• Korean small business review
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• v.42 no.4
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• pp.251-283
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• 2020

The purpose of this study is to provide assistance to the establishment of related policies to improve the level of acceptance and use of smart factories for SMEs in Korea. To this end, the Unified Technology Acceptance Model (UTAUT) was extended to select additional factors that could affect the intention to accept technology, and to demonstrate this. To achieve the research objective, a questionnaire composed of 7-point Likert scales was prepared, and a survey was conducted for manufacturing-related companies. A total of 136 questionnaires were used for statistical processing. As a result of the hypothesis test, performance expectation and social influence had a positive (+) positive effect on voluntary use, but effort expectation and promotion conditions did not have a significant effect. As an extension factor, the network effect and organizational characteristics had a positive (+) effect, and the innovation resistance had a negative effect (-), but the perceived risk had no significant effect. When the size of the company is large, the perceived risk and innovation resistance are low, and the level of influencing factors for veterinary intentions, veterinary intentions, and veterinary behaviors are excluded. Through this study, factors that could have a positive and negative effect on the adoption (reduction) of smart factory-related technologies were identified and factors to be improved and factors to be reduced were suggested. As a result, this study suggests that smart factory-related technologies should be accepted.

• Journal of Broadcast Engineering
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• v.27 no.6
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• pp.936-939
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• 2022

As air combat system technologies developed in recent years, the development of air defense systems is required. In the operating concept of the anti-aircraft defense system, selecting an appropriate armament for the target is one of the system's capabilities in efficiently responding to threats using limited anti-aircraft power. Much of the flying threat identification relies on the operator's visual identification. However, there are many limitations in visually discriminating a flying object maneuvering high speed from a distance. In addition, as the demand for unmanned and intelligent weapon systems on the modern battlefield increases, it is essential to develop a technology that automatically identifies and classifies the aircraft instead of the operator's visual identification. Although some examples of weapon system identification with deep learning-based models by collecting video data for tanks and warships have been presented, aerial vehicle identification is still lacking. Therefore, in this paper, we present a model for classifying fighters, helicopters, and drones using a convolutional neural network model and analyze the performance of the presented model.

• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.4
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• pp.107-118
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• 2022

As connected and automated driving(CAD) technology is entering its commercialization stage, service platforms providing CAD-based mobility services have increased these days. However, CAD-baded mobility services with these platforms need more consideration for the demand for mobility services when determining target areas for CAD-based mobility services because current CAB-based mobility design focus on driving performance and driving stability. For a more efficient design of CAD-based mobility services, we analyzed the applicability for the introduction of CAD-based mobility services in terms of driving difficulty of CAD and demand patterns of current non-CAD based-mobility services, e.g., taxi, demand-responsive transit(DRT), and special transportation systems(STS). In addition, for the spatial analysis of the applicability of the CAD-based mobility service, we propose the Index for Autonomous Driving Applicability (IADA) and analyze the characteristics of the spatial distribution of IADA from the network perspective. The analysis results show that the applicability of CAD-based mobility services depends more on the demand patterns than the driving difficulty of CAV. In particular, the results show that the concentration pattern of demand in a specific road link is more important than the size of demand. As a result, STS service shows higher applicability compared to other mobility services, even though the size of demand for this mobility service is relatively small.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.11
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• pp.403-410
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• 2022

Non-Fungible Tokens (NFTs) created on the basis of blockchain have their own unique value, so they cannot be forged or exchanged with other tokens or coins. Using these characteristics, NFTs can be issued to digital assets such as images, videos, artworks, game characters, and items to claim ownership of digital assets among many users and objects in cyberspace, as well as proving the original. However, interest in NFTs exploded from the beginning of 2020, causing a lot of load on the blockchain network, and as a result, users are experiencing problems such as delays in computational processing or very large fees in the mining process. Additionally, all actions of users are stored in the blockchain, and digital assets are stored in a blockchain-based distributed file storage system, which may unnecessarily expose the personal information of users who do not want to identify themselves on the Internet. In this paper, we propose an NFT platform using cloud computing, access gate, conversion table, and cloud ID to improve usability and privacy problems that occur in existing system. For performance comparison between local and cloud systems, we measured the gas used for smart contract deployment and NFT-issued transaction. As a result, even though the cloud system used the same experimental environment and parameters, it saved about 3.75% of gas for smart contract deployment and about 4.6% for NFT-generated transaction, confirming that the cloud system can handle computations more efficiently than the local system.

• Geophysics and Geophysical Exploration
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• v.25 no.1
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• pp.26-37
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• 2022

Recently, multiple methods using small aircraft for geophysical exploration have been suggested as a result of the development of information and communication technology. In this study, we introduce the hybrid unmanned aircraft electromagnetic system of the Korea Institute of Geosciences and Mineral resources, which is under development. Additionally, data processing and interpretation methods are suggested via the analysis of datasets obtained using the system under development to verify the system. Because the system uses a three-component receiver hanging from a drone, the effects of rotation on the obtained data are significant and were therefore corrected using a rotation matrix. During the survey, the heights of the source and the receiver and their offsets vary in real time and the measured data are contaminated with noise. The noise makes it difficult to interpret the data using the conventional method. Therefore, we developed a recurrent neural network (RNN) model to enable rapid predictions of the apparent resistivity using magnetic field data. Field data noise is included in the training datasets of the RNN model to improve its performance on noise-contaminated field data. Compared with the results of the electrical resistivity survey, the trained RNN model predicted similar apparent resistivities for the test field dataset.

