• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.03a
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• pp.107-111
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• 1998

This paper presents a now approach to the design of intelligent contorl system for track vehicle system using fuzzy logic based on neural network. The proposed control scheme uses a Gaussian function as a unit function in the neural network-fuzzy, and back propagation algorithm to train the fuzzy-neural network controller in the framework of the specialized learning architecture. Moreover, We develop a Windows 95 version dynamic simulator which can simulate a track vehicle model in 3D graphics space. It is proposed a learning controller consisting of two neural network-fuzzy based of independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The dynamic simulator for track vehicle is developed by Microsoft Visual C++. Graphic libraries, OpenGL, by Silicon Graphics, Inc. were utilized for 3D Graphics. The performance of the proposed controller is illustrated by simulation for trajectory tracking of track vehicle speed.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.55 no.3
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• pp.182-188
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• 2006

Contactless power system is base on power transmission by magnetic force. The transformer loss is large because it separated with the gap. Also the system has unstable factor, since the parameters in the secondary can vary with the system movement. This paper proposes light train power transmission system using contactless transformer with multiple primary winding. To increase the system efficiency and to obtain the stable power transmission to the dynamic load, a resonant inverter is adopted. The proposed system was verified by the simulation using Spice and Maxwell. The designed contactless power transmission system is implemented for 5[kW] class and experimental results are discussed.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.05a
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• pp.210-215
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• 1999

In this paper, a new approach to the dynamic control technique for track vehicle system using fuzzy-neural network control technique is proposed. The proposed control scheme uses a Gaussian function as a unit function in the neural network-fuzzy, and back propagation algorithm to train the fuzzy-neural network controller in the framework of the specialized learning architecture. It is proposed a learning controller consisting of two neural network-fuzzy based on independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The performance of the proposed controller is shown by simulation for trajectory tracking of the speed and azimuth of a track vehicle.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1050-1052
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• 1996

This paper presents a neuro-control algorithm for the speed control of a direct drive motor without the knowledge of the dynamics of the motor and the characteristics of a nonlinear load. In the field of motor control, it is not possible to directly use the back-propagation method in order to train a network since the desired output of the network is not known. Hence, we propose an extended back-propagation algorithm to force the closed loop system to give desired results. Experimental results shown that the proposed neuro-controller can reduce the unknown load effects and have the good velocity tracking capabilities.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.69.3-69
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• 2001

This paper presents a new approach to the design of cruise control system of a mobile robot with two drive wheel. The proposed control scheme uses a Gaussian function as a unit function in the fuzzy-neural network, and back propagation algorithm to train the fuzzy-neural network controller in the framework of the specialized learning architecture. It is proposed a learning controller consisting of two neural network-fuzzy based on independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The performance of the proposed controller is shown by performing the computer simulation for trajectory tracking of the speed and azimuth of a mobile robot driven by two independent wheels.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.4
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• pp.75-86
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• 1996

In this paper, we propose a new fuzzy-neural network control scheme for the speed and azimuth control of a mobile robot. The proposed control scheme uses a gaussian function as a unit function in the fuzzy-neural network, and back propagation algorithm to train the fuzzy-neural network controller in the frame-work of the specialized learning architecture. It is proposed a learning controller consisting of two fuzzy-neural networks based on independent reasoning and a connection net woth fixed weights to simply the fuzzy-neural network. The effectiveness of the proposed controller is illustrated by performing the computer simulation for a circular trajectory tracking of a mobile robot driven by two independent wheels.

• Journal of the Korea Society of Computer and Information
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• v.19 no.12
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• pp.187-196
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• 2014

In this paper, Minimize driving energy for operation within a defined distance yeokgan fixed time-resolved and determine the nature of the train is traveling, and to model mathematically. Urban rail car cruise in general by the PID controller is used instead of automatically tracking a target value while traveling in energy consumption to be minimized by using optimal control model railroad charyangreul was designed under real operating conditions the same. The actual track conditions apply to the minimum value or a separate listing of cars around the track facility without a driving energy of the automatic operation and to reduce the driving energy. Therefore, actual route chosen straight line 8 / gradient segment / curve for the measured data analysis, such as sections within the city-minute drive each section and presented how the trains to save energy, depending on the pattern of the train station in the region.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.1
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• pp.111-118
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• 2022

In computer vision research, the two-dimensional human pose is a very extensive research direction, especially in pose tracking and behavior recognition, which has very important research significance. The acquisition of human pose targets, which is essentially the study of how to accurately identify human targets from pictures, is of great research significance and has been a hot research topic of great interest in recent years. Human pose recognition is used in artificial intelligence on the one hand and in daily life on the other. The excellent effect of pose recognition is mainly determined by the success rate and the accuracy of the recognition process, so it reflects the importance of human pose recognition in terms of recognition rate. In this human body gesture recognition, the human body is divided into 17 key points for labeling. Not only that but also the key points are segmented to ensure the accuracy of the labeling information. In the recognition design, use the comprehensive data set MS COCO for deep learning to design a neural network model to train a large number of samples, from simple step-by-step to efficient training, so that a good accuracy rate can be obtained.

• Journal of Korean Society of Transportation
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• v.23 no.4 s.82
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• pp.17-28
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• 2005

About 91.1% of Railway-Highway Crossings (RHC) in Korea use a Constant Distance Warning System(CDWS), while about 8.9% use a Constant Warning Time System(CWTS). The CDWS does not recognize speed differences of approaching trains and provides only waiting times to vehicles and pedestrians based on the highest speed of approaching trains. Under the CDWS, therefore, low speed trains provide unnecessary waiting times at crossings which often generates complains to vehicle drivers and pedestrians and may cause wrong decisions to pass the crossings. The objective of this research is to improve the safety of the RHC by developing accurate a CWTS. In this research a train speed and location detection system was developed with ultra sonic detectors. Locations of the detectors was decided based on the highest speed and the minimum warning time of Saemaul of 160 km/h. To validate the algorithms of the newly developed systems the lab tests were conducted. The results show that the train detection system provides accurate locations of trains and the maximum error between real speeds of trains and those of the system was 0.07m/s.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.05a
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• pp.621-624
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• 2005

Among many auditory feelings for the vehicle interior noise, booming is considered as the most important nuisance to the passenger and developer. Because the main source of booming noise is a power train system including engine, in general, it consists of tonal components related to fundamental engine rotation and its harmonics including the firing frequency. Therefore, it is demanded to extract the effective tonal components only by using pitch extraction algorithm based on the place theory enable to find aurally relevant tonal components. However, there is a difference between booming sensation and pitch perception according to frequency change of tonal component. In this study, subjective listening test using a tracking method was performed to find the difference limen for just noticeable change of booming sensation in frequency. 20 Koreans and 10 Japanese were participated in this test and the results obtained from Koreans and Japanese were compared with each other. Finally, 5Hz was determined as the difference limen for just noticeable change of booming sensation in frequency, and by applying this value to booming analysis using pitch concept, it was confirmed that the degree of prediction of booming sensation was improved.