• Proceedings of the KIEE Conference
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• 2003.07c
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• pp.1867-1869
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• 2003

In this paper, chamber(Circuit breaker compartment of C-GIS) made of stainless steel with 4 mm width is used. Artificial defect was made on enclosure or HV conductor of chamber and $SF_6$ gas was injected into it according to pressure. In this experiment, Acoustic emission sensors of different types was used to compare sensitivity to detect acoustic signal occurred by Partial discharge(PD) of according to types and resonance frequency in $SF_6$ gas atmosphere. Sensors used in tests was R6I, R15I and 2/4/6 Pre-Amplifier connected with R6IU without pre. amp. In case of R6IU, gain was adjusted with 40 dB like other sensors and operated by differential mode. Post amplifier(post. amp) and band pass filter(BPF) were developed Gain of post. amp. is 60 dB and BPF has band width of $50{\sim}300$ kHz. Also, envelope circuit developed reduces frequency of AE sensor. As a result, in $SF_6$ atmosphere, R6IU and R6I had resonance frequency of 60 Hz was better than R15I. Also, R6IU was better than R6I because of type property of pre.amp. had differential mode.

• Restorative Dentistry and Endodontics
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• v.29 no.3
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• pp.267-272
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• 2004

The purpose of this in vitro study was to evaluate the effect of surface defects and cross-sectional configuration of NiTi rotary files on the fatigue life under cyclic loading. Three NiTi rotary files ($K3^{TM},{\;}ProFile^{\circledR},{\;}and{\;}HERO{\;}642^{\circledR}$) with ＃30/.04 taper were evaluated. Each rotary file was divided into 2 subgroups : control (no surface defects) and experimental group (artificial surface defects), A total of six groups of each 10 were tested. The NiTi rotary files were rotated at 300rpm using the apparatus which simulated curved canal (40 degree of curvature) until they fracture. The number of cycles to fracture was calculated and the fractured surfaces were observed with a scanning electron microscope. The data were analyzed statistically. The results showed that experimental groups with surface defects had lower number of cycles to fracture than control group but there was only a statistical significance between control and experimental group in the $K3^{TM}$ (p<0.05), There was no strong correlation between the cross-sectional configuration area and fracture resistance under experimental conditions. Several of fractured files demonstrated characteristic patterns of brittle fracture consistent with the propagation of pre-existing cracks. This data indicate that surface defects of NiTi rotary files may significantly decrease fatigue life and it may be one possible factor for early fracture of NiTi rotary files in clinical practice.

• Journal of the Korean Institute of Gas
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• v.6 no.1 s.17
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• pp.10-16
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• 2002

This paper introduces developed prototype intelligent pig which detects corrosion on pipeline by using Magnetic Flux Leakage technology. The 8 inch developed MFL(Magnetic Flux Leakage) pig is composed of 5 yokes which magnetize pipeline wall and 45 Hall sensors which detect MFL signal. The designed MFL modules are analyzed by using magnetic circuit method in order to confirm whether pipeline wall is fully saturated. A variety of artificial defects are manufactured on 8 inch diameter steel pipeline in order to acquire MFL signals. So leakage flux of the axial, radial and circumferential component was acquired as defects. The results of this paper show that design technique for 8 inch MFL pig can be applied to large diameter MFL pig and 0.5mm defect depth can be detected.

• Journal of the Korean Society for Nondestructive Testing
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• v.24 no.4
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• pp.307-314
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• 2004

A circumferential guided wave method was developed to detect the axial crack on the bent feeder pipe. Dispersion curves of circumferential guided waves were calculated as a function of curvature of the pipe. In the case of thin plate, i.e. infinite curvature, as the frequency increases, the $S_0$ and $A_0$ mode coincide and eventually become Rayleigh wave mode. In the case of pipe, however, as the curvature increases, the lowest modes do not coincide even in the high frequencies. Based on the analysis, a rocking technique using angle beam transducer was applied to detect an axial defect in the bent region of PHWR feeder pipe. Based on the analysis of experimenal data for artificial notches, the vibration modes of each signal were identified. It was found that the notches with the depth of )0% of wall thickness can be detected with the method.

• Archives of Craniofacial Surgery
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• v.22 no.4
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• pp.177-182
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• 2021

