• Research in Plant Disease
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• v.28 no.3
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• pp.113-121
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• 2022

Rice blast is a major plant disease that occurs worldwide and significantly reduces rice yields. Rice blast disease occurs periodically in Korea, causing significant socio-economic damage due to the unique status of rice as a major staple crop. A disease outbreak prediction system is required for preventing rice blast disease. Epidemiological investigations of disease outbreaks can aid in decision-making for plant disease management. Currently, plant disease prediction and epidemiological investigations are mainly based on quantitatively measurable, structured data such as crop growth and damage, weather, and other environmental factors. On the other hand, text data related to the occurrence of plant diseases are accumulated along with the structured data. However, epidemiological investigations using these unstructured data have not been conducted. The useful information extracted using unstructured data can be used for more effective plant disease management. This study analyzed news articles related to the rice blast disease through text mining to investigate the years and provinces where rice blast disease occurred most in Korea. Moreover, the average temperature, total precipitation, sunshine hours, and supplied rice varieties in the regions were also analyzed. Through these data, it was estimated that the primary causes of the nationwide outbreak in 2020 and the major outbreak in Jeonbuk region in 2021 were meteorological factors. These results obtained through text mining can be combined with deep learning technology to be used as a tool to investigate the epidemiology of rice blast disease in the future.

• Journal of Korean Society of Environmental Engineers
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• v.22 no.6
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• pp.1093-1101
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• 2000

The object of this study was to develop new BNR process using fixed-biofilm which could be applied to retrofit the existing wastewater treatment plant or to introduce as tertiary treatment plant. To achieve complete denitrification from typical raw sewage in Korea, external carbon source must be supplied because $SCOD_{cr}/T-N(NH_4{^+}-N+NOx-N)$of raw sewage was lower than other countries. In this study, the ratio of $SCOD_{cr}/NH_4{^+}-N$ was 2.49 and the influent $NH_4{^+}$-N concentration during the experimental period was varied from 25 to 37 mg/L. To enhance nitrogen removal from the sewage, the two processes using fixed biofilm were adopted as R-Hanoxic/mid.settler/aerobic/anoxic/ aerobic) and R-2(aerobic/mid.settlerlanoxic/anoxic/aerobic), respectively. In the comparison of $NH_4{^+}$-N, T-N effluent quality and T-N removal efficiency in both processes without external carbon source, R-1 process was better than R-2 process for nitrogen removal from raw sewage. With respect to $SCOD_{cr}$/NOx-N ratio and total nitrogen removal in each anoxic reactor of two processes, R-1's was more effective than R-2's for distributing organic matters of raw sewage. In the both processes using fixed biofilm, the amount of required alkalinity to remove unit $NH_4{^+}$-N were 5.18 and 5.76($g{\cdot}CaCO_3/g{\cdot}NH_4{^+}-N_{removed}$), respectively and were lower than activated sludge BNR process(7.14).

• Korean Journal of Breeding Science
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• v.41 no.3
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• pp.292-298
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• 2009

A new rice cultivar "Keumo 3" was developed for adopting under double cropping system with after or before cash crop cultivation. It was selected from the cross-combination between YR17202 $F_2$/Shinkeumobyeo//YR15727-B-B-B-102. The parent, YR17202 $F_2$ individual plant, was used for tolerance to lodging, it derived from a cross between Nonganbyeo/Shinkeumobyeo. Nonganbyeo is well known to lodging tolerance cultivar, as well as biotic stress, because it was developed by crossing with Tongil type. And the YR15727-B-B-B-102 line used as another parent with short growth duration, likewise highly resistance to rice blast disease. The pedigree derived from the cross-combination YR17202 $F_2$/Shinkeumobyeo//YR15727-B-B-B-102 were generated to $F_7$, and a best line among them named as Milyang 201. After a series of yield trials, including local adaptability test conducted throughout the peninsular of Korea, Milyang 201 was registered with the name of "Keumo 3" in 2005. The cultivar belongs to a early maturing group and heads 4 days earlier than Keumobyeo, a standard cultivar. It has short culm, and less spikelet number per panicle than Keumobyeo. However, its milled rice yield grown under extremely late transplanting time, 10. July, over the 3 local sites for 2003-2005 years, averaged 4,48 MT/ha, which is 6% higher than the standard, Keumobyeo. "Keumo 3" has showed a durable resistance to leaf blast disease during fourteen blast nurseries screening covered from south to north in Korea for 2003-2007 years. And it was confirmed harbours pi-zt, a durable blast resistance gene. Moreover it was incompatible with 19 blast isolates under artificial inoculation, except one isolate, K1101. Additionally, "Keumo 3" exhibits resistance to $K_1$, $K_2$ and $K_3$ of bacterial blight pathogen, as well as strip virus disease resistance, and moderate resistance to dwarf virus disease. Consequently, the new rice cultivar "Keumo 3" would be well adopted where a bio stress makes a big problem annually.

