• Korean Journal of Metals and Materials
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• v.48 no.5
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• pp.436-444
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• 2010

The hot tear susceptibility of Al alloys was investigated by using a constrained-rod mold designed to quantify 8 types of tear tendency. The severity of the crack was scored by 5 grades on a scale of 0 to 4, with 0 being "no crack formed" and 4 being "complete separation by crack". The Hot Tear Susceptibility index (HTS) which consists of crack type scores and position scores, was proposed to compare the hot tear tendency of Al alloys. A356.0 cast alloy and AA6061 wrought Al alloy showed an HTS value of 27.5 and 53 respectively. The effects of Si, Cu, and Mg content on hot tear tendency were also investigated with a constrained-rod mold. The variation of HTS values with alloying elements represents similar behavior in the variation of the solidification range in a pseudo binary phase diagram.

• Korean Journal of Metals and Materials
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• v.49 no.11
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• pp.874-881
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• 2011

The present study describes a new technique to extract the primary silicon from an Al-Si alloy melt using centrifugal force during its solidification. The primary silicon was separated from an Al-50 wt.%Si alloy by centrifugal force in the form of a foam, which facilitated subsequent acid leaching to extract the pure silicon due to its wide surface area. The foam recovery after centrifugal separation was decreased as centrifugal acceleration was increased. The final recovery after acid leaching became closer to the solid fraction of the alloy, which was calculated from the Al-Si binary phase diagram, with increasing centrifugal acceleration due to the effective removal of the attached Al on the foam. The purity of the primary silicon obtained by the centrifugal separation method was over 99.99%, with only aluminum being also present.

• Korean Journal of Remote Sensing
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• v.38 no.1
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• pp.45-56
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• 2022

The Ieodo Ocean Research Station (IORS) lies between the exclusive economic zone (EEZ) boundaries of Korea, Japan, and China. The geographical positioning of the IORS makes it ideal for monitoring ships in the area. In this study, we introduce ship monitoring results by Automatic Identification System (AIS) and the Broadband 3G^TM radar, which has been developed for use in small ships using the Frequency Modulated Continuous Wave (FMCW) technique. AIS and FMCW radar data were collected at IORS from November 23th to 30th, 2013. The acquired FMCW radar data was converted to 2-D binary image format over pre-processing, including the internal and external noise filtering. The ship positions detected by FMCW radar images were passed into a tracking algorithm. We then compared the detection and tracking results from FMCW radar with AIS information and found that they were relatively well matched. Tracking performance is especially good when ships are across from each other. The results also show good monitoring capability for small fishing ships, even those not equipped with AIS or with a dysfunctional AIS.

• Journal of IKEEE
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• v.26 no.2
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• pp.240-248
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• 2022

Floating-point images that express pixel information as real numbers are used in HDR images. There have been various researches on efficient median filter algorithms, but most of them are applicable to 8-bit depth images and there are only a few number of algorithms applicable to floating-point images, including Gil and Werman's algorithm. In this paper, we propose a median filter algorithm that works efficiently on floating-point images by improving Kim's algorithm, which improved Gil and Werman's algorithm. Experimental results show that the execution time is improved by about 10% compared to the Kim's algorithm by reducing the redundant work for the repetitively used binary search tree and applying the inverted index.

• 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.

• International Journal of Computer Science & Network Security
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• v.22 no.12
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• pp.98-106
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• 2022

A condition of the retina known as hypertensive retinopathy (HR) is connected to high blood pressure. The severity and persistence of hypertension are directly correlated with the incidence of HR. To avoid blindness, it is essential to recognize and assess HR as soon as possible. Few computer-aided systems are currently available that can diagnose HR issues. On the other hand, those systems focused on gathering characteristics from a variety of retinopathy-related HR lesions and categorizing them using conventional machine-learning algorithms. Consequently, for limited applications, significant and complicated image processing methods are necessary. As seen in recent similar systems, the preciseness of classification is likewise lacking. To address these issues, a new CAD HR-diagnosis system employing the advanced Deep Dense CNN Learning (DD-CNN) technology is being developed to early identify HR. The HR-diagnosis system utilized a convolutional neural network that was previously trained as a feature extractor. The statistical investigation of more than 1400 retinography images is undertaken to assess the accuracy of the implemented system using several performance metrics such as specificity (SP), sensitivity (SE), area under the receiver operating curve (AUC), and accuracy (ACC). On average, we achieved a SE of 97%, ACC of 98%, SP of 99%, and AUC of 0.98. These results indicate that the proposed DD-CNN classifier is used to diagnose hypertensive retinopathy.

