• Journal of the Institute of Convergence Signal Processing
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• v.20 no.2
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• pp.78-83
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• 2019

Sounds based machine fault diagnosis recovers all the studies that aim to detect automatically abnormal sound on machines using the acoustic emission by these machines. Conventional methods that use mathematical models have been found inaccurate because of the complexity of the industry machinery systems and the obvious existence of nonlinear factors such as noises. Therefore, any fault diagnosis issue can be treated as a pattern recognition problem. We propose here an automatic fault diagnosis method of hand drills using discrete wavelet transform(DWT) and pattern recognition techniques such as artificial neural networks(ANN). We first conduct a filtering analysis based on DWT. The power spectral density(PSD) is performed on the wavelet subband except for the highest and lowest low frequency subband. The PSD of the wavelet coefficients are extracted as our features for classifier based on ANN the pattern recognition part. The results show that the proposed method can be effectively used not only to detect defects but also to various automatic diagnosis system based on sound.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.9
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• pp.339-348
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• 2021

In recent software defect prediction research, defect prediction between cross projects and cross-version projects are actively studied. Cross-version defect prediction studies assume WP(Within-Project) so far. However, in the CV(Cross-Version) environment, the previous work does not consider the distribution difference between project versions is important. In this study, we propose an automated Bayesian optimization framework that considers distribution differences between different versions. Through this, it automatically selects whether to perform transfer learning according to the difference in distribution. This framework is a technique that optimizes the distribution difference between versions, transfer learning, and hyper-parameters of the classifier. We confirmed that the method of automatically selecting whether to perform transfer learning based on the distribution difference is effective through experiments. Moreover, we can see that using our optimization framework is effective in improving performance and, as a result, can reduce software inspection effort. This is expected to support practical quality assurance activities for new version projects in a cross-version project environment.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1443-1444
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• 2007

지속적으로 증가되는 전기에너지 공급의 신뢰성을 높이기 위하여 전력설비 주요 사고 원인인 부분방전(PD : Partial Discharge)을 검출하고 결함원의 패턴인식 방법의 개발 필요성 날로 증가되고 있다. 본 논문은 부분방전의 패턴인식 확률을 높이기 위하여 검출된 부분방전의 주파수 분석을 이용하여 Conventional PRPD Analysis 방법의 결함 판독확률을 향상시키기 위하여 Advanced PRPD를 제안 한다. 이를 위하여, GIS(Gas Insulated Switchgear)의 주요 사고원인으로 인식되어 있는 결함들을 인위적으로 제작 후 삽입하여 부분방전을 발생시켜 자체 설계 개발된 UHF 내장형 센서를 이용하여 검출하였다. 새로이 제안하는 방법과 기존의 PRPD 방법의 인식률을 상호 비교하기 위하여, 두 가지 그룹을, 즉, 기존의 방법에 의한 것과 부분방전의 주파수 분석이 포함된 방법에 의한 데이터그룹을 구축하고 학습방법은 동일한 인공신경망 MLP (Multilayer Perceptron)를 이용하여 인식률과 학습시간을 동시에 비교하였다. 상호 비교 결과에 의하면, 후자의 방법이 인식확률 뿐만아니라 학습시간도 좋은 결과가 나타났다.

• Proceedings of the KWS Conference
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• 2004.05a
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• pp.21-23
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• 2004

파이프용접에서 특정용접을 하기 위한 최적의 용접조건 선정하는 작업은 대개 많은 시간과 비용을 요구한다. 최근에 인공지능(AI) 기술을 이용하여 용접변수를 결정하기 위해서는 생산성, 용접결함 등 여러 가지 요소를 고려해야 한다고 주장한다. (중략)

• Proceedings of the Materials Research Society of Korea Conference
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• 2009.11a
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• pp.19.2-19.2
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• 2009

금속을 용해 응고시킬 때 생성되는 소위, 주조 결함이나 소결금속 내의 기공은 재료의 성능이나강도를 현저하게 낮추는 결함으로서 예전부터 기피되어 왔다. 또한, 재료공정에있어서도 여하의 기공이나 기포가 없는 치밀한 고강도 및 고기능성 재료를 개발하는 것에 최대한의 주의와 관심을 기울여 왔다. 그렇지만, 우리가 자연계의 천연물이나 인공물을 둘러보면 그 대부분이다공질임을 쉽게 눈치챌 수 있다. 예를 들어 목재, 지엽등의 생물을 시작해서 콘크리트 등의 인공물, 우리 체내의 뼈도 전형적인 다공질구조로 구성되어 있다. 이러한 구조로부터 재료의 재질제어 이외에 구조제어라는 새로운 어프로치를 고려할 수 있고, 최근 들어, 금속재료에 있어서도 이러한 다공질구조에 관한 연구가활성화되어 충격흡수재, 생체재료, 베어링재료 등의 다양한응용이 전개되고 있다. 특히, 원주상의 방향성 기공을 갖는 로터스금속은 기존의 복잡한구조의 다공질금속보다 뛰어난 기계적 성질을 갖는다. 이러한 다공질금속은 일방향응고할 때 생성하는 과포화가스원자를 석출시켜 기공을 일방향으로 성장시킨다. 즉, 융점에서의 고상과 액상의 가스 용해도 차를 이용하는 것으로서 응고시에 고용할 수 없는 가스원자가 기공을 형성한다. 이와같이 제조한 방향성 다공질금속은 BT (인플란트, 생체적합성, 저탄성, 경량), ST (초음속기엔진부품, 경량), IT (고성능수냉모듈), ET(고온촉매, 필터)의 분야로의 응용이 기대된다. 본 강연에서는 방향성 다공질금속의 제조법, 특성 및 응용을 포함하여그 동안의 연구성과 및 앞으로의 과제 등을 소개하고자 한다.

