• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.164-164
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• 2000

This paper proposes a moving window technique that extracts lanes and vehicles using the images captured by a CCD camera equipped inside an automobile in real time. For the purpose, first of all the optimal size of moving window is determined based upon speed of the vehicle, road curvature, and camera parameters. Within the moving windows that are dynamically changing, lanes and vehicles are extracted, and the vehicles within the driving lanes are classified as obstacles. Assuming highway driving, there are two sorts of image-objects within the driving lanes: one is ground mark to show the limit speed or some information for driving, and the other is the vehicle as an obstacle. Using characteristics of three-dimension objects, a neural network can be trained to distinguish the vehicle from ground mark. When it is recognized as an obstacle, the distance from the camera to the front vehicle can be calculated with the aids of database that keeps the models of automobiles on the highway. The correctness of this measurement is verified through the experiments comparing with the radar and laser sensor data.

• 한국자동차공학회논문집
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• 제18권4호
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• pp.115-120
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• 2010

The reduction rate of fuel consumption by showing the fuel injection data for driver was measured in this study. The fuel injection data are composed of injection period, real time fuel economy and average fuel economy. The fuel consumption was measured by processing the voltage signal of injector and driven distance by GPS sensor. The fuel consumption was reduced by driving more carefully, i.e driving more steady without sudden acceleration and deceleration watching these fuel injection data. The reduction rate was up to 37% and the rate increased as the driver is customed to this driving pattern.

• 전기학회논문지
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• 제61권12호
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• pp.1843-1847
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• 2012

Piezo devices have large power density and simple structure. They can generate larger force than the conventional actuators. It has also wide bandwidth with fast response in a compact size. Thus the piezo devices are expected to be used widely in the future for small actuators with fast response time and large actuating force. However, the piezo actuators need high voltage with high driving current due to their large capacitive property. In this paper, we propose a simple method to drive piezo devices using voltage inversion circuit with coil inductance. Experiments with real circuit demonstrates that the proposed scheme can improve the energy efficiency very much.

• 동력기계공학회지
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• 제10권4호
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• pp.11-16
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• 2006

This research is to verify the cooling effect of the acting surface on the rotary motor using heat pipe and conventional cooling fan. In order to show the cooling performance of the rotary motor and heat pipe with the fin-typed heat sink, the surface temperature of the motor and condenser was measured in real time. The experiments were also conducted as for not only cooling device installed with heat pipe only, but with heat pipe and conventional cooling fan simultaneously. The present experiment reveals that the cooling combination of the heat pipe and cooling fan is far superior to the conventional cooling device for the driving motor such as the fin-typed heat sink. When the driving voltage of 20V and 14V were supplied to the driving motor, the cooling performance of the rotary motor with heat pipe was 170% and 500%, respectively better than that without heat pipe on steady state condition.

• 한국기후변화학회지
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• 제6권2호
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• pp.143-158
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• 2015

This study developed the emission intensity estimation method of GHGs by considering the characteristics of the models and time series. The telematics device was installed on the vehicle (OBD-II) to collect information on the operation conditions from each sample vehicle of public authorities. As a result of comparing the mileage distance and fuel consumption, the matching degree is analyzed very high, showed a ${\pm}1{\sim}4%$ error for each vehicle. By comparing driving record diary of vehicles managed by public authorities, this study presents the method that can be used to verify driving information in order to derive the GHGs emission intensity.

• International Journal of Internet, Broadcasting and Communication
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• 제13권1호
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• pp.210-218
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• 2021

CNN (Convolutional Neural Network), a type of deep learning algorithm, is a type of artificial neural network used to analyze visual images. In deep learning, it is classified as a deep neural network and is most commonly used for visual image analysis. Accordingly, an AI autonomous driving model was constructed through real-time image processing, and a crosswalk image of a road was used as an obstacle. In this paper, we proposed a low-cost model that can actually implement autonomous driving based on the CNN model. The most well-known deep neural network technique for autonomous driving is investigated and an end-to-end model is applied. In particular, it was shown that training and self-driving on a simulated road is possible through a practical approach to realizing lane detection and keeping.

