• The Journal of Bigdata
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• v.3 no.2
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• pp.59-70
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• 2018

In order to provide stable district heat supplying service to the certain limited residential area, it is the most important to forecast the short-term future demand more accurately and produce and supply heat in efficient way. However, it is very difficult to develop a universal heat demand forecasting model that can be applied to general situations because the factors affecting the heat consumption are very diverse and the consumption patterns are changed according to individual consumers and regional characteristics. In particular, considering all of the various variables that can affect heat demand does not help improve performance in terms of accuracy and versatility. Therefore, this study aims to develop a demand forecasting model using deep learning based on only limited information that can be acquired in real time. A demand forecasting model was developed by learning the artificial neural network of the Tensorflow using past data consisting only of the outdoor temperature of the area and date as input variables. The performance of the proposed model was evaluated by comparing the accuracy of demand predicted with the previous regression model. The proposed heat demand forecasting model in this research showed that it is possible to enhance the accuracy using only limited variables which can be secured in real time. For the demand forecasting in a certain region, the proposed model can be customized by adding some features which can reflect the regional characteristics.

• Journal of Korean Dental Science
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• v.11 no.2
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• pp.71-81
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• 2018

Purpose: The purpose of this study was to investigate the corrosion behaviors of dental implant alloy after microsized surface modification in electrolytes containing Mn ion. Materials and Methods: $Mn-TiO_2$ coatings were prepared on the Ti-6Al-4V alloy for dental implants using a plasma electrolytic oxidation (PEO) method carried out in electrolytes containing different concentrations of Mn, namely, 0%, 5%, and 20%. Potentiodynamic method was employed to examine the corrosion behaviors, and the alternatingcurrent (AC) impedance behaviors were examined in 0.9% NaCl solution at $36.5^{\circ}C{\pm}1.0^{\circ}C$ using a potentiostat and an electrochemical impedance spectroscope. The potentiodynamic test was performed with a scanning rate of $1.667mV\;s^{-1}$ from -1,500 to 2,000 mV. A frequency range of $10^{-1}$ to $10^5Hz$ was used for the electrochemical impedance spectroscopy (EIS) measurements. The amplitude of the AC signal was 10 mV, and 5 points per decade were used. The morphology and structure of the samples were examined using field-emission scanning electron microscopy and thin-film X-ray diffraction. The elemental analysis was performed using energy-dispersive X-ray spectroscopy. Result: The PEO-treated surface exhibited an irregular pore shape, and the pore size and number of the pores increased with an increase in the Mn concentration. For the PEO-treated surface, a higher corrosion current density ($I_{corr}$) and a lower corrosion potential ($E_{corr}$) was obtained as compared to that of the bulk surface. However, the current density in the passive regions ($I_{pass}$) was found to be more stable for the PEO-treated surface than that of the bulk surface. As the Mn concentration increased, the capacitance values of the outer porous layer and the barrier layer decreased, and the polarization resistance of the barrier layers increased. In the case of the Mn/Ca-P coatings, the corroded surface was found to be covered with corrosion products. Conclusion: It is confirmed that corrosion resistance and polarization resistance of PEO-treated alloy increased as Mn content increased, and PEO-treated surface showed lower current density in the passive region.

• Journal of Adhesion and Interface
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• v.22 no.3
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• pp.91-97
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• 2021

By introducing an organic interlayer on the Parylene C dielectric surface, the electrical device performances and the operating stabilities of organic field-effect transistors (OFETs) were improved. To achieve this goal, hexamethyldisilazane (HMDS) and octadecyltrichlorosilane (ODTS), as the organic interlayer materials, were used to control the surface energy of the Parylene C dielectrics. For the bare case used with the pristine Parylene C dielectrics, the field-effect mobility (μ_FET) and threshold voltage (V_th) of dinaphtho[2,3-b:2',3'-f ]thieno[3,2-b]- thiophene (DNTT) FET devices were measured at 0.12 cm²V^-1s^-1 and - 5.23 V, respectively. On the other hand, the OFET devices with HMDS- and ODTS-modified cases showed the improved μ_FET values of 0.32 and 0.34 cm²V^-1s^-1, respectively. More important point is that the μ_FET and V_th of the DNTT FET device with the ODTS-modified Parylene C dielectric presented the smallest changes during a repeated measurement of 1000 times, implying that it has the most stable operating stability. The results could be meaned that the organic interlayer, especially ODTS, effectively covers the Parylene C dielectric surface with alkyl chains and reduces the charge trapping at the interface region between active layer and dielectric, thereby improving the electrical operating stability.

