• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1996.06a
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• pp.113-118
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• 1996

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1996.06a
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• pp.119-124
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• 1996

• 전기의세계
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• v.51 no.12
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• pp.26-32
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• 2002

• The Journal of Korea Robotics Society
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• v.14 no.2
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• pp.122-130
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• 2019

Collecting a rich but meaningful training data plays a key role in machine learning and deep learning researches for a self-driving vehicle. This paper introduces a detailed overview of existing open-source simulators which could be used for training self-driving vehicles. After reviewing the simulators, we propose a new effective approach to make a synthetic autonomous vehicle simulation platform suitable for learning and training artificial intelligence algorithms. Specially, we develop a synthetic simulator with various realistic situations and weather conditions which make the autonomous shuttle to learn more realistic situations and handle some unexpected events. The virtual environment is the mimics of the activity of a genuine shuttle vehicle on a physical world. Instead of doing the whole experiment of training in the real physical world, scenarios in 3D virtual worlds are made to calculate the parameters and training the model. From the simulator, the user can obtain data for the various situation and utilize it for the training purpose. Flexible options are available to choose sensors, monitor the output and implement any autonomous driving algorithm. Finally, we verify the effectiveness of the developed simulator by implementing an end-to-end CNN algorithm for training a self-driving shuttle.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.2
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• pp.529-546
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• 2012

To address the need for autonomous control of remote and distributed mobile systems, Machine-to-Machine (M2M) communications are rapidly gaining attention from both academia and industry. M2M communications have recently been deployed in smart grid, home networking, health care, and vehicular networking environments. This paper focuses on M2M communications in the vehicular networking context and investigates areas where M2M principles can improve vehicular networking. Since connected vehicles are essentially a network of machines that are communicating, preferably autonomously, vehicular networks can benefit a lot from M2M communications support. The M2M paradigm enhances vehicular networking by supporting large-scale deployment of devices, cross-platform networking, autonomous monitoring and control, visualization of the system and measurements, and security. We also present some of the challenges that still need to be addressed to fully enable M2M support in the vehicular networking environment. Of these, component standardization and data security management are considered to be the most significant challenges.

• Journal of Sensor Science and Technology
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• v.32 no.5
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• pp.267-274
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• 2023

LiDAR, one of the most important sensing methods used in mobile robots and cars with assistive/autonomous driving functions, is used to locate surrounding obstacles or to build maps. For real-time path generation, the detection of potholes or puddles on the driving surface is crucial. To achieve this, we used the coordinates of the reflection points provided by LiDAR as well as the intensity information to classify water areas, which was achieved by applying a linear regression method to the intensity distribution. The rationale for using the LiDAR index as an input variable for linear regression is presented, and we demonstrated that it is not affected by errors in the distance measurement value. Because of LiDAR vertical scanning, if the reflective surface is not uniform, it is divided into different groups according to the intensity distribution, and a mathematical basis for this is presented. Through experiments in an outdoor driving area, we could distinguish between flat ground, potholes, and puddles, and kinematic analysis was performed to calculate the maximum width that could be crossed for a given vehicle body size and wheel radius.

• Journal of Advanced Navigation Technology
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• v.23 no.4
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• pp.296-301
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• 2019

Artificial intelligence is closely linked to our real lives, leading innovation in various fields. Especially, as a means of transportation possessing artificial intelligence, autonomous unmanned vehicles are actively researched and are expected to be put into practical use soon. Autonomous cars and autonomous unmanned aerial vehicles are required to equip accurate navigation system so that they can find out their present position and move to their destination. At present, the navigation of transportation that we operate is mostly dependent on GPS. However, GPS is vulnerable to external intereference. In fact, since 2010, North Korea has jammed GPS several times, causing serious disruptions to mobile communications and aircraft operations. Therefore, in order to ensure safety in the operation of the autonomous unmanned vehicles and to prevent serious accidents caused by the intereference, rapid situation judgment and countermeasure are required. In this paper, based on big data and machine learning technology, we propose a countermeasure system for GPS interference that supports decision making by applying John Boyd's OODA loop cycle (detection - direction setting - determination - action).

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.2
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• pp.115-120
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• 2015

Recently, in automotive technology area, intelligent safety systems have been actively accomplished for drivers, passengers, and pedestrians. Also, many researches are focused on development of autonomous vehicles. This paper propose the application of LiDAR sensors, which takes major role in perceiving environment, terrain classification, obstacle data clustering method, and local map building for autonomous driving. Finally, based on these results, planning for lane change path that vehicle tracking possible were created and the reliability of path generation were experimented.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.323-326
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• 1995

During a fast decade, an automatic control technology makes an aggressive improvement with the developments of computer and communication technology. In large scale and complicated systems, an autonomous decentralized control system is required in which the sub-systems must have some ability such that the self-judgement and self-performance functions. In this paper, we propose an algorithm to realized these functions using micro mobile robot which is applied to a control of a werehouse. The proposed algorithm is based on performance index, and the selecting rules of the task between the sub-systems are induced by the index. Also, it is effected by weighting function which is determined by environment and kind of works. To verify the effectiveness of this algorithm, we develop the simulator to implement the autonomous decentralized control and apply to the micro mobile robot on the PC machine.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.271-281
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• 2022

Manual seed classification challenges can be overcome using a reliable and autonomous seed purity identification and classification technique. It is a highly practical and commercially important requirement of the agricultural industry. Researchers can create a new data mining method with improved accuracy using current machine learning and artificial intelligence approaches. Seed classification can help with quality making, seed quality controller, and impurity identification. Seeds have traditionally been classified based on characteristics such as colour, shape, and texture. Generally, this is done by experts by visually examining each model, which is a very time-consuming and tedious task. This approach is simple to automate, making seed sorting far more efficient than manually inspecting them. Computer vision technologies based on machine learning (ML), symmetry, and, more specifically, convolutional neural networks (CNNs) have been widely used in related fields, resulting in greater labour efficiency in many cases. To sort a sample of 3000 seeds, KNN, SVM, CNN and CNN-SVM hybrid classification algorithms were used. A model that uses advanced deep learning techniques to categorise some well-known seeds is included in the proposed hybrid system. In most cases, the CNN-SVM model outperformed the comparable SVM and CNN models, demonstrating the effectiveness of utilising CNN-SVM to evaluate data. The findings of this research revealed that CNN-SVM could be used to analyse data with promising results. Future study should look into more seed kinds to expand the use of CNN-SVMs in data processing.