• Journal of the Korean Society for Nondestructive Testing
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• v.17 no.4
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• pp.248-261
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• 1997

Ultrasonic pattern recognition is the most effective approach to the problem of discriminating types of flaws in weldments based on ultrasonic flaw signals. In spite of significant progress in the research on this methodology, it has not been widely used in many practical ultrasonic inspections of weldments in industry. Hence, for the convenient application of this approach in many practical situations, we develop an intelligent ultrasonic signature classification software which can discriminate types of flaws in weldments based on their ultrasonic signals using various tools in artificial intelligence such as neural networks. This software shows the excellent performance in an experimental problem where flaws in weldments are classified into two categories of cracks and non-cracks. This performance demonstrates the high possibility of this software as a practical tool for ultrasonic flaw classification in weldments.

• Journal of the Korean Society for Nondestructive Testing
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• v.17 no.3
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• pp.174-183
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• 1997

Turbine rotor disc consists of disc, bore, keyway, hub, and rim in which the typical defects are located. And these part of disc has very complicated geometry, therefore proper transducer selection, wedge design, fabrication, classification and evaluation of the signal identification are required. In this research, test block with the artificial flaws at keyway and boresurface parts have been used in order to establish the ultrasonic inspection technique for flaw detectability on disc. The analysis of the signals from the test blocks was performed. The wedges were designed according to the curvature from the discs. All the ultrasonic signals were collected and identified for evaluation. The ultrasonic inspection technique for the flaw-detection was established from this research. And it is proved that the result of this research can be applicable in the field inspection.

• Proceedings of the Korea Information Processing Society Conference
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• 2024.05a
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• pp.436-438
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• 2024

의료 당국은 가능한 최상의 서비스를 보장하기 위해 코로나19와 같은 전염병 기간뿐만 아니라 일상적인 운영에서도 의료 공급망 프로세스를 효과적으로 관리해야 한다. 제품 리콜, 제품 공급 부족 모니터링, 만료 및 위조는 방지되어야 하는 중요한 의료 공급망 운영 중 일부이다. 본 논문에서는 블록체인과 분산형 스토리지 시스템을 사용한 위조 의약품 유통 방지 시스템을 제안한다. 제안하는 솔루션은 투명성을 높이고, 이해 관계자간의 커뮤니케이션을 개선하며, 제품 조달 일정을 단축하는 동시에 중요한 격차와 결함을 제거한다.

• Journal of the Korean Society for Nondestructive Testing
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• v.32 no.4
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• pp.410-417
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• 2012

Currently domestic thermal and nuclear power plants are comprised of many type's condenser and steam generator tubes to produce the electricity of good quality. There are some methods to inspect these tubes in the event that several defects were discovered in these facilities. Among many non-destructive methods, we used guided wave to inspect the soundness of tubes, because this method is very fast to detect the defect and very simple to install the equipment and also, can inspect up to the long range at a fixed point. Also, this method has a drawback that does not detect a very small size defect. So, we made an effort to overcome this drawback through the experimentation and signal analysis according to the size and shape of the defect through the manufacture of various artificial cracks capable to generate within the small size tube in the study and we anticipate that these detect limits can be overcome along with the development of the signal processing and manufacturing technology of the sensor for the inspection.

• Journal of the Korea institute for structural maintenance and inspection
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• v.23 no.7
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• pp.80-86
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• 2019

As computer performance improves, research using deep learning are being actively carried out in various fields. Recently, deep learning technology has been applying to the safety evaluation for structures. In particular, the internal blades of a turbine structure requires experienced experts and considerable time to detect surface damages because of the difficulty of separation of the blades from the structure and the dark environmental condition. This study proposes a Faster R-CNN deep learning model that can detect surface damages on the internal blades, which is one of the primary elements of the turbine structure. The deep learning model was trained using image data with dent and punch damages. The image data was also expanded using image filtering and image data generator techniques. As a result, the deep learning model showed 96.1% accuracy, 95.3% recall, and 96% precision. The value of the recall means that the proposed deep learning model could not detect the blade damages for 4.7%. The performance of the proposed damage detection system can be further improved by collecting and extending damage images in various environments, and finally it can be applicable for turbine engine maintenance.