• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.04a
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• pp.403-413
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• 1996

Two-phase self-organizing neuro-modeling (SONM). the global SONM and local SONM, is designed for tracking non-stationary manufacturing processes. Radial basis function (RBF) neural network is employed, and self-tuning estimator is also developed for the determination of RBF network parameters on-line. A pattern recognition approach is presented for identifying a correct RBF neural network, which is used for identifying current manufacturing processes. Experimental results showed that the proposed approach is suitable for tracking non-stationary processes.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.6
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• pp.738-746
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• 1998

This paper presents a neural network-based adaptive controller for a single flexible-link. The control for feedback-error loaming of neural network is designed by using the re-definition approach. The neural network controllers are implemented on an single flexible-link experimental test-bed. The tip response is significantly improved and the vibrations of the flexible modes are damped very fast. Experimental and simulation results are presented of the proposed tip position tracking controllers over the conventional PD-type, passive controllers.

• Journal of Power Electronics
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• v.2 no.1
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• pp.46-54
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• 2002

In this paper a neural network controller for achieving maximum power tracking as well as output voltage regulation, for a wind energy conversion system (WECS) employing a permanent magnet synchronous generator is proposed. The permanent magnet generator (PMG) supplies a dc load via a bridge rectifier and two buck-boost converters. Adjusting the switching frequency of the first buck-boost converter achieves maximum power tracking. Adjusting the switching frequency of the second buck-boost converter allows output voltage regulation. The on-time of the switching devices of the two converters are supplied by the developed neural network (NN). The effect of sudden changes in wind speed and/ or in reference voltage on the performance of the NN controller are explored. Simulation results showed the possibility of achieving maximum power tracking and output voltage regulation simulation with the developed neural network controllers. The results proved also the fast response and robustness of the proposed control system.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2000.07a
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• pp.165-168
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• 2000

This paper presents the tracking resistance and mechanical strength due to boiling absorption of epoxy resin. The single network structure specimen(E series) formed of epoxy alone and interpenetrating polymer network(IPN) structure specimen(EM series) which epoxy resin was taken as first network and methacrylic acid resin as second network were manufactured. As adding $SiO^2$ filler classified by o[phr], 50[phr] and 100[phr] to those specimens, six kinds of specimens were manufactured and boiled in water during 2, 4, 8, 16, 32 and 64[hours]. As a result, it was confirmed that the tracking breakdown time of E series showed a abrupt decrease with boiling time increasing, but that of EM series was decreasing smoothly. Also, it was verified that the degrading rates of mechanical strength was lowerd according to improvement of adhension strength in case of EM series.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.5
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• pp.502-508
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• 2012

Heliostat, as a concentrator to reflect the incident solar energy to the receiver, is the most important system in the tower-type solar thermal power plant since it determines the efficiency and ultimately the overall performance of solar thermal power plant. Thus, a good sun tracking ability as well as a good optical property of it are required. Heliostat sun tracking system uses usually an open loop control system. Thus the sun tracking error caused by heliostat's geometrical error, optical error and computational error cannot be compensated. Recently use of sun tracking error model to compensate the sun tracking error has been proposed, where the error model is obtained from the measured ones. This work is a development of heliostat sun tracking error measurement and compensation method using BCS (Beam Characterization System). We first developed an image processing system to measure the sun tracking error optically. Then the measured error is modeled in linear polynomial form and neural network form trained by the extended Kalman filter respectively. Finally error models are used to compensate the sun tracking error. We also developed the necessary image processing algorithms so that the heliostat optical properties such as maximum heat flux intensity, heat flux distribution and total reflected heat energy could be analyzed. Experimentally obtained data shows that the heliostat sun tracking accuracy could be dramatically improved using either linear polynomial type error model or neural network type error model. Neural network type error model is somewhat better in improving the sun tracking performance. Nevertheless, since the difference between two error models in compensation of sun tracking error is small, a linear error model is preferred in actual implementation due to its simplicity.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.24 no.2
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• pp.33-38
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• 2010

Recently, photovoltaic generator system is widely extended by energy policy of the government. Add to this, high efficiency of photocell power generation is steady needed to sun tracking method. However sun tracking method is not widely extended by insufficiency of tracking technology. As method of solving this problem, this paper applied sunlight sensor and neural network control algorithm for maximum power tracking of sun photocell. Sun tracking sensor consists of one upright square pole and form light sensor of east, west, south, north on flat board. Sun tracking dual axes control is operated respectively by two motor. Motor control input is calculated by neural network control algorithm. The function of proposed control method is verified by sun tracking experiment of photocell generation. The sun tracking method of this paper is increased 32[%] efficiency more than fixed method.

• Journal of Broadcast Engineering
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• v.23 no.2
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• pp.186-196
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• 2018

In this paper, we propose an on-line tracking method using convolutional neural network (CNN) for tracking object. It is well known that a large number of training samples are needed to train the model offline. To solve this problem, we use an untrained model and update the model by collecting training samples online directly from the test sequences. While conventional methods have been used to learn models by training samples offline, we demonstrate that a small group of samples are sufficient for online object tracking. In addition, we define a loss function containing color information, and prevent the model from being trained by wrong training samples. Experiments validate that tracking performance is equivalent to four comparative methods or outperforms them.

• Fire Science and Engineering
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• v.9 no.1
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• pp.3-9
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• 1995

This paper is a study to dectect the tracking owing to deterioration of indoor wiring, and to prevent the electrical fire. After analysing the harmonics of waveshapes in load current and tracking current by FFT, a method of identifying the tracking was developed by using neural network. Fluoscent lamp, witch was mostly used in indoor, was chosen as the load used in this study. When the learning number in neural network was more then 30,000 times, an excellent neural net-work which could correctly identify the tracking was established. Therefore, the result of this study can be utilized as a basic material in various measuring instruments, such as an hotline inslation tester, earth tester in vehicles, and tracking fire alarm device, witch can detect the tracking under the condition of hotline.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.489-492
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• 2016

Transportation in the logistics, many business organizations are engaged in monitoring and tracking the vehicles in order to improve logistics services, reduce expenses and secure security in cargo transportation. It is saving time and money by tracking and monitoring vehicles which transport cargo in supply chain of logistics. Therefore the main issue of delivery flow is to improve services, and ensure the safety in transportation system. This article suggests the tracking and monitoring model to keep safety transports on ICT network. It focuses on precise delivery control by monitoring and tracking vehicles to save time and costs. The status of product movement is analyzed for proper decision making. The vehicle embedded with RFID is automatically tracked in the movement process by tracking and monitoring model. The main role keeps safety tracking to reduce costs and to deliver products at proper time and location.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.6
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• pp.1304-1311
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• 2003

Target tracking in a real world situation is difficult problem because of continuous variations in images, huge amounts of data, and high processing speed demands. The problem becomes even harder in the case of sea background. This paper presents an initial study of neural network based method for target detection and tracking in cluttering environment. The approach uses a combination of differential motion analysis, Kohonen self-organizing network and region growing method. The network is capable of detecting the mass-centers of moving objects within one frame. The history of neurons positions in the sequential frames approximates the traces of the targets. The experiments done with the network in simulated environment showed promising results.