maximum perceptible water was recorded in each location was almost the same as the time point at which the typhoon landed at the location. To analyze the accuracy of the GNSS-based perceptible water measurement, the data were compared with radiosonde-based perceptible water data. The mean error was 0.0cm, and the root mean square error and the standard deviation were both 0.3cm, indicating that the GNSS-based perceptible water data were highly accurate and precise. The results of the this study show that the GNSS-based perceptible water data may be used as highly accurate information for the analysis of typhoon tracks over time.

• Clean Technology
• /
• v.25 no.2
• /
• pp.147-152
• /
• 2019

Electrostatic precipitator that shows a good performance for the removal of particulate matter is important for controlling emissions from industrial facilities and power plants. The efficiency of the electrostatic precipitator on the removal of particulate matter is highly affected by the flow pattern inside the electrostatic precipitator. A number of studies have been conducted to obtain uniform flow distribution inside electrostatic precipitators. An electrostatic precipitator (ESP) with a length of 3.5 m and a height of 0.875 m was designed and installed in this study. The ESP included an inlet duct, diffuser, body, and contractor. Three perforated plates were installed in the diffuser of the ESP. Five pitot tubes were installed vertically and used to measure flow distribution in the cross section of the ESP body. Root mean square deviation value (RMS%) was used to examine the flow distribution inside the ESP when the perforated plates were installed in the diffuser. Flow distribution was also investigated in relation to the porosity of the perforated plate. The results showed that the perforated plates improved greatly the flow distribution inside the electrostatic precipitator. In addition, the most uniform flow distribution was found with 40%, 50%, and 50% porous perforated plates located from the inlet of the diffuser.

• Journal of the Korean Society of Environmental Restoration Technology
• /
• v.22 no.4
• /
• pp.1-14
• /
• 2019

The 1st Forest Health Management survey was conducted to examine the health of the forests in Korea. However, in order to understand the health of the forests, which account for 63.7% of the total land area in South Korea, it is necessary to comprehensively spatialize the results of the survey beyond the sampling points. In this regard, out of the sample points of the 1st Forest Health Management survey in Gyeongbuk area, 78 spots were selected. For these spots, the species diversity index was selected from the survey sections, and the spatial interpolation method was applied. Inverse distance weighted (IDW), Ordinary Kriging and Ordinary Cokriging were applied as spatial interpolation methods. Ordinary Cokriging was performed by selecting vegetation indices which are highly correlated with species diversity index as a secondary variable. The vegetation indices - Normalized Differential Vegetation Index(NDVI), Leaf Area Index(LAI), Sample Ratio(SR) and Soil Adjusted Vegetation Index(SAVI) - were extracted from Landsat 8 OLI. Verification was performed by the spatial interpolation method with Mean Error(ME) and Root Mean Square Error(RMSE). As a result, Ordinary Cokriging using SR showed the most accurate result with ME value of 0.0000218 and RMSE value of 0.63983. Ordinary Cokriging using SR was proven to be more accurate than Ordinary Kriging, IDW, using one variable. This indicates that the spatial interpolation method using the vegetation indices is more suitable for spatialization of the biodiversity index sample points of 1st Forest Health Management survey.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.4
• /
• pp.2060-2077
• /
• 2019

Recently, mobile healthcare services have attracted significant attention because of the emerging development and supply of diverse wearable devices. Smartwatches and health bands are the most common type of mobile-based wearable devices and their market size is increasing considerably. However, simple value comparisons based on accumulated data have revealed certain problems, such as the standardized nature of health management and the lack of personalized health management service models. The convergence of information technology (IT) and biotechnology (BT) has shifted the medical paradigm from continuous health management and disease prevention to the development of a system that can be used to provide ground-based medical services regardless of the user's location. Moreover, the IT-BT convergence has necessitated the development of lifestyle improvement models and services that utilize big data analysis and machine learning to provide mobile healthcare-based personal health management and disease prevention information. Users' health data, which are specific as they change over time, are collected by different means according to the users' lifestyle and surrounding circumstances. In this paper, we propose a prediction model of user physical activity that uses data characteristics-based long short-term memory (DC-LSTM) recurrent neural networks (RNNs). To provide personalized services, the characteristics and surrounding circumstances of data collectable from mobile host devices were considered in the selection of variables for the model. The data characteristics considered were ease of collection, which represents whether or not variables are collectable, and frequency of occurrence, which represents whether or not changes made to input values constitute significant variables in terms of activity. The variables selected for providing personalized services were activity, weather, temperature, mean daily temperature, humidity, UV, fine dust, asthma and lung disease probability index, skin disease probability index, cadence, travel distance, mean heart rate, and sleep hours. The selected variables were classified according to the data characteristics. To predict activity, an LSTM RNN was built that uses the classified variables as input data and learns the dynamic characteristics of time series data. LSTM RNNs resolve the vanishing gradient problem that occurs in existing RNNs. They are classified into three different types according to data characteristics and constructed through connections among the LSTMs. The constructed neural network learns training data and predicts user activity. To evaluate the proposed model, the root mean square error (RMSE) was used in the performance evaluation of the user physical activity prediction method for which an autoregressive integrated moving average (ARIMA) model, a convolutional neural network (CNN), and an RNN were used. The results show that the proposed DC-LSTM RNN method yields an excellent mean RMSE value of 0.616. The proposed method is used for predicting significant activity considering the surrounding circumstances and user status utilizing the existing standardized activity prediction services. It can also be used to predict user physical activity and provide personalized healthcare based on the data collectable from mobile host devices.