• Journal of the Semiconductor & Display Technology
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• v.22 no.2
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• pp.40-44
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• 2023

The defect detection based on deep learning requires minimal loss and high accuracy to pinpoint product defects. In this paper, we confirm the loss rate of deep learning training based on disc-shaped artificial scaffold images. It is intended to compare the performance of Cross-Entropy functions used in object detection algorithms. The model was constructed using normal, defective artificial scaffold images and category cross entropy and sparse category cross entropy. The data was repeatedly learned five times using each loss function. The average loss rate, average accuracy, final loss rate, and final accuracy according to the loss function were confirmed.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.6
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• pp.159-166
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• 2005

The pulse amplitude of a search radar signal received by an ES system is not constant pulse by pulse because of the radar's scan characteristics. The variation of the pulse amplitude causes the ES detection loss in the ES system. Therefore, the ES detection range equation should consider the ES detection loss caused by the search radar's scan characteristics. In this paper, we theoretically analyze the ES detection loss for the circular scar and propose the model to evaluate it quantitatively. The experiment results for the real search radar signals demonstrate that the proposed model is suitable for the evaluation model of the ES detection loss related to the circular scan of radars.

• The Journal of the Korea Contents Association
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• v.19 no.9
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• pp.637-645
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• 2019

The RFID group proof is an extension of the yoking proof proving that multiple tags are scanned by a reader simultaneously. Existing group proof schemes provide only delayed tag loss detection which detects loss of tag response in a verification phase. However, delayed tag loss detection is not suitable for real-time applications where tag loss must be detected immediately. In this study, I propose a tag response loss detection scheme which detects loss of tag response in the proof generation process quickly. In the proposed scheme, the tag responds with the sequence number assigned to the tag group, and the reader detects the loss of the tag response through the sequence number. Through an experiment for indistinguishability, I show that the sequence number is secure against an analyzing message attack to distinguish between specific tags and tag groups. In terms of efficiency, the proposed scheme requires fewer transmissions and database operations than existing techniques to determine which tags response is lost.

• Proceedings of the Korean Information Science Society Conference
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• 2001.04a
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• pp.163-165
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• 2001

본 논문에서는 먼저 Forwarding 방식의 라우팅 프로토콜인 MPLS(Multi-Protocol Label Switching)와 네트웍에서의 Traffic Engineering(TE)에 대한 개괄적인 설명과 함께, MPLS 네트웍 내에서의 트래픽 엔지니어링에 대해 기술한다. 그리고 MPLS 도메인 양 끝단에서 단일 경로의 패킷에 대한 MPLS 헤더의 레이블 번호를 이용한 동일한 패킷인지에 대한 확인 방안과 MPLS 도메인 내에서 Loss Detection 메커니즘을 이용한 효율적인 트래픽 엔지니어링방안을 제안한다. 향후 본 연구 방안을 적용하게 되면, 차등 서비스(Differentiated Services, Diffserv)를 제공하는 네트웍 환경의 핵심 망과 Mobile IP 기반의 무선 네트웍 환경에서 유선 네트웍의 Quality of Service(QoS)를 향상시킬 수 있을 것이다.

• Journal of Korean Academy of Nursing
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• v.41 no.6
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• pp.758-767
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• 2011

Purpose: This study was done to examine the threshold value of estimated height loss at which the risk of osteoporosis increases and to verify its discriminative ability in the detection of osteoporosis. Methods: It was conducted based on epidemiological descriptive methods on 732 Korean women at a public healthcare center in Seoul between July and November 2010. ANOVA, Pearson correlation, logistic regression analysis and receiver operating characteristics (ROC) curve were used for data analysis. Results: There was an age-related correlation between bone mineral density (lumbar spine: F=37.88, p<.001; femur: F=54.27, p<.001) and estimated height loss (F=27.68, p<.001). Estimated height loss increased significantly with decreasing bone mineral density (lumbar spine: r=-.23, p<.001; femur: r=-.34, p<.001). The odds ratio for the point at which the estimated height loss affects the occurrence of osteoporosis was found to increase at a cut-off value of 2 cm and the area under ROC curve was .71 and .82 in lumbar spine and femur, respectively. Conclusion: The optimal cut-off value of the estimated height loss for detection of osteoporosis was 2 cm. Height loss is therefore a useful indicator for the self-assessment and prognosis of osteoporosis.

