• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2006.11a
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• pp.642-647
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• 2006

Recently Ministry of National Defense is reconstructing old facilities using modular construction method for enhancing soldiers' quality of living. In the future, the use of modular construction method is expected to be increased. But in beginning stage as ROK military facilities that was built by modular was not designed and constructed efficiently, problems for pilot projects need to be analyzed by phases and construction methods should be improved. For the maximized efficiency, design should be made to minimize on-site works, factory automation in an assembly plant should be installed for the better productivity, roads that will be used for transportation should be checked, and joint and connection methods between modules in on-site assembly should be improved for a better quality.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.5
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• pp.602-608
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• 2011

In this study, a method of designing a neurointerface using neural network (NN) is proposed for controlling nonholonomic mobile robots. According to the concept of virtual master-slave robots, in particular, a partially stable inverse dynamic model of the master robot is acquired online through the NN by applying a feedback-error learning method, in which the feedback controller is assumed to be based on a PD compensator for such a nonholonomic robot. The NN for the online feedback-error learning can composed that the input layer consists of six units for the inputs $x_i$, i=1~6, the hidden layer consists of two hidden units for hidden outputs $o_j$, j=1~2, and the output layer consists of two units for the outputs ${\tau}_k$, k=1~2. A tracking control problem is demonstrated by some simulations for a nonholonomic mobile robot with two-independent driving wheels. The initial q value was set to [0, 5, ${\pi}$].

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.105-108
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• 2010

The vehicle navigation system is a representative driver supporting system that available to present searching and guiding path functions, have been increased for usability. Under competition situation because of following the spreaded navigation market, to meet customer's needs about new given services, there are need differentiated services increasing dramatically. Also now, dash board indicates various vehicle's status and driver can aware of that. However it is not easy to know where is abnormal essentially and there are no devices to give warning to driver. Therefore, It is difficult to preserve accidents because it can't deal with various abnormal functions immediately on driving. In this paper, we proposed vehicle diagnosis program within navigation that is available to manage and to make a diagnosis of vehicle. And this program conform OBD-II standard, so it can transmit diagnosis data from ECU to navigation system using Bluetooth wireless communication protocol. Thus this program give enhanced services to customer as well as multimedia and geometry information services.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.5
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• pp.1095-1100
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• 2013

Carbon dioxide is a major cause for which accelerates Global Warming. Therefore several countries are working on the project recommended to use a bicycle instead of the car when you move to the nearby destination in an effort to reduce the emissions of carbon dioxide. In this paper, It was developed to measure the greenhouse gas reduction using Bluetooth Sensor Node by riding a bicycle instead of a car and management system in order to authenticate the riding record. The developed application provides various information such as individual bicycle mileage, greenhouse gas reductions, bicycle riding path, the number of planted ginkgo trees. This proposed system is expected to be helpful to green house gas emission reduction because the usage rate of bicycle will increase if government combine ways to offer people rewards such as pin money or tax breaks for people who take advantage of the bicycle with the project.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.5
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• pp.45-58
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• 2021

It is generally known that a two-lane roundabout has some problems in safety such as increasing conflicts, typically merging and diverging conflicts and conflicts between entering traffic and exiting as well as turning traffic. To solve these problems, a turbo-roundabout had been developed and has successfully brought safer and more efficient operation in other countries. In this study, micro simulations using VISSIM were conducted to investigate the maximum value of service traffic volume. It was found that operation of turbo-roundabouts was influenced by traffic volume for each turning traffic, and the maximum values of traffic volume were values between 2,400 and 2,800 vehicles per hour as rates of traffic volume for each turning traffic. Typically, turbo-roundabouts have limited to operate in conditions with more than 30% for left-turning traffic volume.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1163-1175
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• 2021

In autonomous driving, road markings are an essential element for object tracking, path planning and they are able to provide important information for localization. This paper presents an approach to update and measure road surface markers with HD maps as well as matching using inverse perspective mapping. The IPM removes perspective effects from the vehicle's front camera image and remaps them to the 2D domain to create a bird-view region to fit with HD map regions. In addition, letters and arrows such as stop lines, crosswalks, dotted lines, and straight lines are recognized and compared to objects on the HD map to determine whether they are updated. The localization of a newly installed object can be obtained by referring to the measurement value of the surrounding object on the HD map. Therefore, we are able to obtain high accuracy update results with very low computational costs and low-cost cameras and GNSS/INS sensors alone.

