• The Korean Journal of Air & Space Law and Policy
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• v.24 no.2
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• pp.187-210
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• 2009

Space law(or outer space law) and the law of the sea are branches of international law dealing with activities in geographical ares which do not or do only in part come under national sovereignty. Legal rules pertaining to the outer space and sea began to develop once activities emerged in those areas: amongst others, activities dealing with transportation, research, exploration, defense and exploitation. Naturally the law of the sea developed first, followed, early in the twentieth century, by air law, and later in the century by space law. Obviously the law of the sea, of the air and of outer space influence each other. Ideas have been borrowed from one field and applied to another. This article examines some analogies and differences between the outer space law and the law of the sea, especially from the perspective of the legal status, the exploration and exploitation of the natural resources and environment. As far as the comparisons of the legal status between the outer space and high seas are concerned the two areas are res extra commercium. The latter is res extra commercium based on both the customary international law and treaty, however, the former is different respectively according to the customary law and treaty. Under international customary law, whilst outer space constitutes res extra commercium, celestial bodies are res nullius. However as among contracting States of the 1967 Outer Space Treaty, both outer space and celestial bodies are declared res extra commercium. As for the comparisons of the exploration and exploitation of natural resources between the Moon including other celestial bodies in 1979 Moon Agreement and the deep sea bed in the 1982 United Nations Convention on the Law of the Sea, the both areas are the common heritage of mankind. The latter gives us very systematic models such as International Sea-bed Authority, however, the international regime for the former will be established as the exploitation of the natural resources of the celestial bodies other than the Earth is about to become feasible. Thus Moon Agreement could not impose a moratorium, but would merely permit orderly attempts to establish that such exploitation was in fact feasible and practicable, by allowing experimental beginnings and thereafter pilot operations. As Professor Carl Christol said until the parties of the Moon Agreement were able to put into operation the legal regime for the equitable sharing of benefits, they would remain free to disregard the Common Heritage of Mankind principle. Parties to one or both of the agreements would retain jurisdiction over national space activities. In so far as the comparisons of the protection of the environment between the outer space and sea is concerned the legal instruments for the latter are more systematically developed than the former. In the case of the former there are growing tendencies of concerning the environmental threats arising from space activities these days. There is no separate legal instrument to deal with those problems.

• Asian-Australasian Journal of Animal Sciences
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• v.13 no.5
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• pp.636-640
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• 2000

Cup-plant (Silphium perfoliatum L.) has potential to produce high biomass and highly digestible forage in the wetlands where other productive forages do not grow or produce well. However, high moisture content at harvest is a considerable disadvantage of cup-plant for the production of high quality silage. This study was conducted to determine the effect of moisture content on the characteristics of cup-plant silage. Harvested cup-plant was ensiled in farm scale plastic bag silos and laboratory silos. In the plastic bag silos, first growth (FG) and regrowth (RG) cup-plant was harvested, wilted and ensiled. Dry matter content of FG and RG was 280 g/kg and 320 g/kg after 48 hr of wilting. The silage made with FG had pH 5.3 and 5.63 g/kg DM of acetate as a major volatile fatty acid. The composition of lactate, butyrate and acetate production was 1.0: 0.9: 2.3. The pH of silage made with RG was 4.5 and lactate was a major fermentation end product (16.8 g/kg DM). In the laboratory silos, wilted and unwilted first growth cup-plant material was ensiled to compare the early fermentation end products at days 2, 4, 11, and 40. Wilting increased dry matter content by 42% in the harvested material. Wilted silage showed about one unit lower pH until day 11. The contents of ammonia nitrogen and acetate were higher in un wilted silage, while that of lactate was higher in wilted silage (p<0.05). Butyrate and propionate were not detected in the wilted silage until day 40. We conclude from the results that moisture control is essential for the production of high quality cup-plant silage and high pH of cup-plant silage is due to low concentrations of fermentation end products.

• Smart Media Journal
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• v.11 no.5
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• pp.48-55
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• 2022

Tomato crops are easy to expose to disease and spread in a short period of time, so late measures against disease are directly related to production and sales, which can cause damage. Therefore, there is a need for a service that enables early prevention by simply and accurately diagnosing tomato diseases in the field. In this paper, we construct a system that applies a deep learning-based model in which ImageNet transition is learned in advance to classify and serve nine classes of tomatoes for disease and normal cases. We use the input of MobileNet, ResNet, with a deep learning-based CNN structure that builds a lighter neural network using a composite product for the image set of leaves classifying tomato disease and normal from the Plant Village dataset. Through the learning of two proposed models, it is possible to provide fast and convenient services using MobileNet with high accuracy and learning speed.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.1
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• pp.59-64
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• 2024

Recently, research on the convergence of drones and artificial intelligence technologies have been conducted in various industrial fields. In this paper, we propose an illegal parking vehicle recognition model using deep learning-based object recognition and classification algorithms. The model of object recognition and classification consist of YOLOv8 and ResNet18, respectively. The proposed model was trained using image data collected in general road environment, and the trained model showed high accuracy in determining illegal parking. From simulation results, it was confirmed that the proposed model has generalization performance to identify illegal parking vehicles from various images.

