• International Journal of Automotive Technology
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• 제8권5호
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• pp.583-591
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• 2007

This paper describes the development of the $H_{\infty}$ lateral control system for an autonomous ground vehicle operating a limited area using the RTK-DGPS(Real Time Kinematic-Differential Global Positioning System). Before engaging in autonomous driving, map data are acquired by the RTK-DGPS and used to construct a reference trajectory. The navigation system contains the map data and computes the reference yaw angle of the vehicle using two consecutive position values. The yaw angle of the vehicle is controlled by the $H_{\infty}$ controller. A prototype of the autonomous vehicle by the navigation method has been developed, and the performance of the vehicle has been evaluated by experiment. The experimental results show that the $H_{\infty}$ controller and the RTK-DGPS based navigation system can sufficiently track the map at low speed. We expect that this navigation system can be made more accurate by incorporating additional sensors.

• 한국자동차공학회논문집
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• 제15권1호
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• pp.16-22
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• 2007

Fuel efficiency is one of the major issues in regard to energy and environment. As customers desire more comfortable vehicles, increase of accessory traction force is necessary. Air conditioning system (ACS) consumes the biggest traction force among accessories, especially during summer. This means ACS is the primary object deteriorating fuel economy among accessories. Since direct measurement of traction force and fuel consumption in practical vehicle is difficult, comparison analysis is taken between vehicle with and without ACS working. For this comparison, real time measurements are carried out to know ACS traction force and fuel consumption. As a result of the comparison, a vehicle without ACS operation was 15.92% superior to a vehicle with ACS operating. It could be used as a fundamental material for improvement ACS for better fuel efficiency.

• Journal of information and communication convergence engineering
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• 제9권4호
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• pp.447-451
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• 2011

Recently, various in-vehicle networks have been developed respectively in order to accomplish their own purposes such as CAN and MOST. Especially, the MOST network is usually adapted to provide entertainment service. The car navigation system is also widely used for guiding driving paths to driver. The position for the navigation system is usually acquired by GPS technology. However, the GPS technique has two serious problems. The first is unavailability in urban canyons. The second is inherent positional error rate. The problems have been studied in many literatures. However, the second still leads to incorrect locational information in some area, especially parallel roads. This paper proposes a performance tuning method of image matching algorithm for the car navigation system. The method utilizes images obtained from in-vehicle MOST network and a real-time image matching algorithm which determines the direction of moving vehicle in parallel section of road. In order to accuracy improvement of image matching algorithm, three conditions are applied. The experimental tests show that the proposed system increases the accuracy.

• 한국정밀공학회지
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• 제21권5호
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• pp.99-105
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• 2004

Internet provides the useful method to monitor the current states of the machine tool no matter where a personnel monitors it. In this paper, a monitoring method of the torque of the machine tool's spindle induction motor using interne is suggested. To estimate the torque accurately, spindle driving system of an CNC lathe is divide into two parts, induction motor part and mechanical part attached to the induction motor spindle. Magnetizing current is calculated from the measured 3 phase currents without speed sensor used to estimate the torque generated by an induction motor. In mechanical part of the system, some of the torque is used to overcome friction and remaining torque is used to overcome cutting force. An equation to estimate friction torque is drawn as a function of cutting torque and rotation speed. Graphical programming is used to implement the suggested algorithm. to monitor the torque of an induction motor in real time and to make the estimated torque monitored on client computers. Torque of the spindle induction motor is well monitored on the client computers in about 3% error range under various cutting conditions.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2009년도 춘계학술대회
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• pp.127-130
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• 2009

본 연구 논문에서는 RTLS(Real Time Location System)를 기반으로 하는 Y/T(Yard Tractor) Multi-Cycle System (RYMS)의 핵심기능으로써 컨테이너터미널 양 적하작업 등 저효율적인 하역 분야 개선에 목적을 둔다. 이와 같은 목적을 달성하는데 있어서는 최적 경로의 탐색과 최단 이동거리를 찾아낼 수 있도록 최적 알고리즘을 적용, Y/T를 선택하고 확정하는 기법이 요구된다. 본 연구논문에서 제안된 방식의 실험결과를 살펴보면, 평균치대비 절감 운전거리율은 12%가 되었고 최대치대비 절감 운전거리율은 23%가 되었음을 확인할 수 있었다.

• 대한임베디드공학회논문지
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• 제17권1호
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• pp.41-49
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• 2022

This paper proposes a novel lane detection algorithm based on inverse perspective transformation and machine learning in lightweight embedded system. The inverse perspective transformation method is presented for obtaining a bird's-eye view of the scene from a perspective image to remove perspective effects. This method requires only the internal and external parameters of the camera without a homography matrix with 8 degrees of freedom (DoF) that maps the points in one image to the corresponding points in the other image. To improve the accuracy and speed of lane detection in complex road environments, machine learning algorithm that has passed the first classifier is used. Before using machine learning, we apply a meaningful first classifier to the lane detection to improve the detection speed. The first classifier is applied in the bird's-eye view image to determine lane regions. A lane region passed the first classifier is detected more accurately through machine learning. The system has been tested through the driving video of the vehicle in embedded system. The experimental results show that the proposed method works well in various road environments and meet the real-time requirements. As a result, its lane detection speed is about 3.85 times faster than edge-based lane detection, and its detection accuracy is better than edge-based lane detection.

