• The Journal of Asian Finance, Economics and Business
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• v.7 no.12
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• pp.123-129
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• 2020

This scholarly work is an effort to capture the effects of oil prices on the actual exchange rate between dollar and rupee. This is done with reference to the U.S. dollar as oil prices are marked in USD (U.S. Dollar) in the international market, and India is among the top five importers of oil. Using monthly data from January 2001 to May 2020. The study used the real GDP, money supply, short-term interest rate difference between two countries, and inflation apart from the crude oil prices per barrel as the factors that help define the exchange rate. The analysis, through cointegration and vector error correction method (VECM), suggests long and short-run causality amid prices of oil and the rate of exchange fluctuations. Oil prices are found to be negatively related to the exchange rate in the long term but positively related in the short term. The result of the Wald test also indicates the short-run causation from the short-term interest rate and the prices of crude oil towards the exchange rate. The present study shows that oil prices are evidence of the existence of short-term and long-term driving associations with short-term interest rates and exchange rates.

• Journal of the Korean Institute of Rural Architecture
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• v.12 no.4
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• pp.29-36
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• 2010

The purpose of this study is to have detailed data of the distribution, locations, and the amount of people in the waiting line of the nursing home. Also, we studied the accessibility to the facilities by using Web GIS to analyze the transit time it takes from the nursing home to health center and hospitals. We can provide the basic data that could contribute when future plans for the nursing homes' locations, health and medical policy are made. The results are as follows. 1. The nursing homes are stiffly concentrated in regions of Seoul and Gyeongi-do where large number of the elderly covered by long-term care insurance and the waiting line was very long for the elderlies to enter the nursing homes. In these cities of Ulsan and Jeju where number of the elderly covered by long-term care insurance is relatively small, there were less facilities. 2. The nursing homes located in urban areas had higher occupancy rate and higher number of people in the waiting line. 3. The average time taken by driving from the nursing homes and health center was 10 minutes and there was not a noticeable difference between the cities. Driving from the nursing homes to hospitals in rural areas took 22 minutes which is 2.5 times of the time taken for urban areas. Daegu-si and Incheon-si had relatively short distance from the nursing homes and the hospitals while Jeju-do had the furthest. For rural areas, it is needed for health center to be equipped with a wider medical coverage, have closely connected with hospitals to minimize the differences they have from ones in rural areas. It is also needed to have ambulances equipped for tele-medical examination and treatment system.

• Journal of Korean Medicine Rehabilitation
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• v.18 no.1
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• pp.141-151
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• 2008

Objectives : The purpose of this study was to investigate effects of long-term taxi driving posture on the balance of musculoskelectal system. Methods : The author practiced Moire topography by using IBS-2000 for 30 male taxi drivers and general people. Then we measured difference of contour lines, difference of shoulder joint height, interval between vertical baseline of pelvis and vertical baseline of neck, ratio of thoracic curve and lumbar curve, difference of width between right and left through Moire topography. After we statistically analyzed difference of Moire topography's data between long-term employed male taxi drivers and general people. Results : 1. Taxi drivers, difference of contour lines in scapular, lumbar, gluteal region was bigger than general people and significant difference(p<0.05) was observed between subject group. 2. Taxi drivers, diference of shoulder joint height was more large than general people and significant difference(p<0.01) was observed between subject group. 3. Taxi drivers, diference of interval between vertical baseline of pelvis and vertical baseline of neck was more large than general people and significant difference((p<0.05) was observed between subject group. 4. Taxi drivers, ratio of thoracic curve was more large than general people and ratio of lumbar curve was more less than general people and significant difference(p<0.05) was observed between subject group. Conclusions : According to above results long-term taxi driving posture might cause musculoskelectal system unbalance.

• Electronics and Telecommunications Trends
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• v.37 no.4
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• pp.11-18
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• 2022

Sharing vehicle data among the companies within a car ecosystem can improve driving experience, increase driver comfort, contribute to social goals such as improving road safety and lowering fuel consumption. Furthermore, by participating in the ecosystem, companies can secure long-term and sustainable new revenue-generating opportunities. In this paper, we will examine the bottleneck and success factors of data sharing, as well as the technological solutions that urgently require development for car data sharing.

• Journal of Internet Computing and Services
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• v.24 no.1
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• pp.39-47
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• 2023

The development of autonomous driving and Advanced Driver Assistance System (ADAS) technology has grown rapidly in recent years. As most traffic accidents occur due to human error, self-driving vehicles can drastically reduce the number of accidents and crashes that occur on the roads today. Obviously, technical advancements in autonomous driving can lead to improved public driving safety. However, due to the current limitations in technology and lack of public trust in self-driving cars (and drones), the actual use of Autonomous Vehicles (AVs) is still significantly low. According to prior studies, people's acceptance of an AV is mainly determined by trust. It is proven that people still feel much more comfortable in personalized ADAS, designed with the way people drive. Based on such needs, a new attempt for a customized ADAS considering each driver's driving style is proposed in this paper. Each driver's behavior is divided into two categories: assertive and defensive. In this paper, a novel customized ADAS algorithm with high classification accuracy is designed, which divides each driver based on their driving style. Each driver's driving data is collected and simulated using CARLA, which is an open-source autonomous driving simulator. In addition, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) machine learning algorithms are used to optimize the ADAS parameters. The proposed scheme results in a high classification accuracy of time series driving data. Furthermore, among the vast amount of CARLA-based feature data extracted from the drivers, distinguishable driving features are collected selectively using Support Vector Machine (SVM) technology by comparing the amount of influence on the classification of the two categories. Therefore, by extracting distinguishable features and eliminating outliers using SVM, the classification accuracy is significantly improved. Based on this classification, the ADAS sensors can be made more sensitive for the case of assertive drivers, enabling more advanced driving safety support. The proposed technology of this paper is especially important because currently, the state-of-the-art level of autonomous driving is at level 3 (based on the SAE International driving automation standards), which requires advanced functions that can assist drivers using ADAS technology.