• Economic and Environmental Geology
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• v.55 no.1
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• pp.1-17
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• 2022

The mechanical and thermal properties of the rock masses can affect the performance associated with both the isolating and retarding capacities of radioactive materials within the deep geological disposal system for High-Level Radioactive Waste (HLW). In this study, the essential parameters for the site descriptive model (SDM) related to the rock mechanics and thermal properties of the HLW disposal facilities site were reviewed, and the technical background was explored through the cases of the preceding site descriptive models developed by SKB (Swedish Nuclear and Fuel Management Company), Sweden and Posiva, Finland. SKB and Posiva studied parameters essential for the investigation and evaluation of mechanical and thermal properties, and derived a rock mechanics site descriptive model for safety evaluation and construction of the HLW disposal facilities. The rock mechanics SDM includes the results obtained from investigation and evaluation of the strength and deformability of intact rocks, fractures, and fractured rock masses, as well as the geometry of large-scaled deformation zones, the small-scaled fracture network system, thermal properties of rocks, and the in situ stress distribution of the disposal site. In addition, the site descriptive model should provide the sensitivity analysis results for the input parameters, and present the results obtained from evaluation of uncertainty.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.5
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• pp.171-182
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• 2022

The safe use of a structure requires it to be maintained in an undamaged state. Thus, a typical factor that determines the safety of a structure is a crack in it. In addition, cracks are caused by various reasons, damage the structure in various ways, and exist in different shapes. Making matters worse, if these cracks are unattended, the risk of structural failure increases and proceeds to a catastrophe. Hence, recently, methods of checking structural damage using deep learning and computer vision technology have been introduced. These methods usually have the premise that there should be a large amount of training image data. However, the amount of training image data is always insufficient. Particularly, this insufficiency negatively affects the performance of deep learning crack detection algorithms. Hence, in this study, a method of augmenting crack image data based on the image translation technique was developed. In particular, this method obtained the crack image data for training a deep learning neural network model by transforming a specific case of a asphalt crack image into a concrete crack image or vice versa . Eventually, this method expected that a robust crack detection algorithm could be developed by increasing the diversity of its training data.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.2
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• pp.59-76
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• 2023

Since the COVID-19 era, the rise in apartment prices has been unconventional. In this uncertain real estate market, price prediction research is very important. In this paper, a model is created to predict the actual transaction price of future apartments after building a vast data set of 870,000 from 2015 to 2020 through data collection and crawling on various real estate sites and collecting as many variables as possible. This study first solved the multicollinearity problem by removing and combining variables. After that, a total of five variable selection algorithms were used to extract meaningful independent variables, such as Forward Selection, Backward Elimination, Stepwise Selection, L1 Regulation, and Principal Component Analysis(PCA). In addition, a total of four machine learning and deep learning algorithms were used for deep neural network(DNN), XGBoost, CatBoost, and Linear Regression to learn the model after hyperparameter optimization and compare predictive power between models. In the additional experiment, the experiment was conducted while changing the number of nodes and layers of the DNN to find the most appropriate number of nodes and layers. In conclusion, as a model with the best performance, the actual transaction price of apartments in 2021 was predicted and compared with the actual data in 2021. Through this, I am confident that machine learning and deep learning will help investors make the right decisions when purchasing homes in various economic situations.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.35 no.6
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• pp.375-380
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• 2022

High-speed railway bridges carry a risk of dynamic response amplification due to resonance caused by train loads, and running safety and riding comfort must therefore be reviewed through dynamic analysis in accordance with design codes. The running safety and ride comfort calculation procedure, however, is time consuming and expensive because dynamic analyses must be performed for every 10 km/h interval up to 110% of the design speed, including the critical speed for each train type. In this paper, a deep-learning-based prediction system that can predict the running safety and ride comfort in advance is proposed. The system does not use dynamic analysis but employs a deep learning algorithm. The proposed system is based on a neural network trained on the dynamic analysis results of each train and speed of the railway bridge and can predict the running safety and ride comfort according to input parameters such as train speed and bridge characteristics. To confirm the performance of the proposed system, running safety and riding comfort are predicted for a single span, straight simple beam bridge. Our results confirm that the deck vertical displacement and deck vertical acceleration for calculating running safety and riding comfort can be predicted with high accuracy.

• Journal of the Korean Electrochemical Society
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• v.25 no.4
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• pp.174-183
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• 2022

Polymer electrolyte fuel cells and water electrolysis are attracting attention in terms of high energy density and high purity hydrogen production. The catalyst layer for the polymer electrolyte fuel cell and water electrolysis is a porous electrode composed of a precious metal-based electrocatalyst and an ionomer binder. Among them, the ionomer binder plays an important role in the formation of a three-dimensional network for ion conduction in the catalyst layer and the formation of pores for the movement of materials required or generated for the electrode reaction. In terms of the use of commercial perfluorinated ionomers, the content of the ionomer, the physical properties of the ionomer, and the type of the dispersing solvent system greatly determine the performance and durability of the catalyst layer. Until now, many studies have been reported on the method of using an ionomer for the catalyst layer for polymer electrolyte fuel cells. This review summarizes the research results on the use of ionomer binders in the fuel cell aspect reported so far, and aims to provide useful information for the research on the ionomer binder for the catalyst layer, which is one of the key elements of polymer electrolyte water electrolysis to accelerate the hydrogen economy era.