• Journal of Information Processing Systems
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• 제18권2호
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• pp.258-267
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• 2022

Weak over-the-horizon perception and blind spot are the main problems in intelligent connected vehicles (ICVs). In this paper, a V2V image transmission-based road condition warning method is proposed to solve them. The encoded road emergency images which are collected by the ICV are transmitted to the on-board unit (OBU) through Ethernet. The OBU broadcasts the fragmented image information including location and clock of the vehicle to other OBUs. To satisfy the channel quality of the V2X communication in different times, the optimal fragment length is selected by the OBU to process the image information. Then, according to the position and clock information of the remote vehicles, OBU of the receiver selects valid messages to decode the image information which will help the receiver to extend the perceptual field. The experimental results show that our method has an average packet loss rate of 0.5%. The transmission delay is about 51.59 ms in low-speed driving scenarios, which can provide drivers with timely and reliable warnings of the road conditions.

• International Journal of Computer Science & Network Security
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• 제23권11호
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• pp.67-72
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• 2023

In the past decade, Autonomous Vehicle Systems (AVS) have advanced at an exponential rate, particularly due to improvements in artificial intelligence, which have had a significant impact on social as well as road safety and the future of transportation systems. The fusion of light detection and ranging (LiDAR) and camera data in real-time is known to be a crucial process in many applications, such as in autonomous driving, industrial automation and robotics. Especially in the case of autonomous vehicles, the efficient fusion of data from these two types of sensors is important to enabling the depth of objects as well as the classification of objects at short and long distances. This paper presents classification of objects using CNN based vision and Light Detection and Ranging (LIDAR) fusion in autonomous vehicles in the environment. This method is based on convolutional neural network (CNN) and image up sampling theory. By creating a point cloud of LIDAR data up sampling and converting into pixel-level depth information, depth information is connected with Red Green Blue data and fed into a deep CNN. The proposed method can obtain informative feature representation for object classification in autonomous vehicle environment using the integrated vision and LIDAR data. This method is adopted to guarantee both object classification accuracy and minimal loss. Experimental results show the effectiveness and efficiency of presented approach for objects classification.

• International journal of advanced smart convergence
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• 제12권4호
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• pp.182-189
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• 2023

A recently research, object detection and segmentation have emerged as crucial technologies widely utilized in various fields such as autonomous driving systems, surveillance and image editing. This paper proposes a program that utilizes the QT framework to perform real-time object detection and precise instance segmentation by integrating YOLO(You Only Look Once) and Mask R CNN. This system provides users with a diverse image editing environment, offering features such as selecting specific modes, drawing masks, inspecting detailed image information and employing various image processing techniques, including those based on deep learning. The program advantage the efficiency of YOLO to enable fast and accurate object detection, providing information about bounding boxes. Additionally, it performs precise segmentation using the functionalities of Mask R CNN, allowing users to accurately distinguish and edit objects within images. The QT interface ensures an intuitive and user-friendly environment for program control and enhancing accessibility. Through experiments and evaluations, our proposed system has been demonstrated to be effective in various scenarios. This program provides convenience and powerful image processing and editing capabilities to both beginners and experts, smoothly integrating computer vision technology. This paper contributes to the growth of the computer vision application field and showing the potential to integrate various image processing algorithms on a user-friendly platform

• Tribology and Lubricants
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• 제40권1호
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• pp.24-27
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• 2024

A monotype valve body for a dual clutch transmission has the potential to reduce costs, weight, and manufacturing time by modularizing various parts, including those of existing solenoid packs and valve bodies, into one through the application of super-precision die casting technology. However, this approach may lead to challenges such as reduced rigidity and increased interference due to modularization and compactness, impacting both product performance due to the reduced weight as well as durability and reliability. Unlike existing products, this approach requires a high-precision thin-wall block to avoid more complicated flow line formation, interference between flow lines, and leaks, as well as a strict quality requirement standard and precise inspections including detection of internal defects. To conduct precise inspections, we built an equivalent model corresponding to a driving distance of 300,000 km. Testing involved simulating actual road loads using a real vehicle and a chassis dynamometer in the FTP-75 mode (EPA Federal Test Procedure). The aim of the study was to establish a vehicle load-based part durability model for manufacturing a mono-type valve body and to develop fundamental technology for part weight reduction through preliminary design by introducing analytical weight reduction technology based on the derived results.