• Journal of Electrical Engineering and Technology
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• v.8 no.5
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• pp.1157-1162
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• 2013

We proposed AlGaN/GaN high-electron-mobility transistors (HEMTs) using thermal oxidation for NiOx passivation. Auger electron spectroscopy, secondary ion mass spectroscopy, and pulsed I-V were used to study oxidation features. The oxidation process diffused Ni and O into the AlGaN barrier and formed NiOx on the surface. The breakdown voltage of the proposed device was 1520 V while that of the conventional device was 300 V. The gate leakage current of the proposed device was 3.5 ${\mu}A/mm$ and that of the conventional device was 1116.7 ${\mu}A/mm$. The conventional device exhibited similar current in the gate-and-drain-pulsed I-V and its drain-pulsed counterpart. The gate-and-drain-pulsed current of the proposed device was about 56 % of the drain-pulsed current. This indicated that the oxidation process may form deep states having a low emission current, which then suppresses the leakage current. Our results suggest that the proposed process is suitable for achieving high breakdown voltages in the GaN-based devices.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2004.07b
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• pp.1010-1014
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• 2004

High quality GaN layer and $In_xGa_{1-x}N$ alloy were obtained on (0001)sapphire substrate using ammonia$(NH_3)$ and dimethylhydrazine$(DMH_y)$ as a nitrogen source by gas source molecular hem epitaxy(GSMBE) respectively. As a result, RHEED is used to investigate the relaxation processes which take place during the growth of GaN and $In_xGa_{1-x}N$. The full Width at half maximum of the x-ray diffraction(FWHM) rocking curve measured from Plane of GaN has exhibitted as narrow as 8 arcmin. Photoluminescence measurement of GaN and $In_xGa_{1-x}N$ were investigated at room temperature, where the intensity of the band edge emission is much stronger than that of deep level emission. In content of $In_xGa_{1-x}N$ epitaxial layer according to growth condition was investigated.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.3
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• pp.1014-1025
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• 2020

Cloud computing services changed the way the data are managed across the healthcare system that can improve patient care. Currently, most healthcare organizations are using cloud-based applications and related services to deliver better healthcare facilities. But architecting a cloud-based healthcare system needs deep knowledge about the working nature of these services and the requirements of the healthcare environment. The success is based on the usage of appropriate cloud services in the architecture to manage the data flow across the healthcare system.Cloud service providers offer a wide variety of services to ingest, store and process healthcare data securely. The top three public cloud providers- Amazon, Google, and Microsoft offers advanced cloud services for the solution that the healthcare industry is looking for. This article proposes a framework that can effectively utilize cloud services to handle the data flow among the various stages of the healthcare infrastructure. The useful cloud services for ingesting, storing and analyzing the healthcare data for the proposed framework, from the top three cloud providers are listed in this work. Finally, a cloud-based healthcare architecture using Amazon Cloud Services is constructed for reference.

• Korean Journal of Remote Sensing
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• v.37 no.4
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• pp.747-761
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• 2021

With the increasing application of UAVs in intelligent transportation systems, vehicle detection for aerial images has become an essential engineering technology and has academic research significance. In this paper, a vehicle detection method for aerial images based on the YOLOv4 deep learning algorithm is presented. At present, the most known datasets are VOC (The PASCAL Visual Object Classes Challenge), ImageNet, and COCO (Microsoft Common Objects in Context), which comply with the vehicle detection from UAV. An integrated dataset not only reflects its quantity and photo quality but also its diversity which affects the detection accuracy. The method integrates three public aerial image datasets VAID, UAVD, DOTA suitable for YOLOv4. The training model presents good test results especially for small objects, rotating objects, as well as compact and dense objects, and meets the real-time detection requirements. For future work, we will integrate one more aerial image dataset acquired by our lab to increase the number and diversity of training samples, at the same time, while meeting the real-time requirements.

• Structural Monitoring and Maintenance
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• v.9 no.3
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• pp.289-303
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• 2022

In this study, the impact of assigned pixel labels on the accuracy of crack image identification of steel structures is examined by using an atrous separable convolution neural network (ASCNN). Firstly, images containing fatigue cracks collected from steel structures are classified into four datasets by assigning different pixel labels based on image features. Secondly, the DeepLab v3+ algorithm is used to determine optimal parameters of the ASCNN model by maximizing the average mean-intersection-over-union (mIoU) metric of the datasets. Thirdly, the ASCNN model is trained for various image sizes and hyper-parameters, such as the learning rule, learning rate, and epoch. The optimal parameters of the ASCNN model are determined based on the average mIoU metric. Finally, the trained ASCNN model is evaluated by using 10% untrained images. The result shows that the ASCNN model can segment cracks and other objects in the captured images with an average mIoU of 0.716.