• Transactions of the Korean Society of Automotive Engineers
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• v.22 no.6
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• pp.104-112
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• 2014

Generally, numerical approaches of evaluation for vehicle seat comfort have been studied without considering time-dependent characteristics and the only seating moment have been considered in seat design. However, the comfort not only at the seating moment but also in the long-term should be evaluated because the passengers are sitting repeatedly on the seat to drive the vehicle for hours. So, the aim of this paper is to carry out a quantitative evaluation of the time-dependent mechanical characteristics of seat foams and to suggest a process for predicting the viscoelastic deformation of seat foam in response to long-term driving. To characterize the seat materials, uniaxial compression and tension tests were carried out for the seat foam and stress relaxation tests were performed for evaluating the viscoelastic behavior of the seat foam. A unit solid element model was used to verify the reliability of the material model with respect to the compression behavior of the seat foam. It is not straightforward to evaluate the time-dependent compression of foams using the explicit solver because the viscoelastic material model is limited. To use the explicit solver, the material model must be modified using stress-degradation data. Normalized stress relaxation moduli were added to the stress-strain curves obtained under static conditions to achieve a time-dependent set of stress-strain relations that were compatible with the implicit solver. There was good agreement between the analysis results and experimental data.

• Journal of the Korea Society of Computer and Information
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• v.23 no.3
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• pp.63-70
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• 2018

In this work, we analyze the relationship between the accumulated mileage of hybrid electric vehicle(HEV) and the data provided from vehicle parts. Data were collected while traveling over 70,000 Km in various paths. The data collected in seconds are aggregated for 10 minutes and characterized in terms of centrality, variability, normality, and so on. We examined whether the statistical properties of vehicle parts are different for each cumulative mileage interval of a hybrid car. When the cumulative mileage interval is categorized into =< 30,000, <= 50,000, and >50,000, the statistical properties are classified by the mileage interval as 82.3% accuracy. This indicates that if the data of the vehicle parts is collected by operating the hybrid vehicle for 10 minutes, the cumulative mileage interval of the vehicle can be estimated. This makes it possible to detect the abnormality of the vehicle part relative to the accumulated mileage. It can be used to detect abnormal aging of vehicle parts and to inform maintenance necessity.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.1
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• pp.216-238
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• 2023

In intelligent transportation systems, traffic management is an important task. The accurate forecasting of traffic characteristics like flow, congestion, and density is still active research because of the non-linear nature and uncertainty of the spatiotemporal data. Inclement weather, such as rain and snow, and other special events such as holidays, accidents, and road closures have a significant impact on driving and the average speed of vehicles on the road, which lowers traffic capacity and causes congestion in a widespread manner. This work designs a model for multivariate short-term traffic congestion prediction using SLSTM_AE-BiLSTM. The proposed design consists of a Bidirectional Long Short Term Memory(BiLSTM) network to predict traffic flow value and a Convolutional Neural network (CNN) model for detecting the congestion status. This model uses spatial static temporal dynamic data. The stacked Long Short Term Memory Autoencoder (SLSTM AE) is used to encode the weather features into a reduced and more informative feature space. BiLSTM model is used to capture the features from the past and present traffic data simultaneously and also to identify the long-term dependencies. It uses the traffic data and encoded weather data to perform the traffic flow prediction. The CNN model is used to predict the recurring congestion status based on the predicted traffic flow value at a particular urban traffic network. In this work, a publicly available Caltrans PEMS dataset with traffic parameters is used. The proposed model generates the congestion prediction with an accuracy rate of 92.74% which is slightly better when compared with other deep learning models for congestion prediction.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.12
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• pp.1500-1505
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• 2019

Currently, as part of the ICT Smart City, the company is building C-ITS(Cooperative-Intelligent Transport Systems) for solving urban traffic problems. In order to realize autonomous driving service with C-ITS, the role of advanced road infrastructure is important. In addition to the study of mid- to long-term C-ITS and autonomous driving services, it is necessary to present more realistic solutions for road traffic safety in the short term. Therefore, in this paper, we propose a highway accident detection alarm system that can detect and analyze traffic flow and risk information, which are essential information of C-ITS, based on IoT guard rail and provide immediate alarm and remote control. Intelligent IoT guard rail is expected to be used as an intelligent advanced road infrastructure that provides data at actual road sites that are required by C-ITS and self-driving services in the long term.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1259-1268
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• 2023

This study proposes a throttle and steering control technology using visual sensors based on deep learning's convolutional and recurrent neural networks. It collects camera image and control value data while driving a training track in clockwise and counterclockwise directions, and generates a model to predict throttle and steering through data sampling and preprocessing for efficient learning. Afterward, the model was validated on a test track in a different environment that was not used for training to find the optimal model and compare it with a CNN (Convolutional Neural Network). As a result, we found that the proposed deep learning model has excellent performance.