• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.26 no.3
• /
• pp.436-444
• /
• 2002

The measurement of residual stresses by the hole-drilling method has been commonly used to evaluate residual stresses in structural members. In this method, eccentricity can usually occur between the hole center and rosette gage center. In this study, the error due to the hole eccentricity is predicted using the artificial neural network. The neural network has trained training examples of stress ratio, normalized eccentricity, off-centered direction and stress error using backpropagation learning process. The prediction results of the error using the trained neural network are good agreement with FE analyzed ones.

• Proceedings of the KSME Conference
• /
• 2001.06a
• /
• pp.956-963
• /
• 2001

The measurement of residual stresses by the hole-drilling method has been commonly used to evaluate residual stresses in structural members. In this method, eccentricity can usually occur between the hole center and rosette gage center. In this study, the error due to the hole eccentricity is predicted using the artificial neural network. The neural network has trained training examples of stress ratio, normalized eccentricity, off-centered direction and stress error using backpropagation loaming process. The prediction results of the error using the trained neural network are good agreement with FE analyzed ones.

• The Transactions of the Korean Institute of Electrical Engineers
• /
• v.39 no.12
• /
• pp.1306-1316
• /
• 1990

A controller using a multilayer neural network is proposed to the dynamic control of a PUMA 560 robot arm. This controller is developed based on an error back-propagation (BP) neural network. Since the neural network can model an arbitrary nonlinear mapping, it is used as a commanded feedforward torque generator. A Proportional Derivative (PD) feedback controller is used in parallel with the feedforward neural network to train the system. The neural network was trained by the current state of the manipulator as well as the PD feedback error torque. No a priori knowledge on system dynamics is needed and this information is rather implicitly stored in the interconnection weights of the neural network. In another experiment, the neural network was trained with the current, past and future positions only without any use of velocity sensors. Form this thim window of position values, BP network implicitly filters out the velocity and acceleration components for each joint. Computer simulation demonstrates such powerful characteristics of the neurocontroller as adaptation to changing environments, robustness to sensor noise, and continuous performance improvement with self-learning.

• Journal of Advanced Navigation Technology
• /
• v.11 no.3
• /
• pp.313-318
• /
• 2007

This paper explains the design and implementation of the FDA(Fault Detection Agent). FDA is a system that is suitable for detecting software error for multimedia distance education based on home network environment. This system consists of an ED, and ES. ED is an agent that detects an error by hooking techniques for multimedia distance education based on home network environment. ES is an agent that is an error sharing system for multimedia distance education based on home network environment. From the perspective of multimedia collaborative environment, an error application becomes another interactive presentation error is shared with participants engaged in a cooperative work. Performance analysis is done by Comparison of Hooking with Snatching Method.

• Proceedings of the IEEK Conference
• /
• 2003.11c
• /
• pp.183-186
• /
• 2003

In this paper, performance evaluation of HNCP home network is using stochastic activity network models is proposed. HNCP is a home network protocol for controling and monitoring home appliances using power line communication. a CSMA/CA with packet drop method is used in HNCP MAC layer. Using the proposed stochastic activity network models. performances of HNCP home networks with error-free environment and error environment are evaluated.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.26 no.1
• /
• pp.17-26
• /
• 2008

The 1st order level network of Korea was adjusted simultaneously in 1987. After that, the 1 st order level network of Korea was adjusted simultaneously by National Geographic Information Institute in 2006. The levelling data were acquired by digital level with invar staff from 2001 through 2006. The 1st order level network consists of 36 level lines. Among them, 34 level lines comprise 11 level loops. Among 36 level lines, 4 level lines have fore & back error larger than the regulations for the 1st order levelling of NGII, Korea. Also, the closing error of 3 loops of level network exceed the regulation for the 1st order levelling of NGII. The standard error of fore and back leveling between bench marks(${\eta}_1$) are distributed between 0.2 $mm/{\surd}km$ and 1.7 $mm/{\surd}km$. The standard error of loop closing(${\eta}_2$) is 2.0 $mm/{\surd}km$. This result means that the 1st order level network of Korea qualifies for the high precision leveling defined by International Geodetic Association in 1948. As the result of the 1st order level network adjustment, the reference standard error($\hat{{\sigma}_0}$) of the level network was 1.8 $mm/{\surd}km$, which is twice as good as that of the 1st adjustment of level networks in 1987.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
• /
• 2007.04a
• /
• pp.7-10
• /
• 2007

The 1st order vertical network of Korea was adjusted in 1987 at first time. This is the second adjustment of the 1st order vertical network of Korea by National Geographic Information Institute. All the levelling data were acquired by digital level with invar staff. The number of 1st order level lines are 36, and 34 level lines comprise 11 circles of level network. Backward and forward error of a few level lines are larger than the regulations of NGII, Korea. Also, 3 circles of vertical network has circuit closure error that is exceed the regulation. As the result of 1st order vertical network adjustment, the reference standard error of the vertical network was $1.8mm/{\surd}km$.

• Journal of Institute of Control, Robotics and Systems
• /
• v.18 no.5
• /
• pp.502-508
• /
• 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 Institute of Control, Robotics and Systems
• /
• v.16 no.7
• /
• pp.711-719
• /
• 2010

Heliostat, as a concentrator reflecting the incident solar energy to the receiver located at the tower, is the most important system in the tower-type solar thermal power plant, since it determines the efficiency and performance of solar thermal plower plant. Thus, a good sun tracking ability as well as its good optical property are required. In this paper, we propose a method to compensate the heliostat sun tracking error. We first model the sun tracking error, which could be measured using BCS (Beam Characterization System), by multilayered neural network. Then the extended Kalman filter was employed to train the neural network. Finally the model is used to compensate the sun tracking errors. Simulated result shows that the method proposed in this paper improve the heliostat sun tracking performance dramatically. It also shows that the training of neural network by the extended Kalman filter provides faster convergence property, more accurate estimation and higher measurement noise rejection ability compared with the other training methods like gradient descent method.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.26 no.3
• /
• pp.613-621
• /
• 2016

Since the error control problem is a critical and sensitive issue in the one-way network, we can adopt a forward error correction code method or data retransmission method based on the response of reception result. In this paper, we propose error control method and continuous data transmission protocol in the one-way network which has unidirectional data transmission channel and special channel to receive only the response of reception result. Furthermore we present data encryption and key update mechanism which is based on the pre-shared key distribution scheme and suggest some ASDU(Application Service Data Unit) formats to implement it in the one-way network.