• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2003.04a
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• pp.293-298
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• 2003

This study deals with the source location method of defect which can be occurred in structure. The existing methods was very difficult to be applied to predict it because of using very complex numerical formula. The triangulation method which was proposed in his study can predict the source location predicted easily with small amount of data. Wave arrival time data can be directly converted into source-sensor distance is known. For this purpose, the propagation velocity was measured by Rayleigh wave, and the propagation behavior was analyzed. For the consideration of jointed part in structure, the source location method was applied to the welded workpiece. The signal propagation velocity was measured in welding part for the purpose of application to the part and the revised algorithm of source location was proposed.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2006.04a
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• pp.82-88
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• 2006

In People usually think that smart materials and smart structures have not been developed until recent years. But those kinds of sensors have already been used for sensing damage in a variety of materials and structures. Two typical examples are piezoelectric materials (e.g., PZT) and electric strain gauges. Load cell is an example that utilizes the piezoelectric property to measure the change in physical quantities occurred by applied loads, while strain gauges are used to measure the deformation of compressive and tension members. The feasibility of using smart materials is realized for a monitoring technology when those sensors are used to monitor damages at inside or outsider of the structures. In this study, a fundamental study on the development of self diagnostic smart concrete using PZT, and unsaturated polyester electric resistance sensor.

• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.135-137
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• 2006

To improve device yield and throughput, faults in plasma processing equipment should be quickly and accurately diagnosed. Despite many useful information of ex-situ sensor measurements, their applications to recognize plasma faultshave not been investigated. In this study, a new technique to identify fault causes by recognizing X-ray photoelectron spectroscopy (XPS) using neural network and continuous wavelet transformation (CWT). The presented technique was evaluated with the plasma etch data. A totalof 17 experiments were conducted for model construction. Model performance was investigated from the perspectives of training error, testing error, and recognition accuracy with respect to various thresholds. CWT-based BPNN models demonstrated a higher prediction accuracy of about 26%. Their advantages over pure XPS-based models were conspicuous in all three measures at small networks.

• Proceedings of the KIEE Conference
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• 1997.11a
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• pp.32-34
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• 1997

For a given brushless DC linear motor, we suggest the numerical prediction methods to analyze it's thrust characteristics. First, we calculate the magnetic flux density by the finite element method, and we then compute the maximum thrust with three computational methods - a Lorentz equation, a Maxwell stress method and a virtual work method. To confirm the accuracy of the computational methods, we measure the thrust of the linear motor made by our laboratory with a force-torque sensor. Also, we calculate the thrust by the measured back electromotive force. To choose the appropriate method for a specified application, we compare the maximum thrusts of the computational method and the calculation by the back electromotive force with the measured one. We conclude that the Maxwell stress method is turned out the best because it has the most accurate results among three computational methods and it is more convenient than the calculation method by the back electromotive force.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.10a
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• pp.40-45
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• 1999

The demands for robotic and automatic system are continually increasing in manufacturing fields. There have been many studies to monitor and predict the system, but they have mainly focused upon measuring cutting force, and current of motor spindle, and upon using acoustic sensor, etc.In this study, time series sequence of cutting force was acquired by taking advantage of piezoelectric type tool dynamometer. Radial cutting force was obtained from it and was available for useful observation data. The parameter was estimated using PAA (parameter adaptation algorithm) from observation data. ARMA(auto regressive moving average) model was selected for system model and second order was decided according to parameter estimation. Uncorrelation test was also carried out to verify convergence of parameter.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.10a
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• pp.591-596
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• 1999

Due to the development of CNC machining centers and the complexity of machined part geometry, the ball-end milling became the most widely used the cutting process. Generally, the tool runout defined as the eccentricity of a rotating tool set in the holder involved the spindle runout and the problem of tool runout generated to remove the workpiece is a main factor affecting the machining accuracy. In this paper, the relationship of tool runout(zero-to-peak, P-K) and surface shape on the change of cutting conditions is studied and it is proposed the probability of prediction of surface shape from the in-process tool runout measurements with high response displacement sensor in the ball-end milling

• Transactions of the Korean Society of Machine Tool Engineers
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• v.18 no.2
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• pp.178-186
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• 2009

Most of automotive plastic parts are injection molding products. Inspection of total product is impossible, because number of product to inspect is too many and various. Condition-based Monitoring was proposed to decrease cost and time for inspecting. In this research, a system that predicts quality of part at fabrication point of time, and confirms informations through the internet was developed. Cavity sensors were installed inside of mold, and gathered signals as measuring, and through this process Sensor-based Monitoring system can be observed manufacturing of a part. Monitoring system transmits signals to client through the internet, and finally developed system provides manufacturing informations and predictions of quality as web-based monitoring.

• Geomechanics and Engineering
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• v.23 no.4
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• pp.381-392
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• 2020

The difficulties, challenges and limitations faced in standard pressuremeter testing in the measurement of low soil deformations led a number of researchers to think about the possible modification of the equipment, and especially the replacement of the volumeter by a Hall Effect sensor. This article is a major contribution in this direction. It makes an attempt to detail the design, manufacture and operation of the new equipment. The calibration of the various components was carried out according to the rules presently in force. This proposal was applied, on an exploratory basis, to the data of a real site located in France. The authors present the preliminary results of some cyclic pressuremeter tests, previously carried out in the laboratory, on a sandy material, and they then provide a basic interpretation of these results. The findings indicated that the proposed apparatus is capable of providing high-quality information about constraints and deformations. Although these tests were performed within the laboratory, it was possible to analyze the power, quality, performance and insufficiencies of the proposed equipment.

• Journal of the Society of Naval Architects of Korea
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• v.48 no.4
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• pp.336-341
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• 2011

Ship infrared signature is the cause of detection and tracking by infrared sensor and anti-ship missile seeker. Recent warships have been applied the infrared stealth technology to reduce own ship infrared signature and tested to validate own ship infrared signature level. This study describes the two issues. Firstly, we describe the infrared measurement concept and infrared signature level establishment method that have been performed. Secondly, we compare and analyze the error components between the infrared measurement and simulation result.

• Journal of Sensor Science and Technology
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• v.13 no.3
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• pp.199-206
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• 2004

Epileptic seizures result from a temporary electrical disturbance of the brain. In this paper, a method of discriminating EEG for diagnoses of temporal lobe epilepsy is proposed. The proposed method for classification of epilepsy and sleep EEG is based on the wavelet transform and the fuzzy c-means. The magnitude and mean of wavelet coefficients for each EEG band are applied to the cluster of the FCM classifier. The proposed system show a little more accurate diagnosis for EEG by analysis of frequency for Wavelet and the success rate of 95% classification using FCM. From the simulation results by the implemented system, we demonstrated this research can be reduce doctor's labors and realize quantitative diagnosis of EEG.