• Korean Journal of Culture and Social Issue
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• v.21 no.3
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• pp.481-496
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• 2015

The purpose of this study was to search for empirical evidence between internalized shame with self-control, aggression, and addiction potential. Also examine the differences between clinical group and normal group. The data was collected from 100 AA participants and 380 Non-AA participants. For measuring each variable, internalizes shame scale(ISS), self-control rating scale(SCRS), Buss-Durkee Hostility inventory(BDH), addiction potential scale(APS) were used. The survey result was analyzed with Pearson Correlation, Partial correlation and t-test. The results shows: Frist, the internalized shame and the aggression, the addiction potential shows a positive correlation but the self-control shows negative correlation. Second, after control self-control, internalized shame positively correlate to addiction potential and aggression. Third, clinical group recorded a higher level in internalized shame, aggression and addiction potential than normal group; a lower level in self-control. Based on the finding, the implications of understandings, the interventions, the limitations of this study, and the suggestions were discussed.

• Journal of the Korean Society of Propulsion Engineers
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• v.26 no.6
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• pp.21-27
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• 2022

C_f/SiC composites have low density, high mechanical strength, and good thermal stability, making them promising materials for high-temperature applications such as rocket propulsion and military fields. However, the remaining Si deteriorates physical and thermal properties. In this paper, the de-siliconization was introduced as a method to remove the Si of the C_f/SiC composite fabricated through Liquid Silicon Infiltration(LSI) process. The stability of composite has been tested under an oxyacetylene torch flame for up to 5 minutes. The oxidized surface and cross section of specimens were characterized by 3D scanning, X-ray diffraction(XRD), Optical microscope(OM) and Scanning electron microscope(SEM).

• The korean journal of orthodontics
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• v.52 no.2
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• pp.112-122
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• 2022

Objective: This study aimed to present and evaluate a new deep learning model for determining cervical vertebral maturation (CVM) degree and growth spurts by analyzing lateral cephalometric radiographs. Methods: The study sample included 890 cephalograms. The images were classified into six cervical stages independently by two orthodontists. The images were also categorized into three degrees on the basis of the growth spurt: pre-pubertal, growth spurt, and post-pubertal. Subsequently, the samples were fed to a transfer learning model implemented using the Python programming language and PyTorch library. In the last step, the test set of cephalograms was randomly coded and provided to two new orthodontists in order to compare their diagnosis to the artificial intelligence (AI) model's performance using weighted kappa and Cohen's kappa statistical analyses. Results: The model's validation and test accuracy for the six-class CVM diagnosis were 62.63% and 61.62%, respectively. Moreover, the model's validation and test accuracy for the three-class classification were 75.76% and 82.83%, respectively. Furthermore, substantial agreements were observed between the two orthodontists as well as one of them and the AI model. Conclusions: The newly developed AI model had reasonable accuracy in detecting the CVM stage and high reliability in detecting the pubertal stage. However, its accuracy was still less than that of human observers. With further improvements in data quality, this model should be able to provide practical assistance to practicing dentists in the future.

• Journal of The Korean Society of Agricultural Engineers
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• v.66 no.4
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• pp.27-39
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• 2024

Soil texture is determined by the proportions of sand, silt, and clay within the soil, which influence characteristics such as porosity, water retention capacity, electrical conductivity (EC), and pH. Traditional classification of soil texture requires significant sample preparation including oven drying to remove organic matter and moisture, a process that is both time-consuming and costly. This study aims to explore an alternative method by developing an AI model capable of predicting soil texture from images of pre-sorted soil samples using computer vision and deep learning technologies. Soil samples collected from agricultural fields were pre-processed using sieve analysis and the images of each sample were acquired in a controlled studio environment using a smartphone camera. Color distribution ratios based on RGB values of the images were analyzed using the OpenCV library in Python. A convolutional neural network (CNN) model, built on PyTorch, was enhanced using Digital Image Processing (DIP) techniques and then trained across nine distinct conditions to evaluate its robustness and accuracy. The model has achieved an accuracy of over 80% in classifying the images of pre-sorted soil samples, as validated by the components of the confusion matrix and measurements of the F1 score, demonstrating its potential to replace traditional experimental methods for soil texture classification. By utilizing an easily accessible tool, significant time and cost savings can be expected compared to traditional methods.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.1
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• pp.17-22
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• 2024