• Journal of agriculture & life science
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• v.52 no.6
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• pp.127-138
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• 2018

In this study, the operational characteristics of a domestic vegetable transplanter were investigated. The main functional components and power path of the tranplanter were analyzed. The link structure of transplanting device waskinematically analyzed, and 3D modeling and dynamic simulation were performed. Based on this analysis, the trajectory of the bottom end of the transplanting hopper was analyzed. Also, the plant spacing according to the engine speed and the shifting stage of transplanting transmission was analyzed and verified by field test. As main results of this study, the transplanting device is one degree of freedom(DOF) 4-bar link type mechanism which comprises 10 links and 13 rotating joints. The transplanting hopper plants seedlings in a vertical direction while maintaining a constant posture by the links of transplanting device. The power is transmitted to both the driving part and transplanting part from the engine, and the maximum and minimum plant spacing of the transplanting device were 428.97 mm and 261.20 mm.

• Journal of Broadcast Engineering
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• v.26 no.6
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• pp.738-747
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• 2021

Today, with the improvement of deep learning technology, computer vision areas such as image classification, object detection, object segmentation, and object tracking have shown remarkable improvements. Various applications such as intelligent surveillance, robots, Internet of Things, and autonomous vehicles in combination with deep learning technology are being applied to actual industries. Accordingly, the requirement of an efficient compression method for video data is necessary for machine consumption as well as for human consumption. In this paper, we propose an object-based compression of thermal infrared images for machine vision. The input image is divided into object and background parts based on the object detection results to achieve efficient image compression and high neural network performance. The separated images are encoded in different compression ratios. The experimental result shows that the proposed method has superior compression efficiency with a maximum BD-rate value of -19.83% to the whole image compression done with VVC.

• The Journal of Korea Robotics Society
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• v.17 no.2
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• pp.142-151
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• 2022

With the advent of the 4th industrial revolution, autonomous driving technology is being commercialized in various industries. However, research on autonomous driving so far has focused on platforms with wheel-type platform. Research on a tracked platform is at a relatively inadequate step. Since the tracked platform has a different driving and steering method from the wheel-type platform, the existing research cannot be applied as it is. Therefore, a path-tracking algorithm suitable for a tracked platform is required. In this paper, we studied a path-tracking algorithm for a tracked platform based on a GPS sensor. The existing Pure Pursuit algorithm was applied in consideration of the characteristics of the tracked platform. And to compensate for "Cutting Corner", which is a disadvantage of the existing Pure Pursuit algorithm, an algorithm that changes the LAD according to the curvature of the path was developed. In the existing pure pursuit algorithm that used a tracked platform to drive a path including a right-angle turn, the RMS path error in the straight section was 0.1034 m and the RMS error in the turning section was measured to be 0.2787 m. On the other hand, in the variable LAD algorithm, the RMS path error in the straight section was 0.0987 m, and the RMS path error in the turning section was measured to be 0.1396 m. In the turning section, the RMS path error was reduced by 48.8971%. The validity of the algorithm was verified by measuring the path error by tracking the path using a tracked robot platform.

• The Journal of Information Systems
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• v.31 no.3
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• pp.47-65
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• 2022

Purpose With the recent development of Big Data and Artificial Intelligence technology, self-driving technology has developed into three stages (partial self-driving) or four stages (conditional self-driving), it is expected to bring a new paradigm to transportation in the city. Although many researchers are researching related technologies, there is no research on self-driving for disabled persons. In this study, the basic research was conducted based on the assumption that the shared self-driving car used by the disabled person is similar to the special transportation currently driving. Design In this study, data analysis and machine learning techniques were utilized to analyze the mobility patterns of disabled persons by type and to search for leading factors affecting the traffic volume of special transportation. Findings The study found that external physical disorders and developmental disorders often visit general welfare centers, internal organ disorders often visit general hospitals, and the elderly and mental disorders have various destinations. In addition, machine learning analysis showed that the main transportation routes for the disabled person use arterial roads and auxiliary arterial roads and that the ratio of building usage-related variables affecting the use of special transportation for a disabled person is high. In addition, the distance to the subway and bus stops was also mentioned as a meaningful variable. Based on these analysis results, it is expected that the necessary infrastructure for shared self-driving cars for disability person traffic will be used as meaningful research data in the future.