• Proceedings of the Korean Environmental Sciences Society Conference
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• 2019.10a
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• pp.83-83
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• 2019

Increasing the CO₂ concentration in the atmosphere induce high temperature and rising sea levels. So the technology that capture and reuse of the CO₂ have been recently become popular. Among other methods, CRR(CO2₂ reduction reaction) is typical method of CO₂ reusing. Electrocatalyst can show more higher efficiencies in CRR than photocatalyst because it doesn't use nature source. Nowadays, finding high efficient electrocatalyst by controlling electronic (affected by stoichiometry) and geometric (affected by atomic arrangement) factors are very important issues. Mono-atomic electro-catalyst has limitations on controlling binding energy because each intermediate has own binding energy range. So the Multi-metallic electro-catalyst is important to stabilize intermediate at the same time. Carbon monoxide(CO) which is our target product and important feedstock of useful products. Au is known for the most high CO production metal. With copper, Not only gold/copper has advantages which is they have FCC packing for easily forming solid solution regardless of stoichiometry but also presence of adsorbed CO on Cu promotes the desorption of CO on Au because of strong repulsion. And gold/copper bi-metal catalyst can show high catalytic activity(mass activity) although it has low selectivity relatively Gold. Actually, multi-metallic catalyst structure control method is limited in the solution method which is takes a lot of time. In here, we introduce CTS(carbo thermal shock) method which is using heat to make MMNP in a few seconds for making gold-copper system. This method is very simple and efficient in terms of time(very short reaction time and using carbon substrate as a direct working electrode) and increasing reaction sites(highly dispersed and mixing alloy structures). Last one is easy to control degree of mixing and it can induce 5 or more metals in one alloy system. Gold/copper by CTS can show higher catalytic activity depending on metal ratio which is altered easily by changing simple variables. The ultimate goals are making CO₂ test system by CTS which can check the selectivity depending on metal types in a very short time.

• Atmosphere
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• v.33 no.5
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• pp.493-503
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• 2023

This study investigates the downward influences of sudden stratospheric warming (SSW) in February 2018 using a subseasonal-to-seasonal forecast model, Global Seasonal forecasting system version 6 (GloSea6). To quantify the influences of SSW on the tropospheric prediction skills, free-evolving (FREE) forecasts are compared to stratospheric nudging (NUDGED) forecasts where zonal-mean flows in the stratosphere are relaxed to the observation. When the models are initialized on 8 February 2018, both FREE and NUDGED forecasts successfully predicted the SSW and its downward influences. However, FREE forecasts initialized on 25 January 2018 failed to predict the SSW and downward propagation of negative Northern Annular Mode (NAM). NUDGED forecasts with SSW nudging qualitatively well predicted the downward propagation of negative NAM. In quantity, NUDGED forecasts exhibit a higher mean squared skill score of 500 hPa geopotential height than FREE forecasts in late February and early March. The surface air temperature and precipitation are also better predicted. Cold and dry anomalies over the Eurasia are particularly well predicted in NUDGED compared to FREE forecasts. These results suggest that a successful prediction of SSW could improve the surface prediction skills on subseasonal-to-seasonal time scale.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1049-1056
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• 2023

The height of the atmospheric boundary layer indicates the peak developed when turbulence is generated by mixing heat and water vapor, and is generally determined through thermodynamic methods. Wind profilers produce atmospheric information from the scattering of signals sent into the atmosphere. A method for making the spectrum of turbulent components, turbulent kinetic energy dissipation rate, and refractive index structure coefficient was presented to determine the atmospheric boundary layer depth. Compared with the vertical distribution characteristics of potential temperature and specific humidity based on radiosonde data, the determination method of the atmospheric boundary layer height from wind profiler output was evaluated as very useful.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1269-1276
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• 2023

Crop estimation is essential for the multinational meal and powerful demand due to its numerous aspects like soil, rain, climate, atmosphere, and their relations. The consequence of climate shift impacts the farming yield products. We operate the dataset with temperature, rainfall, humidity, etc. The current research focuses on feature selection with multifarious classifiers to assist farmers and agriculturalists. The crop yield estimation utilizing the feature selection approach is 96% accuracy. Feature selection affects a machine learning model's performance. Additionally, the performance of the current graph classifier accepts 81.5%. Eventually, the random forest regressor without feature selections owns 78% accuracy and the decision tree regressor without feature selections retains 67% accuracy. Our research merit is to reveal the experimental results of with and without feature selection significance for the proposed ten algorithms. These findings support learners and students in choosing the appropriate models for crop classification studies.

