• Journal of the Korean GEO-environmental Society
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• v.10 no.4
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• pp.49-57
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• 2009

The governmental investment to SOC facilities currently has increasedthe construction of new roads and the consistent extension of already-existing roads or the line-shape revision of those roads. As a result, the road cut slopes have been increasingrapidly. Unfortunately, human-life damages and property damages frequently occur due to the rockfall and the landslide every year. To reduce those damages, many studies have been performed. The present regulation regarding rockfall protection facilities follows the "Guide for Installation and Management of Road Safety Facilities" issued by MLTM (the Ministry of Land, Transport, and Maritime Affairs) that indicates the standard size of facilities and energy absorbing efficiencies. Most domestic road slopes use standardized rockfall protection facilities based on the regulation. However, there have been doubted about the effectiveness of rockfall protection facilities and the damages caused by rockfalls havebeen increasedevery year. Thus, it seems that relevant studies are necessary on the rockfall protection net being capable of supporting rockfall energies. Accordingly, this study reviews previous literature to investigate the function and the feature of rockfall protection nets and analyzetheir limitations by each type. After that, by using the pullout test device for a rockfall protection net, an experiment on the PVC coating net and the expanded metal is performed under the exact same condition. Finally, the features of the Expended metal is explained with the comparison analysises of load-variation and the confirmation of damaged forms. As a result, there have been founded the problemsof net breaking down and not being able to support due to PVC coating net's material property of disintegration. On the other hand, the Expanded Metal might be expected as a substituteof rockfall protection net according to its capability of support and integration.

• Journal of Biomedical Engineering Research
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• v.45 no.3
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• pp.109-117
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• 2024

We developed a device that integrates digital otoscope and stethoscope for telemedicine. The integrated device was utilized for the collection of tympanic membrane images and cardiac auscultation data. Data accumulated on the platform server can support real-time diagnosis of heart and eardrum diseases using artificial intelligence. Public data from Kaggle were used for deep learning. After comparing with various deep learning models, the MobileNetV2 model showed superior performance in analyzing tympanic membrane data, and the VGG16 model excelled in analyzing cardiac data. The classification algorithm achieved an accuracy of 89.9% for eardrums data and 100% for heart sound data. These results demonstrate the possibility of diagnosing diseases without the limitations of time and space by using this platform.

• Proceedings of the Korean Vacuum Society Conference
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• 2016.02a
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• pp.390.1-390.1
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• 2016

The present work explains the interfacial energetics of all oxide transparent photodiodes. The optical, structural and morphological of copper oxides were systematically analyse by UV-Visible spectrometer, X-Ray diffraction, Raman spectroscopy, Scanning electron microscopy (SEM) and Atomic force microscopy measurements (AFM). The UV-Visible result exhibits optical bandgap of Cu2O and CuO as 2.2 and 2.05 eV respectively. SEM and AFM result shows a uniform grain size distribution in Cu2O and CuO thin films with the average grain size of 45 and 40 nm respectively. The results of Current-Voltage and Kelvin probe force microscope characteristics describe the electrical responses of the Cu2O/ZnO and CuO/ZnO heterojunctions photodiodes. The obtained electrical response depicts the approximately same knee voltages with a measurable difference in the absolute value of net terminal current. More over the present study realizes the all oxide transparent photodiode with zero bias photocurrent. The presented results lay the template for fabricating and analysing the self-bias all oxide transparent photodetector.

• Journal of Korea Society of Digital Industry and Information Management
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• v.12 no.4
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• pp.81-87
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• 2016

Recently, as the heterogeneous network (HetNet) has been deployed widely to support various kinds of Radio Access Networks(RANs) with a combination of Macro, Pico, and/or Femto cells, research and standardization efforts have been very active regarding the concept of Licensed Shared Access (LSA) for supporting spectrum sharing. In order for a mobile device to efficiently support the spectrum sharing, the mobile device shall be reconfigurable, meaning that its radio application code has to be adaptively changed in accordance with the hopping of desired spectral band. Especially, Working Group 2 (WG2) of Technical Committee (TC) Reconfigurable Radio System (RRS) of European Telecommunications Standards Institute (ETSI) has been a main driving force for developing standard architecture for Multi-mode Mobile Device (MD) that can be applied to the LSA system. In this paper, we introduce the Multi-mode MD architecture for supporting LSA-based spectrum sharing. An implementation of a test-bed of Multi-mode MD is presented in order to verify the feasibility of the standard MD architecture for the purpose of LSA-based spectrum sharing through various experimental tests.

• Nuclear Engineering and Technology
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• v.54 no.12
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• pp.4422-4430
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• 2022

Studying the effects of gamma radiation on the instrumentation and control (I&C) system of a nuclear power plant is critical to the successful and reliable operation of the plant. In the accidental scenario, the adverse environment of ionizing radiation affects the performance of the I&C system and it leads to inaccurate and incomprehensible results. This paper reports the effects of gamma radiation on the AT89C51RD2, a commercial-off-the-shelf 8-bit high-performance flash microcontroller. The microcontroller, selected for the device under test for this study is used in the remote terminal unit for a nuclear power plant. The custom circuits were made to test the microcontroller under different gamma doses using a ⁶⁰Co gamma source in both ex-situ and in-situ modes. The device was exposed to a maximum dose of 1.5 kGy. Under this hostile environment, the performance of the microcontroller was studied in terms of device current and voltage changes. It was observed that the microcontroller device can operate up to a total absorbed dose of approximately 0.6 kGy without any failure or degradation in its performance.

