• The Bulletin of The Korean Astronomical Society
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• v.46 no.2
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• pp.46.3-47
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• 2021

Recently a multilayer spectral inversion (MLSI) model has been proposed to infer the physical parameters of plasmas in the solar chromosphere. The inversion solves a three-layer radiative transfer model using the strong absorption line profiles, H alpha and Ca II 8542 Å, taken by the Fast Imaging Solar Spectrograph (FISS). The model successfully provides the physical plasma parameters, such as source functions, Doppler velocities, and Doppler widths in the layers of the photosphere to the chromosphere. However, it is quite expensive to apply the MLSI to a huge number of line profiles. For example, the calculating time is an hour to several hours depending on the size of the scan raster. We apply deep neural network (DNN) to the inversion code to reduce the cost of calculating the physical parameters. We train the models using pairs of absorption line profiles from FISS and their 13 physical parameters (source functions, Doppler velocities, Doppler widths in the chromosphere, and the pre-determined parameters for the photosphere) calculated from the spectral inversion code for 49 scan rasters (~2,000,000 dataset) including quiet and active regions. We use fully connected dense layers for training the model. In addition, we utilize a skip connection to avoid a problem of vanishing gradients. We evaluate the model by comparing the pairs of absorption line profiles and their inverted physical parameters from other quiet and active regions. Our result shows that the deep learning model successfully reproduces physical parameter maps of a scan raster observation per second within 15% of mean absolute percentage error and the mean squared error of 0.3 to 0.003 depending on the parameters. Taking this advantage of high performance of the deep learning model, we plan to provide the physical parameter maps from the FISS observations to understand the chromospheric plasma conditions in various solar features.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.42 no.7 s.337
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• pp.41-48
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• 2005

We design and fabricate 3-dB tandem directional coupler using the coplanar waveguide structure which is applicable to balanced amplifiers and mixers for 60 GHz wireless local area network system. The coupler comprises the multiple-sectional parallel-coupled lines to facilitate the fabrication process, and enable smaller device size and higher directivity than those of the conventional 3-dB coupler employing the edge-coupled line. In this study, we adopt the structure of two-sectional parallel-coupled lines of which each single-coupled line has a coupling coefficient of -8.34 dB and airbridge structure to monolithically materialize the uniplanar coupler structure instead of using the conventional multilayer or bonded structure. The airbridge structure also supports to minimize the parasitic components and maintain desirable device performance in V-band (50$\~$75 GHz). The measured results from the fabricated couplers show couplings of 3.S$\~$4 dB and phase differences of 87.5$^{\circ}{\pm}1^{\circ}$ in V-band range and show directivities higher than 30 dB at a frequency of 60 GHz.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.23 no.3
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• pp.23-43
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• 2020

The purpose of this paper is to create ecological values for urban rivers. For this, the paper looks into river bed structures and how nearby lands are used. This study was performed to set the specific sections for analysis through a field investigation of the infrastructure conditions, surrounding land use, and the inter habitat structure of Seongnaecheon(Stream). A total of 780 individuals from 31 species of wild birds appeared in Seongnaecheon(Stream). According to foraging guild's habitat, there were 9 species of water, 8 types of water edges, 5 types of crowns, 5 types of shrubs, 2 types of tree trunks, 2 types of birds of raptors, and 355 individuals of water, 243 shrubs, 90 crowns, 84 water edges and 5 raptors. Many water birds were observed at the site where the Seongnaecheon(Stream) sandy plains and wetland herbaceous area were developed and the open water was secured. Most of the forest birds appeared on levee slope connected with forest around and riverside with fewer facilities for use. The species diversity index of Shannon, the entire section of Seongnaecheon(Stream), was 2.2697 and the downstream ecological landscape conservation area of Seongnaecheon(Stream) was found to be useful as a habitat for wild birds in the city compared to other sections. Some sections of Seongnaecheon (Stream) had low species diversity index due to lack of green space and surrounding urbanization areas. In choosing target species, I researched the special features of the habitats and the habitation structure of wild birds in each zone. Regarding detailed plans, by classifying the breeding place & roosting site and the roosting site & shelter that took account of the inhabitation characteristics of the target species in different sections, this paper suggested the major plant species and multilayer planting structures. Moreover, this study proposed the development of habitats for water birds and forest birds along with the connection of the green network for improving the Eco-corridor linkage and inhabitation features in Seongnaecheon(Stream).

