• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.5
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• pp.595-603
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• 2019

To alleviate the demand on private car that is constantly increasing, Busan Metropolitan City (BMC) has established Bus Rapid Transit (BRT) to revitalize public transportation. But there are no unified lane system between BRT and general bus stations, which makes off-lane turning general bus to contribute to congestion. And as the bottleneck phenomenon at entrance/exit accelerates the congestion, there has been huge dissatisfaction among commuting drivers. Therefore, this study identifies efficient methods to operate better through measuring civilian awareness. We evaluate both satisfaction and drawbacks on BRT service with Importance-Performance Analysis (IPA). We first distinguish the groups by the awareness on BRT and their main transit usage, and then clarify the difference between the groups. And as a result, the group who is positive to BRT and uses buses often demands improvement in bus indoor comfort and curbing jaywalking. On the other hand, group who is negative to BRT and uses private cars often demands improvement in lane changing and the moving speed of private cars. We next examines the groups with MDPREF, one method of Multidimensional Scaling (MDS). And we have clarified that the evaluating criteria and the individual attributes of the groups corresponds very well.

• Journal of the Korean Society of Hazard Mitigation
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• v.11 no.1
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• pp.85-91
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• 2011

RV-GIUH must be applied at an outlet or a junction of highest order stream of a subbasin because the model was derived for basins following Horton's ordering system. However hydrograph is calculated at various locations which does not fit to the desirable points. Therefore, some guideline is required for RV-GIUH application in practice. This study would like to suggest the outlet location criteria for appling RV-GIUH at un-gauged basin. Locations were selected by moving to upstream from outlet of Sanganmi basin and unit hydrograph using derived and simple RV-GIUH were estimated at each location. As the results, the peaks of RV-GIUH in upstream were exaggerated because of distortion of length ratio and total stream length. To avoid this error, the location must be selected at 60% downstream of highest stream length. To apply RV-GIUH at various places, equations correcting distortion of total stream length were suggested. With the correcting equations, it can be possible that RV-GIUH is applied at 20% downstream of highest stream length. Application and precision of RV-GIUH will be improved through this research.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.6
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• pp.701-707
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• 2021

In this study, we examined the sea surface temperature (SST), air temperature (AT), and their time lag in response to an extreme cold wave in 2018 and a weak cold wave in 2019, cross-correlating these to the northern wind direction frequency. The data used in this study include SST observations of seven ocean buoys Real-time Information System for Aquaculture Environment provided by the National Institute of Fisheries Science and automatic weather station AT near them recorded every hour; null data was interpolated. A finite impulse response filter was used to identify the appropriate data period. In the extreme cold wave in 2018, the seven locations indicated low SST caused by moving cold air through the northern wind direction. A warm cold wave in 2019, the locations showed that the AT data was similar to the normal AT data, but the SST data did not change notably. During the extreme cold wave of 2018, data showed a high correlation coefficient of about 0.7 and a time lag of about 14 hours between AT and SST; during the weak cold wave of 2019, the correlation coefficient was 0.44-0.67 and time lag about 20 hours between AT and SST. This research will contribute to rapid response to such climate phenomena while minimizing aquaculture damage.

• Journal of Korean Society of Forest Science
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• v.110 no.3
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• pp.419-430
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• 2021

This study investigated spatiotemporal trends of sediment-related disasters in North Korea from 1960 to 2019 and post-disaster recovery cases based on a literature review and satellite images. Results showed that occurrence status of sediment-related disasters was initially externally reported in 1995 (during the Kim Jongil era); their main triggering factor was heavy summer rainfall. Furthermore, forest degradation rate was positively correlated with population density (R² = 0.4347, p = 0.02) and occurrence number of sediment-related disasters was relatively high on the west coast region, where both variables showed high values. This indicates that human activity was a major cause of forest degradation and thus, significantly affected sediment-related disasters in mountain regions. Finally, sediment- related disasters due to shallow landslides, debris flow, and slow-moving landslides were observed in undisturbed forest regions and human-impacted forest regions, including terraced fields, opencast mines, forest roads, and post-wildfire areas, via satellite image analysis. These disaster-hit areas remained mostly abandoned without any recovery works, whereas hillside erosion control work (e.g., treeplanting with terracing) or torrent erosion control work (e.g., check dam, debris flow guide bank) were implemented in certain areas. These findings can provide reference information to expand inter-Korean exchange and cooperation in forest rehabilitation and erosion control works of North Korea.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.4
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• pp.611-620
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• 2021

The maintenance industry is mainly progressing based on condition-based maintenance after corrective maintenance and preventive maintenance. In condition-based maintenance, maintenance is performed at the optimum time based on the condition of equipment. In order to find the optimal maintenance point, it is important to accurately understand the condition of the equipment, especially the remaining useful life. Thus, using simulation data (C-MAPSS), a prediction model is proposed to predict the remaining useful life of a turbofan engine. For the modeling process, a C-MAPSS dataset was preprocessed, transformed, and predicted. Data pre-processing was performed through piecewise RUL, moving average filters, and standardization. The remaining useful life was predicted using principal component analysis and the k-NN method. In order to derive the optimal performance, the number of principal components and the number of neighbor data for the k-NN method were determined through 5-fold cross validation. The validity of the prediction results was analyzed through a scoring function while considering the usefulness of prior prediction and the incompatibility of post prediction. In addition, the usefulness of the RUL prediction model was proven through comparison with the prediction performance of other neural network-based algorithms.

