• Journal of the Korean Society for Nondestructive Testing
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• v.24 no.3
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• pp.246-252
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• 2004

Electronic speckle pattern interferometry (ESPI) for quantitative evaluation of delaminations inside of a composite material plate is described. Delaminations caused by the impact on composite materials are difficult to detect visual inspection and ultrasonic testing due to non-homeogenous structure. This paper proposes the quantitative evaluation technique of the defects made in the composite plates by impact load. Artificial defects are introduced inside of the composite plate for the development of a reliable ESPI inspection technique. Real defects produced by impact tester are inspected and compared with the results of visual inspection which shows a good agreement within 5% error.

• KIPS Transactions on Computer and Communication Systems
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• v.7 no.2
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• pp.27-32
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• 2018

One of the crucial issues in mobile edge cloud computing environments is to monitor mobile devices. Due to the inherit properties of mobile devices, they are prone to unstable behavior that leads to failures. In order to satisfy the service level agreement (SLA), the mobile edge cloud administrators should take appropriate measures through a monitoring scheme. In this paper, we propose a monitoring scheme of mobile devices based on artificial intelligence in mobile edge cloud computing environments. The proposed monitoring scheme is able to measure faults of mobile devices based on previous and current monitoring information. To this end, we adapt the hidden markov chain model, one of the artificial intelligence technologies, to monitor mobile devices. We validate our monitoring scheme based on the hidden markov chain model. The proposed monitoring scheme can also be used in general cloud computing environments to monitor virtual machines.

• Journal of the Korean Society for Nondestructive Testing
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• v.24 no.3
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• pp.275-281
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• 2004

Socket weldment used to change the flow direction of fluid nay have flaws such as lack of fusion and cracks. Liquid penetrant testing or Radiography testing have been applied as NDT methods for flaw detection of the socket weldment. But it is difficult to detect the flaw inside of the socket weldment with these methods. In order to inspect the flaws inside the socket weldment, a ultrasonic testing method is established and a ultrasonic transducer and automated ultrasonic testing system are developed for the inspection. The automated ultrasonic testing system is based on the portable personal computer and operated by the program based Windows 98 or 2000. The system has a pulser/receiver, 100MHz high speed A/D board, and basic functions of ultrasonic flaw detector using the program. For the automated testing, motion controller board of ISA interface type is developed to control the 4-axis scanner and a real time iC-scan image of the automated testing is displayed on the monitor. A flaws with the size of less than 1mm in depth are evaluated smaller than its actual site in the testing, but the flaws larger than 1mm appear larger than its actual size on the contrary. This tendency is shown to be increasing as the flaw size increases. h reliable and objective testing results are obtained with the developed system, so that it is expected that it can contribute to safety management and detection of repair position of pipe lines of nuclear power plants and chemical plants.

• Journal of the Korean Society for Nondestructive Testing
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• v.15 no.2
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• pp.378-385
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• 1995

The ultrasonic non-destructive testing uses the directivity of the ultrasonic pulse wave which propagates in one direction. The directivity is expressed as the relationship between the propagate direction and its sound pressure. The directivity of ultrasonic wave is closely related to determination of probe arrangement, testing sensitivity, scanning pitch and defect location and characterization. The paper measured the directivity of shear wave, which reflected from artificial defect located in weld metal zone in butt welded joint similar model made of pyrex glass by using visualization method. 2 MHz and 4 MHz angle probes were used to measure the directivity of reflection wave at the artificial defect. The directivity of shear waves reflected from the defect was different according to the probe position and the shape of butt welded joint. The difference of directivity of reflection wave was existed between 2 MHz and 4 MHz angle probes. The angle of reflection wave became equal to angle of incidence as increase of the height of excess metal.

• Journal of the Korean Society of Propulsion Engineers
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• v.10 no.2
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• pp.102-109
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• 2006

In this Paper, Support Vector Machine(SVM) and Artificial Neural Network(ANN) are used for developing the defect diagnostic algorithm of the aircraft turbo-shaft engine. The system that uses the ANN falls in a local minima when it learns many nonlinear data, and its classification accuracy ratio becomes low. To make up for this risk, the Separate Learning Algorithm(SLA) of ANN has been proposed by using SVM. This is the method that ANN learns selectively after discriminating the defect position by SVM, then more improved performance estimation can be obtained than using ANN only. The proposed SLA can make the higher classification accuracy by decreasing the nonlinearity of the massive data during the training procedure.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.7
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• pp.149-159
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• 1998

