• The Journal of the Convergence on Culture Technology
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• v.9 no.1
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• pp.649-654
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• 2023

This paper proposes the design of a neural network structure search model using graph convolutional neural networks. Deep learning has a problem of not being able to verify whether the designed model has a structure with optimized performance due to the nature of learning as a black box. The neural network structure search model is composed of a recurrent neural network that creates a model and a convolutional neural network that is the generated network. Conventional neural network structure search models use recurrent neural networks, but in this paper, we propose GC-NAS, which uses graph convolutional neural networks instead of recurrent neural networks to create convolutional neural network models. The proposed GC-NAS uses the Layer Extraction Block to explore depth, and the Hyper Parameter Prediction Block to explore spatial and temporal information (hyper parameters) based on depth information in parallel. Therefore, since the depth information is reflected, the search area is wider, and the purpose of the search area of the model is clear by conducting a parallel search with depth information, so it is judged to be superior in theoretical structure compared to GC-NAS. GC-NAS is expected to solve the problem of the high-dimensional time axis and the range of spatial search of recurrent neural networks in the existing neural network structure search model through the graph convolutional neural network block and graph generation algorithm. In addition, we hope that the GC-NAS proposed in this paper will serve as an opportunity for active research on the application of graph convolutional neural networks to neural network structure search.

• Journal of the Korea Institute of Building Construction
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• v.23 no.3
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• pp.305-313
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• 2023

With an increasing interest in the commercialization of research results in the present societal climate, especially in the construction industry, preliminary product analysis plays a critical role when introducing a new product to the market. It significantly influences the product's success or failure. In this context, this study aims to investigate the utility of Importance-Performance Analysis (IPA) as a management strategy tool for preliminary analysis in the commercialization of new architectural technologies. The study specifically assesses a smart ball product engineered for pipeline inspection. The evaluation is carried out based on product quality, convenience, and usability categories. Seventeen factors are recognized as sub-items, and a survey is conducted among relevant experts and consumer groups. From the survey, four key items are chosen: "Keep up the good work," "Concentrate here," "Low priority," and "Possible overkill." Suitable strategic measures are derived for each item. By conducting a correlation analysis between product importance and performance, this study offers a method to establish priority directions for future development. This analysis assists in identifying areas that necessitate improvement or additional focus to increase the product's commercial potential. On the whole, this study contributes to understanding and applying Importance-Performance Analysis as a valuable tool in the preliminary analysis and commercialization of novel technologies in the field of architecture.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.6
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• pp.199-206
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• 2017

The experience sampling method (ESM) is an innovative research method to study the immediate real emotional experience experienced in real life through the immediate reaction of research participants. ESM, which has received significant attention in recent, is considered as the research method particularly for child care teachers' emotions and happiness. This method has been shown to be able to overcome the limitations in current research methods, based on teachers' recall or surveys, in assessing child care teachers' emotional states or stress levels. Despite the expectation that the need for further research on the increased stress and negative emotional experiences of child care teachers and its appropriateness as the alternative research method to study child care teachers' immediate emotional experience, ESM has deficiencies in that research participants need to have their pencil-and-paper survey packages on hand whenever their electronic beepers randomly beep. Furthermore, ESM demands much more researcher energy and efforts to handle the voluminous data collected from each participant in effectively creating a database. In this paper, in order to apply ESM successfully to the study of child care teachers' emotional experience, we aim to develop a software program that uses mobile communication technology. Given that traditional types of data collection methods in social science research can prove too burdensome to encourage participation in surveys in the first place or ensure the return of completed surveys, the present study adopts a convergent research approach to develop a software program that is able to obtain ESM participants' answers immediately on their personal smart phones. This study deals with system construction and prototyping for software development as a basic research and evaluates the research results through indepth interview with experts.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.5
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• pp.230-238
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• 2023