• Journal of Korea Water Resources Association
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• v.54 no.5
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• pp.301-309
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• 2021

In this study, we tried to improve the performance of the existing U-net-based deep learning rainfall prediction model, which can weaken the meaning of time series order. For this, ConvLSTM2D U-Net structure model considering temporal consistency of data was applied, and we evaluated accuracy of the ConvLSTM2D U-Net model using a RainNet model and an extrapolation-based advection model. In addition, we tried to improve the uncertainty in the model training process by performing learning not only with a single model but also with 10 ensemble models. The trained neural network rainfall prediction model was optimized to generate 10-minute advance prediction data using four consecutive data of the past 30 minutes from the present. The results of deep learning rainfall prediction models are difficult to identify schematically distinct differences, but with ConvLSTM2D U-Net, the magnitude of the prediction error is the smallest and the location of rainfall is relatively accurate. In particular, the ensemble ConvLSTM2D U-Net showed high CSI, low MAE, and a narrow error range, and predicted rainfall more accurately and stable prediction performance than other models. However, the prediction performance for a specific point was very low compared to the prediction performance for the entire area, and the deep learning rainfall prediction model also had limitations. Through this study, it was confirmed that the ConvLSTM2D U-Net neural network structure to account for the change of time could increase the prediction accuracy, but there is still a limitation of the convolution deep neural network model due to spatial smoothing in the strong rainfall region or detailed rainfall prediction.

• BMB Reports
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• v.54 no.2
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• pp.118-123
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• 2021

The bacterial effector protein RavZ from a pathogen can impair autophagy in the host by delipidating the mammalian autophagy-related gene 8 (mATG8)-phosphatidylethanolamine (PE) on autophagic membranes. In RavZ, the membrane-targeting (MT) domain is an essential function. However, the molecular mechanism of this domain in regulating the intracellular localization of RavZ in cells is unclear. In this study, we found that the fusion of the green fluorescent protein (GFP) to the MT domain of RavZ (GFP-MT) resulted in localization primarily to the cytosol and nucleus, whereas the GFP-fused duplicated-MT domain (GFP-2xMT) localized to Rab5- or Rab7-positive endosomes. Similarly, GFP fusion to the catalytic domain (CA) of RavZ (GFP-CA) resulted in localization primarily to the cytosol and nucleus, even in autophagy-induced cells. However, by adding the MT domain to GFP-CA (GFP-CA-MT), the cooperation of MT and CA led to localization on the Rab5-positive endosomal membranes in a wortmannin-sensitive manner under nutrient-rich conditions, and to autophagic membranes in autophagy-induced cells. In autophagic membranes, GFP-CA-MT delipidated overexpressed or endogenous mATG8-PE. Furthermore, GFP-CA△α3-MT, an α3 helix deletion within the CA domain, failed to localize to the endosomal or autophagic membranes and could not delipidate overexpressed mATG8-PE. Thus, the CA or MT domain alone is insufficient for stable membrane localization in cells, but the cooperation of MT and CA leads to localization to the endosomal and autophagic membranes. In autophagic membranes, the CA domain can delipidate mATG8-PE without requiring substrate recognition mediated by LC3-interacting region (LIR) motifs.

• Journal of Navigation and Port Research
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• v.44 no.6
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• pp.550-556
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• 2020

Over the past years, the role of ports in the global network of supply chains has becoming increasingly important, not merely as a physical location for loading and unloading goods, but also as an essential center of economic activity where additional value is added to cargo. Due to the overall growing importance of ports, each country has chosen to adopt hub growth as a primary economic strategy. Northeast Asia in particular, due to its high population density, experiences intense competition between its ports. Busan's port, as a result, has used the establishment of Distripark in order to attract high and stable trade volume, and compete more effectively with other ports in the region. This study estimates the unit cost of the logistic process for the all principal cargos handled at Busan New Port, with the findings revealing that unit cost increases gradually starting with chemical products, LME bulk goods, automobile parts, LME containers, general cargoes, and LME inland transportation goods coming in last. Future research will look more closely at all all categories of cargo handled in the Distrpark of Busan New Port, thereby enabling us to better understand the value created by the port, and how to best implement effective trade volume-attraction strategy.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.1
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• pp.768-776
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• 2021

