• 대한전기학회논문지:전기물성ㆍ응용부문C
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• 제54권12호
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• pp.553-557
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• 2005

This paper describes the application results of an ultrasonic detection system for the power transformer. The ultrasonic detection system with 6 sensors was applied to detect partial discharge in a 154kV transformer with a dangerous levels of $C_{2}H_{2},\;C_{2}H_4$ and $CH_{4}$ gases. The ultrasonic detection tests were carried out 2 times, respectively, to confirm the existence and location of the partial discharge in the transformer. As a result of internal inspection, the arc trace between the pressure ring and core due to the partial discharge was found at the estimated position based on the amplitude and arriving time of the ultrasonic signals. Therefore, it was verified that the ultrasonic detection system is effective as a preventive diagnosis method for the power transformer. Also, the reliability of the ultrasonic detection system in detecting partial discharges in the transformer was also confirmed. It is expected, therefore, that the ultrasonic detection system will have beneficial effects on applications and verifications in detecting partial discharges for the power transformer.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2010년도 추계학술대회 논문집
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• pp.533-534
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• 2010

Fiber optic sensor has bee widely used in the industrial applications. For the application of acoustic detection of the high voltage electric transformer Sagnac interferometer can be used. In this paper several different materials of mandrel were used in the fiber sensor array. Based on the transformer oil fiber optic sensor is more sensitive than in the air.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1991년도 추계학술대회 논문집 학회본부
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• pp.415-418
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• 1991

This paper described an instrument for the detection and geometric location of partial discharge(PD) sources in oil transformer. This instrument measures electric current pulse and ultrasonic pulse simultaneously in order to determine the geometric location of PD in transformer. Laboratory experiment systems are made for detection and location of PD in oil transformer. It was observed that there are effects of the barrier, such as insulation papers, silicon steel plate and actual transformer with location and detection of PD in model transformer. Through the laboratory actual test, it was clarified that this measurement device could be used satisfactorily for location of pd in oil transformer.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2005년도 하계학술대회 논문집 Vol.6
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• pp.574-574
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• 2005

To found out the degradation characteristic of transformer insulation, insulation material was depisited into transformer oil and heated. Due to the thermal stress which added to insulation, the density of carbon dioxide which included in transformer oil was mesured by using the gas density detection equipment of gas sensor and air circulation method. As a result, it didn't match with the transformer supervision standard. But it was found that as thermal stress increased, the density of carbon dioxide propertionally increased.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2021년도 추계학술대회
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• pp.112-114
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• 2021

도시 상태를 탐지하기 위해서는 운송 수단 수, 교통 흐름등이 필수적으로 파악되어야 할 요소이다. 본 논문에서는 기존의 Mask R-CNN을 이용하여 다양한 차량의 형태를 학습하고, 드론으로 촬영한 도시항공 영상에서 특정 유형의 차량 들을 검출하는 시스템을 오늘날 NLP 분야에서 널리 쓰이게 된 Transformer 모델을 컴퓨터 비전 문제에 도입하여 기존의 컨볼루션 신경망보다 높은 성능을 보여준 Swin Transformer 모델을 이용하여 기존의 연구에서 보여주었던 검출 시스템 능력을 향상시켰다.

• 대한임베디드공학회논문지
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• 제17권6호
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• pp.319-327
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• 2022

Transformers are the most famous deep learning models that has achieved great success in natural language processing and also showed good performance on computer vision. In this survey, we categorized transformer-based models for computer vision, particularly object detection tasks and perform comprehensive comparative experiments to understand the characteristics of each model. Next, we evaluated the models subdivided into standard transformer, with key point attention, and adding attention with coordinates by performance comparison in terms of object detection accuracy and real-time performance. For performance comparison, we used two metrics: frame per second (FPS) and mean average precision (mAP). Finally, we confirmed the trends and relationships related to the detection and real-time performance of objects in several transformer models using various experiments.

• International journal of advanced smart convergence
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• 제13권2호
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• pp.195-204
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• 2024

In this paper, we investigate the comparative performance of two leading object detection architectures, YOLOv7 and Detection Transformer (DETR), across varying levels of data augmentation using CycleGAN. Our experiments focus on chest scan images within the context of biomedical informatics, specifically targeting the detection of abnormalities. The study reveals that YOLOv7 consistently outperforms DETR across all levels of augmented data, maintaining better performance even with 75% augmented data. Additionally, YOLOv7 demonstrates significantly faster convergence, requiring approximately 30 epochs compared to DETR's 300 epochs. These findings underscore the superiority of YOLOv7 for object detection tasks, especially in scenarios with limited data and when rapid convergence is essential. Our results provide valuable insights for researchers and practitioners in the field of computer vision, highlighting the effectiveness of YOLOv7 and the importance of data augmentation in improving model performance and efficiency.

• KIEE International Transactions on Electrophysics and Applications
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• 제2C권6호
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• pp.297-303
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• 2002

We have developed an on-line ultrasonic detector to monitor partial discharge in an operating transformer. The ultrasonic sensor has 150[KHz] resonance frequency and contains a pre-amplifier with 60[㏈] gain. The on-line ultrasonic detector has 50~300[KHz] frequency band-pass filter to remove electrical and mechanical noises from the transformer. This detector has an ultrasonic signal discrimination algorithm which discriminates ultrasonic signals due to partial discharge in a transformer. A moving average method of ultrasonic signal number was employed to effectively monitor the increasing trend of the partial discharge. This paper describes an experience of partial discharge detection in a 154[㎸] operating transformer using an ultrasonic detector. With regards to gas analysis in oil, C2H2 gas was produced with a warning level in this transformer We detected ultrasonic signals on the transformer steel wall, and estimated the position of partial discharge. With further inspection, we found carbonized marks due to partial discharge on the supporting bolt which fastens the windings.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2005년도 추계학술대회 논문집 Vol.18
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• pp.323-324
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• 2005

To found out the degradation characteristic of transformer insulation, insulation materials and electrodes are deposited into transformer oil. They used to heated and make flashover. Due to the thermal and electrical stress added to insulation materials, the density of carbon dioxide and hydrogen included in transformer oil was increased. The gas density can measured by using the gas density detection equipment of gas sensor and air circulation method.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2022년도 제66차 하계학술대회논문집 30권2호
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• pp.67-70
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• 2022

NLP 분야에서 적극 활용되기 시작한 Transformer 모델을 Vision 분야에서 적용하기 시작하면서 object detection과 segmentation 등 각종 분야에서 기존 CNN 기반 모델의 정체된 성능을 극복하며 향상되고 있다. 또한, label 데이터 없이 이미지들로만 자기지도학습을 한 ViT(Vision Transformer) 모델을 통해 이미지에 포함된 여러 중요한 객체의 영역을 검출하는 saliency map을 추출할 수 있게 되었으며, 이로 인해 ViT의 자기지도학습을 통한 object detection과 semantic segmentation 연구가 활발히 진행되고 있다. 본 논문에서는 ViT 모델 뒤에 classifier를 붙인 모델에 일반 학습한 모델과 자기지도학습의 pretrained weight을 사용해서 전이학습한 모델의 시각화를 통해 각 saliency map들을 비교 분석하였다. 이를 통해, 클래스 분류 학습 기반 전이학습이 transformer의 saliency map에 미치는 영향을 확인할 수 있었다.