• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.3
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• pp.707-725
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• 2021

Energy management systems (EnMS) contribute to sustainable energy saving and greenhouse gas reduction by emphasizing the role of energy management in production-oriented economies. Although understanding the methods used to measure energy performance is a key factor in constructing successful EnMS, few attempts have been made to examine these methods, their applicability, and their utility in practice. To fill this research gap, this study aimed to deepen the understanding of energy performance measures by focusing on four energy performance indicators (EnPIs) proposed by ISO 50006, namely the measured energy value, ratio between measured values, linear regression model, and nonlinear regression model. This paper presents policy and managerial implications to facilitate the effective use of these measures. An analytic hierarchy process (AHP) analysis was conducted with 41 experts to analyze the preference for EnPIs and their key selection criteria by the industry sector, and organization and user type. The findings suggest that the most preferred EnPI is the ratio between the measured values followed by the measured energy value. The ease of use was considered to be most important while choosing EnPIs.

• Journal of the Korean Crystal Growth and Crystal Technology
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• v.31 no.3
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• pp.116-126
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• 2021

Diesel particulate filter (DPF) is a typical application field of cordierite (Mg₂Al₄Si₅O₁₈) honeycomb. Green body for DPF honeycomb was extruded using slurry paste and sintered at the temperature range of 980~1450℃. Quantitative crystal phase analysis was carried out by using Rietveld refinement method for powder XRD data. In conjunction with the quantitative Rietveld analysis, SEM-EDS analysis was carried for the crystal phases (indialite, cordierite, cristobalite, alumina, spinel, mullite, pro-enstatite). After removing amorphous phase on the sintered surfaces by chemical etching method, the shape and composition of the crystal phases can be clearly identified by SEM-EDS method. By combining the Rietveld refinement method and SEM-EDS analysis, crystal phase evolution process in DPF cordierite ceramics could be clarified. In addition, the coefficient of thermal expansion (CTE) of the DPF honeycombs were measured and compared with the calculated CTEs based on the quantitative crystal phase analysis results.

• Journal of the Korea institute for structural maintenance and inspection
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• v.24 no.6
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• pp.206-216
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• 2020

As the number of aging railway bridges in Korea increases, maintenance costs due to aging are increasing and continuous management is becoming more important. However, while the number of old facilities to be managed increases, there is a shortage of professional personnel capable of inspecting and diagnosing these old facilities. To solve these problems, this study presents an improved model that can detect Local damage to structures using machine learning techniques of AI technology. To construct a damage detection machine learning model, an analysis model of the bridge was set by referring to the design drawing of a non-ballasted plate-girder railroad bridge. Static strain data according to the damage scenario was extracted with the analysis model, and the Local damage index based on the reliability of the bridge was presented using statistical techniques. Damage was performed in a three-step process of identifying the damage existence, the damage location, and the damage severity. In the estimation of the damage severity, a linear regression model was additionally considered to detect random damage. Finally, the random damage location was estimated and verified using a machine learning-based damage detection classification learning model and a regression model.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.1
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• pp.92-111
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• 2021

As fine dust negatively affects disease, industry and economy, the people are sensitive to fine dust. Therefore, if the occurrence of fine dust can be predicted, countermeasures can be prepared in advance, which can be helpful for life and economy. Fine dust is affected by the weather and the degree of concentration of fine dust emission sources. The industrial sector has the largest amount of fine dust emissions, and in industrial complexes, factories emit a lot of fine dust as fine dust emission sources. This study targets regions with old industrial complexes in local cities. The purpose of this study is to explore the factors that cause fine dust and develop a predictive model that can predict the occurrence of fine dust. weather data and fine dust data were used, and variables that influence the generation of fine dust were extracted through multiple regression analysis. Based on the results of multiple regression analysis, a model with high predictive power was extracted by learning with a machine learning regression learner model. The performance of the model was confirmed using test data. As a result, the models with high predictive power were linear regression model, Gaussian process regression model, and support vector machine. The proportion of training data and predictive power were not proportional. In addition, the average value of the difference between the predicted value and the measured value was not large, but when the measured value was high, the predictive power was decreased. The results of this study can be developed as a more systematic and precise fine dust prediction service by combining meteorological data and urban big data through local government data hubs. Lastly, it will be an opportunity to promote the development of smart industrial complexes.