• Journal of the Korean Society of Clothing and Textiles
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• v.47 no.6
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• pp.1057-1079
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• 2023

This paper examines the use of semiotics for analyzing fashion advertisements in the fast fashion industry. While previous studies have explored the use of semiotics in various industries, the application of this theory in the fashion sector-especially regarding fast fashion's commercial videos related to sustainability-remains underexplored. The paper adopts Roland Barthes' Semiotics Theory to analyze the advertising videos related to the sustainability of major fast fashion brands such as H&M, MANGO, and ZARA. The research approach involved reviewing all commercial videos related to sustainability on these brands' official YouTube accounts and conducting comprehensive analyses of advertisements using the binary opposition analysis framework. The paper's findings indicate that these commercial videos serve as a platform to mold a brand's sustainability image and promote the notion that fast fashion brands are leading the charge toward sustainability, preparing for an unpredictable future, guiding people toward hope, and offering ultimate freedom. This research high-lights the necessity for a critical examination of advertising videos related to sustainability in the fast fashion industry to guarantee accountability and transparency.

• Annual Conference of KIPS
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• 2007.05a
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• pp.3-5
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• 2007

본 논문에서는 새로운 내용기반 이미지 검색 기법으로 식물 잎의 윤곽선에 대하여 동적 시간 정합 기법을 이용하여 유사한 이미지를 효과적으로 검색하는 방법을 제안한다. 이를 위하여 우선 식물 잎의 기준점에 대하여 잎의 가장자리를 따라 가면서 구해지는 거리의 곡선을 통하여 잎의 외형 특성을 표현하였다. 추출된 곡선 정보의 효율적인 저장과 처리를 위하여 곡선의 특성을 표현할 수 있는 퓨리에 계수(Fourier Coefficients)를 계산하고 이를 바탕으로 유사한 이미지를 계산하였다. 이런 과정에서 생기는 문제점으로는 복잡한 형태의 곡선에 대해서는 퓨리에 계수를 통하여 저장하고 복원하는 과정에서 원본 곡선의 세부적인 형태 정보를 상실하게 된다. 이러한 문제를 해결하기 위해서는 복잡한 곡선 유형에 대해서는 복원시 상실되는 정보가 최소화될 수 있는 작은 단위의 구간으로 나누고 이에 대한 퓨리에 계수를 계산하는 방법으로 다수의 퓨리에 계수 세트를 추출하는 이진 구간 분할 (Binary Range Reduction) 알고리즘을 사용하였고 질의 이미지와 저장된 이미지들을 비교하는 과정에서 검색의 정확도를 향상시키기 위하여 동적 시간 정합(Dynamic Time Warping) 알고리즘을 사용하였다. 그리고 검색의 효율을 더욱 높이기 위하여 추출된 외형 정보를 기반으로 잎의 유형을 다양한 카테고리로 분류하는 외형 기형 기반의 잎 분류 기법을 제안하였다. 다양한 실험을 통하여 제안한 기법이 식물 잎 검색에 우수한 성능을 나타냄을 보인다.

• Journal of radiological science and technology
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• v.33 no.2
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• pp.97-107
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• 2010

The objectives of this study is to figure out the unknown image processing methods of commercial CR system. We have implemented the processing curve of each Look up table(LUT) in REGIUS 150 CR system by using virtual digital test pattern method. The characteristic of Dry Imager was measured also. First of all, we have generated the virtual digital test pattern file with binary file editor. This file was used as an input data of CR system (REGIUS 150 CR system, KONICA MINOLTA). The DICOM files which were automatically generated output files by the CR system, were used to figure out the processing curves of each LUT modes (THX, ST, STM, LUM, BONE, LIN). The gradation curves of Dry Imager were also measured to figure out the characteristics of hard copy image. According to the results of each parameters, we identified the characteristics of image processing parameter in CR system. The processing curves which were measured by this proposed method showed the characteristics of CR system. And we found the linearity of Dry Imager in the middle area of processing curves. With these results, we found that the relationships between the curves and each parameters. The G value is related to the slope and the S value is related to the shift in x-axis of processing curves. In conclusion, the image processing method of the each commercial CR systems are different, and they are concealed. This proposed method which uses virtual digital test pattern can measure the characteristics of parameters for the image processing patterns in the CR system. We expect that the proposed method is useful to analogize the image processing means not only for this CR system, but also for the other commercial CR systems.

• Journal of Korea Multimedia Society
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• v.8 no.11
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• pp.1496-1509
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• 2005

In this paper, an adaptive license plate recognition system which detects and recognizes license plate at real-time by using projected plane convolution and Decision Tree Classifier is proposed. And it was tested in circumstances which presence of complex background. Generally, in expressway tollgate or gateway of parking lots, it is very difficult to detect and segment license plate because of size, entry angle and noisy problem of vehicles due to CCD camera and road environment. In the proposed algorithm, we suggested to extract license plate candidate region after going through image acquisition process with inputted real-time image, and then to compensate license size as well as gradient of vehicle with change of vehicle entry position. The proposed algorithm can exactly detect license plate using accumulated edge, projected convolution and chain code labeling method. And it also segments letter of license plate using adaptive binary method. And then, it recognizes license plate letter by applying hybrid pattern vector method. Experimental results show that the proposed algorithm can recognize the front and rear direction license plate at real-time in the presence of complex background environments. Accordingly license plate detection rate displayed $98.8\%$ and $96.5\%$ successive rate respectively. And also, from the segmented letters, it shows $97.3\%$ and $96\%$ successive recognition rate respectively.