• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.1
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• pp.17-27
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• 2023

Weeds are one of the factors that reduce crop yield through nutrient and photosynthetic competition. Quantification of weed density are an important part of making accurate decisions for precision weeding. In this study, we tried to quantify the density of weeds in images of maize fields taken by unmanned aerial vehicle (UAV). UAV image data collection took place in maize fields from May 17 to June 4, 2021, when maize was in its early growth stage. UAV images were labeled with pixels from maize and those without and the cropped to be used as the input data of the semantic segmentation network for the maize detection model. We trained a model to separate maize from background using the deep learning segmentation networks DeepLabV3+, U-Net, Linknet, and FPN. All four models showed pixel accuracy of 0.97, and the mIOU score was 0.76 and 0.74 in DeepLabV3+ and U-Net, higher than 0.69 for Linknet and FPN. Weed density was calculated as the difference between the green area classified as ExGR (Excess green-Excess red) and the maize area predicted by the model. Each image evaluated for weed density was recombined to quantify and visualize the distribution and density of weeds in a wide range of maize fields. We propose a method to quantify weed density for accurate weeding by effectively separating weeds, maize, and background from UAV images of maize fields.

• Journal of the Institute of Convergence Signal Processing
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• v.22 no.3
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• pp.141-148
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• 2021

In this paper, we propose an algorithm of welding bead detection and evaluation using geodesic active contour algorithm and high pass filter with image processing technique. The algorithm uses histogram equalization and high pass filter as gaussian filter to improve contrast. The image processing techniques smoothens the welding beads reduce the noise on an image. Then, the algorithm detects the welding bead area by applying the geodesic active contour algorithm and morphological ooperation. It also applies the balloon force that either inflates in, or deflates out the evolving contour for a better segmentation. After that, we propose a method for determining the quality of welding bead using effective length and width of the detected bead. In the experiments, our algorithm achieved the highest recall, precision, F-measure and IOU as 0.9894, 0.9668, 0.9780, and 0.8957 respectively. We compared the proposed algorithm with the conventional algorithms to evaluate the performance of the proposed algorithm. The proposed algorithm achieved better performance compared to the conventional ones with a maximum computational time of 0.6 seconds for segmenting and evaluating one welding bead.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1623-1631
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• 2022

Since oil spills can be a significant threat to the marine ecosystem, it is necessary to obtain information on the current contamination status quickly to minimize the damage. Satellite-based detection of marine oil spills has the advantage of spatiotemporal coverage because it can monitor a wide area compared to aircraft. Due to the recent development of computer vision and deep learning, marine oil spill detection can also be facilitated by deep learning. Unlike the existing studies based on Synthetic Aperture Radar (SAR) images, we conducted a deep learning modeling using PlanetScope optical satellite images. The blind test of the DeepLabV3+ model for oil spill detection showed the performance statistics with an accuracy of 0.885, a precision of 0.888, a recall of 0.886, an F1-score of 0.883, and a Mean Intersection over Union (mIOU) of 0.793.

• The Korean Journal of Physiology
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• v.28 no.2
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• pp.151-157
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• 1994

Intracellular free $Ca^{2+}$ contributes to regulation of various events occurring in vascular smooth muscle cells. One of these events is modulating the membrane iou currents. Single smooth muscle cells were isolated from rabbit mesenteric artery. Three kinds of $Ca^{2+}-activated\;current$ were studied with the patch clamp method. $Ca^{2+}-activated\;K^+\;current$ with a large oscillation was recorded in the depolarized potential range. The single channel conductance of this current was about 250 pS. It was abolished by replacing intracellular $K^+\;with\;Cs^+$. A $Ca^{2+}-activated$ nonselective cation current was observed in both the depolarized and hyperpolarized potential ranges. And it was blocked by replacement of extracellular $Na^+$ with N-methylglucamine (NMG) or extracellular application of $Cd^{2+}$. $Ca^{2+}-activated\;Cl^-\;current$ was revealed in the whole voltage range and was blocked by niflumic acid. These results indicate that at least three kinds of $Ca^{2+}-activated$ ionic currents exist in smooth muscle cells from rabbit superior mesenteric artery.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.46 no.6
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• pp.941-947
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• 2013

To understand the relationship between various oceanographic factors and seaweed production, we examined the annual accumulated aquaculture production of Undaria pinnatifida with respect to water temperature, salinity, dissolved oxygen, current patterns and nutrients over 21 years (1990-2010) (this date range does not add up to over 21 years) along the coast of Busan, Korea. According to the results of the cross-correlation function, annual production of U. pinnatifida was closely related to the following conditions: low water temperature, low salinity, strong Tsushima Warm Current, and high concentrations of dissolved oxygen and nutrients. In this study, we also considered the Index of Oceanographic factors for U. pinnatifida (IOU) by computation of a simple equation. This index will be used for the prediction of U. pinnatifida aquaculture production off the coast of Busan.