• Advances in nano research
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• v.12 no.5
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• pp.489-499
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• 2022

Nanofluids have recently triggered a substantial scientific interest as cooling media. However, their stability is challenging for successful engagement in industrial applications. Different factors, including temperature, nanoparticles and base fluids characteristics, pH, ultrasonic power and frequency, agitation time, and surfactant type and concentration, determine the nanofluid stability regime. Indeed, it is often too complicated and even impossible to accurately find the conditions resulting in a stabilized nanofluid. Furthermore, there are no empirical, semi-empirical, and even intelligent scenarios for anticipating the stability of nanofluids. Therefore, this study introduces a straightforward and reliable intelligent classifier for discriminating among the stability regimes of alumina-water nanofluids based on the Zeta potential margins. In this regard, various intelligent classifiers (i.e., deep learning and multilayer perceptron neural network, decision tree, GoogleNet, and multi-output least squares support vector regression) have been designed, and their classification accuracy was compared. This comparison approved that the multilayer perceptron neural network (MLPNN) with the SoftMax activation function trained by the Bayesian regularization algorithm is the best classifier for the considered task. This intelligent classifier accurately detects the stability regimes of more than 90% of 345 different nanofluid samples. The overall classification accuracy and misclassification percent of 90.1% and 9.9% have been achieved by this model. This research is the first try toward anticipting the stability of water-alumin nanofluids from some easily measured independent variables.

• Smart Structures and Systems
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• v.31 no.1
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• pp.45-56
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• 2023

Vibration-based structural health monitoring is used to ensure the safety of structures by installing sensors in structures. The peak picking method, one of the applications of vibration-based structural health monitoring, is a method that analyze the dynamic characteristics of a structure using the peaks of the frequency response function. However, the results may vary depending on the person predicting the peak point; further, the method does not predict the exact peak point in the presence of noise. To overcome the limitations of the existing peak picking methods, this study proposes a new method to automate the modal analysis process by utilizing long short-term memory, a type of recurrent neural network. The method proposed in this study uses the time series data of the frequency response function directly as the input of the LSTM network. In addition, the proposed method improved the accuracy by using the phase as well as amplitude information of the frequency response function. Simulation experiments and lab-scale model experiments are performed to verify the performance of the LSTM network developed in this study. The result reported a modal assurance criterion of 0.8107, and it is expected that the dynamic characteristics of a civil structure can be predicted with high accuracy using data without experts.

• Journal of the Korean Wood Science and Technology
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• v.46 no.1
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• pp.73-84
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• 2018

Recently, the importance of flame retardation treatment technology has been emphasized due to the increase in urban fire accidents and fire damage incidents caused by building exterior materials. Particularly, in the utilization of wood-based building materials, the flame retarding treatment technology is more importantly evaluated. An Intumescent system is one of the non-halogen flame retardant treatment technologies and is a system that realizes flame retardancy through foaming and carbonization layer formation. To apply the Intumescent system, composite material was prepared by using Ethylene vinyl acetate (EVA) as a matrix. To enhance the flame retardant properties of the Intumescent system, a nano-clay was applied together. Composite materials with Intumescent system and nano - clay technology were processed into sheet - like test specimens, and then a new structure of cross laminated timber with improved flame retardant properties was fabricated. In the evaluation of combustion characteristics of composite materials using Intumescent system, it was confirmed that the maximum heat emission was reduced efficiently. Depending on the structure attached to the surface, the CLT had two stages of combustion. Also, it was confirmed that the maximum calorific value decreased significantly during the deep burning process. These characteristics are expected to have a delayed combustion diffusion effect in the combustion process of CLT. In order to improve the performance, the flame retardation treatment technique for the surface veneer and the optimization technique of the application of the composite material are required. It is expected that it will be possible to develop a CLT structure with improved fire characteristics.

• Journal of the Korea Computer Graphics Society
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• v.23 no.3
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• pp.31-38
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• 2017

In this paper, we propose a system which estimates Manhattan coordinate systems for urban scene images using a convolutional neural network (CNN). Estimating the Manhattan coordinate system from an image under the Manhattan world assumption is the basis for solving computer graphics and vision problems such as image adjustment and 3D scene reconstruction. We construct a CNN that estimates Manhattan coordinate systems based on GoogLeNet [1]. To train the CNN, we collect about 155,000 images under the Manhattan world assumption by using the Google Street View APIs and calculate Manhattan coordinate systems using existing calibration methods to generate dataset. In contrast to PoseNet [2] that trains per-scene CNNs, our method learns from images under the Manhattan world assumption and thus estimates Manhattan coordinate systems for new images that have not been learned. Experimental results show that our method estimates Manhattan coordinate systems with the median error of $3.157^{\circ}$ for the Google Street View images of non-trained scenes, as test set. In addition, compared to an existing calibration method [3], the proposed method shows lower intermediate errors for the test set.

• Geophysics and Geophysical Exploration
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• v.6 no.2
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• pp.87-94
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• 2003

ULF geomagnetic field anomalies related to earthquakes have been reported and a mechnism that magnetic field variations could be generated by the induced telluric current due to the high frequency fluctuation of conductivity in a fault Bone have been proposed. In this study, we calculated electromagnetic anomalies using a simple fault model and investigated the possibility of significant perturbation. Since low frequency electromagnetic fields are modulated by the high frequency oscillation of conductivity and the modulated fields are concentrated in a narrow ULF band, the electromagnetic fields in ULF band could be perturbed significantly. The amplitude of electromagnetic field anomaly depends on various factors: the geometry and conductivity of fault zone, the magnitude and frequency of conductivity fluctuation, the resistivity structure of crust or mantle, the frequency bandwidth of observational data and so on. Therefore, it is strongly required to reveal the deep resistivity structure of crust a.: well ah the structure of fault zone and to ,select the optimal observation frequency band for the observation of electromagnetic activities related with earthquakes.