The importance of soundness and safety evaluation in weld zone using by the ultrasonic wave has been recently increased rapidly because of the collapses of huge structures and safety accidents. Especially, the ultrasonic method that has been often used for a major non-destructive testing(NDT) technique in many engineering fields plays an important role as a volume test method. Hence, the defecting any defects of weld Bone in austenitic stainless steel type 304 using by ultrasonic wave and neural network is explored in this paper. In order to detect defects, a distance amplitude curve on standard scan sensitivity and preliminary scan sensitivity represented of the relation between ultrasonic probe, instrument, and materials was drawn based on a quantitative standard. Also, a total of 93％ of defect types by testing 30 defect patterns after organizing neural network system, which is learned with an accuracy of 99％, based on ultrasonic evaluation is distinguished in order to classify defects such as holes or notches in experimental results. Thus, the proposed ultrasonic wave and neural network is useful for defect detection and Ultrasonic Non-Destructive Evaluation(UNDE) of weld zone in austenitic stainless steel 304.

• Journal of the Korean Society for Nondestructive Testing
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• v.32 no.1
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• pp.12-19
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• 2012

This study conducted infrared (IR) thermography tests using pipe and plate specimens with artificial wall-thinning defects to find an optimal condition for IR thermography test on the wall-thinned nuclear piping components. In the experiment halogen lamp was used to heat the specimens. The distance between the specimen and the lamp and the intensity of halogen lamp were regarded as experimental parameter. When the distance was set to 1~2 m and the lamp intensity was above 60 % of full power, a single scanning of IR thermography detected all artificial wall-thinning defects, whose minimum dimension was $2{\Theta}=90^{\circ}$, d/t=0.5, and $L/D_o=0.25$, within the pipe of 500 mm in length. Regardless of the distance between the specimen and the lamp, the image of wall-thinning defect in IR thermography became distinctive as the intensity of halogen lamp increased. The detectability of IR thermography was similar for both plate and pipe specimens, but the optimal test condition for IR thermography depended on the type of specimen.

• Journal of the Korean Society for Nondestructive Testing
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• v.30 no.2
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• pp.91-97
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• 2010

This paper proposes method to evaluate the location and size of the internal defects of metallic specimens by the shearing phase lock-in infrared thermography. Until now, infrared thermography test for metal specimen of STS304 and Cu-Zn were conducted to find the best test conditions. However, In unspecified situation of the form and existence of defects, there was a disadvantage to takes a long time for finding the optimal experimental conditions. The defect detection and evaluation was performed at 60 MHz signal using lock-in and shearing-phase method under limited heating conditions. By shearing-phase distribution method, Defects for the maximum, minimum and zero points were quantitatively detected at the size and location of the subsurface. As results, application of the proposed technique was verified for STS304 and Cu7-Zn3 with artificial defect and factors affected defect evaluation were searched and analyzed.

• Journal of Korea Foundry Society
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• v.42 no.4
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• pp.209-217
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• 2022

In the present study, in order to prevent the misrun defects that occur during traditional brass casting, a method for selecting the proper casting process conditions is proposed. A learning model was developed and demonstrated to be able to learn the presence or absence of defects according to the casting process conditions and to predict the occurrence of defects depending on the certain process given. Appropriate process conditions were determined by applying the proposed method, and the determined conditions were verified through a comparison of different simulation results with additional conditions. With this method, it is possible to determine the casting process conditions that will prevent defects in the desired sand model. This technology is expected to contribute to realization of smart traditional brass farming workshops.

• Proceedings of the KIEE Conference
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• 2004.07c
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• pp.1894-1896
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• 2004

전력을 공급하는 변전소 등의 주요 위치에 시설되는 GIS는 사고를 미연에 방지하기 위해 여러 가지 진단방법을 이용하여 이상여부를 판별한다. 이러한 진단 방법 중 현재 국내외적으로 각광을 받고 있는 방법이 UHF센서를 이용한 부분방전 검출방법이다. 따라서, 본 논문에서는 부분방전을 발생시키기 위한 인공결함을 제작하여 GIS 내부에 삽입하고 부분 방전을 발생시켰으며, 이때 발생된 부분방전 신호를 UHF센서를 이용하여 검출하였다. 검출된 부분방전 신호는 phi-f-q 방법으로 분석 하였으며, 그 결과 발생된 파라메터를 인공신경망에 적용하여 각각의 결함에 따른 인식률에 대하여 알아보았다.