• Progress in Superconductivity
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• v.8 no.1
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• pp.8-15
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• 2006

The size of the superconducting domains and the critical current density inside these domains have to be enhanced for most of cryomagnetic applications of melt-textured YBCO bulks. To enlarge the size of the domains we studied the multi-seeding technique based on a well-established procedure for preparing high quality YBCO monoliths using self-made SmBCO seeds. The distance between the seeds was optimised as a result of the investigation of the effects of various seed distances on the characteristics of the grain boundary Junctions. The influences of a-b plane intersections and c-axis misalignments were researched. Thereby, a small range of tolerance of the misorientations between the seed crystals was found. Field mapping was applied to control the materials quality and the superconductor's grain structure was investigated using polarisation microscopy. YBCO function elements with iou. seeds in a line and an arrangement of making type (100)/(100) and (110)/(110) boundary junctions, respectively, were processed. The trapped field profile in both sample types shows single domain behaviour. To demonstrate the potential of the multi-seeding method a ring-shaped sample was processed by placing sixteen seeds in a way to make both (100)/(100) and (110)/(110) grain junctions at the same time. The results up to now are very promising to prepare large single domain melt-textured YBCO semi-finished products in complex shapes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.11
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• pp.4426-4442
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• 2020

Since scene segmentation is becoming a hot topic in the field of autonomous driving and medical image analysis, researchers are actively trying new methods to improve segmentation accuracy. At present, the main issues in image semantic segmentation are intra-class inconsistency and inter-class indistinction. From our analysis, the lack of global information as well as macroscopic discrimination on the object are the two main reasons. In this paper, we propose a Densely connected residual Attention network (DA-Res2Net) which consists of a dense residual network and channel attention guidance module to deal with these problems and improve the accuracy of image segmentation. Specifically, in order to make the extracted features equipped with stronger multi-scale characteristics, a densely connected residual network is proposed as a feature extractor. Furthermore, to improve the representativeness of each channel feature, we design a Channel-Attention-Guide module to make the model focusing on the high-level semantic features and low-level location features simultaneously. Experimental results show that the method achieves significant performance on various datasets. Compared to other state-of-the-art methods, the proposed method reaches the mean IOU accuracy of 83.2% on PASCAL VOC 2012 and 79.7% on Cityscapes dataset, respectively.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.1
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• pp.11-18
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• 2022

The use of multi-spectral cameras is essential for day and night pedestrian detection. In this paper, a color camera and a thermal imaging infrared camera were used to detect pedestrians near a crosswalk for 24 hours at an intersection with a high risk of traffic accidents. For pedestrian detection, the YOLOv5 object detector was used, and the detection performance was improved by using color images and thermal images at the same time. The proposed system showed a high performance of 0.940 mAP in the day/night multi-spectral (color and thermal image) pedestrian dataset obtained from the actual crosswalk site.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.535-538
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• 2022

근감소증은 영양부족, 운동량 감소 그리고 노화 등으로 정상적인 근육의 양과 근력 및 근 기능이 감소하는 질환을 말한다. 근감소증은 보편적으로 유럽 근감소증 실무그룹분석(EWGSOP)에서 정의한 측정 방법을 따른다. 본 논문에서는 근감소증 진단을 위한 영상 분할 모델을 개발하고 외부검증하는 방법에 대해서 제안한다. 우리는 CT 영상에서 L3 영역을 선별하여 자동으로 근육, 피하지방, 내장지방을 분할할 수 있는 인공지능 모델을 U-Net을 사용하여 개발하였다. 또한 모델의 성능을 평가하기 위해서 분할영역의 IOU(Intersection over Union)를 계산하여 내부검증을 진행하였으며, 타 병원의 데이터를 이용하여 같은 방법으로 외부검증을 진행한 결과를 보인다. 검증 결과를 토대로 문제점과 해결방안에 대해서 고찰하고 보완하고자 했다.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.1036-1038
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• 2020

This paper proposes a novel automated liver segmentation using Multi-Dilated U-Nets. The proposed multidilation segmentation model has the advantage of considering both local and global shapes of the liver image. We use the CT images subject-wise, every 2D image is concatenated to 3D to calculate the IOU score and DICE score. The experimental results on Jeonbuk National University hospital dataset achieves better performance than the conventional U-Net.