• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.12
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• pp.2475-2482
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• 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 compensated using the neural network. The neural network has trained training examples of normalized eccentricity, eccentric direction and direction of maximum stress at eccentric case using backpropagation learning process. The trained neural network could compensated the error of measured residual stress in experiments with hole eccentricity. The proposed neural network is very useful for compensation of the error due to hole eccentricity in hole-drilling method.

• Proceedings of the KSME Conference
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• 2001.11a
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• pp.412-418
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• 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 corrected using the neural network. The neural network has trained training examples of normalized eccentricity, eccentric direction and direction of maximum stress at eccentric case using backpropagation learning process. The trained neural network could corrected the error of measured residual stress in experiments with hole eccentricity. The proposed neural network is very useful for correction of the error due to hole eccentricity in hole-drilling method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.5
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• pp.797-802
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• 2008

High Speed Rail Train Control System consists of CTC, IXL and ATC. IXL and ATC perform train control and command via interchanging relevant information between a signal room and CTC. However, it is proved that IXL and ATC are attributed to train delay error since those systems are highly sensitive to trackside conditions. Especially, network error on IXL blocks transmitting signal information to adjacent signal room so that its effects give rise to system overall problems. In order to figure out the measures for which minimizing the occurrence rate of train delay error due to HSR TCS, This paper is performed analysis on communication network structure, the length of SSI network roof and SSI-TFM distance by examining and analyzing the error cases related to IXL in a network aspect.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.10
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• pp.2687-2702
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• 1996

It is desirable that the implementation of next generation information infrastructure is the Integrated Network combining the satellite and the terrestrial network. The application of the ATM network being the dominant infrastrure of terrestrial network to the satellite network is being studied variously. Considering these concepts, this paper analyzes due to ATM transport via satellite, evaluates the degradation of QoS and proposes reliable method of ATM cell transport via satellite. Because ATM is investigated with the optical fiber which is almost error free characteristics, the practical application of ATM transport via satellite essentially need the channel coding(FEC:Forward Error Correction) to enhance BER performance. But using the FEC coding, satellite link has burst error characteristics which evoke severe performance degradation fo ATM QoS. Therefore in satellite link, we analyze burst error characteristics using experimental results of computer simulation. Then to compensate these characteristics, based on this analysis and HEC dual mode algorithm we propose various interleaver structures(Block interleaver, Intra interlever, and Inter-Intra interleaver) to improve cell transmission QoS. We execute performance evaluations of iterleaver structures by computer simulation.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.6
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• pp.221-227
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• 2002

In this paper, the system model for the compensation of the low-cost personal navigation system is derived and the error compensation method using GPS is also proposed. WNS(Walking Navigation System) is a kind of personal navigation system using the number of a walk, stride and azimuth. Because the accuracy of these variables determines the navigation performance, computational methods have been investigated. The step is detected using the walking patterns, stride is determined by neural network and azimuth is calculated with gyro output. The neural network filters off unnecessary motions. However, the error compensation method is needed, because the error of navigation information increases with time. In this paper, the accumulated error due to the step detection error, stride error and gyro bias is compensated by the integrating with GPS. Loosely coupled Kalman filter is used for the integration of WNS and GPS. It is shown by simulation that the error is bounded even though GPS signal is blocked.

• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.300-302
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• 2006

Middleware offers function that user application program can transmit data independently of network device. Connection management about network connection of module is important for normal service of module base personal robot. Unpredictable network disconnection is influenced to whole robot performance in module base personal robot. For this, Middleware must be offer two important function. The first is function of error detection and reporting about abnormal network disconnection. Therefore, middleware need method for network error detection and module management to consider special quality that each network device has. The second is the function recovering that makes the regular service possible. When the module closed from connection reconnects, as this service reports connection state of the corresponding module, the personal robot resumes the existing service. In this paper proposed method of network connection management for to support fault tolerant about network error of network module based personal robot.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.7
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• pp.728-733
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• 2009

This paper presents the compensation of mechanical deflection error in SCARA robot. End of robot gripper is deflected by weight of arm and pay-load. If end of robot gripper is deflected constantly regardless of robot configuration, it is not necessary to consider above mechanical deflection error. However, deflection in end of gripper varies because that moment of each axis varies when robot moves, it affects the relative accuracy. I propose the compensation method of deflection error using neural network. FEM analysis to obtain the deflection of gripper end was carried out on various joint angle, the results is used in neural network teaming. The result by simulation showed that maximum relative accuracy reduced maximum 9.48% on a given working area.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2021.05a
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• pp.223-224
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• 2021

Recently, the demand for high fluidity concrete has been increased due to skyscrapers. However, it has its own limits. First of all, high fluidity concrete has large variation and through trial & error it costs lots of money and time. Neural network model has repetitive learning process which can solve the problem while training the data. Therefore, the purpose of this study is to predict optimum mix design of 40MPa, 60MPa high fluidity concrete by using neural network model and verifying compressive strength by applying real data. As a result, comparing collective data and predicted compressive strength data using MATLAB, 40MPa mix design error rate was 1.2%~1.6% and 60MPa mix design error rate was 2%~3%. Overall 40MPa mix design error rate was less than 60MPa mix design error rate.

• Journal of Advanced Navigation Technology
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• v.12 no.6
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• pp.666-672
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• 2008

This paper explains late comer and error recovery process for home network environment with session management. This system consists of an ED, ES, and ER. ED is an agent that detects an error by hooking techniques for multimedia distance education based on home network environment with session management. ES is an agent that is an error sharing system for multimedia distance education based on home network environment with session management. ER is a system that is suitable for recovering software error for multimedia distance education based on home network environment with session management. This paper explains a performance analysis of an error recovery system running on distributed multimedia environment using the rule-based DEVS modeling and simulation techniques. The proposed method is more efficient than the other method in comparison with error ration and processing time.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.7
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• pp.170-178
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• 1994

The nearest neighbor classification rule is widely used because it is not only simple but the error rate is asymptotically less than twice Bayes theoretical minimum error. But the method basically use the whole training patterns as the reference vectors. so that both storage and classification time increase as the number of training patterns increases. LVQ(Learning Vector Quantization) resolved this problem by training the reference vectors instead of just storing the whole training patterns. But it is a heuristic algorithm which has no theoretic background there is no terminating condition and it requires a lot of iterations to get to meaningful result. This paper is to propose a new training method of the reference vectors. which minimize the given error function. The nearest neighbor network,the network version of the nearest neighbor classification rule is proposed. The network is funtionally identical to the nearest neighbor classification rule is proposed. The network is funtionally identical to the nearest neighbor classification rule and the reference vectors are represented by the weights between the nodes. The network is trained to minimize the error function with respect to the weights by the steepest descent method. The learning algorithm is derived and it is shown that the proposed method can adjust more reference vectors than LVQ in each iteration. Experiment showed that the proposed method requires less iterations and the error rate is smaller than that of LVQ2.