This paper introduces the implementation of a secondhand clothing trading system equipped with virtual fitting functionality based on deep learning. The proposed system provides users with the ability to visually try on secondhand clothing items online and assess their fit. To achieve this, it utilizes the Convolutional Neural Network (CNN) algorithm to create virtual representations of users considering their body shape and the design of the clothing. This enables buyers to pre-assess the fit of clothing items online before actually wearing them, thereby aiding in their purchase decisions. Additionally, sellers can present accurate clothing sizes and fits through the system, enhancing customer satisfaction. This paper delves into the CNN model's training process, system implementation, user feedback, and validates the effectiveness of the proposed system through experimental results.

• Convergence Security Journal
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• v.23 no.4
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• pp.61-70
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• 2023

Clustering is an unsupervised learning method that involves grouping data based on features such as distance metrics, using data without known labels or ground truth values. This method has the advantage of being applicable to various types of data, including images, text, and audio, without the need for labeling. Traditional clustering techniques involve applying dimensionality reduction methods or extracting specific features to perform clustering. However, with the advancement of deep learning models, research on deep clustering techniques using techniques such as autoencoders and generative adversarial networks, which represent input data as latent vectors, has emerged. In this study, we propose a deep clustering technique based on deep learning. In this approach, we use an autoencoder to transform the input data into latent vectors, and then construct a vector space according to the cluster structure and perform k-means clustering. We conducted experiments using the MNIST and Fashion-MNIST datasets in the PyTorch machine learning library as the experimental environment. The model used is a convolutional neural network-based autoencoder model. The experimental results show an accuracy of 89.42% for MNIST and 56.64% for Fashion-MNIST when k is set to 10.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.27-65
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• 2020

Many companies on information and communication technology make public their own developed AI technology, for example, Google's TensorFlow, Facebook's PyTorch, Microsoft's CNTK. By releasing deep learning open source software to the public, the relationship with the developer community and the artificial intelligence (AI) ecosystem can be strengthened, and users can perform experiment, implementation and improvement of it. Accordingly, the field of machine learning is growing rapidly, and developers are using and reproducing various learning algorithms in each field. Although various analysis of open source software has been made, there is a lack of studies to help develop or use deep learning open source software in the industry. This study thus attempts to derive a strategy for adopting the framework through case studies of a deep learning open source framework. Based on the technology-organization-environment (TOE) framework and literature review related to the adoption of open source software, we employed the case study framework that includes technological factors as perceived relative advantage, perceived compatibility, perceived complexity, and perceived trialability, organizational factors as management support and knowledge & expertise, and environmental factors as availability of technology skills and services, and platform long term viability. We conducted a case study analysis of three companies' adoption cases (two cases of success and one case of failure) and revealed that seven out of eight TOE factors and several factors regarding company, team and resource are significant for the adoption of deep learning open source framework. By organizing the case study analysis results, we provided five important success factors for adopting deep learning framework: the knowledge and expertise of developers in the team, hardware (GPU) environment, data enterprise cooperation system, deep learning framework platform, deep learning framework work tool service. In order for an organization to successfully adopt a deep learning open source framework, at the stage of using the framework, first, the hardware (GPU) environment for AI R&D group must support the knowledge and expertise of the developers in the team. Second, it is necessary to support the use of deep learning frameworks by research developers through collecting and managing data inside and outside the company with a data enterprise cooperation system. Third, deep learning research expertise must be supplemented through cooperation with researchers from academic institutions such as universities and research institutes. Satisfying three procedures in the stage of using the deep learning framework, companies will increase the number of deep learning research developers, the ability to use the deep learning framework, and the support of GPU resource. In the proliferation stage of the deep learning framework, fourth, a company makes the deep learning framework platform that improves the research efficiency and effectiveness of the developers, for example, the optimization of the hardware (GPU) environment automatically. Fifth, the deep learning framework tool service team complements the developers' expertise through sharing the information of the external deep learning open source framework community to the in-house community and activating developer retraining and seminars. To implement the identified five success factors, a step-by-step enterprise procedure for adoption of the deep learning framework was proposed: defining the project problem, confirming whether the deep learning methodology is the right method, confirming whether the deep learning framework is the right tool, using the deep learning framework by the enterprise, spreading the framework of the enterprise. The first three steps (i.e. defining the project problem, confirming whether the deep learning methodology is the right method, and confirming whether the deep learning framework is the right tool) are pre-considerations to adopt a deep learning open source framework. After the three pre-considerations steps are clear, next two steps (i.e. using the deep learning framework by the enterprise and spreading the framework of the enterprise) can be processed. In the fourth step, the knowledge and expertise of developers in the team are important in addition to hardware (GPU) environment and data enterprise cooperation system. In final step, five important factors are realized for a successful adoption of the deep learning open source framework. This study provides strategic implications for companies adopting or using deep learning framework according to the needs of each industry and business.

