• Journal of the Korean Institute of Rural Architecture
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• v.24 no.2
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• pp.45-52
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• 2022

Of the 17,106 domestic reservoirs(as of December 2020), 14,611 are older than 50 years, and these old reservoirs will gradually increase over time. The injection grouting method is most applied to the reinforcement method of the aging reservoir. However, the injection grouting method is not accurate in uniformity and reinforced area. An laboratory model test was conducted to evaluate the applicability of the deep mixing method, which compensated for these shortcomings, as a reservoir reinforcement method. As a result of calculating the hydraulic conductiveity for each method through the model test results, the injection grouting method was calculated as a hydraulic conductiveity value that was about 7.5 times larger than that of the deep mixing method. As a result of measuring the water level change in the laboratory model test, it was found that the water level change decreased in the injection method and deep mixing method compared to the non-reinforcement method. In addition, deep mixing method showed a water level change of about 15% based on 40 hours compared to the injection method, indicating that the water-reducing effect was superior to that of the injection method.

• Korean Journal of Materials Research
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• v.11 no.1
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• pp.55-60
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• 2001

The effect of high energy B ion implantation and initial oxygen concentration upon defect formation and gettering of metallic impurities in Czochralski silicon wafer has been studied by applying DLTS( Deep Level Transient Spectroscopy), SIMS(Secondary ton Mass Spectroscopy), BMD (Bulk Micro-Defect) analysis and TEM(Transmission Electron Microscopy). DLTS results show the signal of the deep levels not only in as-implanted samples but also in low and high temperature annealed samples. Vacancy-related deep levels in as- implanted samples were changed to metallic impurities-related deep levels with increase of annealing temperature. In the case of high temperature anneal, by showing the lower deep level concentration with increase of initial oxygen concentration, high initial oxygen concentration seems to be more effective compared with the lower initial oxygen one.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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• pp.3099-3120
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• 2023

The present fast-moving era brings a serious stress issue that affects elders and youngsters. Everyone has undergone stress factors at least once in their lifetime. Stress is more among youngsters as they are new to the working environment. whereas the stress factors for elders affect the individual and overall performance in an organization. Electroencephalogram (EEG) based stress level classification is one of the widely used methodologies for stress detection. However, the signal processing methods evolved so far have limitations as most of the stress classification models compute the stress level in a predefined environment to detect individual stress factors. Specifically, machine learning based stress classification models requires additional algorithm for feature extraction which increases the computation cost. Also due to the limited feature learning characteristics of machine learning algorithms, the classification performance reduces and inaccurate sometimes. It is evident from numerous research works that deep learning models outperforms machine learning techniques. Thus, to classify all the emotions based on stress level in this research work a hybrid deep learning algorithm is presented. Compared to conventional deep learning models, hybrid models outperforms in feature handing. Better feature extraction and selection can be made through deep learning models. Adding machine learning classifiers in deep learning architecture will enhance the classification performances. Thus, a hybrid convolutional neural network model was presented which extracts the features using CNN and classifies them through machine learning support vector machine. Simulation analysis of benchmark datasets demonstrates the proposed model performances. Finally, existing methods are comparatively analyzed to demonstrate the better performance of the proposed model as a result of the proposed hybrid combination.

• International journal of advanced smart convergence
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• v.5 no.3
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• pp.8-15
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• 2016

In recent years, one of deep learning models called Deep Belief Network (DBN) which formed by stacking restricted Boltzman machine in a greedy fashion has beed widely used for classification and recognition. With an ability to extracting features of high-level abstraction and deal with higher dimensional data structure, this model has ouperformed outstanding result on image and speech recognition. In this research, we assess the applicability of deep learning in dna classification level. Since the training phase of DBN is costly expensive, specially if deals with DNA sequence with thousand of variables, we introduce a new encoding method, using decimal-binary vector to represent the sequence as input to the model, thereafter compare with one-hot-vector encoding in two datasets. We evaluated our proposed model with different contrastive algorithms which achieved significant improvement for the training speed with comparable classification result. This result has shown a potential of using decimal-binary vector on DBN for DNA sequence to solve other sequence problem in bioinformatics.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2004.09a
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• pp.453-454
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• 2004

Hydrogeochemical and isotopic characteristics were investigated for groundwater of Tertiary basin in southeastern part of Korea where deep drilling is in progress for geothermal investigation. According to geology, aquifer was distinguished as alluvial, tertiary sedimentary bedrock (bedrock groundwater), and fractured volcanic rock (deep groundwater). Groundwater of each aquifer is distinctively separated in Eh-pH conditions and concentrations of Cl, F, B and HCO$_3$. Deep groundwater has very low level 3H and 14C whereas alluvial groundwater has those of recent precipitation level. However one of deep groundwater show mixed characteristics in terms of hydrochemistry which indicates effect of pumping. Deep groundwater have temperature of 38 to 43$^{\circ}C$ whereas bedrock and alluvial groundwater have temperature less than 2$0^{\circ}C$. Fractured basement rock aquifer has different hydrogeologicalsetting from bedrock and alluvial aquifer considering hydrogeochemical and isotopic characteristics, and temperature.

