• Geophysics and Geophysical Exploration
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• v.1 no.1
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• pp.49-56
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• 1998

Seismic tomography has been widely used as high resolution subsurface imaging techniques in engineering applications. Although most of the techniques have been using travel time inversion, waveform method is being driven forward owing to the progress of computational environments. Although full-waveform inversion method has been known as the best method in terms of model resolving power without high-frequency restriction and weak scattering approximation, it has practical disadvantage that it is apt to get stuck in local minimum if the initial guess is far from the actual model and it consumes so much time to calculate. In this study, 2-D full-waveform inversion algorithm in acoustic medium is developed, which uses result of traveltime tomography as initial model. From the application on synthetic data, it is proved that this approach can efficiently reduce the problem of conventional approaches: our algorithm shows much faster convergence rate and improvement of model resolution. Result of application on physical modeling data also shows much improvement. It is expected that this algorithm can be applicable to real data.

• 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.

• Fire Science and Engineering
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• v.32 no.6
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• pp.46-54
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• 2018

A total of 19 elevators for evacuation were installed in the Lotte World Tower and it is planned to operate the shuttle using the manual key from five refuge floors to the 1st floor in an emergency. In the event of a fire or other disaster, it is necessary to conduct intensive analysis to determine how much RSET reduction could be achieved using the evacuation elevator compared to the existing evacuation plans. When the optimal transportation sharing ratio by the evacuation elevators was 40% at the Lotte World Tower, the RSET of the evacuation scenario in parallel with the elevators in the entire building was calculated to be 1 hour and 2 minutes. The RSET of a conventional evacuation scenario (Walking along the stairs without using the elevators) was calculated to be 1 hour 29 minutes, therefore, the former evacuation scenario were found to have a shortening effect of approximately 27 minutes compared to the latter. On the other hand, to maintain this effect, each part of the evacuation route using the elevator must have the capability to protect the evacuee from any hazards caused by fires, such as smoke, flame, and radiant heat during the evacuation. Moreover, the evacuation route should be continuous from the residence position of the elevator user to the final evacuation site, and be recognized easily.

• Journal of Oriental Neuropsychiatry
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• v.30 no.3
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• pp.209-220
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• 2019

Objectives: As a general emotion, everyone can temporarily experience depression, but depressive disorder is a disease that excessively affects daily life. Among the various causes of depression, the deficiency of monoamine-based neurotransmitters such as serotonin and epinephrine are considered significant. Thus, antidepressants that target monoamines are used frequently. However, side effects such as nausea, vomiting, insomnia, anxiety, and sexual dysfunction are observed. Thus, it is necessary to develop a new therapeutic agent with fewer side effects. In this study, we investigated the antidepressant effect of JG02, used to treat depression by normalizing the flow of qi (氣) in Korean medicine. Methods: C57BL/6 mice were selected and randomly divided into six groups: normal, control, amitriptyline, and JG02 (50, 125, 250 mg/kg), respectively. Except for normal, depression was induced by applying restraint stress at the same time for six hours daily for 14 consecutive days. Saline, amitriptyline or JG02 samples were orally administered two hours before applying the stress. After that, a forced swimming test and an open field test were performed. Additionally, serum corticosterone, serotonin mRNA, BDNF mRNA, and protein in the hippocampal region were measured and compared. Results: JG02 decreased immobility time rate in the FST and increased the zone transition number and travel distance in the OFT. Also, JG02 inhibited the release of serum corticosterone, and increased serotonin, BDNF gene expression, and BDNF protein in the hippocampus. Conclusions: In this study, JG02 showed significant antidepressant effects on the chronic restraint stress mice model. When further research is performed based on JG02, the development of a new antidepressant is considered highly possible.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.1
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• pp.119-131
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• 2021

This study quantitatively derived the direct socio-economic effects of the Gyeongbu Expressway, which opened in 1970, and suggested a methodological approach for more reliable results. The scenario was set when the Gyeongbu Expressway was not constructed in 1970, the opening of the Gyeongbu Expressway was delayed by 10 years, and the toll road between Seoul and Daejeon, or between Seoul and Gangneung was opened instead of the Gyeongbu Expressway as suggested by the World Bank. In addition, direct benefits were estimated by calculating and comparing the current vehicle operating costs, travel time costs, traffic accident costs, and environmental pollution costs. As a result, it was estimated that about 351 trillion won in direct benefits occurred, and it can be seen that the promotion of the construction project of the Gyeongbu Expressway at that time had a huge impact on South Korea's social economy.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.2
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• pp.1-14
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• 2021

The exclusive bus lanes in the Yeongdong Expressway were implemented in the Singal to Yeoju section in 2017, but the capacity of both exclusive bus lanes and general-purpose lanes of the Yeongdong Expressway decreased and the travel time increased, reducing it to the Singal to Deokpyeong section. Therefore, it is necessary to increase the efficiency of exclusive bus lanes to revitalize public transportation and improve environmental problems. This study calculated the willingness to pay and the social benefits of permission for Green cars to drive on exclusive bus lanes in Yeongdong Expressway. A survey was conducted on two groups of Green car users and Expressway users, and the willingness to pay was estimated using the CVM method. As a result, the average WTP of Green car users were estimated to be 218.7(won/km·person), and that of Expressway users were estimated to be approximately 235.5(won/km·person). The direct benefits were estimated to be approximately 7.9 billion won for Green car users, and 8.5 billion won for Expressway users in 2019. Finally, the value of time saving was estimated to be approximately 8.0 billion won.

