• 한국전기전자재료학회논문지
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• 제24권11호
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• pp.915-919
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• 2011

We analyzed various forces affective to the charged particles in closed space, to explain the image degradation and lifetime-shortening phenomena because of particle lumping which is one of the serious problems in reflective displays. It is possible to predict the quantity of q/m which is the most important parameter in determining the optical and electrical characteristics, by calculating the image force and kinetic energy. For stable driving, the quantity of q/m must be in the defined range but it changes during the fabrication process, so we added the filtering process to solve this problem and obtained the well-defined nonlinear driving voltage coinciding with the threshold voltage. And we obtained the fully-driving property which prevents the particle lumping and decides the image quality and lifetime of panel from the optical characteristics and occupation surface of moving particles.

• 자동차안전학회지
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• 제15권1호
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• pp.45-54
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• 2023

In the commercialization stage of level 4 or higher autonomous driving, the need for new legal system related to drive safely has increased in order to meet the improved level of technological development. Especially human drivers should not be legally accountable for road safety in the era of autonomous vehicles and thus safety standards for operation of autonomous vehicles are significant. To address this issue, the German Road Traffic Act was revised in 2021, adding provisions corresponding to the commercialization of self-driving vehicle of level 4 and in the similar context the Japanese Road Traffic Ac was amended in 2022. This Article draws implications for legislative discussions on driving-related responsibilities of driverless autonomous vehicle to ensure driving safety in Korea through recent amendments in Germany and Japan.

• 인터넷정보학회논문지
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• 제23권2호
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• pp.53-59
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• 2022

3단계 자율주행 차량 이후, 4, 5단계의 자율주행 기술은 차량의 완벽한 주행뿐만 아니라 탑승객의 상태를 최적으로 유지하기 위해 노력하고 있다. 그러나 현재 자율주행 기술은 LiDAR, 전방 카메라 등 시각적 정보에 과하게 의존하기 때문에 지정된 도로 이외의 도로에서 완벽하게 자율주행을 실행하기 힘들다. 따라서 본 논문은 차량이 시각 정보 외의 데이터를 사용하여 도로의 상태를 분류하고, 도로 상태와 주행 상태에 따라 최적의 제동 강도를 계산하는 BSCS (Braking Strength Calculation System)를 제안한다. 본 논문에서 제안하는 BSCS는 KNN 알고리즘을 기반으로 도로의 상태를 분류하는 RCDM (Road Condition Definition Module)과 RCDM의 결과와 현재 주행 상태를 통해 주행 중 최적의 제동 강도를 계산하는 BSCM (Braking Strength Calculation Module)로 구성된다. 본 논문의 실험 결과, KNN 알고리즘에 가장 적합한 K의 수를 찾을 수 있었고, 비지도 학습인 K-means 알고리즘보다 본 논문에서 제안한 RCDM이 더 정확한 것이 증명되었다. 해당 논문의 BSCS는 시각 정보뿐만 아니라 서스펜션에 가해지는 진동 데이터를 사용함으로써, 시각 정보가 제한되는 여러 환경에서 자율주행 차량의 제동을 더 원활하게 만들 수 있다.

• 스마트미디어저널
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• 제9권4호
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• pp.9-16
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• 2020

최근 이미지 분할(Image Segmentation)에 관련되어 스마트 공장 산업과 의료 분야 등에 접목하려는 연구가 다수 진행되고 있다. 특히 딥러닝 알고리즘을 사용한 이미지 분할 시스템들은 대용량의 데이터를 높은 정확도로 학습할 만큼 발전되었다. 자율주행 분야에서도 이미지 분할을 이용하기 위해선 대용량의 데이터들에 대한 충분한 학습량이 필요하며, 실시간으로 운전자의 데이터를 처리하는 스트리밍 환경은 고속도로, 어린이보호구역 등으로 안전운행에 대한 정확도가 중요하다. 따라서 본 논문에서는 다양한 도로환경에 적용할 수 있는 기존 FCN(Fully Convoulutional Network) 알고리즘을 강화한 DFCN 알고리즘을 제안하였으며, DFCN 알고리즘의 성능이 FCN 알고리즘과 비교하여 손실 값 측면에서 1.3% 개선하였음을 증명하였으며, 기존 U-Net 알고리즘에 DFCN 알고리즘을 적용하여 이미지 내의 주파수의 정보를 유지하여 더 좋은 결과치를 도출함으로써 결과적으로 자율주행 환경에서 DFCN 알고리즘이 FCN 알고리즘보다 성능이 향상되었다는 것을 증명하였다.