• IEMEK Journal of Embedded Systems and Applications
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• v.13 no.6
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• pp.313-319
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• 2018

We propose a post-processing algorithm to improve the quality of the RGB image generated by deep learning based colorization from the gray-scale image of an infrared camera. Wavelet fusion is used to generate a new luminance component of the RGB image luminance component from the deep learning model and the luminance component of the infrared camera. PSNR is increased for all experimental images by applying the proposed algorithm to RGB images generated by two deep learning models of SegNet and DCGAN. For the SegNet model, the average PSNR is improved by 1.3906dB at level 1 of the Haar wavelet method. For the DCGAN model, PSNR is improved 0.0759dB on the average at level 5 of the Daubechies wavelet method. It is also confirmed that the edge components are emphasized by the post-processing and the visibility is improved.

• Computers and Concrete
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• v.29 no.3
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• pp.145-159
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• 2022

In this study, the extreme learning machine and deep learning models were devised to estimate the bond strength of corroded reinforcement in concrete. The six inputs and one output were used in this study. The compressive strength, concrete cover, bond length, steel type, diameter of steel bar, and corrosion level were selected as the input variables. The results of bond strength were used as the output variable. Moreover, the Analysis of variance (Anova) was used to find the effect of input variables on the bond strength of corroded reinforcement in concrete. The prediction results were compared to the experimental results and each other. The extreme learning machine and the deep learning models estimated the bond strength by 99.81% and 99.99% accuracy, respectively. This study found that the deep learning model can be estimated the bond strength of corroded reinforcement with higher accuracy than the extreme learning machine model. The Anova results found that the corrosion level was found to be the input variable that most affects the bond strength of corroded reinforcement in concrete.

• The Journal of Engineering Geology
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• v.34 no.1
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• pp.117-136
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• 2024

Deep geological disposal of high-level radioactive waste is imperative to national safety and environmental protection and it relies on establishing siting criteria suited to the geological and social conditions of each country. This paper compares the various geological and social criteria applied by different countries in the process of securing sites for the deep geological disposal of high-level radioactive waste. The present comparative analysis considers the siting criteria established by the worlds leading countries in high-level radioactive waste disposal with the aims of establishing detailed criteria appropriate to Korea's conditions and applying the criteria to explore safe and suitable sites for deep geological disposal. The findings of this research are expected to serve as a foundation for establishing criteria for the selection of disposal sites for high-level radioactive waste in Korea and are anticipated to contribute significantly to sustainable national development and environmental protection.

• The Journal of Fisheries Business Administration
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• v.31 no.1
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• pp.95-113
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• 2000

This paper aims to analyze the competitiveness of the Korean Deep-sea Fisheries firms in the firm level. The extant researches on this topic have been done mainly in the macro-or industry-level perspectives and depended on the quantitative analyses using the aggregated data. The results of these researches are useful to figure out the main features of the industy, however, hardly give any implications on the strategic or competitiveness-related problems in the firm level. To accomplish the research purposes this study analyzes the competitiveness of the Korean Fisheries firms on the value chain scheme using qualitative tools. Specifically this paper focuses on the industry competition characteristics, key success factors, the competitiveness, and the supporting systems and policies of the Korean Government. Data are gathered by questionaire and analyzed by factor analysis and Kruska-Wallis one-way ANOVA. The results shows that the competitiveness of the Korean Deep-sea Fisheries firms is not behind the foreign competitors. However the resource securing, the market development, R&D investment are the main obstacles to the firms. The governmental supports are kedined to improve the competitiveness of the Korean Deep-sea Fisheries firms.

• Journal of the Korean Physical Society
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• v.73 no.12
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• pp.1879-1883
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• 2018

We analyzed the deep-trap states of GaN/InGaN ultraviolet light-emitting diodes (UV LEDs) before and after electrical stress. After electrical stress, the light output power dropped by 5.5%, and the forward leakage current was increased. The optical degradation mechanism could be explained based on the space-charge-limited conduction (SCLC) theory. Specifically, for the reference UV LED (before stress), two sets of deep-level states which were located 0.26 and 0.52 eV below the conduction band edge were present, one with a density of $2.41{\times}10^{16}$ and the other with a density of $3.91{\times}10^{16}cm^{-3}$. However, after maximum electrical stress, three sets of deep-level states, with respective densities of $1.82{\times}10^{16}$, $2.32{\times}10^{16}cm^{-3}$, $5.31{\times}10^{16}cm^{-3}$ were found to locate at 0.21, 0.24, and 0.50 eV below the conduction band. This finding shows that the SCLC theory is useful for understanding the degradation mechanism associated with defect generation in UV LEDs.