Background: Although prompt surgery after an orbital fracture is preferable, the actual timing of surgery in real-world settings varies. Therefore, this study investigated the outcomes of implant surgery for inferior orbital wall fractures by comparing three groups according to the time interval between the injury and surgery. Methods: A retrospective review was conducted of patients' medical charts and initial computed tomography images from 2009 to 2020. The time to treatment was chosen by patients or their guardians based on the patients' comorbidities and the physician's explanation. The patients were divided into three groups according to the time of surgery (group 1: 3-7 days, group 2: 8-14 days, group 3: 15 or more days). Data were collected on age, the time interval until surgery, the dimensions of the defect, the operation time, the follow-up period, and the postoperative paresthesia score (ranging from 0 to 10). The outcomes were evaluated using a 4-point scale: 4=good (no complications), 3=fair (no subjective symptoms), 2=poor (remaining paresthesia), and 1=very poor (strabismus and/or enophthalmos). Results: The study included 85 patients with unilateral fractures who underwent surgery from 3 to 93 days after injury. The overall score distribution of the surgical outcomes was as follows: good=63, fair=7, poor=6, and very poor=9. The three groups showed no significant differences in the transverse dimension of the injury (p=0.110) or the anteroposterior dimension (p=0.144). In groups 1, 2, and 3, the postoperative outcome scores were 3.84±0.37, 3.63±0.87, and 2.93±1.33 (p=0.083), and the percentage of patients with good outcomes was 84%, 81.25%, and 57.14%, respectively. Conclusion: Performing surgery using an artificial implant within 2 weeks of the injury showed better outcomes and fewer postoperative complications than when treatment was delayed.

• 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 Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.213-215
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• 2021

With the development of IoT technology, various technologies such as artificial intelligence and automation are being grafted into industrial sites, and accordingly, the importance of data processing is increasing. In particular, a system based on a digital image may cause a malfunction due to noise in the image due to a sensor defect or a communication environment problem. Therefore, research on image processing has been continued as a pre-processing process, and an effective noise reduction technique is required depending on the type of noise and the characteristics of the image. In this paper, we propose a modified spatial weight filter to protect edge components in the impulse noise reduction process. The proposed algorithm divides the filtering mask into four regions and calculates the standard deviation of each region. The final output was filtered by applying a spatial weight to the region with the lowest standard deviation value. Simulation was conducted to evaluate the performance of the proposed algorithm, and it showed superior impulse noise reduction performance compared to the existing method.

• Journal of Korean Society for Quality Management
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• v.51 no.2
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• pp.283-296
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• 2023

Purpose: The purpose of this study is to actually implement and verify whether welding defects can be detected in real time by utilizing deep learning AI solutions in the welding process of electric vehicle hairpin winding motors. Methods: AI's function and technological elements using synthetic neural network were applied to existing electric vehicle hairpin winding motor laser welding process by making special hardware for detecting electric vehicle hairpin motor laser welding defect. Results: As a result of the test applied to the welding process of the electric vehicle hairpin winding motor, it was confirmed that defects in the welding part were detected in real time. The accuracy of detection of welds was achieved at 0.99 based on mAP@95, and the accuracy of detection of defective parts was 1.18 based on FB-Score 1.5, which fell short of the target, so it will be supplemented by introducing additional lighting and camera settings and enhancement techniques in the future. Conclusion: This study is significant in that it improves the welding quality of hairpin winding motors of electric vehicles by applying domestic artificial intelligence solutions to laser welding operations of hairpin winding motors of electric vehicles. Defects of a manufacturing line can be corrected immediately through automatic welding inspection after laser welding of an electric vehicle hairpin winding motor, thus reducing waste throughput caused by welding failure in the final stage, reducing input costs and increasing product production.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.570-573
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• 2022

This paper presents the identification of void diameters for a cast-resin transformer using an artificial neural network (ANN) model. A PD signal was measured by the Rogowski coil sensor which has the planar and thin structures fabricated on a printed circuit board (PCB), and the PD electrode system was fabricated to simulate a PD defect by a void. In addition, void samples with different diameters were fabricated by injecting air in a cylindrical aluminum frame using a syringe during the epoxy curing process. To identify the diameter of void defects, PD characteristics such as the discharge magnitude, pulse count, and phase angle were extracted and back propagation algorithm (BPA) was designed using virtual instrument (VI) based on the Labview program. From the experimental results, the BPA algorithm proposed in this paper has over 90% accurate rate to identify the diameter of void defects and is expected to use reference data of maintenance and replacement of insulation for cast-resin transformers in the on-site PD measurement.

• The Transactions of the Korea Information Processing Society
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• v.13 no.3
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• pp.130-139
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• 2024

Artificial intelligence models are being used to detect facility anomalies using physics data such as vibration, current, and temperature for predictive maintenance in the manufacturing industry. Since the types of facility anomalies, such as facility defects and failures, anomaly detection methods using autoencoder-based unsupervised learning models have been mainly applied. Normal or abnormal facility conditions can be effectively classified using the reconstruction error of the autoencoder, but there is a limit to identifying facility anomalies specifically. When facility anomalies such as unbalance, misalignment, and looseness occur, the facility vibration frequency shows a pattern different from the normal state in a specific frequency range. This paper presents an N-segmentation anomaly detection method that performs anomaly detection by dividing the entire vibration frequency range into N regions. Experiments on nine kinds of anomaly data with different frequencies and amplitudes using vibration data from a compressor showed better performance when N-segmentation was applied. The proposed method helps materialize them after detecting facility anomalies.