• Journal of Convergence for Information Technology
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• v.11 no.11
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• pp.10-16
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• 2021

As the implementation of smart factories spreads widely, the need for research to improve data efficiency is raised by prioritizing massive amounts of big data using IoT devices in terms of relevance and quality. The purpose of this study is to investigate whether prioritizing big data in management accounting decisions such as cost volatility estimation and recipe optimization can improve smart solution performance and decision-making effectiveness. Based on the survey answers of 84 decision makers at domestic small and medium-sized manufacturers who operate smart solutions such as ERP and MES that link manufacturing data in real time, empirical research was conducted. As a result, it was analyzed that setting prioritization of big data has a positive effect on decision-making in management accounting. became In addition, it was found that big data prioritization has a mediating effect that indirectly affects smart solution performance by using big data in management accounting decision making. Through the research results, it will be possible to contribute as a prior research to develop a scale to evaluate the correlation between big data in the process of business decision making.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.2
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• pp.117-124
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• 2021

This study is a study to recognize hand movements using a neural network for wrist rehabilitation. The rehabilitation of the hand aims to restore the function of the injured hand to the maximum and enable daily life, occupation, and hobby. It is common for a physical therapist, an occupational therapist, and a security tool maker to form a team and approach a doctor for a hand rehabilitation. However, it is very inefficient economically and temporally to find a place for treatment. In order to solve this problem, in this study, patients directly use smart devices to perform rehabilitation treatment. Using this will be very helpful in terms of cost and time. In this study, a wrist rehabilitation dataset was created by collecting data on 4 types of rehabilitation exercises from 10 persons. Hand gesture recognition was constructed using a neural network. As a result, the accuracy of 93% was obtained, and the usefulness of this system was verified.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.2
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• pp.24-35
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• 2021

Due to increasing awareness on the treatment of end-of-use/life products, disassembly has been a fast-growing research area of interest for many researchers over recent decades. This paper introduces a novel lot-sizing problem that has not been studied in the literature, which is the service-parts lot-sizing with disassembly option. The disassembly option implies that the demands of service parts can be fulfilled by newly manufactured parts, but also by disassembled parts. The disassembled parts are the ones recovered after the disassembly of end-of-use/life products. The objective of the considered problem is to maximize the total profit, i.e., the revenue of selling the service parts minus the total cost of the fixed setup, production, disassembly, inventory holding, and disposal over a planning horizon. This paper proves that the single-period version of the considered problem is NP-hard and suggests a heuristic by combining a simulated annealing algorithm and a linear-programming relaxation. Computational experiment results show that the heuristic generates near-optimal solutions within reasonable computation time, which implies that the heuristic is a viable optimization tool for the service parts inventory management. In addition, sensitivity analyses indicate that deciding an appropriate price of disassembled parts and an appropriate collection amount of EOLs are very important for sustainable service parts systems.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.19 no.4
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• pp.459-467
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• 2021

In the majority of countries, the upper limit of buffer temperature in a repository is set to below 100℃ due to the possible illitization. This smectite-to-illite transformation is expected to be detrimental to the swelling functions of the buffer. However, if the upper limit is increased while preventing illitization, the disposal density and cost-effectiveness for the repository will dramatically increase. Thus, understanding the characteristics and creating a database related to the buffer under the elevated temperature conditions is crucial. In this study, a strategy to investigate the bentonite found in Korea under the elevated temperatures from a mineral transformation and radionuclides retardation perspective was proposed. Certain long-term hydrothermal reactions generated the bentonite samples that were utilized for the investigation of their mineral transformation and radionuclide retardation characteristics. The bentonite samples are expected to be studied using in-situ synchrotron-based X-Ray Diffraction (XRD) technique to determine the smectite-to-illite transformation. Simultaneously, the 'high-temperature and high-pressure mineral alteration measurement system' based on the Diamond Anvil Cell (DAC) will control and provide the elevated temperature and pressure conditions during the measurements. The kinetic models, including the Huang and Cuadros model, are expected to predict the time and manner in which the illitization will become detrimental to the performance and safety of the repository. The sorption reactions planned for the bentonite samples to evaluate the effects on retardation will provide the information required to expand the current knowledge of repository optimization.

• Korean Journal of Construction Engineering and Management
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• v.23 no.1
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• pp.113-117
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• 2022

Additive manufacturing (AM, also known as 3D printing) technology is digitalized technology, making it easy to predict and manage quality and also, have design freedom ability. With these advantages, AM technology is applied to various industries. In particular, a method of manufacturing buildings and infrastructure with AM technology is being proposed to the construction industry. However, the application of AM technology is restricted due to problems such as insufficient history and quality of technology, lack of construction management methods, and certification of manufacturing products. Therefore, the manufacture of architectural products is implemented with indirect AM technology. In particular, it manufactures formwork using AM and injecting building materials to implement the architectural product. In this study, hybrid type material extrusion AM is used to manufacture large-sized formwork and implement building products. Moreover, we identify factors that can predict productivity and economic feasibility in the additive manufacturing process. As a result, design optimization results are proposed to reduce the production cost and time of architecture buildings.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.3
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• pp.443-449
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• 2022