• Fire Science and Engineering
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• v.33 no.6
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• pp.120-125
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• 2019

A real-scale combustion test was conducted on a vinyl flooring in a divided space, with 50 mL of an inflammable liquid sprayed on it. The combustion behavior of the vinyl flooring was studied in real time, and the carbonization patterns of the surface and cross-sections of the carbonized vinyl floor were analyzed. When the flame ignited by gasoline reached its peak, a continuously flaming region, intermittent flaming region, plume region, etc., were formed. The combustion of 50 mL gasoline on vinyl flooring took 26 s, and a halo pattern was observed. This test involved spraying kerosene evenly on the vinyl flooring and attempting to ignite the flooring using a gas torch, which failed. After the combustion of the vinyl flooring was complete, its carbonized range was measured to be 600 mm in length and 380 mm in width, and the carbonized area was 1000 ㎟. Heat transformed the coated layer of surface of the carbonized vinyl flooring into a carbonized layer, which became harder. The analysis of cross-section of the boundary surface of the carbonized vinyl flooring using a stereoscopic microscope showed that the vinyl flooring was bubbling, and that the white boundary layer at the bottom of the coated layer had disappeared.

• Clean Technology
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• v.19 no.2
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• pp.113-120
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• 2013

The treatment of chemically stable perflourocompounds (PFCs) requires a large amount of energy. An energy efficient arc plasma system has been developed to overcome such disadvantage. $CF_4$, $SF_6$ and $NF_3$ were injected into the plasma torch directly, and net plasma power was estimated from the measurement of thermal efficiency of the system. Effects of net plasma power, waste gas flow rate and additive gases on the destruction and removal efficiency (DRE) of PFCs were examined. The calculation of thermodynamic equilibrium composition was also conducted to compare with experimental results. The average thermal efficiency was ranged from 60 to 66% with increasing waste gas flow rate, while DRE of PFCs was decreased with increasing gas flow rate. On the other hand, DRE of each PFCs was increased with the increasing input power. Maximum DREs of $CF_4$, $SF_6$ and $NF_3$ were 4%, 15% and 90%, respectively, without reaction gas at the fixed input power and waste gas flow rate of 3 kW and 70 L/min. A rapid increase of DRE was found using hydrogen or oxygen additional gases. Hydrogen was more effective than oxygen to decompose PFCs and to control by-products. The major by-product in the arc plasma process with hydrogen was hydrofluoric acid that is easy to be removed by a wet scrubber. DREs of $CF_4$, $SF_6$ and $NF_3$ were 25%, 39% and 99%, respectively, using hydrogen additional gas at the waste gas flow rate of 100 L/min and the input power of 3 kW.

• Journal of the Korea Concrete Institute
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• v.20 no.5
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• pp.565-572
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• 2008

It is a trend to increase safekeeping properties in financial company as the world economy situation has been globalized and advanced. The development of a securable vault door resisting to malicious trespass is needed. Therefore, this study focuses on developing high performance concrete placed at the inside of the vault door, and all materials used in this study is easy to obtain in domestic considering economic competitiveness. The compressive strength over 170 MPa was targeted, and structurally strengthening was also planned in order to resist to over $3,000^{\circ}C$ heating by torch and extreme impact loading by hammer drilling machine. Several types of fibers and reinforcing structures were used in order to resist those external heating and loading. This purpose was required to satisfy UL 608 standard of a vault door. Consequently, the result from this study is expected to be applied to construction field of major facilities, which should guarantee the safety from an external attack such as terror.