• Nuclear Engineering and Technology
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• v.56 no.6
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• pp.2412-2420
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• 2024

The International Atomic Energy Agency (IAEA) mandates safeguards to ensure non-proliferation of nuclear materials. Among inspection techniques used to detect partial defects within spent nuclear fuel (SNF), gamma emission tomography (GET) has been reported to be reliable for detection of partial defects on a pin-by-pin level. Conventional GET, however, is limited by low detection efficiency due to the high density of nuclear fuel rods and self-absorption. This paper proposes a new type of GET named Spent Fuel Internal Tomography (SFIT), which can acquire sinograms at the guide tube. The proposed device consists of the housing, shielding, C-shaped collimator, reflector, and gadolinium aluminum gallium garnet (GAGG) scintillator. For accurate attenuation correction, the source-distinguishable range of the SFIT device was determined using MC simulation to the region away from the proposed device to the second layer. For enhanced inspection accuracy, a proposed specific source-discrimination algorithm was applied. With this, the SFIT device successfully distinguished all source locations. The comparison of images of the existing and proposed inspection methods showed that the proposed method, having successfully distinguished all sources, afforded a 150 % inspection accuracy improvement.

• Journal of the Korean Wood Science and Technology
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• v.45 no.6
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• pp.797-808
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• 2017

Automatic wood species identification systems have enabled fast and accurate identification of wood species outside of specialized laboratories with well-trained experts on wood species identification. Conventional automatic wood species identification systems consist of two major parts: a feature extractor and a classifier. Feature extractors require hand-engineering to obtain optimal features to quantify the content of an image. A Convolutional Neural Network (CNN), which is one of the Deep Learning methods, trained for wood species can extract intrinsic feature representations and classify them correctly. It usually outperforms classifiers built on top of extracted features with a hand-tuning process. We developed an automatic wood species identification system utilizing CNN models such as LeNet, MiniVGGNet, and their variants. A smartphone camera was used for obtaining macroscopic images of rough sawn surfaces from cross sections of woods. Five Korean softwood species (cedar, cypress, Korean pine, Korean red pine, and larch) were under classification by the CNN models. The highest and most stable CNN model was LeNet3 that is two additional layers added to the original LeNet architecture. The accuracy of species identification by LeNet3 architecture for the five Korean softwood species was 99.3%. The result showed the automatic wood species identification system is sufficiently fast and accurate as well as small to be deployed to a mobile device such as a smartphone.

• Nutrition Research and Practice
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• v.13 no.6
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• pp.521-528
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• 2019

BACKGROUND/OBJECTIVES: The aim of this study was to develop Korean food image detection and recognition model for use in mobile devices for accurate estimation of dietary intake. MATERIALS/METHODS: We collected food images by taking pictures or by searching web images and built an image dataset for use in training a complex recognition model for Korean food. Augmentation techniques were performed in order to increase the dataset size. The dataset for training contained more than 92,000 images categorized into 23 groups of Korean food. All images were down-sampled to a fixed resolution of $150{\times}150$ and then randomly divided into training and testing groups at a ratio of 3:1, resulting in 69,000 training images and 23,000 test images. We used a Deep Convolutional Neural Network (DCNN) for the complex recognition model and compared the results with those of other networks: AlexNet, GoogLeNet, Very Deep Convolutional Neural Network, VGG and ResNet, for large-scale image recognition. RESULTS: Our complex food recognition model, K-foodNet, had higher test accuracy (91.3%) and faster recognition time (0.4 ms) than those of the other networks. CONCLUSION: The results showed that K-foodNet achieved better performance in detecting and recognizing Korean food compared to other state-of-the-art models.

• Fisheries and Aquatic Sciences
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• v.18 no.3
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• pp.325-332
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• 2015

An active stimulating method for juvenile fishes to drive escaping from mesh of the codend was examined by shaking canvas in the bottom trawl followed by shrimp beam trawl. Field fishing trials by a bottom trawl were carried out between the Geomoondo and Jejudo in west of South sea, Korea by conver-net methods to examine the effect on the reduction of juvenile fish as a discard catch by generating a shaking movement of the codend using two pieces of asymmetrical semi-circular canvas. The mean period of the shaking motion with the round canvas was 10-15 s, and the range of amplitude as a vertical depth change was up to 0.4-0.6 m when towing speed 3.4-4.3 k't as estimated by peak event analysis. The escape rate of juvenile fish in conver-net by total juvenile bycatch (codend and cover-net) in 14 trials increased from 20% in a steady codend to 34% using a shaking codend in the bottom trawl, while the marketing catch or total bycatch was similar between steady and shaking cod ends. There was no difference in the body size of the fish and species composition between the steady and shaking cod ends. Above results demonstrate a new method for bycatch reduction actually up to 18% using an active stimulating device, although further experiments are needed to increase an effective shaking motion of the codend in amplitude and period for more bycatch reduction.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.498-500
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• 2022

CCTV protects people and assets safely by identifying dangerous situations and responding promptly. However, it is difficult to continuously monitor the increasing number of CCTV images. For this reason, there is a need for a device that continuously monitors CCTV images and notifies when abnormal behavior occurs. Recently, many studies using artificial intelligence models for image data analysis have been conducted. This study simultaneously learns spatial and temporal characteristic information between image data to classify various abnormal behaviors that can be observed in CCTV images. As an artificial intelligence model used for learning, we propose a multi-classification deep learning model that combines an end-to-end 3D convolutional neural network(CNN) and ResNet.