• Journal of the Korea Institute of Military Science and Technology
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• v.27 no.1
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• pp.1-7
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• 2024

Weather is one of the main causes of aircraft accidents, and among the phenomena caused by weather, icing is a phenomenon in which an ice layer is formed when an object exposed to an atmosphere below a freezing temperature collides with supercooled water droplets. If this phenomenon occurs in the rotor blades, it causes defects such as severe vibration in the airframe and eventually leads to loss of control and an accident. Therefore, it is necessary to foresee the icing situation so that it can ascend and descend at an altitude without a freezing point. In this study, vibration data in normal and faulty conditions was acquired, data features were extracted, and vibration was predicted through deep learning-based algorithms such as CNN, LSTM, CNN-LSTM, Transformer, and TCN, and performance was compared to evaluate blade icing. A method for minimizing operating loss is suggested.

• Journal of the Korean Society for Heat Treatment
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• v.37 no.2
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• pp.49-57
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• 2024

We investigated the changes in microstructure and surface roughness of the compound layer of GC250D gray cast iron, commonly used in brake discs, during gas nitriding. The gas atmosphere of the nitriding process was controlled with a hydrogen partial pressure of 49.5%, and the process was conducted at a nitriding temperature of 520℃ with various process times. As the nitriding process time of the GC250D material increased, both the depth of hardening and the thickness of the compound layer increased, with a maximum surface hardness of approximately 1265 HV_0.1 was measured. Additionally, the surface roughness increased with the process time. Phase analysis of the compound layer revealed an increase in the proportion of the γ' phase as the nitriding process time increased. Changes in the formation of the compound layer were observed depending on the orientation of graphite within the material, leading to the formation of wedges. Therefore, the increase in surface roughness appears to be attributed to the uneven compounds, the expansion of the compound layer and wedges formed on the surface during the nitriding process.

• Atmosphere
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• v.34 no.2
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• pp.177-185
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• 2024

As increasing global interest in renewable energy due to the ongoing climate crisis, there is a growing need for efficient technologies to manage such resources. This study focuses on the predictive skill of daily solar power generation using weather observation and forecast data. Meteorological data from the Korea Meteorological Administration and solar power generation data from the Korea Power Exchange were utilized for the period from January 2017 to May 2023, considering both inland (Daejeon) and coastal (Busan) regions. Temperature, wind speed, relative humidity, and precipitation were selected as relevant meteorological variables for solar power prediction. All data was preprocessed by removing their systematic components to use only their residuals and the residual of solar data were further processed with weighted adjustments for homoscedasticity. Four models, MLR (Multiple Linear Regression), RF (Random Forest), DNN (Deep Neural Network), and RNN (Recurrent Neural Network), were employed for solar power prediction and their performances were evaluated based on predicted values utilizing observed meteorological data (used as a reference), 1-day-ahead forecast data (referred to as fore1), and 2-day-ahead forecast data (fore2). DNN-based prediction model exhibits superior performance in both regions, with RNN performing the least effectively. However, MLR and RF demonstrate competitive performance comparable to DNN. The disparities in the performance of the four different models are less pronounced than anticipated, underscoring the pivotal role of fitting models using residuals. This emphasizes that the utilized preprocessing approach, specifically leveraging residuals, is poised to play a crucial role in the future of solar power generation forecasting.

• Journal of Conservation Science
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• v.30 no.2
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• pp.137-148
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• 2014