• Journal of Broadcast Engineering
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• v.28 no.3
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• pp.285-292
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• 2023

Recently, the impact of fine dust on health has become a major topic. Fine dust is dangerous because it can penetrate the body and affect the respiratory system, without being filtered out by the mucous membrane in the nose. Since fine dust is directly related to the industry, it is practically impossible to completely remove it. Therefore, if the concentration of fine dust can be predicted in advance, pre-emptive measures can be taken to minimize its impact on the human body. Fine dust can travel over 600km in a day, so it not only affects neighboring areas, but also distant regions. In this paper, wind direction and speed data and a time series prediction model were used to predict the concentration of fine dust in Seoul, and the correlation between the concentration of fine dust in Seoul and the concentration in each region was confirmed. In addition, predictions were made using the concentration of fine dust in each region and in Seoul. The lowest MAE (mean absolute error) in the prediction results was 12.13, which was about 15.17% better than the MAE of 14.3 presented in previous studies.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.2
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• pp.1-14
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• 2023

This study derives quantitative data on how much the fiscal deficit of subway operation agencies can be reduced in the process of charging free rides for the elderly in metropolitan subways during peak periods. In smart card data, every trip of elderly is recorded except fares. Therefore, it is required to establish a methodology for estimating the fares of elderly passengers and distributing them to subway opertation agencies as income. This study builds a simultaneous dynamic traffic allocation model that reflects the assumption that elderly selects a minimum time route based on the departure time. The travel route of the elderly is estimated, and the distance-proportional fare charged to the elderly is calculated based on this, and the fare is distributed by reflecting the connected railway revenue allocation principle of the metropolitan subway operating agencies. As a result of conducting a case study for before and after COVID-19 in 2019 and 2020, it is analyzed that Seoul Metro's annual free loss of 360 billion won could be reduced 6~8% at the morning peak (07:00-08:59), and 13~16% at the morning and afternoon peak (18:00-19:59).

• SUVANNABHUMI
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• v.4 no.1
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• pp.57-90
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• 2012

This essay is for a study on the survey of buddhist cultural literary document about the Southeast Asia by Nanqishu(南齊書), Liangshu(梁書), Chiu T'angshu(舊唐書), Hsin T'ang shu(新唐書) which are included in the Chinese Official History and Jí-shénzhōu-sānbăo-găntōng-lù(集神州三寶感通錄), Weioshu Shilao Ji (魏書 釋老志). These documents allows us to imagine next some historical states. First, these documents are recording this area, especially Funan, as a plentiful diggings of gold, silver, tin, copper, etc. These are important materials for gilt bronze sculptures. Further, this local produced gold called 'Yangmai(楊邁)' is recognized as same as Zĭmòjīn(紫磨金) in china, and these documents explains the process of producing the bronze images and golden ornaments in Southeast Asia. Specially, this plentiful materials leads them to make a 10 wei(圍) tall golden-silver image which worshiped as hindu god or sometimes buddhist images. Second, Vietnam and Funan in Northern and Southern Dynasty periods were in antagonistic relationship not only in the political but also in religious between Vietnamese Hinduism and Funan's Buddhism. Under this situation, the monk Nagaxian(那伽仙) who had came from India was accredited to Southern Qi court as a delegate to build a good relationship by the common religion Buddhism. It means the Buddhism of Southeast Asia also took a role of diplomacy. Third, these documents proved the active Southeast Asian cultural exchange in early 3th century. At this time, Funan delegate Suwu(蘇物) visited the court of Kushan Dynasty in India and he is seemed to travel the city of Pātaliputra. It oppressed us to extend our outlook which have been restricted in the relationship between Southern india and Southeast Asia to more broaden area. In addition, the buddhist art of Southern India and Bodhgaya temple was imported to Southeast Asia directly to send to Southern China. For example, the wooden buddha image, Bodhgaya stupa image, and Sri Lanka style's buddha images are looked be introduced to Southern China at this time throughout the Southeast Asia. At last, we got to know that the court music of Kucha in the northern silk route was imported to the Southeast Asia in early middle age. Even it may be introduced by China, but this document is very important to make the surmise is not unreasonable that the buddhist culture of northern silk route imported to the Southeast Asia accompanied with Kucha music. The buddha images in Gandharan style which are excavated from some site of Southeast Asia may demonstrate this literary sources is authentic.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.6B
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• pp.551-560
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• 2009

This paper describes a newly developed pollutant transport model named ARPTM which was designed to simulate the transport and characteristics of pollutant materials after an accidental spill in upstream of river system up to a given position in the downstream. In particular, the ARPTM incorporated ADCP data to compute longitudinal dispersion coefficient and advection velocity which are necessary to apply one-dimensional advection-dispersion equation. ARPTM was built on top of the geographic information system platforms to take advantage of the technology's capabilities to track geo-referenced processes and visualize the simulated results in conjunction with associated geographic layers such as digital maps. The ARPTM computes travel distance, time, and concentration of the pollutant cloud in the given flow path from the river network, after quickly finding path between the spill of the pollutant material and any concerned points in the downstream. ARPTM is closely connected with a recently developed GIS-based Arc River database that stores inputs and outputs of ARPTM. ARPTM thereby assembles measurements, modeling, and cyberinfrastructure components to create a useful cyber-tool for determining and visualizing the dynamics of the clouds of pollutants while dispersing in space and time. ARPTM is expected to be potentially used for building warning system for the transport of pollutant materials in a large basin.