• Korean Chemical Engineering Research
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• v.60 no.1
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• pp.1-6
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• 2022

As the demand for wearable devices increases, many studies have been studied on the development of flexible electrode materials recently. In particular, the development of high-performance flexible electrode materials is very important for wearable sensors for healthcare because it is necessary to continuously monitor and accurately detect body information such as body temperature, heart rate, blood glucose, and oxygen concentration in real time. In this study, we fabricated the nonenzymatic glucose sensor based on polyaniline/carbon nanotube fiber (PANI/CNT fiber) electrode. PANI layer was synthesized on the flexible CNT fiber electrode through electrochemical polymerization process in order to improve the performance of a flexible CNT fiber based electrode material. Surface morphology of the PANI/CNT fiber electrode was observed by scanning electron microscopy. And its electrochemical characteristics were investigated by chronoamperometry, cyclic voltammetry, electrochemical impedance spectroscopy. Compared to bare CNT fiber electrode, this PANI/CNT fiber electrode exhibited small electron transfer resistance, low peak separation potential and large surface area, resulting in enhanced sensing properties for glucose such as wide linear range (0.024~0.39 and 1.56~50 mM), high sensitivity (52.91 and 2.24 ㎂/mM·cm²), low detection limit (2 μM) and good selectivity. Therefore, it is expected that it will be possible to develop high performance CNT fiber based flexible electrode materials using various nanomaterials.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.3
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• pp.465-475
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• 2022

Due to the impact of COVID-19, people are paying attention to convenience and health, and the use of IoT devices to help them is increasing. In order to embed a lightweight security element in IoT devices that need to handle sensitive information even with limited resources, the development of a lightweight S-box is essential. Until 2021, it was common to develop a lightweight 4-bit S-box by a heuristic method, and to develop an extended structure or repeat the same operation for a larger size lightweight S-box. However, in January 2022, a paper that proposed a heuristic algorithm to find an 8-bit S-box with better differential uniformity and linearity than the S-box generated with an MISTY extended structure, although non-bijective, was published [1]. The heuristic algorithm proposed in this paper generates an S-box by adding AND operations one by one. Whenever an AND operation is added, they use a method that pre-removes the S-box for which the calculated differential uniformity does not reach the desired criterion. In this paper, we improve the performance of this heuristic algorithm. By increasing the amount of pre-removal using not only differential uniformity but also other differential property, and adding a process of calculating linearity for pre-removing, it is possible to satisfy not only differential security but also linear security.

• Nuclear Engineering and Technology
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• v.54 no.1
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• pp.318-325
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• 2022

In this study, composites containing undoped and barium-doped Bi₂WO₆:Ba²⁺were investigated for their shielding against diagnostic X-ray. At first, Bi₂WO₆ and barium-doped Bi₂WO₆ were synthesized with different weight percentages of barium oxide through a hydrothermal process. The as-synthesized nanostructures were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS) and Raman spectroscopy (RS). After that, some shields were generated with undoped and barium-doped Bi₂WO₆:Ba²⁺ nanostructure particles incorporated into polyvinyl chloride (PVC) polymer with different thicknesses and 15% weight of the nanostructure. Finally, the prepared samples were exposed to an X-ray tube at 40, 80, and 120 kV voltages, 10 mAs and, 44.5 cm SID (i.e. the distance from the X-ray beam source to the specimen). Linear and mass attenuation coefficients were also calculated for different samples. The results indicated that, among the samples, the one with 7.5 mmol barium-doped Bi₂WO₆ had the most attenuation at the voltage of 40kV, and the attenuation coefficients would increase with an increase in the amount of barium. The samples with 15 and 17.5 mmol barium-doped Bi₂WO₆ had higher attenuation than the others at 80 and 120 kV. Moreover, the half-value layer (HVL), tenth-value layer (TVL) and 0.25 mm lead equivalent thickness were calculated for all the samples. The lowest HVL value was for the sample with 7.5 mmol barium-doped Bi₂WO₆. As the result clearly show, an increment in the barium-doping content leads to a decrease in both HVL and TVL. In every three voltages, 0.25 mm lead equivalent thickness of the barium-doped composites (7.5 mmol and 15 mmol) had less than the other composites. The lowest value of 0.25 mm lead equivalent thickness was 7.5 barium-doped in 40 kV voltage and 15 mmol barium-doped in 80 kV and 120 kV voltages. These results were obtained only for 15% weight of the nanostructure.