Current protection-coordination methods use the reverse time characteristics of the T-C curve, which is not effective for a LVDC distribution system because the protective operation time of converters and DC circuit breakers is much faster than AC protection devices. Therefore, an algorithm is proposed for fault-section isolation using the fault current slope to minimize the blackout region and coordinate between converters and protection devices in a rapid and accurate manner. The method deals with the slope characteristics of a fault current, which may depend on the fault location in an LVDC distribution system. Thus, an LVDC distribution system can be operated in a stable manner by isolating the fault section selectively before the shutdown of the main converter using slope characteristics, which change in proportion to the line impedance and fault location. A 1.5-kV LVDC distribution system was modeled to verify the effectiveness of the proposed algorithm using PSCAD/EMTDC. The system is composed of a distribution substation, LVDC converter, and distribution lines. The simulation results confirm that the proposed algorithm is a useful tool for minimizing the fault section in an LVDC distribution system.

• Journal of Astronomy and Space Sciences
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• v.38 no.1
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• pp.31-38
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• 2021

In this paper, we present observations of the Space Radiation Detectors (SRDs) onboard the Next Generation Small Satellite-1 (NEXTSat-1) satellite. The SRDs, which are a part of the Instruments for the study of Stable/Storm-time Space (ISSS), consist of the Medium-Energy Particle Detector (MEPD) and the High-Energy Particle Detector (HEPD). The MEPD can detect electrons, ions, and neutrals with energies ranging from 20 to 400 keV, and the HEPD can detect electrons over an energy range from 0.35 to 2 MeV. In this paper, we report an event where particle flux enhancements due to substorm injections are clearly identified in the MEPD A observations at energies of tens of keV. Additionally, we report a specific example observation of the electron distributions over a wide energy range in which we identify electron spatial distributions with energies of tens to hundreds of keV from the MEPD and with energy ranging up to a few MeV from the HEPD in the slot region and outer radiation belts. In addition, for an ~1.5-year period, we confirm that the HEPD successfully observed the well-known outer radiation belt electron flux distributions and their variations in time and L shell in a way consistent with the geomagnetic disturbance levels. Last, we find that the inner edge of the outer radiation belt is mostly coincident with the plasmapause locations in L, somewhat more consistent at subrelativistic energies than at relativistic energies. Based on these example events, we conclude that the SRD observations are of reliable quality, so they are useful for understanding the dynamics of the inner magnetosphere, including substorms and radiation belt variations.

• Journal of Space Technology and Applications
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• v.1 no.1
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• pp.64-75
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• 2021

On the moon as well as the earth, one of the easiest ways to understand geological formation history of any region is to observe the stratigraphy if it is available, the order in which the strata build up. By analyzing stratigraphy, it is possible to infer what geological events have occurred in the past. Mare Nubium also has an unique normal fault called Rupes Recta that shows stratigraphy. However, a rover moving with wheels is incompetent to explore the cliff since the Rupes Recta has an inclination of 10° - 30°. Therefore, a quadruped walking robot must be employed for stable expedition. To exploration a fault with a four-legged walking robot, it is necessary to design an expedition route by taking account of whether the stratigraphy is well displayed, whether the slope of the terrain is moderate, and whether there are obstacles and rough texture in the terrain based on the remote sensing data from the previous lunar missions. For the payloads required for fault surface exploration we propose an optical camera to grasp the actual appearance, a spectrometer to analyze the composition, and a drill to obtain samples that are not exposed outward.

• The Journal of the Convergence on Culture Technology
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• v.8 no.4
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• pp.11-17
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• 2022

Currently, the income and funds of the local economy are flowing out to the metropolitan area, and talented people, the driving force for regional development, also gather in the metropolitan area, and the local economy is facing a serious crisis. Local currency is issued by local governments and is a currency with auxiliary and complementary functions that can be used only within the area concerned. In order to revitalize the local economy, as local governments have focused their attention on the introduction of local currency, studies on the issuance and use of local currency are continuously being conducted. In this study, by using big data from data materials such as portals and SNS, the consumer image of local currency issued in local governments was identified through big data analysis, and based on the research results, the issuance and operation of local currency was conducted. The purpose is to present implications for The results of this study are as follows. First, by inducing local consumption through the policy issuance of local currency, it is showing the effect of increasing the economic income of the region. Second, local governments are exerting efforts to revitalize the economy and establish a virtuous cycle system for the local economy by issuing and distributing local currency. Third, the introduction of blockchain technology shows the stable operation of local currency. With academic significance, it was possible to grasp the changed appearance and effect of local currency through big data analysis and the policy direction of local currency.