• The Journal of Bigdata
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• v.4 no.2
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• pp.13-22
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• 2019

This study was conducted with funding from the government (Ministry of Agriculture, Food and Rural Affairs) in 2018 with support from the Agricultural, Food, and Rural Affairs Agency, 318069-03-HD040, and is based on artificial intelligence-based HPAI spread analysis and patterning. The inflow of highly pathogenic avian influenza is coming through migratory birds from abroad, but it is not known exactly what pathways provide the farm with the cause of the infection. And the transition between farms from the generated farms only assumes that the vehicle is the main cause, and the main cause of the spread is not exactly known. Based on the call detailed records (CDR) data provided by KT, the study aims to see how people visiting migratory bird-watching sites, presumed to be the site of the outbreak, will flow through infected farms.

• Journal of the Korea Convergence Society
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• v.9 no.1
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• pp.253-259
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• 2018

The Korea Convergence Society. Recently, as the 4th industrial revolution is rising, there are many changes in various field of industries. Among these industries, autonomous vehicles based on artificial intelligence, big data and internet of things is one of the most promising industry. Autonomous vehicle stray from classical car domain of manufacturers and suppliers, IT/Electronics suppliers and communication companies are widen their business area to autonomous vehicle technology. In this paper, we analysis the state of art of autonomous vehicle technology and development direction of industries/research institute. Finally, we discuss the social/economic effects of autonomous vehicle.

• Journal of Digital Convergence
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• v.20 no.5
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• pp.463-476
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• 2022

After industrialization in the past, the automobile industry has continued to grow centered on internal combustion engines, but is facing a major change with the recent 4th industrial revolution. Most companies are preparing for the transition to electric vehicles and autonomous driving. Therefore, in this study, topic modeling was performed based on LDA algorithm by collecting 4,002 domestic papers and 68,372 overseas papers that contain keywords related to CASE (Connectivity, Autonomous, Sharing, Electrification), which represent future automobile trends. As a result of the analysis, it was found that domestic research mainly focuses on macroscopic aspects such as traffic infrastructure, urban traffic efficiency, and traffic policy. Through this, the government's technical support for MaaS (Mobility-as-a-Service) is required in the domestic shared car sector, and the need for data opening by means of transportation was presented. It is judged that these analysis results can be used as basic data for the future automobile industry.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.846-848
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• 2018

최근 4차 산업혁명이 이슈가 되면서 빅 데이터나 인공지능에 대한 연구가 활발해지고, 이를 통해 자동화 및 자율화가 제조 공정이나 차량 운행 등에서 활용되고 있다. 또한 이를 위해서 데이터를 분석하고 정제하며 시각화를 효과적으로 하는 방법에 대한 관심도 같이 늘어나고 있다. 본 논문에서는 자동화 공장의 제품을 관리함에 있어 데이터를 쉽게 이해할 수 있도록 시각화하는 방법에 대한 연구를 수행했다. 이를 위해 D3 자바스크립트 라이브러리를 통해 웹기반으로 구현한 제품과 장애를 효과적으로 관리할 수 있는 시스템을 개발했다. 제안하는 관리 시스템은 자동화 공장의 제조 공정 중 제품이나 장애 상황에 대한 이해를 빠르게 하도록 하여 의사결정 하는데 기여할 것이다.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.6
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• pp.1123-1130
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• 2020

This study aims to classify the severity in car crashes using five classification learning models. The dataset used in this study contains 21,013 vehicle crashes, obtained from Korea Expressway Corporation, between the year of 2015-2017 and the LightGBM(Light Gradient Boosting Model) performed well with the highest accuracy. LightGBM, the number of involved vehicles, type of accident, incident location, incident lane type, types of accidents, types of vehicles involved in accidents were shown as priority factors. Based on the results of this model, the establishment of a management strategy for response of highway traffic accident should be presented through a consistent prediction process of accident severity level. This study identifies applicability of Machine Learning Models for Predicting of the Severity of Car Traffic Accidents on a Highway and suggests that various machine learning techniques based on big data that can be used in the future.

• Electronics and Telecommunications Trends
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• v.30 no.5
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• pp.39-48
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• 2015

최근 스마트카 기술은 안전운전지원 센서 기반의 단독형 시스템에서 도로 인프라와 빅 데이터를 기반으로 ICT와 연계하여 교통흐름을 예측하고 대응할 수 있는 협력형 자율주행 서비스 개발로 발전하고 있다. 협력형 자율주행 서비스를 실현하거나, IoT 환경과 스마트카의 융합을 위해서는 스마트카에서 커넥티비티 역량이 무엇보다도 중요하게 되었으며 이를 위한 요소기술로 차량용 이더넷, V2X(Vehicle to Everything) 네트워킹, 네트워크 보안기술을 꼽을 수 있다. 본고에서는 스마트카에 적용되는 차량 내부 네트워크 및 게이트웨이, V2X 네트워킹 및 보안에 관한 최근 표준 기술동향 및 이슈를 살펴보고자 한다.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.1-8
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• 2023