• Korean Journal of Labor Studies
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• v.24 no.2
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• pp.215-245
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• 2018

The purpose of this study is to provide policy implications on the settlement of regional labor market of young women through detailed study on the experience of regional labor market in Jeollanam-do Province. For this purpose, this study analyzed the labor mobility experience in the regional labor market of young women, which lacked specific case studies. In this study, we have identified the causes of job changes by dividing the labor mobility of young women into intra-career moves and inter-career moves. For the causes of labor mobility we divided into two aspects: problems in preparation for employment and employment conditions. The inter-career moves included more diverse factors than intra-career moves. In the inter-career moves, problems in preparation for employment were highlighted as the causes of job changes. In the case of moving within the career, young women would leave because of the employment conditions such as the expiration of the employment period, but the turnover appears to be the way of retaining their previous career. On the other hand, in the case of intra-career moves, the strong desire to maintain the career was shown, and at the same time, the possibility of leaving the region was also high. Based on the case study, this study proposed systematic career counseling for career match, and construction of career management system to support continuous career development.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.310-318
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• 2018

Recently, the importance of big data analysis is increasing as a large amount of data is generated by various devices connected to the Internet with the advent of Internet of Things (IoT). Especially, it is necessary to analyze various large-scale IoT streaming sensor data generated in real time and provide various services through new meaningful prediction. This paper proposes a real-time indoor PM10 concentration prediction LSTM model based on streaming data generated from IoT sensor using AWS. We also construct a real-time indoor PM10 concentration prediction service based on the proposed model. Data used in the paper is streaming data collected from the PM10 IoT sensor for 24 hours. This time series data is converted into sequence data consisting of 30 consecutive values from time series data for use as input data of LSTM. The LSTM model is learned through a sliding window process of moving to the immediately adjacent dataset. In order to improve the performance of the model, incremental learning method is applied to the streaming data collected every 24 hours. The linear regression and recurrent neural networks (RNN) models are compared to evaluate the performance of LSTM model. Experimental results show that the proposed LSTM prediction model has 700% improvement over linear regression and 140% improvement over RNN model for its performance level.

• Journal of radiological science and technology
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• v.42 no.3
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• pp.217-222
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• 2019

The purpose of this study is to evaluate the dose distribution by gantry rotation and MLC moving speed on treatment planning system(TPS) and linear accelerator. The dose analyzer phantom(Delta 4) was scanned by CT simulator for treatment planning. The planning target volumes(PTVs) of prostate and pancreas was prescribed 6,500 cGy, 5,000 cGy on VMAT(Volumetric Modulated Arc Therapy) by TPS while MLC speed changed. The analyzer phantom was irradiated linear accelerator using by planned parameters. Dose distribution of PTVs were evaluated by the homogeneity index, conformity index, dose volume histogram of organ at risk(rectum, bladder, spinal cord, kidney). And irradiated dose analysis were evaluated dose distribution and conformity by gamma index. The PTV dose of pancreas was 4,993 cGy during 0.1 cm/deg leaf and gantry that was the most closest prescribed dose(5,000 cGy). The dose of spinal cord, left kidney, and right kidney were accessed the lowest during 0.1 cm/deg, 1.5 cm/deg, 0.3 cm/deg. The PTV dose of prostate was 6,466 cGy during 0.1 cm/deg leaf and gantry that was the most closest prescribed dose(6,500 cGy). The dose of bladder and rectum were accessed the lowest during 0.3 cm/deg, 2.0 cm/deg. For gamma index, pancreas and prostate were analyzed the lowest error 100% at 0.8, 1.0 cm/deg and 99.6% at 0.3, 0.5 cm/deg. We should used the optimal leaf speed according to the gantry rotation if the treatment cases are performed VMAT.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.6
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• pp.243-250
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• 2019

Because of a moving UAV has a lot of potential/kinetic energy, if the UAV falls to the ground, it may have a lot of impact. Because this can lead to human casualities, in this paper, the population density area on the UAV flight path is defined as a dangerous area. The conventional UAV path flight was a passive form in which a UAV moved in accordance with a path preset by a user before the flight. Some UAVs include safety features such as a obstacle avoidance system during flight. Still, it is difficult to respond to changes in the real-time flight environment. Using public Big Data for UAV path flight can improve response to real-time flight environment changes by enabling detection of dangerous areas and avoidance of the areas. Therefore, in this paper, we propose a method to detect and avoid dangerous areas for UAVs by utilizing the Big Data collected in real-time. If the routh is designated according to the destination by the proposed method, the dangerous area is determined in real-time and the flight is made to the optimal bypass path. In further research, we will study ways to increase the quality satisfaction of the images acquired by flying under the avoidance flight plan.

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