• Journal of Korean Society for Geospatial Information Science
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• v.2 no.2 s.4
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• pp.81-88
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• 1994

The CNS(Car Navigation System) is used more generally in driver aid system than ALV(Auto nomous Land Vehicle) research area. In this paper we developed a new position tracking algorithm for the Global Path Planning in the CNS. In japan, CNS is already well developed and, thesedays they sell CNS products about $400{\sim}500$ thousands per year, and USA and European Communications(EC), too. In Korea, studies of the first generation CNS, which finds current location of a navigating vehicle and displays its location in a Digital-Map with real-time are progressing but still in the beginning step. Therefore a new position tracking algorithm is presented, which reduces vehicle position error dramatically by fusing GPS and dead-reckoning sensors. And the validity of our algorithm is demonstrated by the experimental results with the real car.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.8
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• pp.327-342
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• 2017

In this paper, we introduce an integrated processing system of smart sensor data for IoT service which collects sensor data and efficiently processes it. Based on the technology of collecting sensor data to the development of the IoT field and sending it to the network · Based on the receiving technology, as various projects such as smart homes, autonomous running vehicles progress, the sensor data is processed and effectively An autonomous control system to utilize has been a problem. However, since the data type of the sensor for monitoring the autonomous control system varies according to the domain, a sensor data integration processing system applying the autonomous control system to various different domains is necessary. Therefore, in this paper, we introduce the Smart Sensor Data Integrated Processing System, apply it and use the window as a reference to process internal and external sensor data 1) receiveData, 2) parseData, 3) addToDatabase 3 With the process of the stage, we provide and implement the automatic window opening / closing system "Smart Window" which ventilates to create a comfortable indoor environment by autonomous control system. As a result, standby information is collected and monitored, and machine learning for performing statistical analysis and better autonomous control based on the stored data is made possible.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.3
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• pp.687-692
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• 2018

In this paper, the development of an automotive thermal imaging camera providing image information for ADAS (Advanced Driver Assist System) and autonomous vehicles is described and an improved technique to enhance the details of the image is proposed. Thermal imaging cameras are used in various fields, such as the medical, industrial and military fields, for the purpose of temperature measurement and night vision. In automobiles, they are utilized for night vision systems. For their utilization in ADAS and autonomous vehicles, appropriate image resolution and enhanced detail are required for object recognition. In this study, a $640{\times}480$ resolution thermal imaging camera that can be applied to automobiles is developed and the BDE (Block-Range Detail Enhancement) technique is applied to improve the details of the image. In order to improve the image detail obtained in various driving environments, the block-range values between the target pixel and the surrounding 8 pixels are calculated and classified into 5 levels. Then, different factors are added or subtracted to obtain images with high utilization. The improved technique distinguishes the dark part of the image by the resulting temperature difference of 130mK and shows an improvement in the fine detail in both the bright and dark parts of the image. The developed thermal imaging camera using the improved detail enhancement technique is applied to a test vehicle and the results are presented.

• Smart Media Journal
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• v.11 no.4
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• pp.9-18
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• 2022

Recently, research and system using AI is rapidly increasing in various fields. Smart farm using artificial intelligence and information communication technology is also being studied in agriculture. In addition, data-based precision agriculture is being commercialized by convergence various advanced technology such as autonomous driving, satellites, and big data. In Korea, the number of commercialization cases of facility agriculture among smart agriculture is increasing. However, research and investment are being biased in the field of facility agriculture. The gap between research and investment in facility agriculture and open-air agriculture continues to increase. The fields of fruit trees and plant factories have low research and investment. There is a problem that the big data collection and utilization system is insufficient. In this paper, we are proposed the system for determining the fruit tree growth stage using a deep learning-based object detection model. The system was proposed as a hybrid app for use in agricultural sites. In addition, we are implemented an object detection function for the fruit tree growth stage determine.

• The Journal of the Convergence on Culture Technology
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• v.7 no.4
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• pp.705-710
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• 2021

Nowadays, cutting-edge information communication technology is the genuine core technology of the fourth Industrial Revolution and is still making great progress rapidly among various technology fields. The biggest issue in ICT fields is the machine learning based Artificial Intelligence applications using big data in cloud computing environment on the basis of wireless network, and also the technology fields of autonomous control applications such as Autonomous Car or Mobile Robot. Since value of the high-tech ICT technology depends on the surrounded environmental factors and is very flexible, the precise technology valuation method is urgently needed in order to get successful technology transfer, transaction and commercialization. In this research, we analyze the characteristics of the high-tech ICT technology and the main factors in technology transfer or commercialization process, and propose the precise technology valuation method that reflects the characteristics of the ICT technology through phased analysis of the existing technology valuationmodel.