• Smart Structures and Systems
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• v.29 no.1
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• pp.29-39
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• 2022

Crack detection plays an important role in the maintenance and protection of steel box girder of bridges. However, since the cracks only occupy an extremely small region of the high-resolution images captured from actual conditions, the existing methods cannot deal with this kind of image effectively. To solve this problem, this paper proposed a novel three-stage method based on deep learning technology and morphology operations. The training set and test set used in this paper are composed of 360 images (4928 × 3264 pixels) in steel girder box. The first stage of the proposed model converted high-resolution images into sub-images by using patch-based method and located the region of cracks by CBAM ResNet-50 model. The Recall reaches 0.95 on the test set. The second stage of our method uses the Attention U-Net model to get the accurate geometric edges of cracks based on results in the first stage. The IoU of the segmentation model implemented in this stage attains 0.48. In the third stage of the model, we remove the wrong-predicted isolated points in the predicted results through dilate operation and outlier elimination algorithm. The IoU of test set ascends to 0.70 after this stage. Ablation experiments are conducted to optimize the parameters and further promote the accuracy of the proposed method. The result shows that: (1) the best patch size of sub-images is 1024 × 1024. (2) the CBAM ResNet-50 and the Attention U-Net achieved the best results in the first and the second stage, respectively. (3) Pre-training the model of the first two stages can improve the IoU by 2.9%. In general, our method is of great significance for crack detection.

• Journal of the Korean Society for Heat Treatment
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• v.16 no.1
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• pp.2-9
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• 2003

The aluminum alloy which is light and has excellent thermal conductivity and iron base alloy that is remarkable heat-resistece and wear resistence properties were bonded together. The bond was created between a stationary and a rotating member by using the frictional heat generated between them while subjected to high normal forces on the interface of Al alloy and iron base alloy. The microstructure of the bonded interface of friction welding and the strength in the bonded interface formed under various bonding conditions were examined through TEM, SEM with EDX and triple bending test. In interface of bonding materials formed after various heat treatment, bonding strength was substantially different, resulting from formation of intermetallic compound or softening during annealing.

• 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 Multimedia Society
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• v.24 no.3
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• pp.382-395
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• 2021

In this study, character recognition using deep learning is performed among the various defects in the PCB, the purpose of which is to check whether the printed characters are printed correctly on top of components, or the incorrect parts are attached. Generally, character recognition may be perceived as not a difficult problem when considering MNIST, but the printed letters on the PCB component data are difficult to collect, and have very high redundancy. So if a deep learning model is trained with original data without any preprocessing, it can lead to over fitting problems. Therefore, this study aims to reduce the redundancy to the smallest dataset that can represent large amounts of data collected in limited production sites, and to create datasets through data enhancement to train a flexible deep learning model can be used in various production sites. Moreover, ResNet model verifies to determine which combination of datasets is the most effective. This study discusses how to reduce and augment data that is constantly occurring in real PCB production lines, and discusses how to select coresets to learn and apply deep learning models in real sites.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.30-40
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• 2022

Electrocardiogram (ECG) signal's shape and characteristic varies through each individual, so it is difficult to classify with one neural network. It is difficult to classify the given data directly, but if corresponding normal beat is given, it is relatively easy and accurate to classify the beat by comparing two beats. In this study, we classify the ECG signal by generating the reference normal beat through the template cluster, and combining with the input ECG signal. It is possible to detect abnormal beats of various individual's records with one neural network by learning and classifying with the imaged ECG beats which are combined with corresponding reference normal beat. Especially, various neural networks, such as GoogLeNet, ResNet, and DarkNet, showed excellent performance when using the comparative learning. Also, we can confirmed that GoogLeNet has 99.72% sensitivity, which is the highest performance of the three neural networks.

• Korean journal of applied entomology
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• v.46 no.1 s.145
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• pp.153-158
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• 2007

This study was conducted to develope the observing methods for density of stink bugs in soybean reproductive stage. The adults and nymphs of bean bug, Riptortus clavatus, red-banded shield bug, Piezodous hybneri, green stink bug, Nezara antennata, Sole bug, Dolycoris baccarum, and brown marmorated stink bug, Halyomorpha halys were observed by three observing methods such as beating, sweeping net, and visual counting methods in the full bloom (R2), full pod (R4) and beginning maturity (R7) of soybean. As a result, total number of stink bugs observed was the highest with 5,214.2 by beating method, and then was 2,581.8 by visual counting method, and was the lowest with 103.1 by sweeping net method. Total number of stink bugs observed by the beating and visual counting methods was P. hybneri, followed by N. antennata, H. halys, R. clavatus and D. baccarum with clear difference in observed number of each stink bugs while total number of stink bugs observed by sweeping net method was very low in the range of 18 to 23. Accordingly, the observed density of stink bugs exception of R. clavatus adult by beating method was generally high. However, the number of R. clavatus adult was more observed by flushing method than that by beating method from the beginning bloom (R1) to full maturity (R8), and was more observed at morning time than that at afternoon time. Therefore, two observation methods that flushing method for R. clavatus and beating method for the other stink bugs were recommended for the occurring density of stink bugs in soybean because both bean bug and pentatomidae stink bugs have distinct behavior characteristics such as flying and dropping.