• The Journal of Korea Robotics Society
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• v.10 no.1
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• pp.33-41
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• 2015

Recent developments in robotics and intelligent vehicle area, bring interests of people in an autonomous driving ability and advanced driving assistance system. Especially fully automatic parking ability is one of the key issues of intelligent vehicles, and accurate parked vehicles detection is essential for this issue. In previous researches, many types of sensors are used for detecting vehicles, 2D LiDAR is popular since it offers accurate range information without preprocessing. The L shape feature is most popular 2D feature for vehicle detection, however it has an ambiguity on different objects such as building, bushes and this occurs misdetection problem. Therefore we propose the accurate vehicle detection method by using a 3D complete vehicle model in 3D point clouds acquired from front inclined 2D LiDAR. The proposed method is decomposed into two steps: vehicle candidate extraction, vehicle detection. By combination of L shape feature and point clouds segmentation, we extract the objects which are highly related to vehicles and apply 3D model to detect vehicles accurately. The method guarantees high detection performance and gives plentiful information for autonomous parking. To evaluate the method, we use various parking situation in complex urban scene data. Experimental results shows the qualitative and quantitative performance efficiently.

• The Journal of Korean Association of Computer Education
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• v.8 no.2
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• pp.33-40
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• 2005

It has been found that the learning method based on conventional CAI(Computer Assisted Instruction) to be inadequate and inefficient as it is designed without considering the individual learning characteristics of the learners. In order to rectify and remedy the problem, the development of an ITS(Intelligent Tutoring System) that is adequately equipped with an artificial intelligence that successfully interprets the individual learning ability characteristics through accumulated individual data is in order. This study attempts to verify the individual acquisition ability and the possible error committed by learners in the process of learning in order to present the elements to be considered for designing a successful student module that enables the effective learning through the 'learner ability grouping' for learning Electronic Calculator Architecture.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.427-430
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• 2002

Recently the subject of "wavelet analysis" has be drawn by both mathematical and engineering application fields such as Signal Processing, Compression/Decomposition, Wavelet-Neural Network, Statistics and etc. Even though its similar to Fourier analysis, wavelet is a versatile tool with much mathematical content and great potential for applications. Especially, wavelet transform uses localizable various mother wavelet functions in time-frequency domain. Therefore, wavelet transform has good time-analysis ability for high frequency component, and has good frequency-analysis ability for low frequency component. Using the discriminative ability is more easy method than other conventional techniques. In this paper, Morlet wavelet transform was applied to discriminate the kind of line fault by acquired data from real power transformation network. The experimental result presented that Morlet wavelet transform is easier,and more useful method than the FFT (Fast Fourier Transform).

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.1037-1040
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• 1999

In this paper, we designed the extended ATA(AT Attachment interface with extension) interface that combines with goods price and ability and intellectual behavior of SCSI, for make progress the ability and structure of ordinary interface for connect with device of using PC. ATA is establish a standard of IDE(Intelligent Drive Electronics) public in small form factor. SCSI bus is device behaving intellectual and have stable hardware structure, calssified instructions structure. But it is device that difficult to buy, because of price of more than two times. The other side, ATA device is worse than SCSI bus in part of ability, but it came to SCSI in part of speed after improve and it's price is less expensive. another improvement of ATA is a standard of ARAP(AT Attachment Packet Interface) and use method of packet transmission and behaves as if SCSI use a method. Finally, improvement of ATAPI behave from interface of only HDD to ability of ordinary interface. This paper propose the structure of extended interface that satisfied the price and ability.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.255-260
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• 2013

Nowadays, the intelligent education system has been studied using the self-directed learning ability. It can connect to the online virtual university and it is based on web technology that can be accessed anywhere anyplace. In order to implement the intelligent tutoring system, the student's weak and strong subjects must be first determined in real time, it proposed level learning capabilities and security algorithms in this paper. Moreover, in this paper, to implement the intelligent education tutoring system it proposed qr code and student level learning simulation.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.1156-1161
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• 1990

The concept of the manipulability measure of the robotic mechanism is extended to the dual arm holding a single object. This is a measure of manipulating ability of the dual arm forming a closed kinematic chain in positioning and orienting the object. Dual arm manipulability measure is defined and compared to the single arm manipulability measure, and some properties are investigated.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.27-34
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• 1996

GENIE is a learning-based engine for building intelligent systems. Learning in GENIE proceeds by incrementally modeling its human or technical environment using a neural network and a genetic algorithm. The neural network is used to represent the knowledge for solving a given task and has the ability to grow its structure. The genetic algorithm provides the neural network with training examples by actively exploring the example space of the problem. Integrated into the training examples by actively exploring the example space of the problem. Integrated into the GENIE system architecture, the genetic algorithm and the neural network build a virtually self-teaching autonomous learning system. This paper describes the structure of GENIE and its learning components. The performance is demonstrated on a robot learning problem. We also discuss the lessons learned from experiments with GENIE and point out further possibilities of effectively hybridizing genetic algorithms with neural networks and other softcomputing techniques.

• Journal of the Korea Institute of Military Science and Technology
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• v.23 no.3
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• pp.267-276
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• 2020

Threats targeting cyberspace are becoming more intelligent and increasing day by day. To cope with such cyber threats, it is essential to improve the coping ability of system security officers. In this paper, we propose a stateful traffic generator that provide network background traffic for cyber warfare training. The proposed architecture is designed for generating traffic similar to real system traffic, so the trainee can perform more realistic training.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1297-1300
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• 1993

Machine learning in an uncertain or unknown environment is of vital interest to those working with intelligent systems. The ability to garner new information, process it, and increase the understanding/ capability of the machine is crucial to the performance of autonomous systems. The field of artificial intelligence provides two major approaches to the problem of knowledge engineering-expert systems and neural networks. Harnessing the power of these two techniques in a hybrid, cooperating system holds great promise.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.11a
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• pp.19-22
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• 2000

In this paper, we present forecasting ozone concentration with decision support system. Forecasting ozone concentration with decision support system is acquired to information from human knowledge and experiment data. Fuzzy clustering method uses the acquisition and dynamic polynomial neural network gives us a good performance for ozone prediction with ability of superior data approximation and self-organization.