In this study, in order to improve the disadvantages of the environmental error of the infusion set that performs infusion therapy in the existing clinical practice and to maximize the user's convenience by miniaturizing the existing infusion pump system, the structure of the muscle pump of the human vein was imitated. As a double check valve method, a method for preventing the backflow of fluid and discharging a constant fluid in one direction by external pressure was proposed. The proposed bio-mimic muscle pump uses a check valve that controls the flow of fluid in one direction and a silicone tube with elasticity, and a chamber is constructed. A peristaltic pump for applying intermittent pressure to the tube chamber was constructed using a multi-cam structure roller. In order to verify the performance of the proposed pump, optimization was performed while changing the number of multi-cam rollers and adjusting the speed of the roller driving motor, and the reproducibility of the instantaneous discharge amount and the continuous discharge amount of the pump was compared and tested. The performance of the muscle pump proposed in this study was verified through experiments that it can inject up to 1L of fluid within 12 hours, and that it is possible to inject the fluid with an accuracy of ±0.1ml. Real-time monitoring of the fluid injection volume through the bio-mimic muscle pump proposed in this study not only increases the convenience of the administrator, but also provides a precise fluid administration environment to more patients at a low cost, and additionally applies bubble detection and occlusion detection technology If so, it is believed that a safer medical environment can be provided to patients.

• Korean Journal of Optics and Photonics
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• v.32 no.6
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• pp.259-265
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• 2021

Recently, lens manufacturing and assembly technology has greatly improved. However, tight requirements of manufacturing and assembly lead to an increase in cost and manufacturing time, and in some cases the performance of an optical system may deteriorate depending on the operating environment's conditions, such as temperature or vibration. In addition, the use of a compensator is an effective method to reduce sensitivity in an ultra-precision optical system, but in the case of a small lens, such as that in an endoscope, it is difficult to use a compensator due to the size limitation of the lens barrel. Therefore, minimizing lens sensitivity is the most important technology in lens design. For this reason, there have been various attempts to reduce the lens sensitivity, and there is a trend to add functions to reduce the sensitivity in the lens design S/W. In this paper, we introduce a design technology that minimizes lens sensitivity. We first design a lens with quite good performance, then analyze the sensitivity of this lens, make a multi-configuration with high-sensitivity element error, and then reoptimize it. We prove with an example that this design technique is very effective.

• Fisheries and Aquatic Sciences
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• v.25 no.2
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• pp.76-89
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• 2022

The Lophius litulon liver was used as raw material for the extraction of fish oil via various extraction methods. The extraction rate by water extraction, potassium hydroxide (KOH) hydrolysis and protease hydrolysis were compared and the results revealed the protease hydrolysis extraction had a higher extraction rate with good protein-lipid separation as observed by optical microscope. Furthermore, subsequent experiments determined neutrase to be the best hydrolytic enzyme in terms of extraction rate and cost. The extraction conditions of neutrase hydrolysis were optimized by single-factor experiment and response surface analysis, and the optimal extraction rate was 58.40 ± 0.25% with the following conditions: enzyme concentration 2,000 IU/g, extraction time 1.0 h, liquid-solid ratio 1.95:1, extraction temperature 40.5℃ and pH 6.5. The fatty acids composition in fish oil from optimized extraction condition was composed of 19.75% saturated fatty acids and 80.25% unsaturated fatty acids. The content of docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA) were 8.06% and 1.19%, respectively, with the ratio (6.77:1) surpassed to the recommendation in current researches (5:1). The results in this study suggest protease treatment is an efficient method for high-quality fish oil extraction from Lophius litulon liver with a satisfactory ratio of DHA and EPA.

• The Journal of the Acoustical Society of Korea
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• v.42 no.1
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• pp.32-41
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• 2023

Recently, studies on reverberation suppression using Non-negative Matrix Factorization (NMF) have been actively conducted. The NMF method uses a cost function based on the Kullback-Leibler divergence for optimization. And some constraints are added such as temporal continuity, pulse length, and energy ratio between reverberation and target. The tendency of constraints are controlled by hyperparameters. Therefore, in order to effectively suppress reverberation, hyperparameters need to be optimized. However, related studies are insufficient so far. In this paper, the reverberation suppression performance according to the three hyperparameters of the NMF was analyzed by using sea experimental data. As a result of analysis, when the value of hyperparameters for time continuity and pulse length were high, the energy ratio between the reverberation and the target showed better performance at less than 0.4, but it was confirmed that there was variability depending on the ocean environment. It is expected that the analysis results in this paper will be utilized as a useful guideline for planning precise experiments for optimizing hyperparameters of NMF in the future.

• Korean Journal of Materials Research
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• v.33 no.5
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• pp.175-188
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• 2023

Water electrolysis holds great potential as a method for producing renewable hydrogen fuel at large-scale, and to replace the fossil fuels responsible for greenhouse gases emissions and global climate change. To reduce the cost of hydrogen and make it competitive against fossil fuels, the efficiency of green hydrogen production should be maximized. This requires superior electrocatalysts to reduce the reaction energy barriers. The development of catalytic materials has mostly relied on empirical, trial-and-error methods because of the complicated, multidimensional, and dynamic nature of catalysis, requiring significant time and effort to find optimized multicomponent catalysts under a variety of reaction conditions. The ultimate goal for all researchers in the materials science and engineering field is the rational and efficient design of materials with desired performance. Discovering and understanding new catalysts with desired properties is at the heart of materials science research. This process can benefit from machine learning (ML), given the complex nature of catalytic reactions and vast range of candidate materials. This review summarizes recent achievements in catalysts discovery for the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER). The basic concepts of ML algorithms and practical guides for materials scientists are also demonstrated. The challenges and strategies of applying ML are discussed, which should be collaboratively addressed by materials scientists and ML communities. The ultimate integration of ML in catalyst development is expected to accelerate the design, discovery, optimization, and interpretation of superior electrocatalysts, to realize a carbon-free ecosystem based on green hydrogen.