• Journal of the Korea Convergence Society
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• v.12 no.9
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• pp.11-19
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• 2021

A software system is required to change during its life cycle due to various requirements such as adding functionalities, fixing bugs, and adjusting to new computing environments. Such program code modification should be considered as carefully as a new system development becase unexpected software errors could be introduced. In addition, when reusing open source programs, we can expect higher quality software if code changes of the open source program are predicted in advance. This paper proposes a Convolutional Neural Network (CNN)-based deep learning model to predict source code changes. In this paper, the prediction of code changes is considered as a kind of a binary classification problem in deep learning and labeled datasets are used for supervised learning. Java projects and code change logs are collected from GitHub for training and testing datasets. Software metrics are computed from the collected Java source code and they are used as input data for the proposed model to detect code changes. The performance of the proposed model has been measured by using evaluation metrics such as precision, recall, F1-score, and accuracy. The experimental results show the proposed CNN model has achieved 95% in terms of F1-Score and outperformed the multilayer percept-based DNN model whose F1-Score is 92%.

• Journal of the Korean Ceramic Society
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• v.49 no.5
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• pp.475-483
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• 2012

High capacitance X5R MLCCs based on $BaTiO_3$ ceramic dielectric layers exhibit a single broad, asymmetric arc shape impedance and modulus response over the wide frequency range between 1 MHz to 0.01 Hz. Analysis according to the conventional brick-layer model for polycrystalline conductors employing a series connection of multiple RC parallel circuits leads to parameters associated with large errors and of little physical significance. A new parametric impedance model is shown to satisfactorily describe the experimental spectra, which is a parallel network of one resistor R representing the DC conductivity thermally activated by 1.32 eV, one ideal capacitor C exactly representing bulk capacitance, and a constant phase element (CPE) Q with complex capacitance $A(i{\omega})^{{\alpha}-1}$ with ${\alpha}$ close to 2/3 and A thermally activated by 0.45 eV or ca. 1/3 of activation energy of DC conductivity. The feature strongly indicate the CK1 model by J. R. Macdonald, where the CPE with 2/3 power-law exponent represents the polarization effects originating from mobile charge carriers. The CPE term is suggested to be directly related to the trapping of the electronic charge carriers and indirectly related to the ionic defects responsible for the insulation resistance degradation.

• Journal of the Korea Society for Simulation
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• v.29 no.1
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• pp.11-21
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• 2020

Cardio-cerebrovascular diseases (CCD) account for 24% of the causes of death to Koreans and its proportion is the highest except cancer. Currently, the risk of the cardiovascular disease for domestic patients is based on the Framingham risk score (FRS), but accuracy tends to decrease because it is a foreign guideline. Also, it can't score the risk of cerebrovascular disease. CCD is hard to predict, because it is difficult to analyze the features of early symptoms for prevention. Therefore, proper prediction method for Koreans is needed. The purpose of this paper is validating IG-MLP (Information Gain - Multilayer Perceptron) evaluation based feature selection method using CCD data with simulation. The proposed method uses the raw data of the 4^th ~ 7^th of The Korea National Health and Nutrition Examination Survey (KNHANES). To select the important feature of CCD, analysis on the attributes using IG-MLP are processed, finally CCD prediction ANN model using optimize feature set is provided. Proposed method can find important features of CCD prediction of Koreans, and ANN model could predict more accurate CCD for Koreans.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.1
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• pp.672-679
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• 2018

This paper describes a methodology for shape design using an optimal design system, whereas generally a three dimensional analysis is required for such designs. An automatic finite element mesh generation technique, which is based on fuzzy knowledge processing and computational geometry techniques, is incorporated into the system, together with a commercial FE analysis code and a commercial solid modeler. Also, with the aid of multilayer neural networks, the present system allows us to automatically obtain a design window, in which a number of satisfactory design solutions exist in a multi-dimensional design parameter space. The developed optimal design system is successfully applied to evaluate the structures that are used. This study used a stress gauge to measure the maximum stress affecting the parts of the side housing bracket which are most vulnerable to cracking. Thereafter, we used a tool to interpret the maximum stress value, while maintaining the same stress as that exerted on the spot. Furthermore, a stress analysis was performed with the typical shape maintained intact, SM490 used for the material and the minimizing weight safety coefficient set to 3, while keeping the maximum stress the same as or smaller than the allowable stress. In this paper, a side housing bracket with a comparably simple structure for 36 tons was optimized, however if the method developed in this study were applied to side housing brackets of different classes (tons), their quality would be greatly improved.