• 드라이브 ㆍ 컨트롤
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• 제21권1호
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• pp.53-58
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• 2024

With the recent development of intelligence and automation technologies for construction machinery, the demand for safety and efficiency of road-cutting operations has continued to increase. In response to this, a double-blade road cutter has been developed that can automatically cut roads. However, a double-blade road cutter has a load difference between the two blades due to the ground and wear conditions of the cutting blades. The difference in load between the two blades distorts the direction of travel of the cutter. In this study, a vision sensor-based driving guide technology was developed to correct the driving path of road cutters. In addition, we developed a load-sensing technology that detects blade loads in real-time and controls driving speed in the event of overload.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 B
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• pp.546-549
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• 1999

For the safty driving of an automobile which is become individual requisites, a new Neural Network algorithm which recognized the load vehicles in real time is proposed. The proposed neural network technique is the real time computation method through the inter-node diffusion. In the network, a node corresponds to a state in the quantized input space. Each node is composed of a processing unit and fixed weights from its neighbor nodes as well as its input terminal. The most reliable algorithm derived for real time recognition of vehicles, is a dynamic programming based algorithm based on sequence matching techniques that would process the data as it arrives and could therefore provide continuously updated neighbor information estimates. Through several simulation experiments, real time reconstruction of the nonlinear image information is processed 1-D LIPN hardware has been composed and various experiments with static and dynamic signals have been implmented.

• Journal of Positioning, Navigation, and Timing
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• 제7권1호
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• pp.43-52
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• 2018

In recent years, the need of high accuracy navigation for vehicles has increased due to the development of autonomous driving vehicles and increase in land transportation convenience. This study is performed for vehicle users to achieve a performance of centimeter-level positioning accuracy by utilizing Compact Network Real-time Kinematic (RTK) that is applicable as a national-level infrastructure. To this end, medium-baseline RTK was implemented in real time to estimate accurate integer ambiguities between reference stations for reliable generation of Network RTK correction using the linear combination of carrier-phase observations and L1/L2 pseudo-range measurements. The residual tropospheric error was estimated in real time to improve the accuracy of double-differenced integer ambiguity resolution between network configuration reference stations that have at least 30 km or longer baseline distance. In addition, C++ based software was developed to enable real-time generation and broadcasting of Compact Network RTK correction information by utilizing an accurately estimated double-differenced integer ambiguity values. As a result, the horizontal and vertical 95% accuracy was 2.5cm and 5.2cm, respectively, without performance degradation due to user's position change within the network.

• 지적과 국토정보
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• 제53권2호
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• pp.53-66
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• 2023

최근 자율주행 분야에서 실세계와 유사한 디지털트윈 기반의 가상 시뮬레이션 환경 구축에 대한 관심이 크게 증가하고 있다. 디지털트윈 기반의 시뮬레이션 환경에서 자율주행의 성능 및 기능 검증뿐만 아니라, 딥러닝을 위한 가상 학습데이터 생성 연구도 활발히 수행되고 있다. 그러나, 이러한 디지털트윈 기반 자율주행 시뮬레이션 시스템은 고정밀 데이터 구축과 시스템 개발에 많은 시간과 비용을 필요로 하는 문제를 가지고 있다. 이에 본 연구에서는 기 구축된 3차원 입체모형과 정밀도로지도만을 이용하여 디지털트윈 기반의 실시간 자율주행 시뮬레이션 시스템을 신속히 설계하고 구현하기 위한 방안을 제시하고자 한다. 구체적으로 부산 EDC 지역에 대한 FBX의 3D 입체모형과 NGII HD Map을 CARLA에 통합하는 방법과 CARLA 기능을 추가 및 수정하는 방법을 제시한다. 본 연구 결과는 기존의 3D 입체모형과 NGII HD 맵을 활용하면 저렴한 비용으로 신속한 시뮬레이션 시스템의 설계 및 구현이 가능함을 보여준다. 또한, 시뮬레이션 시나리오 구성, 사용자 맞춤형 주행, 실시간 신호등 상태 시뮬레이션 등의 다양한 기능도 지원할 수 있다. 향후 광범위한 지역에 대하여 시스템이 적용되는 경우에 시스템의 활용도가 크게 향상될 것으로 기대된다.