• Journal of Korean Society for Geospatial Information Science
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• v.23 no.2
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• pp.97-104
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• 2015

Due to the introduction of various IT devices, including the recently smartphones and the widespread use of the car navigation system to the location-based information service space has been increased. Spatial information users have been requiring higher levels of quality. In this paper, we study how to build accurate three-dimensional space information by integrating MMS(Moblie Mapping System) survey and airborne survey data. Thus, to analyze the tendency of deviation between the MMS survey and airborne survey data observed in the experimental region, the deviation tendency of the data, it was confirmed that was not consistent. Deviation correction model to select how to change the georeferencing information directly contained in the GPS/INS processing results for the determination, classifies the standard is a method for acquiring the correction reference point coordinates using the calibration model, and analyzed their advantages and disadvantages. With the information of the reference point obtained by airborne photograph of a project, using the method of correcting the MMS survey data. Not only clear the deviation existing between the MMS survey data, it was possible to confirm that the deviation exists between the airborne survey data and MMS survey data was also almost erased.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.5
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• pp.40-51
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• 2020

Much time has passed since Korea's expressway congestion-threshold speed was revised in 2011. In the meantime, various expressway environments have changed owing to improved performance of vehicles, expanded operations of transport competition (i.e., the KTX), and increased speed limits along some expressway sections. In addition, the speed that expressway users expect to travel at is also increasing. Therefore, through a survey, this study investigates expressway users' perceptions of congestion, and reviews the adjustment of the expressway speed congestion threshold by analyzing expressway traffic flow. One result of the survey confirms that the threshold speed expressway users consider to be congestion has slightly increased. Analyzing traffic and speed data through a K-means algorithm found that the threshold speed for congestion is 60 km/h. In addition, assuming the congestion threshold speed increase from 40 km/h to 50 km/h and 60 km/h, frequently congested expressway sections are identified, determining that 50 km/h is appropriate as a congestion threshold for proper expressway mobility management.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.566-566
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• 2016

정부의 4대강 물 관리 종합대책에 따르면, 수계 전체 오염원중 비점오염원이 차지하는 오염부하가 22~37%에 달하는 것으로 추정되고 있다. 하지만 이러한 중요성에도 불구하고 농업지역 비점오염물질 저감을 위한 대책은 논과 밭과 같이 농경지에 관한 것이 대부분이었으며, 축산은 관리 기준의 가장 기초라고 할 수 있는 지목분류기준에 조차 별도의 기준이 없는 실정이다. 가축분뇨공공처리시설과 가축분뇨자원화시설은 가축분뇨를 처리하여야 하는 점오염원이지만, 차량 운반시 발생되는 일부 분뇨와 처리장 세척 시 발생되는 일부 오염물질들이 비점오염원으로 작용하고 있으며 이에 대한 관리가 미흡한 실정이다. 따라서 본 연구에서는 가축분뇨공공처리시설과 가축분뇨자원화시설에서 강우시 발생되는 유출특성을 분석하였으며, 이를 통해 가축분뇨처리시설의 비점오염 관리 처리시설 설치 시에 중요한 기초자료로 활용하고자 한다. 본 연구에서는 경상북도 영천시, 경기도 용인시, 전라북도 정읍시, 강원도 횡성군 등 축산밀집 지역을 대상으로 연 5회 강우시 모니터링을 실시하였으며, 모니터링자료를 바탕으로 유량가중평균농도(Event Mean Concentration, EMC)를 산정하였다. 영천시 가축분뇨자원화시설의EMC 산정결과 평균 BOD 5.1 mg/L, TN 6.90 mg/L, TP 0.91 mg/L로 산정되었으며, 용인시 개별처리농가의 경우 BOD 6.8 mg/L, TN 3.74 mg/L, TP 1.04 mg/L로, 횡성군 가축분뇨공공처리장의 경우 BOD 4.5 mg/L, TN 3.56 mg/L, TP 1.60 mg/L로, 정읍시 가축분뇨공공자원화시설의 경우 BOD 4.3 mg/L, TN 6.82 mg/L, TP 0.48 mg/L로 산정되었다. BOD, TN은 영천시 가축분뇨자원화시설에서 가장 높게 나타났고, TP의 경우 횡성군 축산폐수공공처리장의 경우 높게 나타났다. 유출특성을 분석한 결과 가축분뇨자원화시설의 경우 대부분 콘크리트 기반으로 조성된 토지위에 조성되어 강우시 유량은 급격하게 상승하며, 강우가 종료되면 바로 감소하는 불투수층 지역의 특성을 나타났다. 본 연구에서 분석된 유츨특성과 EMC는 비점오염 처리시설이나 가축분뇨공공처리시설 설치시 기초데이터로 활용이 가능할 것으로 판단되며, 향후 가축분뇨처리시설의 지속적인 모니터링과 모니터링지점 확대로 자원화시설 강우유출수의 DataBase화를 통한 지속적인 연구 및 관리가 되어야 할 것으로 판단된다.

• 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.