• Journal of the Korea Society of Computer and Information
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• v.17 no.1
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• pp.211-218
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• 2012

Fossil fuel is used all over the world and it produces greenhouse gases due to fossil fuel use. Therefore, it cause global warming and is serious environmental pollution. In order to decrease the environmental pollution, we should use renewable energy which is clean energy. Among several renewable energy, wind energy is the most promising one. Wind power generation is does not produce environmental pollution and could not be exhausted. However, due to wind power generation has irregular power output, it is important to predict generated electrical energy accurately for smoothing wind energy supply. There, we consider use ramp characteristic to forecast accurate wind power output. The ramp increase and decrease rapidly wind power generation during in a short time. Therefore, it can cause problem of unbalanced power supply and demand and get damaged wind turbine. In this paper, we make prediction models using power ramp rate as well as wind speed and wind direction to increase prediction accuracy. Prediction model construction algorithm used multilayer neural network. We built four prediction models with PRR, wind speed, and wind direction and then evaluated performance of prediction models. The predicted values, which is prediction model with all of attribute, is nearly to the observed values. Therefore, if we use PRR attribute, we can increase prediction accuracy of wind power generation.

• Journal of Korea Water Resources Association
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• v.55 no.spc1
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• pp.1283-1293
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• 2022

The purpose of this study is to predict residual chlorine in order to maintain stable residual chlorine concentration in sedimentation basin by using artificial intelligence algorithms in water treatment process employing pre-chlorination. Available water quantity and quality data are collected and analyzed statistically to apply into mathematical multiple regression and artificial intelligence models including multi-layer perceptron neural network, random forest, long short term memory (LSTM) algorithms. Water temperature, turbidity, pH, conductivity, flow rate, alkalinity and pre-chlorination dosage data are used as the input parameters to develop prediction models. As results, it is presented that the random forest algorithm shows the most moderate prediction result among four cases, which are long short term memory, multi-layer perceptron, multiple regression including random forest. Especially, it is result that the multiple regression model can not represent the residual chlorine with the input parameters which varies independently with seasonal change, numerical scale and dimension difference between quantity and quality. For this reason, random forest model is more appropriate for predict water qualities than other algorithms, which is classified into decision tree type algorithm. Also, it is expected that real time prediction by artificial intelligence models can play role of the stable operation of residual chlorine in water treatment plant including pre-chlorination process.

• Nutrition Research and Practice
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• v.17 no.4
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• pp.682-697
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• 2023

BACKGROUND/OBJECTIVES: Tibetan tea is a kind of dark tea, due to the inherent complexity of natural products, the chemical composition and beneficial effects of Tibetan tea are not fully understood. The objective of this study was to unravel the composition of Tibetan tea using knowledge-guided multilayer network (KGMN) techniques and explore its potential antioxidant and hypolipidemic mechanisms in mice. MATERIALS/METHODS: The C57BL/6J mice were continuously gavaged with Tibetan tea extract (T group), green tea extract (G group) and ddH2O (H group) for 15 days. The activity of total antioxidant capacity (T-AOC) and superoxide dismutase (SOD) in mice was detected. Transcriptome sequencing technology was used to investigate the molecular mechanisms underlying the antioxidant and lipid-lowering effects of Tibetan tea in mice. Furthermore, the expression levels of liver antioxidant and lipid metabolism related genes in various groups were detected by the real-time quantitative polymerase chain reaction (qPCR) method. RESULTS: The results showed that a total of 42 flavonoids are provisionally annotated in Tibetan tea using KGMN strategies. Tibetan tea significantly reduced body weight gain and increased T-AOC and SOD activities in mice compared with the H group. Based on the results of transcriptome and qPCR, it was confirmed that Tibetan tea could play a key role in antioxidant and lipid lowering by regulating oxidative stress and lipid metabolism related pathways such as insulin resistance, P53 signaling pathway, insulin signaling pathway, fatty acid elongation and fatty acid metabolism. CONCLUSIONS: This study was the first to use computational tools to deeply explore the composition of Tibetan tea and revealed its potential antioxidant and hypolipidemic mechanisms, and it provides new insights into the composition and bioactivity of Tibetan tea.