• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.6
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• pp.89-100
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• 2021

The conservation of stream habitats has been gaining more public attention and fish habitat suitability index (HSI) is an important measure for ecological stream habitat assessment. The fish habitat preference is affected not only by physical stream conditions but also by water quality of which HSI was not available due to the lack of field data. The purpose of this study is to estimate the HSI of Zacco platypus for water quality parameters of water temperature, dissolved oxygen (DO), and biochemical oxygen demand (BOD) using the water environment monitoring data provided by the Ministry of Environment (ME). Fish population data merged with water quality were constructed by spatio-temporal matching of nationwide water quality monitoring data with bio-monitoring data of the ME. Two types of the HSI were calculated by the Instream Flow and Aquatic Systems Group (IFASG) method and probability distribution (Weibull) fitting for the four major river basins. Both the HSIs by the IFASG and Weibull fitting appeared to represent the overall distribution and magnitude of fish population and this can be used in stream fish habitat evaluation considering water quality.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.3
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• pp.75-85
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• 2021

This study evaluated variations in the paddy rice water demand based on the continuous changing in rice transplanting period and ponding depth at the four study paddy fields, which represent typical rice producing regions in Korea. Total 7 scenarios on rice transplanting periods were applied while minimum ponding depth of 0 and 20 mm were applied in accordance with maximum ponding depth ranging from 40 mm to 100 mm with 20 mm interval. The weather data from 2013 to 2019 was also considered. The results indicated that the highest rice water demand occurred at high temperature and low rainfall region. Increased rice transplanting periods showed higher rice water demand. The rice water demand for 51 transplanting days closely matched with the actual irrigation water supply. In case of ponding depth, the results showed that the minimum ponding depth had a proportional relationship with rice water demand, while maximum ponding depth showed the contrary results. Minimum ponding depth had a greater impact on rice water demand than the maximum ponding depth. Therefore, these results suggest that increasing the rice transplanting period, which reflects the current practice is desirable for a reliable estimation of rice water demand.

• Journal of Information Processing Systems
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• v.15 no.2
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• pp.386-398
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• 2019

The transmission capacity of transmission lines is affected by environmental parameters such as ambient temperature, wind speed, wind direction and so on. The environmental parameters can be measured by the installed measuring devices. However, it is impossible to install the environmental measuring devices throughout the line, especially considering economic cost of power grid. Taking into account the limited number of measuring devices and the distribution characteristics of environment parameters and transmission lines, this paper first studies the environmental parameter estimating method of inverse distance weighted interpolation and ordinary Kriging interpolation. Dynamic thermal rating of transmission lines based on IEEE standard and CIGRE standard thermal equivalent equation is researched and the key parameters that affect the load capacity of overhead lines is identified. Finally, the distributed thermal rating of transmission line is realized by using the data obtained from China meteorological data network. The cost of the environmental measurement device is reduced, and the accuracy of dynamic rating is improved.

• Journal of the Korean Society of Mineral and Energy Resources Engineers
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• v.55 no.6
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• pp.649-659
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• 2018

In this study, after operational data for a landfill gas power plant were collected, the methane gas concentration was predicted using a deep learning method. Concentrations of methane gas, carbon dioxide, hydrogen sulfide, oxygen concentration, as well as data related to the valve opening degree, air temperature and humidity were collected from 23 pipeline bases for 88 matches from January to November 2017. After the deep learning model learned the collected data, methane gas concentration was estimated by applying other data. Our study yielded extremely accurate estimation results for all of the 23 pipeline bases.

• Journal of Korea Society of Waste Management
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• v.34 no.2
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• pp.140-147
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• 2017

This study was conducted to evaluate the effects of physical characteristics. Twelve specific odorous compounds and various sources of bacteria were tested via treatment of food waste using an ultra-thermophilic aerobic composting process. Food waste was mixed with seed material and operated for 47 days. During composting, the temperature was maintained at $80-90^{\circ}C$. The variations in $O_2$, $CO_2$ and $NH_3$ production suggested typical microorganism-driven organic decomposition patterns. After composting, the concentrations of 12 specific odorous compounds other than ammonia did not exceed the allowable exhaust limits for odor. After composting, thermophiles represented 50% of all bacteria. After composting, the percentage of thermophile bacterial increased by 15%. Therefore, both stable composting operation and economic benefit can be expected when an ultra-thermophilic composting process is applied to food waste.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.893-895
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• 2016

Data center has a lot of management efforts for the facility, energy, and efficient usage monitoring. Data center power management is important to make the data center have reliable service and cost-effective business. In this paper, IoT based energy measurements monitoring which gives support to energy consumption analysis including indoor, outdoor temperature condition. This converged information for energy analysis gives various aspects of energy consumption effects. With IoT big data, energy machine learning system can give the relation of energy components and measurements, it is the key information of the quick energy analysis in the just one month data trend for the prediction and estimation.