If there is a defect in the wheel bearing, which is a major part of the car, it can cause problems such as traffic accidents. In order to solve this problem, big data is collected and monitoring is conducted to provide early information on the presence or absence of wheel bearing failure and type of failure through predictive diagnosis and management technology. System development is needed. In this paper, to implement such an intelligent wheel hub bearing maintenance system, we develop an embedded system equipped with sensors for monitoring reliability and soundness and algorithms for predictive diagnosis. The algorithm used acquires vibration signals from acceleration sensors installed in wheel bearings and can predict and diagnose failures through big data technology through signal processing techniques, fault frequency analysis, and health characteristic parameter definition. The implemented algorithm applies a stable signal extraction algorithm that can minimize vibration frequency components and maximize vibration components occurring in wheel bearings. In noise removal using a filter, an artificial intelligence-based soundness extraction algorithm is applied, and FFT is applied. The fault frequency was analyzed and the fault was diagnosed by extracting fault characteristic factors. The performance target of this system was over 12,800 ODR, and the target was met through test results.

• Journal of the Economic Geographical Society of Korea
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• v.21 no.4
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• pp.335-349
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• 2018

This study analyzes the effect of the changes in traffic environments such as transportation speeds on the time-distance accessibility for the public transportation passengers. To do this, we use passenger transaction databases of the Seoul metropolitan public transportation system: one week for each of the three years (2011, 2013, and 2015). These big data contain the information about time and space on the traffic trajectories of every passenger. In this study, the time-distances of links between subway stations and bus stops of the public transportation system at each time are calculated based on the actual travel time extracted from the traffic-card transaction database. The changes in the time-distance accessibility of the integrated transportation network from the experimental results can be summarized in two aspects. First, the accessibility tends to decline as the year goes by. This is because the transportation network becomes more complicated and then the average moving speed of the vehicles is lowered. Second, the accessibility tends to increase on the weekend in the analysis of accessibility changes by day. This tendency is because the bus speeds on bus routes on the weekend are faster than other days. In order to analyze the accessibility changes, we illustrate graphs of the vehicle speeds and the numbers of passengers by year and day.

• Journal of the Korea institute for structural maintenance and inspection
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• v.28 no.3
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• pp.10-18
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• 2024

Vehicle loading is one of the main causes of bridge deterioration. Although WiM (Weigh in Motion) can be used to measure vehicle loading on a bridge, it has disadvantage of high installation and maintenance cost due to its contactness. In this study, a non-contact method is proposed to estimate the vehicle loading history of bridges using deep learning and CCTV images. The proposed method recognizes the vehicle type using an object detection deep learning model and estimates the vehicle loading based on the load-based vehicle type classification table developed using the weights of empty vehicles of major domestic vehicle models. Faster R-CNN, an object detection deep learning model, was trained using vehicle images classified by the classification table. The performance of the model is verified using images of CCTVs on actual bridges. Finally, the vehicle loading history of an actual bridge was obtained for a specific time by continuously estimating the vehicle loadings on the bridge using the proposed method.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.302-302
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• 2021

지하차도의 경우 국지 및 돌발홍수가 발생할 경우 대부분 침수됨에도 불구하고 2020년 7월 23일 부산 지역에 밤사이 시간당 80mm가 넘는 폭우가 발생하면서 순식간에 지하차도 천장까지 물이 차면서 선제적인 차량 통제가 우선적으로 수행되지 못하여 미처 대피하지 못한 3명의 운전자 인명사고가 발생하였다. 수재해를 비롯한 재난 관리를 빠르게 수행하기 위해서는 기존의 정부 및 관주도 중심의 단방향의 재난 대응에서 벗어나 정형 데이터와 비정형 데이터를 총칭하는 빅데이터의 통합적 수집 및 분석을 수행이 필요하다. 본 연구에서는 부산지역의 지하차도와 인접한 지하터널 CCTV 자료(센서)를 통한 재난 발생 시 인명피해를 최소화 정보 제공을 위한 Object Detection(객체 탐지)연구를 수행하였다. 지하터널 침수가 발생한 부산지역의 CCTV 영상을 사용하였으며, 영상편집에 사용되는 CCTV 자료의 음성자료를 제거하는 인코딩을 통하여 불러오는 영상파일 용량파일 감소 효과를 볼 수 있었다. 지하차도에 진입하는 물체를 탐지하는 방법으로 YOLO(You Only Look Once)를 사용하였으며, YOLO는 가장 빠른 객체 탐지 알고리즘 중 하나이며 최신 GPU에서 초당 170프레임의 속도로 실행될 수 있는 YOLOv3 방법을 적용하였으며, 분류작업에서 보다 높은 Classification을 가지는 Darknet-53을 적용하였다. YOLOv3 방법은 기존 객체탐지 모델 보다 좀 더 빠르고 정확한 물체 탐지가 가능하며 또한 모델의 크기를 변경하기만 하면 다시 학습시키지 않아도 속도와 정확도를 쉽게 변경가능한 장점이 있다. CCTV에서 오전(일반), 오후(침수발생) 시점을 나눈 후 Car, Bus, Truck, 사람을 분류하는 YOLO 알고리즘을 적용하여 지하터널 인근 Object Detection을 실제 수행 하였으며, CCTV자료를 이용하여 실제 물체 탐지의 정확도가 높은 것을 확인하였다.