• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.5
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• pp.1135-1144
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• 2018

An electric wheelchair used to be utilized as a piece of equipment for the disabled and the elderly in the past, but the recent changes to its functions and forms have made it available across various fields and purposes. In this paper, we propose a continuous track type of electric wheelchair prototype to be used in various fields and environments and a monitoring system to control it. A frame stress design was applied to improve its stability during driving compared with the previous wheelchairs. In addition, we provide a convenience for free and easy operation of them using the App. based on android. A monitoring system based on C# was also added to control a large number of electric wheelchairs. As a result of the implementation and performance evaluation, the von Mises stress value was measured 4.401% within the normal range through five times of stress interpretations, and its accuracy of communication for system manipulation was recorded about 98.75%, which means that it has been proven to be safer than the previous wheelchairs.

• Journal of Korea Technology Innovation Society
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• v.20 no.4
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• pp.939-969
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• 2017

The interest in social regulation and its influence on innovation are increasing as the society concerns more for environment and safety. There have been plenty of literature about the impact of social regulation on innovation and its mechanism. Majority of research have been influenced by or based on the famous Porter's hypothesis. However, majority of the literature focus on internal factors such as expected benefits from change of regulations, and it is hard to find one studying social regulation's influence on innovation through external factors such as market or industrial structure. This study addresses this issue of the impact of social regulation on innovation by analyzing the case of fire-retardant coating material for steel structure industry in Korea. It scrutinizes the impact of social regulation which affects competition and innovation on substitute competing market, and tries to reveal that there might exist the other path to innovation, besides the way that the expected benefit from compliance of regulation directly drives innovation. As a result of the case study, we have found that changes in social regulation may act like economic regulation and restructure the market segment and this effect may lead to innovation. It can be explained by the fact that expected benefits from compliance of regulation can be a direct source of innovation, as Porter suggested, but the change of industry structure and competitive strength caused by the change in social regulation can also act as a driving force of innovation.

• Journal of Soil and Groundwater Environment
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• v.9 no.4
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• pp.15-23
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• 2004

Deicer operations provide traffic safety during winter driving conditions in urban areas. Using large quantities of de-icing chemicals (i.e., $CaCl_2$ and NaCl) can cause serious environmental problems and may change behaviors of heavy metals in roadside sediments, resulting in an increase in mobilization of heavy metals due to complexation of heavy metals with chloride ions. To examine effect of de-icing salt concentration on the leaching behaviors and mobility of heavy metals (cadmium, zinc, copper, lead, arsenic, nickel, chromium, cobalt, manganese, and iron), leaching experiments were conducted on roadside sediments collected from Seoul city using de-icing salt solutions having various concentrations (0.01-5.0M). Results indicate that zinc, copper, and manganese in roadside sediments were easily mobilized, whereas chromium and cobalt remain strongly fixed. The zinc, copper and manganese concentrations measured in the leaching experiments were relatively high. De-icing salts can cause a decrease in partitioning between adsorbed (or precipitated) and dissolved metals, resulting in an increase in concentrations of dissolved metals in salt laden snowmelt. As a result, run-off water quality can be degraded. The de-icing salt applied on the road surface also lead to infiltration and contamination of heavy metal to groundwater.

• Journal of Korean Society of Transportation
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• v.33 no.6
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• pp.554-563
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• 2015

This study dealt with developing an accident model for rural signalized intersections with random parameter negative binomial method. The limitation of previous count models(especially, Poisson/Negative Binomial model) is not to explain the integrated variations in terms of time and the distinctive characters a specific point/segment has. This drawback of the traditional count models results in the underestimation of the standard error(t-value inflation) of the derived coefficient and finally affects the low-reliability of the whole model. To solve this problem, this study improves the limitation of traditional count models by suggesting the use of random parameter which takes account of heterogeneity of each point/segment. Through the analyses, it was found that the increase of traffic flow and pedestrian facilities on minor streets had positive effects on the increase of traffic accidents. Left turning lanes and median on major streets reduced the number of accidents. The analysis results show that the random parameter modeling is an effective method for investigating the influence on traffic accident from road geometries. However, this study could not analyze the effects of sequential changes of driving conditions including geometries and safety facilities.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.4
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• pp.115-123
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• 2021

In recent years, the automobile industry has been facing a major change with the introduction of new technologies represented by autonomous driving, electrification, and digitalization. Major domestic and overseas automakers are trying to use a systematic approach to customer satisfaction through user interfaces to provide customers with a special experience and value beyond just making products with high performance. This study proposes the Kano model as a systematic and qualitative research method for satisfaction. As a case study, 17 functions of a product were sorted (3 operation functions, 7 safety functions, and 7 convenience functions). This was done by analyzing the use case and the customers' requirements for a smart trunk system. 18 new functions were derived via creative ideation codes. In addition, a scientific analysis method is proposed for product quality attributes and the strength of customer satisfaction. Using the Kano methodology, 25 functions were classified into quality attributes: 18 attractive qualities, 3 one-dimensional qualities, and 4 complex qualities, which are combinations of one-dimension qualities and must-have qualities. The functions that have one-dimensional quality and complex qualities were found to have higher customer ratings than the functions that have attractive qualities. Based on this, enterprises could effectively reduce customer complaints and enhance customer satisfaction.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.2
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• pp.43-57
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• 2021

The Forward Collision Warning, a representative technology of the Advanced Driver Assistance Systems, was selected as the target technology. The cognitive response time, deceleration, and impact were selected as the measures of effectiveness. And the amount of change with and without the Forward Collision Warning was measured. The experimental scenarios included a sudden stop event (1) of the vehicle in front of the driver and an event (2) in which the vehicle intervened in the next lane. All experiments were divided into day and night. As a result of the analysis, response time and the deceleration rate decreased when the forward collision warning system was installed. It was analyzed that the driver's risk situation could be detected quickly and the number of front-end collisions could be reduced as a result. Reflecting the driver's operating habits and diversifying the experimental scenarios will increase the installation effectiveness of ADAS and be used to estimate the effectiveness of other technologies.