• Journal of Korea Foundry Society
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• v.41 no.6
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• pp.535-542
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• 2021

In this study, to improve the interfacial bond strength of cast iron-aluminum dissimilar materials, graphite was removed to a certain depth from the cast iron surface through de-graphitization heat treatment. As the heat treatment time increased, the depth at which graphite was removed increased, showing a linear relationship between the heat treatment time and depth. Aluminum was filled to a certain depth on the de-graphitized cast iron surface through die-casting method, and no intermetallic compounds were formed on the cast iron-aluminum interface. The interfacial bonding strength showed a value of 90 MPa regardless of the heat treatment time, which is very high compared to the 12MPa bonding strength of the material without de-graphitization heat treatment. This result is thought to be due to the mechanical bonding of the undercut structure as the liquid aluminum, penetrated by the high pressure die-casting process, solidified in the de-graphitized region of the cast iron.

• Journal of the Korea Computer Graphics Society
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• v.27 no.5
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• pp.1-11
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• 2021

The purpose of our research is to efficiently deform a 3D models which is composed of a triangular mesh and a skeleton. We designed a novel inverse kinematics (IK) solver that calculates the updated positions of mesh vertices with fewer computing operations. Through our user interface, one or more markers are selected on the surface of the model and their target positions are set, then the system updates the positions of surface vertices to construct a deformed model. The IK solving process for updating vertex positions includes many computations for obtaining transformations of the markers, their affecting joints, and their parent joints. Many of these computations are often redundant. We precompute those redundant terms in advance so that the 3-nested loop computation structure was improved to a 2-nested loop structure, and thus the computation time for a deformation is greatly reduced. This novel IK solver can be adopted for efficient performance in various research fields, such as handling 3D models implemented by LBS method, or object tracking without any markers.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1141-1180
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• 2022

The application of satellite imageries has increased in the field of hydrology and water resources in recent years. However, challenges have been encountered on obtaining accurate evapotranspiration and soil moisture. Therefore, present researches have emphasized the necessity to obtain estimations of satellite-based evapotranspiration and soil moisture with related development researches. In this study, we presented the research status in Korea by investigating the current trends and methodologies for evapotranspiration and soil moisture. As a result of examining the detailed methodologies, we have ascertained that, in general, evapotranspiration is estimated using Energy balance models, such as Surface Energy Balance Algorithm for Land (SEBAL) and Mapping Evapotranspiration with Internalized Calibration (METRIC). In addition, Penman-Monteith and Priestley-Taylor equations are also used to estimate evapotranspiration. In the case of soil moisture, in general, active (AMSR-E, AMSR2, MIRAS, and SMAP) and passive (ASCAT and SAR)sensors are used for estimation. In terms of statistics, deep learning, as well as linear regression equations and artificial neural networks, are used for estimating these parameters. There were a number of research cases in which various indices were calculated using satellite-based data and applied to the characterization of drought. In some cases, hydrological cycle factors of evapotranspiration and soil moisture were calculated based on the Land Surface Model (LSM). Through this process, by comparing, reviewing, and presenting major detailed methodologies, we intend to use these references in related research, and lay the foundation for the advancement of researches on the calculation of satellite-based hydrological cycle data in the future.