• Journal of Korea Multimedia Society
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• v.23 no.9
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• pp.1219-1228
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• 2020

In this study, the factors affecting the acceptance intention for level 4-5 of autonomous vehicles were investigated by applying TAM(Technology Acceptance Model). To this end, 332 ordinary persons interested in autonomous vehicle and experienced in driving car were analyzed by using SEM(Structural Equation Modeling). The results showed that self-efficacy and personal innovation had a positive effect on perceived usefulness. On the other hand personal innovation has been shown to have a negative effect on perceived usefulness. Perceived ease of use has a positive effect on perceived usefulness, perceived ease of use and perceived usefulness has a positive effect on acceptance intention. Safety and Privacy has been shown to have a positive effect on trust, trust has a positive effect on acceptance intention. Lastly, autonomous vehicles have a higher impact on their 20s and 30s. The result of this study is expected to be a very useful basic research for the development of target autonomous vehicles, the selection of targets, the direction of corporate marketing strategies, and the preparation of government policies.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.45 no.6
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• pp.55-59
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• 2008

For effective mobility control of UGV(unmanned ground vehicle), the terrain cover classification is an important component as well as terrain geometry recognition and obstacle detection. The vision based terrain cover classification algorithm consists of pre-processing, feature extraction, classification and post-processing. In this paper, we present a method to classify terrain covers based on the color and texture information. The color space conversion is performed for the pre-processing, the wavelet transform is applied for feature extraction, and the SVM(support vector machine) is applied for the classifier. Experimental results show that the proposed algorithm has a promising classification performance.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2016.06a
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• pp.200-203
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• 2016

최근 자율주행자동차에 대한 관심이 증가함에 따라 교통 상황을 인식하는 방법에 대한 연구도 활발하게 진행되고 있다. 특히 교통신호등의 인식은 치명적인 결과를 야기하는 교통사고와 밀접하게 연관된다는 점에서 중요성이 더욱 부각되고 있다. 본 논문에서는 컴퓨터 비전 시스템을 기반으로 한 교통신호등 인식 방법을 제안한다. 차선, 표지판 등과는 다르게 교통신호등은 빛을 발하는 특징이 있으며 그 모양과 형태 또한 규격화 되어 있다. 이러한 특징 중 색상과 형태 특징을 이용하여 두 단계의 추출과정을 거쳐 교통신호등을 인식한다. 먼저 HSV 색 공간에서 적색, 녹색, 주황색의 빛을 발하는 영역을 찾아낸 뒤, 신호의 원형 특징을 이용해 가로, 세로 사이즈와 크기로 신호의 후보를 추출한다. 다음, 신호등의 검은 박스 영역을 찾기 위해 추출한 신호 후보군의 주변부가 검정색인지를 확인한다. 최종적으로 신호등의 박스 부분을 검출하여 신호를 발하는 위치를 기반으로 신호를 인식한다. 실험결과 많은 계산량을 요구하는 기계학습을 사용하지 않고도 실시간 처리와 높은 인식률로 교통 신호를 인식할 수 있음을 확인하였다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.01a
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• pp.373-376
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• 2019

최근 AI 기술이 학술적 연구단계를 넘어 산업계에서 활용이 확산됨에 따라, 실시간 시스템 분야에서도 AI를 접목하려는 움직임이 증가하고 있다. 실시간 시스템은 시간 제약이 있는 시스템으로써, 자율주행 시스템과 같은 안전 최우선 시스템에서는 시간 제약 위반으로 인한 예외 상황 발생 시, 심각한 피해로 이어질 수 있기 때문에 예외 상황 처리를 위한 신뢰성 있는 시스템 설계가 필요하다. 본 논문에서는 실시간 임베디드 AI를 위한 스트림 데이터 처리 언어인 Splash에 신뢰성 향상을 위한 예외 처리 메커니즘을 구현하였다. 구체적으로 예외 감지와 처리 부분으로 나눠서, 예외 상황을 감지하는 시점 및 방법을 정의하고 예외 발생 시 이를 처리하는 과정을 구현하였다. 예외 처리 메커니즘은 실시간 발행/구독 기반의 통신 미들웨어인 DDS 상에 라이브러리 형태로 구현되었으며, 시계열 센서 데이터를 사용하여 확인한 결과, 응용 개발자가 명시한 제약 사항 위반 시 발생하는 예외를 성공적으로 감지하고 예외 처리 동작을 수행하는 것을 확인하였다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.6
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• pp.1099-1110
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• 2023

The recognition system of autonomous driving and robot navigation performs vision work such as object recognition, tracking, and lane detection after multi-sensor fusion to improve performance. Currently, research on a deep learning model based on the fusion of a camera and a lidar sensor is being actively conducted. However, deep learning models are vulnerable to adversarial attacks through modulation of input data. Attacks on the existing multi-sensor-based autonomous driving recognition system are focused on inducing obstacle detection by lowering the confidence score of the object recognition model.However, there is a limitation that an attack is possible only in the target model. In the case of attacks on the sensor fusion stage, errors in vision work after fusion can be cascaded, and this risk needs to be considered. In addition, an attack on LIDAR's point cloud data, which is difficult to judge visually, makes it difficult to determine whether it is an attack. In this study, image scaling-based camera-lidar We propose an attack method that reduces the accuracy of LCCNet, a fusion model (camera-LiDAR calibration model). The proposed method is to perform a scaling attack on the point of the input lidar. As a result of conducting an attack performance experiment by size with a scaling algorithm, an average of more than 77% of fusion errors were caused.