Small and medium-sized food manufacturing enterprises are largely reliant on manual labor, from inputting raw materials to palletizing the final product. Recently, there has been a trend toward smartness and digitization through the implementation of robotics and sensor data technology. In this study, we examined the effectiveness of improvement through 3D simulation on two repetitive work processes within a food manufacturing company. These processes involve workers whose speed cannot match the capacity of the applied equipment. Two manual processes were selected: the weighing and packing process performed by workers after skewer assembly, and the manual batch process of counting randomly delivered frozen foods, packing (both internal and external), and palletizing. The production volume, utilization rate, and number of workers were chosen as verification indicators. As a result of the simulation for improving the 3D process, production increased by 13.5% and 56.8% compared to the existing process, respectively. This was particularly evident in the process of applying palletizing robots. In both processes, as the utilization rate and number of input workers decreased, robots could replace tasks with high worker fatigue, thereby reducing work overload. This study demonstrates the potential to visually compare the process flow improvement using 3D simulations and confirms the possibility of pre-validation for improvement.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.171-180
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• 2023

Hydrogen energy technology is gaining importance in the era of the Fourth Industrial Revolution, offering military advantages when applied to military vehicles due to its characteristics such as reduced greenhouse gas emissions, noise, and low vibration. Korea's military has initiated the Army Tiger 4.0 plan, focusing on hydrogen application, downsizing, and AI-based smart features. The Ministry of National Defense plans to collaborate with the Ministry of Environment to expand hydrogen charging stations nationwide, anticipating increased deployment of military hydrogen vehicles. However, considering the Jet Fire and VCE(Vapor Cloud Explosion) nature of hydrogen, ensuring safety during installation is crucial. Current military guidelines specify a minimum safety distance of 2m from adjacent buildings for charging stations. Scientific methods have been employed to quantitatively assess the accident damage range of hydrogen, proposing a minimum safety distance beyond the affected area.

• Smart Structures and Systems
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• v.32 no.4
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• pp.267-279
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• 2023

Data modelling and interpretation for structural health monitoring (SHM) field data are critical for evaluating structural performance and quantifying the vulnerability of infrastructure systems. In order to improve the data modelling accuracy, and extend the application range from data regression analysis to out-of-sample forecasting analysis, an improved most likely heteroscedastic Gaussian process (iMLHGP) methodology is proposed in this study by the incorporation of the outof-sample forecasting algorithm. The proposed iMLHGP method overcomes this limitation of constant variance of Gaussian process (GP), and can be used for estimating non-stationary typhoon-induced response statistics with high volatility. The first attempt at performing data regression and forecasting analysis on structural responses using the proposed iMLHGP method has been presented by applying it to real-world filed SHM data from an instrumented cable-stay bridge during typhoon events. Uncertainty quantification and correlation analysis were also carried out to investigate the influence of typhoons on bridge strain data. Results show that the iMLHGP method has high accuracy in both regression and out-of-sample forecasting. The iMLHGP framework takes both data heteroscedasticity and accurate analytical processing of noise variance (replace with a point estimation on the most likely value) into account to avoid the intensive computational effort. According to uncertainty quantification and correlation analysis results, the uncertainties of strain measurements are affected by both traffic and wind speed. The overall change of bridge strain is affected by temperature, and the local fluctuation is greatly affected by wind speed in typhoon conditions.

• Korean Journal of Environmental Agriculture
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• v.42 no.4
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• pp.408-417
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• 2023

This study aimed to investigate the effects of an aquaponics system utilizing catfish recirculating water on the growth and water quality in lettuce and chicory cultivation. After 60 d of catfish cultivation in the BFT-AP, the survival rate was 96.0% and average weight gain was 178.5 g. The WGR grew by 241.4% and the SGR was 3.0%. A comparison between lettuce and chicory cultivation using BFT-AP and hydroponics revealed significant increases in shoot height, leaf count per week, and biomass of the cultivated lettuce and chicory. The SPAD values also increased significantly by 15.4-43.3%. Water quality analysis of the BFT-AP systems and crop cultivation beds showed significant differences, except for K and Na. The average NO₃^- concentration in the sediment pond was significantly higher at 45.3±1.0. Comparison of water temperature, pH, EC, DO, and ORP in the BFT-AP system and crop cultivation beds revealed similar monthly water temperatures from July to September. The pH in the catfish culture pond was 0.3-1.2 lower than that in the crop cultivation beds, and the EC was approximately 0.01- 0.03 dS/m lower in the catfish culture pond than the crop cultivation beds. The monthly average ORP in the catfish culture bed steadily increased to 479.9 mV in October. Bacillus velezensis (AFB2-2) and Chlorella fusca (CF) were applied to the biological activation bed; the population density of CF showed a continuous increase, whereas that of AFB2-2 decreased after 15 d and gradually increased thereafter. Thus, the application of the recirculating aquaponics cultivation technology with catfish (BFT) enhances catfish cultivation and promotes the growth of lettuce and chicory. Therefore, it is considered suitable for the environment-friendly cultivation of lettuce and chicory.

