• Journal of Digital Convergence
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• v.19 no.5
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• pp.31-39
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• 2021

This study collected 1,870 English news articles related to research and development of autonomous vehicles in order to identify various issues emerging in the research and development process of autonomous vehicles at home and abroad, and conducted topic modeling after data pre-processing. As a result of topic modeling, we extracted 20 topics, and we performed naming operations for topics and interpreted their meanings. A logical model for autonomous vehicle research and development projects was presented in response to the R&D process of input, activity, output, and outcome of derived topics. The analysis results of this study will be used as basic data to accurately determine the progress of domestic and foreign self-driving car research and development projects and prepare for the rapidly changing technology development.

• Journal of Drive and Control
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• v.16 no.3
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• pp.67-74
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• 2019

The automotive market has recently been investing much time and costs in improving existing technologies such as ABS (Anti-lock Braking System) and TCS (Traction Control System) and developing new technologies. Additionally, various methods have been applied and developed to reduce this. Among them, the development method using the simulation has been mainly used and developed. In this paper, we have studied a method to develop SILS (Software In the Loop Simulation) for TCS which can test various environment variables under the same conditions. We modeled hardware (vehicle engine and ABS module) and software (control logic) of TCS using MATLAB/Simulink and Carsim. Simulation was performed on the climate, road surface, driving course, etc. to verify the TCS logic. By using SILS to develop TCS control logic and controller, it is possible to verify before production and reduce the development period, manpower and investment costs.

• Journal of the Semiconductor & Display Technology
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• v.17 no.2
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• pp.76-80
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• 2018

In recent years, with the development of automobile technology, smart system technology that assists safe driving has been developed. A camera is installed on the front and rear of the vehicle as well as on the left and right sides to detect and warn of collision risks and hazards. Beyond the technology of simple black-box recording via cameras, we are developing intelligent systems that combine various computer vision technologies. However, most related studies have been developed to optimize performance in laboratory-like environments that do not take environmental factors such as weather into account. In this paper, we propose a method to detect object by restoring visibility in image with degraded image due to weather factors such as fog. First, the image quality degradation such as fog is detected in a single image, and the image quality is improved by restoring using an intermediate value filter. Then, we used an adaptive feature extraction method that removes unnecessary elements such as noise from the improved image and uses it to recognize objects with only the necessary features. In the proposed method, it is shown that more feature points are extracted than the feature points of the region of interest in the improved image.

• Journal of Positioning, Navigation, and Timing
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• v.7 no.4
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• pp.235-243
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• 2018

Conventional Real Time Kinematics (RTK) collect measurements in stationary state for several minutes to resolve the integer ambiguity in the carrier phase measurement or resolve the integer ambiguity on the move assuming low maneuvering movement. In this paper, an On The Move-RTK (OTM-RTK) technique that resolves the integer ambiguity on the move for fast and precise positioning of ground vehicles such as high maneuvering vehicles was proposed. The OTM-RTK estimates the precise amount of movement between epochs using the carrier phase measurements acquired on the move, and by using this, resolves the integer ambiguity within a short period of time by evaluating the integer ambiguity candidates for each epoch. This study analyzed the integer ambiguity resolution performance using field driving experiment data in order to verify the performance of the proposed method. The results of the experiment showed that the precise trajectory including the initial position bias can be obtained prior to resolving the integer ambiguity, and after resolving the integer ambiguity on the move, it was possible to obtain the bias-corrected precise position solution. It was confirmed that the integer ambiguity can be resolved by collecting measurements of about 10 epochs from the moving vehicle using a dual frequency receiver.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.380-383
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• 2014

Hub BLDC (Brushless Direct Current) motor is a multi-pole outer rotor-type high-efficiency electric motors and the Direct Drive Motor having permanent magnet rotor to drive shaft of the wheel, also called wheel-in motor. In this study, we design a speed controller with vector control technique using the dsPIC30f2010 16 bit micro-controller to drive Hub BLDC motor. Especially, we propose vector control method which reduce complex operation time, and design directly MOSFET inverter directly which gain high economics.

• Journal of the Korean Society of Industry Convergence
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• v.23 no.6_2
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• pp.979-987
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• 2020

This study used the Deep Learning models used in previous studies, we selected the basic model. The selected model was selected as ZFNet among ZFNet, Googlenet and ResNet, and the object was detected using a ZFNet based FRCNN. In order to reduce the detection error rate of FRCNN, location of four types of objects detected inside the image was designed by SVM classifier and location-based filtering was applied. As simulation results, it showed similar performance to the lane marking classification method with conventional 경계 detection, with an average accuracy of about 88.8%. In addition, studies using the Linear-parabolic Model showed a processing speed of 165.65ms with a minimum resolution of 600 × 800, but in this study, the resolution was treated at about 33ms with an input resolution image of 1280 × 960, so it was possible to classify lane marking at a faster rate than the previous study by CNN-based End to End method.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.3
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• pp.83-91
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• 2021

A crawler crane, which consists of a lattice boom, a driving system, and a movable vehicle, is widely used on construction sites. The crawler crane often traverses rough terrain at these sites; as a result, an overload limiter needs to be installed on the crane to prevent it from overturning and breaking. In this paper, we studied the distributed load change in relation to boom length and the angle of rotation of the roller that comes in direct contact with the grounded track shoe. First, we developed a 3D model of a crawler crane and meshed it for finite elements. Then, we performed finite element analysis to derive the load on the roller. Finally, we graphed and examined the roller distributed load data of the case according to boom length and rotation angle. By detecting the load on the roller of the crawler crane, we can predict the potential for the crane to overturn before it happens.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.21 no.9
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• pp.92-97
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• 2022

In this paper, as a research on autonomous steering for agriculture, a sensor module for furrow recognition was developed through a low-cost distance sensor combination. The developed sensor module was applied to the vehicle, and when driving in a furrow curve, the autonomous steering success rate was 100% at a curvature of 20 m or more, and 70% at a curvature of 15 m or less. The self-steering success rate according to the ground condition showed a 100% success rate regardless of soil, weeds, or mulching film.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.1
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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 Korean Society for Quality Management
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• v.50 no.3
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• pp.533-543
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• 2022

Purpose: The purpose of this study is to identify and resolve the causes of defects in the unmanned aerial vehicle launch system(propulsion wire fracture, rear rail deformation) and to prevent recurrence. Methods: The causes of the two defects were derived through fault tree analysis for each of the two defects and fault reproduction tests. In the case of propulsion wire, the installation of a high speed camera to check the behavior of wire was the driving force behind the defect resolution. Results: The results of this study are as follows; It was determined that the thickness of the washer was less than the maximum tolerance of the pulley was the cause of the propulsion wire fracture defect. Failure to comply with the launch procedure and insufficient safety margin were judged as the cause of the rear rail deformation defect. Accordingly, the configuration was changed to remove each defect. Conclusion: The case of this study was conducted to eliminate defects in the launch system for UAV. The causes of defects were estimated through fault tree analysis. After the configuration change, Structural analysis and launch tests were performed to demonstrate the safety and effectiveness of the modified configuration. As a result, the effect of the modified configuration was verified.