Large scale anoxic chamber system(volume $28m^3$) was developed and installed at The National Folk Museum of Korea for the first time in Korea. In order to get optimal anoxic treatment condition, we compared the disinfection of adults, larvae and eggs of cigarette beetles using nitrogen and argon. The time for complete disinfection of cigarette beetles in pine wooden blocks exposed to nitrogen at oxygen concentration 0.01% and 50% in relative humidity were 15 days at $20^{\circ}C$, 10 days at $25^{\circ}C$, and 7 days $30^{\circ}C$. Time were 10 days at $20^{\circ}C$, 7 days at $25^{\circ}C$, and 5 days $30^{\circ}C$ in argon anoxic atmosphere. From the mortality of cigarette beetles, optimal disinfection condition was oxygen concentration 0.01%, $25^{\circ}C$ in temperature, 50% in relative humidity and exposure time 21 days at nitrogen atmosphere. And when large scale anoxic chamber system was supplied nitrogen by nitrogen generator for anoxic treatment of many collections or large collections, it could be operated stably. To verify optimal disinfection condition, museum insects(adults, larvae, pupae and eggs of cigarette beetles in pine wooden blocks, cotton fabrics and Korean paper book, adults and larvae of drugstore beetles in pine wooden blocks, cotton fabrics and Korean paper book, larvae of varied carpet beetles in pine wooden block and silk fabrics, adults and larvae of hide beetles and adults of rice weevils in breeding boxes) which exposed at optimal disinfection condition, were completely killed.

• Korean Journal of Remote Sensing
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• v.27 no.4
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• pp.481-494
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• 2011

Investigation of the $CO_2$ exchange between biosphere and atmosphere at regional, continental, and global scales can be directed to combining remote sensing with carbon cycle process to estimate vegetation productivity. NASA Earth Observing System (EOS) currently produces a regular global estimate of gross primary productivity (GPP) and annual net primary productivity (NPP) of the entire terrestrial earth surface at 1 km spatial resolution. While the MODIS GPP algorithm uses meteorological data provided by the NASA Data Assimilation Office (DAO), the sub-pixel heterogeneity or complex terrain are generally reflected due to coarse spatial resolutions of the DAO data (a resolution of $1{\circ}\;{\times}\;1.25{\circ}$). In this study, we estimated inputs retrieved from MODIS products of the AQUA and TERRA satellites with 5 km spatial resolution for the purpose of finer GPP and/or NPP determinations. The derivatives included temperature, VPD, and solar radiation. Seven AmeriFlux data located in the Corn Belt region were obtained to use for evaluation of the input data from MODIS. MODIS-derived air temperature values showed a good agreement with ground-based observations. The mean error (ME) and coefficient of correlation (R) ranged from $-0.9^{\circ}C$ to $+5.2^{\circ}C$ and from 0.83 to 0.98, respectively. VPD somewhat coarsely agreed with tower observations (ME = -183.8 Pa ~ +382.1 Pa; R = 0.51 ~ 0.92). While MODIS-derived shortwave radiation showed a good correlation with observations, it was slightly overestimated (ME = -0.4 MJ $day^{-1}$ ~ +7.9 MJ $day^{-1}$; R = 0.67 ~ 0.97). Our results indicate that the use of inputs derived MODIS atmosphere and land products can provide a useful tool for estimating crop GPP.

• Korean Journal of Agricultural and Forest Meteorology
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• v.12 no.1
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• pp.11-22
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• 2010

According to IPCC 4th Assessment Report, concentration of carbon dioxide has been increasing by 30% since Industrial Revolution. Most of IPCC $CO_2$ emission scenarios estimate that the concentration will reach up to double of its present level within 100-year if the current tendency continues. The global warming has resulted in the agro-climate change over the Korean Peninsula as well. Accordingly, it is necessary to understand the future agro-climate induced by the increase of greenhouse gases in terms of the agro-climatic indices in the Korean peninsula. In this study, the future climate is simulated by an atmosphere/ocean/land surface/sea ice coupled general circulation climate model, Pusan National University Coupled General Circulation Model(hereafter, PNU CGCM), and by a regional weather prediction model, Weather Research and Forecasting Model(hereafter, WRF) for the purpose of a dynamical downscaling. The changes of the vegetable period and the crop growth period, defined as the total number of days of a year exceeding daily mean temperature of 5 and 10, respectively, have been analyzed. Our results estimate that the beginning date of vegetable and crop growth periods get earlier by 3.7 and 17 days, respectively, in spring under the $CO_2$-doubled climate. In most of the Korean peninsula, the predicted frost days in spring decrease by 10 days. Climatic production index (CPI), which closely represent the productivity of rice, tends to increase in the double $CO_2$ climate. Thus, it is suggested that the future $CO_2$ doubled climate might be favorable for crops due to the decrease of frost days in spring, and increased temperature and insolation during the heading date as we expect from the increased CPI.