• Korean Journal of Environmental Agriculture
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• v.42 no.4
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• pp.331-337
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• 2023

Physiological disorders in pear fruit are mainly caused by problems during the growing season, such as lack of calcium in the soil, poor drainage, low porosity, vigorous pruning, and excessive fruiting. In this study, soil physicochemical properties and leaf characteristics were analyzed in pear orchards in four regions of Korea where chlorosis symptoms occurred to determine the causes of chlorosis. The color of chlorotic leaves was diagnosed using the naked eye or SPAD and Hunter values. The soil of the chlorotic orchard had a significantly higher soil pH than that of the regular orchard. Although adequate soil depth was not significantly associated with chlorosis, combined with over-fertilization of the soil with lime, it could potentially impair plant iron uptake. Chlorotic leaves had significantly lower iron and calcium contents and significantly higher magnesium contents than those of regular leaves. Therefore, the intensive occurrence of chlorosis during secondary shoot development around June and July when it is hot and humid may be due to impaired iron and calcium absorption, leading to physiological disorders. To solve this problem, avoiding the over-application of lime and applying foliar fertilizers containing chelated iron is recommended.

• KIPS Transactions on Software and Data Engineering
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• v.13 no.1
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• pp.1-16
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• 2024

Over recent years, 6 Sigma has become a key methodology in manufacturing for quality improvement and cost reduction. However, challenges have arisen due to the difficulty in analyzing large-scale data generated by smart factories and its traditional, formal application. To address these limitations, a big data-based 6 Sigma approach has been developed, integrating the strengths of 6 Sigma and big data analysis, including statistical verification, mathematical optimization, interpretability, and machine learning. Despite its potential, the practical impact of this big data-based 6 Sigma on manufacturing processes and management performance has not been adequately verified, leading to its limited reliability and underutilization in practice. This study investigates the efficiency impact of DX SS, a big data-based 6 Sigma, on manufacturing processes, and identifies key success policies for its effective introduction and implementation in enterprises. The study highlights the importance of involving all executives and employees and researching key success policies, as demonstrated by cases where methodology implementation failed due to incorrect policies. This research aims to assist manufacturing companies in achieving successful outcomes by actively adopting and utilizing the methodologies presented.

• Journal of the Korea Society for Simulation
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• v.33 no.2
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• pp.1-11
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• 2024

A cyber-physical system is a technology that connects the physical systems of a manufacturing environment with a cyber space to enable simulation. One of the major challenges in this technology is the seamless communication between these two environments. In complex manufacturing processes, it is crucial to adapt to various protocols of manufacturing equipment and ensure the transmission and reception of a large volume of data without delays or errors. In this study, we propose a method for constructing agent models for real-time simulation-capable cyberphysical systems. To achieve this, we design data collection units as independent agent models and effectively integrate them with existing simulation tools to develop the overall system architecture. To validate the proposed structure and ensure reliability, we conducted empirical testing by integrating various equipment from a real-world smart microfactory system to assess the data collection capabilities. The experiments involved testing data delay and data gaps related to data collection cycles. As a result, the proposed approach demonstrates flexibility by enabling the application of various internal data collection methods and accommodating different data formats and communication protocols for various equipment with relatively low communication delays. Consequently, it is expected that this approach will promote innovation in the manufacturing industry, enhance production line efficiency, and contribute to cost savings in maintenance.