• Environmental and Resource Economics Review
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• v.28 no.2
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• pp.307-326
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• 2019

The electric vehicle is a representative measure to reduce greenhouse gas and local air pollutants in the transportation sector. Most countries provide purchase subsidies and tax reductions to promote electric vehicle sales. The electric vehicles have been considered as zero-emission vehicles(ZEV) in light of the fact that there has been no pollutant emission during driving. However, recent studies have pointed out that the pollutant emitted from the process of generating electricity used for charging the electric vehicles need to be treated as emissions of the electric vehicles. Furthermore, the environmental benefits of electric vehicle replacing the internal combustion vehicle vary with the power mix. In line with the recent studies, this study analyzes the impact of electric vehicles based on the current power mix and future energy transition scenarios in Korea. To estimate the precise air pollutants emission profile, this study uses hourly electricity generation and TMS emission data for each power plant from 2015 to 2016. The estimation results show that the electric vehicles under the current power mix generate the environmental benefits of only -0.41~10.83 won/km. Also, we find that the environmental benefit of electric vehicle will significantly increase only when the ratio of the coal-fired power plant is reduced to a considerable extent.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.3
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• pp.95-105
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• 2019

In recent years, image processing techniques for detecting road surface damaged spot have been actively researched. Especially, it is mainly used to acquire images through a smart phone or a black box that can be mounted in a vehicle and recognize the road surface damaged region in the image using several algorithms. In addition, in conjunction with the GPS module, the exact damaged location can be obtained. The most important technology is image processing algorithm. Recently, algorithms based on artificial intelligence have been attracting attention as research topics. In this paper, we will also discuss artificial intelligence image processing algorithms. Among them, an object detection method based on an region-based convolution neural networks method is used. To improve the recognition performance of road surface damage objects, 600 road surface damaged images and 1500 general road driving images are added to the learning database. Also, supervised learning using background object recognition method is performed to reduce false alarm and missing rate in road surface damage detection. As a result, we introduce a new method that improves the recognition performance of the algorithm to 8.66% based on average value of mAP through the same test database.

• 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.

• Journal of Convergence for Information Technology
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• v.11 no.6
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• pp.33-39
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• 2021

Speech recognition technology is being combined with deep learning and is developing at a rapid pace. In particular, voice recognition services are connected to various devices such as artificial intelligence speakers, vehicle voice recognition, and smartphones, and voice recognition technology is being used in various places, not in specific areas of the industry. In this situation, research to meet high expectations for the technology is also being actively conducted. Among them, in the field of natural language processing (NLP), there is a need for research in the field of removing ambient noise or unnecessary voice signals that have a great influence on the speech recognition recognition rate. Many domestic and foreign companies are already using the latest AI technology for such research. Among them, research using a convolutional neural network algorithm (CNN) is being actively conducted. The purpose of this study is to determine the non-voice section from the user's speech section through the convolutional neural network. It collects the voice files (wav) of 5 speakers to generate learning data, and utilizes the convolutional neural network to determine the speech section and the non-voice section. A classification model for discriminating speech sections was created. Afterwards, an experiment was conducted to detect the non-speech section through the generated model, and as a result, an accuracy of 94% was obtained.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.3
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• pp.137-149
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• 2021

Cable-stayed bridges are infrastructure facilities of a highly public nature; therefore, it is essential to ensure operational safety and prompt response in the event of a collapse or damage caused by natural and social disasters. Among social disasters, impact accidents can occur in bridges when a vehicle collides with a pier or when crashes occur due to aircraft defects. In the case of offshore bridges, ship collisions will occur at the bottom of the pylon. In this research, a procedure to evaluate the structural behavior of a cable-stayed bridge for aircraft impact is suggested based on a numerical analysis approach, and the feasibility of the procedure is demonstrated by performing an example assessment. The suggested procedure includes 1) setting up suitable aircraft impact hazard scenarios, 2) structural modeling considering the complex behavior mechanisms of cable-stayed bridges, and 3) structural behavior evaluation of cable-stayed bridges using numerical impact simulation. It was observed that the scenario set in this study did not significantly affect the target bridge. However, if impact analysis is performed through various scenarios in the future, the load position and critical load level to cause serious damage to the bridge could be identified. The scenario-based assessment process employed in this study is expected to facilitate the evaluation of bridge structures under aircraft impact in both existing bridges and future designs.

• Journal of the Institute of Convergence Signal Processing
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• v.22 no.4
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• pp.149-155
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• 2021

In this study, we intend to develop a defective road surface object recognition model that automatically detects road surface defects that restrict the movement of the transportation handicapped using electric mobile devices with deep learning. For this purpose, road surface information was collected from the pedestrian and running routes where the electric mobility aid device is expected to move in five areas within the city of Busan. For data, images were collected by dividing the road surface and surroundings into objects constituting the surroundings. A series of recognition items such as the detection of breakage levels of sidewalk blocks were defined by classifying according to the degree of impeding the movement of the transportation handicapped in traffic from the collected data. A road surface object recognition deep learning model was implemented. In the final stage of the study, the performance verification process of a deep learning model that automatically detects defective road surface objects through model learning and validation after processing, refining, and annotation of image data separated and collected in units of objects through actual driving. proceeded.