• Smart Structures and Systems
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• v.29 no.1
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• pp.153-166
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• 2022

Identifying fine cracks in steel bridge facilities is a challenging task of structural health monitoring (SHM). This study proposed an end-to-end crack image segmentation framework based on a one-step Convolutional Neural Network (CNN) for pixel-level object recognition with high accuracy. To particularly address the challenges arising from small object detection in complex background, efforts were made in loss function selection aiming at sample imbalance and module modification in order to improve the generalization ability on complicated images. Specifically, loss functions were compared among alternatives including the Binary Cross Entropy (BCE), Focal, Tversky and Dice loss, with the last three specialized for biased sample distribution. Structural modifications with dilated convolution, Spatial Pyramid Pooling (SPP) and Feature Pyramid Network (FPN) were also performed to form a new backbone termed CrackDet. Models of various loss functions and feature extraction modules were trained on crack images and tested on full-scale images collected on steel box girders. The CNN model incorporated the classic U-Net as its backbone, and Dice loss as its loss function achieved the highest mean Intersection-over-Union (mIoU) of 0.7571 on full-scale pictures. In contrast, the best performance on cropped crack images was achieved by integrating CrackDet with Dice loss at a mIoU of 0.7670.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.3
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• pp.109-114
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• 2021

GAN(Generative Adversarial Network) is an image generation model, which is composed of a generator network and a discriminator network, and generates an image similar to a real image. Since the image generated by the GAN should be similar to the actual image, a loss function is used to minimize the loss error of the generated image. However, there is a problem that the loss function of GAN degrades the quality of the image by making the learning to generate the image unstable. To solve this problem, this paper analyzes GAN-related studies and proposes an edge GAN(eGAN) using edge detection. As a result of the experiment, the eGAN model has improved performance over the existing GAN model.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.12
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• pp.1803-1808
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• 2013

To maintain contact between catenary and pantograph copper is important in order to transmit power smoothly on Current collection system. But, Arc discharge with strong light is generated because of contact loss. Therefore, Arc discharge detection is important measurement factor judging performance of current collection system. In this paper, It is described to results of arc discharge applying UV detection technology using arc generator. And Arc discharge was detected using the most commonly used processing catenary and rigid catenary and pantograph copper of electric rolling stock for securing arc detection instrument reliability. Results of contact loss detection instrument in this paper will be used for maintenance of current collection quality and system.

• Journal of Ocean Engineering and Technology
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• v.8 no.1
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• pp.62-70
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• 1994

In order to estimate the detection range of a active SONAR system, the SONAR equation is commonly used. In this paper, an algorithm to calculate detection range in active SONAR system as function of SONAR depth and target depth is presented. For given SONAR parameters and environment, the transmission loss and background level are found, signal excess is computed. Using log-normal distribution, signal excess is converted to detection probability at each range. Then, the detection range is obtained by integrating the detection probability as function of range for each depth. The proposed algorithm have been applied to the case of omni-directional source with center frequency 30Hz for summer and winter sound profiles. It is found that the optimal search depth is the source depth since the detection range increase at source depth where the signal excess is maximized.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.8
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• pp.2199-2213
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• 2024

To enhance the quality of defect detection for Printed Circuit Board Assembly (PCBA) during electronic product manufacturing, this study primarily focuses on optimizing the YOLOv7-based method for PCBA defect detection. In this method, the Mish, a smoother function, replaces the Leaky ReLU activation function of YOLOv7, effectively expanding the network's information processing capabilities. Concurrently, a Squeeze-and-Excitation attention mechanism (SEAM) has been integrated into the head of the model, significantly augmenting the precision of small target defect detection. Additionally, considering angular loss, compared to the CIoU loss function in YOLOv7, the SIoU loss function in the paper enhances robustness and training speed and optimizes inference accuracy. In terms of data preprocessing, this study has devised a brightness adjustment data enhancement technique based on split-filtering to enrich the dataset while minimizing the impact of noise and lighting on images. The experimental results under identical training conditions demonstrate that our model exhibits a 9.9% increase in mAP value and an FPS increase to 164 compared to the YOLOv7. These indicate that the method proposed has a superior performance in PCBA defect detection and has a specific application value.