• 자동차안전학회지
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• 제8권4호
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• pp.31-37
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• 2016

Since real road experiments have many restrictions, a multi-vehicle traffic simulator can be an effective tool to develop and evaluate fully automated driving systems. This paper presents multi-vehicle environment simulation tool to develop and evaluate motorway automated driving systems. The proposed simulation tool consists of following two main parts: surrounding vehicle model and environment sensor model. The surrounding vehicle model is designed to quickly generate rational complex traffic situations of motorway. The environment sensor model depicts uncertainty of environment sensor. As a result, various traffic situations with uncertainty of environment sensor can be proposed by the multi-vehicle environment simulation tool. An application to automated driving system has been conducted. A lane changing algorithm is evaluated by performance indexes from the multi-vehicle environment simulation tool.

• 전자통신동향분석
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• 제36권1호
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• pp.22-31
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• 2021

In this thesis, we examine the development status of autonomous mobility services using various artificial intelligence algorithms and propose a solution by combining edge and cloud computing to overcome technical difficulties. A fully autonomous vehicle with enhanced safety and ethics can be implemented using the proposed solution. In addition, for the future of 2035, we present a new concept that enables two- and three-dimensional movement via cooperation between ecofriendly, low-noise, and modular fully autonomous vehicles. The zero-error autonomous driving system will safely and conveniently transport people, goods, and services without time and space constraints and contribute to the autonomous mobility services that are free from movement in connection with various mobility.

• 산업경영시스템학회지
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• 제23권57호
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• pp.51-62
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• 2000

In this study, the psycho-physiological response of drivers was investigated in terms of EEG(Electroencephalogram), especially with the fractal dimensions computed by Chaotic algorithm. The Chaotic algorithm Is well Known to sensitively analyze the non-linear information such as brain waves. An automobile with a fully equipped data acquisition system was used to collect the data. Ten healthy subjects participated in the experiment. EEG data were collected while subjects were driving the car between Won-ju and Shin-gal J.C. on Young-Dong highway The results were presented in terms of 3-Dimensional attractor to confirm the chaotic nature of the EEG data. The correlation dimension and fractal dimension were calculated to evaluate the complexity of the brain activity as the driving duration changes. In particular, the fractal dimension indicated a difference between the driving condition and non-driving condition while other spectral variables showed inconsistent results. Based upon the fractal dimension, drivers processed the most information at the beginning of the highway driving and the amount of brain activity gradually decreased and stabilized. No particular decrease of brain activity was observed even after 100 km driving. Considering the sensitivity and consistency of the analysis by Chaotic algorithm, the fractal dimension can be a useful parameter to evaluate the psycho-physiological responses of human brain at various driving conditions.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2004년도 춘계학술발표회
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• pp.11-19
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• 2004

When pile foundation constructed by driving method, it is desirable to perform monitoring and estimation of pile drivability and bearing capacity using some suitable tools. Dynamic Pile Monitoring yields information regarding the hammer, driving system, and pile and soil behaviour that can be used to confirm the assumptions of wave equation analysis. Dynamic Pile Monitoring is performed with the Pile Driving Analyser. The Pile Driving Analyser (PDA) uses wave propagation theory to compute numerous variables that fully describe the condition of the hammer-pile-soil system in real time, following each hammer impact. This approach allows immediate field verification of hammer performance, driving efficiency, and an estimate of pile capacity. The PDA has been used widely as a most effective control method of pile installations. A set of PDA test was performed at the site of Donghea-1 Gas Platform Jacket which is located east of Ulsan. The drilling core sediments of location of jacket subsoil are composed of mud and sand, silt. In this case study, the results of PDA test which was applied to measurement and estimation of large diameter open ended steel pipe pile driven by underwater hydraulic hammer, MHU-800S, at the marine sediments were summarized.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권9호
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• pp.4518-4540
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• 2019

With the development of IT convergence technology, autonomous driving has gradually developed; however, the vehicle is still operated by the driver, who should always be in good health - but sometimes, this is not the case. It is especially dangerous to drive when drowsy, and unable to fully concentrate on driving, such as when taking certain medicines, or through fatigue. Drowsy driving is at least eight times more dangerous than normal driving, and as dangerous as drunk driving. Previous research has looked at technology to detect drowsiness, in order to wake up drivers when necessary, or to safely stop the vehicle. Furthermore, many studies have been conducted to find out when drowsiness occurs. However, it is more desirable for the driver to take sufficient rest during a break, in order to be able to continue to focus and drive. In other words, it is important to maintain a normal state before drowsiness. In this study, we introduce a sound source to increase driver concentration and prevent drowsiness, another that can improve the quality of sleep, and a system that produces these sound sources. The proposed system has a noise reduction effect of about 15 dB. We have confirmed that the proposed sound induces an EEG of the desired form.

• 한국정보디스플레이학회:학술대회논문집
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• 한국정보디스플레이학회 2002년도 International Meeting on Information Display
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• pp.211-214
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• 2002

We present gray scale implementation method based on QMA driving technique. We clarified the mechanism of wall charge quantization through discharge current measurement. We used three wall charge states to implement gray scale. The cells would be one of fully-ON, half-On, and OFF states. We built a five sub-fields 243 level gray scale with sustain pulse count of 2, 6, 18, 54, and 162.