• Journal of Korean Tunnelling and Underground Space Association
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• v.20 no.6
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• pp.1161-1175
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• 2018

An unexpected event could be easily followed by a large secondary accident due to the limitation in sight of drivers in road tunnels. Therefore, a series of automated incident detection systems have been under operation, which, however, appear in very low detection rates due to very low image qualities on CCTVs in tunnels. In order to overcome that limit, deep learning based tunnel incident detection system was developed, which already showed high detection rates in November of 2017. However, since the object detection process could deal with only still images, moving direction and speed of moving vehicles could not be identified. Furthermore it was hard to detect stopping and reverse the status of moving vehicles. Therefore, apart from the object detection, an object tracking method has been introduced and combined with the detection algorithm to track the moving vehicles. Also, stopping-reverse discrimination algorithm was proposed, thereby implementing into the combined incident detection processes. Each performance on detection of stopping, reverse driving and fire incident state were evaluated with showing 100% detection rate. But the detection for 'person' object appears relatively low success rate to 78.5%. Nevertheless, it is believed that the enlarged richness of image big-data could dramatically enhance the detection capacity of the automatic incident detection system.

• Journal of Internet Computing and Services
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• v.20 no.4
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• pp.91-102
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• 2019

In autonomous driving systems, the ability to classify pedestrians in images captured by cameras is very important for pedestrian safety. In the past, after extracting features of pedestrians with HOG(Histogram of Oriented Gradients) or SIFT(Scale-Invariant Feature Transform), people classified them using SVM(Support Vector Machine). However, extracting pedestrian characteristics in such a handcrafted manner has many limitations. Therefore, this paper proposes a method to classify pedestrians reliably and effectively using CNN's(Convolutional Neural Network) deep features and transfer learning. We have experimented with both the fixed feature extractor and the fine-tuning methods, which are two representative transfer learning techniques. Particularly, in the fine-tuning method, we have added a new scheme, called M-Fine(Modified Fine-tuning), which divideslayers into transferred parts and non-transferred parts in three different sizes, and adjusts weights only for layers belonging to non-transferred parts. Experiments on INRIA Person data set with five CNN models(VGGNet, DenseNet, Inception V3, Xception, and MobileNet) showed that CNN's deep features perform better than handcrafted features such as HOG and SIFT, and that the accuracy of Xception (threshold = 0.5) isthe highest at 99.61%. MobileNet, which achieved similar performance to Xception and learned 80% fewer parameters, was the best in terms of efficiency. Among the three transfer learning schemes tested above, the performance of the fine-tuning method was the best. The performance of the M-Fine method was comparable to or slightly lower than that of the fine-tuningmethod, but higher than that of the fixed feature extractor method.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.6
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• pp.121-132
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• 2018

Weather is an important factor affecting roadway transportation in many aspects such as traffic flow, driver 's driving patterns, and crashes. This study focuses on the relationship between weather and road surface condition and develops a model to estimate the road surface condition using machine learning. A road surface sensor was attached to the probe vehicle to collect road surface condition classified into three categories as 'dry', 'moist' and 'wet'. Road geometry information (curvature, gradient), traffic information (link speed), weather information (rainfall, humidity, temperature, wind speed) are utilized as variables to estimate the road surface condition. A variety of machine learning algorithms examined for predicting the road surface condition, and a two - stage classification model based on 'Random forest' which has the highest accuracy was constructed. 14 days of data were used to train the model and 2 days of data were used to test the accuracy of the model. As a result, a road surface state prediction model with 81.74% accuracy was constructed. The result of this study shows the possibility of estimating the road surface condition using the existing weather and traffic information without installing new equipment or sensors.

• The Journal of the Korea Contents Association
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• v.19 no.5
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• pp.350-362
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• 2019

The purpose of this study was to examine the effects of job characteristics and health on accident experience by analyzing the data of transportation workers according to age. The analysis used data from 'the fourth Korean Working Conditions Survey(KWCS)'. A total of 1,997 transport workers data were finally analyzed, and correlation analysis, crossover analysis and logistic regression analysis were performed. It was confirmed that there was no correlation between the age of the transport workers and the accident experience. In the relationship between the characteristics of transportation workers and the experience of the accident, it was found that, in the case of older workers, there was a significant effect in the order of 'at mistake someone else hurt', 'musculoskeletal problem', 'cardiovascular problem' and 'repetitive movements of hands or arms', the model explaining power was 56.9%(p <.01). In the case of non-older workers, it was found that 'depression and anxiety disorder', 'relationship between job and safety', 'at mistake someone else hurt' and 'labor union', the model explaining power was 21.8%(p <.01). Therefore, in order to promote prevent accidents of transportation workers in future, it is necessary to consider various variables such as health and job characteristics besides age.