• Journal of Astronomy and Space Sciences
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• v.36 no.3
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• pp.105-113
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• 2019

Spectroscopic observations of barium star ${\zeta}$ Capricornus (HD204075) obtained at the 8.2 m telescope of the European Southern Observatory, with a spectral resolving power R = 80,000 and signal to noise ratio greater than 300, were used to refine the atmospheric parameters. We found new values for effective temperature ($T_{eff}=5,300{\pm}50K$), surface gravity ($log\;g=1.82{\pm}0.15$), micro-turbulent velocity ($v_{micro}=2.52{\pm}0.10km/s$), and iron abundance ($log\;N(Fe)=7.32{\pm}0.06$). Previously published abundances of chemical elements in the atmosphere of HD204075 were analyzed and no correlations of these abundances with the second ionization potentials of these elements were found. This excludes the possible influence of accretion of hydrogen and helium atoms from the interstellar or circumstellar environment to the atmosphere of this star. The accretion of nuclear processed matter from the evolved binary companion was primary cause of the abundance anomalies. The young age of HD204075 allows an estimation of the time-scale for the creation of the abundance anomalies arising from accretion of interstellar hydrogen and helium as is the case of stars with low magnetic fields; which we estimate should exceed $10^8$ years.

• Journal of Integrative Natural Science
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• v.14 no.2
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• pp.41-49
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• 2021

The causes of climate change are natural and artificial. Natural causes include changes in temperature and sunspot activities caused by changes in solar radiation due to large-scale volcanic activities, while artificial causes include increased greenhouse gas concentrations and land use changes. Studies have shown that excessive carbon use among artificial causes has accelerated global warming. Climate change is rapidly under way because of this. Due to climate change, the frequency and cycle of infectious disease viruses are greater and faster than before. Currently, the world is suffering greatly from coronavirus infection-19 (COVID-19). Korea is no exception. The first confirmed case occurred on January 20, 2020, and the number of infected people has steadily increased due to several waves since then, and many confirmed cases are occurring in 2021. In this study, we conduct a study on climate change before and after COVID-19 using weather data from Korea to determine whether climate change affects infectious disease viruses through logistic regression analysis. Based on this, we want to classify before and after COVID-19 through a logistic regression model to see how much classification rate we have. In addition, we compare monthly classification rates to see if there are seasonal classification differences.

• Korean Journal of Metals and Materials
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• v.49 no.11
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• pp.893-899
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• 2011

Fabrication of a complex aluminum alloy by the ARB process using dissimilar aluminum alloys has been carried out. Two-layer stack ARB was performed for up to six cycles at ambient temperature without a lubricant according to the conventional procedure. Dissimilar aluminum sheets of AA1050 and AA5052 with thickness of 1 mm were degreased and wire-brushed for the ARB process. The sheets were then stacked together and rolled to 50% reduction such that the thickness became 1 mm again. The sheet was then cut into two pieces of identical length and the same procedure was repeated for up to six cycles. A sound complex aluminum alloy sheet was successfully fabricated by the ARB process. The tensile strength increased as the number of ARB cycles was increased, reaching 298 MPa after 5 cycles, which is about 2.2 times that of the initial material. The average grain size was $24{\mu}m$ after 1 cycle, and became $1.8{\mu}m$ after 6 cycles.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.4
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• pp.1193-1215
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• 2021

The measurement of the key product quality index plays an important role in improving the production efficiency and ensuring the safety of the enterprise. Since the actual working conditions and parameters will inevitably change to some extent with time, such as drift of working point, wear of equipment and temperature change, etc., these will lead to the degradation of the quality variable prediction model. To deal with this problem, the selective integrated moving windows based principal component regression (SIMV-PCR) is proposed in this study. In the algorithm of traditional moving window, only the latest local process information is used, and the global process information will not be enough. In order to make full use of the process information contained in the past windows, a set of local models with differences are selected through hypothesis testing theory. The significance levels of both T - test and χ² - test are used to judge whether there is identity between two local models. Then the models are integrated by Bayesian quality estimation to improve the accuracy of quality variable prediction. The effectiveness of the proposed adaptive soft measurement method is verified by a numerical example and a practical industrial process.