• The Korean Journal of Malacology
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• v.22 no.1 s.35
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• pp.27-38
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• 2006

We investigated the reproductive cycle with gonad developmental phases of Solen grandis by histological observations. Seasonal changes in biochemical components of the adductor muscle, visceral mass, foot muscle and mantle were studied by biochemical analysis, from January to December, 2005. The reproductive cycle of this species can be classified into five successive stages: early active stage (December to January), late active stage (January to March), ripe stage (March to July), partially spawned stage (June to July) and spent/inactive stage (July to December). Total protein content was the highest in the foot muscle, the content was high in January (early active stage), the lowest in April (ripe stage), and was the highest in August (partially spawned stage). In the visceral mass, total protein content began to increase in February (late active stage) and reached a maximum in March (ripe stage). Thereafter, it gradually decreased between June and July (partially spawned stage). There was a strong negative correlation in total protein contents between visceral mass and mantle (r = -0.594, p = 0.042). Meanwhile there was a positive correlation between the adductor muscle and foot muscle, the correlation was not statistically significant (r = 0.507, p = 0.093). Total lipid content was the highest in the visceral mass; it was more than 2 to 5-fold higher than that in the adductor muscle, foot muscle, and mantle. Monthly changes in total lipid content were also most dynamic in the visceral mass. It was relatively higher between January and February, showed a maximum in March (the ripe stage), decreased rapidly from April to July (ripe and partially spawned stage), and gradually decreased from September to December (spent/inactive stage). There was a strong positive correlation in total lipid content between foot muscle and adductor muscle (r = 0.639, p = 0.025). Tthough a negative correlation was found between visceral mass and mantle (r = -0.392), the correlation was not statistically significant (p = 0.208). Glycogen contents changed within relatively narrow range and were similar among different tissues. There was no statistically significant correlation in glycogen contents among tissues.

• Journal of forensic and investigative science
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• v.1 no.2
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• pp.5-19
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• 2006

Data mining is an information extraction activity to discover hidden facts contained in databases. Using a combination of machine learning, statistical analysis, modeling techniques and database technology, data mining finds patterns and subtle relationships in data and infers rules that allow the prediction of future results. Typical applications include market segmentation, customer profiling, fraud detection, evaluation of retail promotions, and credit risk analysis. Law enforcement agencies deal with mass data to investigate the crime and its amount is increasing due to the development of processing the data by using computer. Now new challenge to discover knowledge in that data is confronted to us. It can be applied in criminal investigation to find offenders by analysis of complex and relational data structures and free texts using their criminal records or statement texts. This study was aimed to evaluate possibile application of data mining and its limitation in practical criminal investigation. Clustering of the criminal cases will be possible in habitual crimes such as fraud and burglary when using data mining to identify the crime pattern. Neural network modelling, one of tools in data mining, can be applied to differentiating suspect's photograph or handwriting with that of convict or criminal profiling. A case study of in practical insurance fraud showed that data mining was useful in organized crimes such as gang, terrorism and money laundering. But the products of data mining in criminal investigation should be cautious for evaluating because data mining just offer a clue instead of conclusion. The legal regulation is needed to control the abuse of law enforcement agencies and to protect personal privacy or human rights.

• Korean Journal of Remote Sensing
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• v.34 no.6_1
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• pp.1041-1053
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• 2018

In this study, a real-time storage level and capacity monitoring and forecasting system for Yongdam Dam watershed was developed using high resolution satellite image. The drought indices such as Standardized Precipitation Index (SPI) from satellite data were used for storage level monitoring in case of drought. Moreover, to predict storage volume we used a statistical method based on Principle Component Analysis (PCA) of Singular Spectrum Analysis (SSA). According to this study, correlation coefficient between storage level and SPI (3) was highly calculated with CC=0.78, and the monitoring and predictability of storage level was diagnosed using the drought index calculated from satellite data. As a result of analysis of principal component analysis by SSA, correlation between SPI (3) and each Reconstructed Components (RCs) data were highly correlated with CC=0.87 to 0.99. And also, the correlations of RC data with Normalized Water Surface Level (N-W.S.L.) were confirmed that has highly correlated with CC=0.83 to 0.97. In terms of high resolution satellite image we developed a water detection algorithm by applying an exponential method to monitor the change of storage level by using Multi-Spectral Instrument (MSI) sensor of Sentinel-2 satellite. The materials of satellite image for water surface area detection in Yongdam dam watershed was considered from 2016 to 2018, respectively. Based on this, we proposed the possibility of real-time drought monitoring system using high resolution water surface area detection by Sentinel-2 satellite image. The results of this study can be applied to estimate of the reservoir volume calculated from various satellite observations, which can be used for monitoring and estimating hydrological droughts in an unmeasured area.

• Journal of Korean Society of Forest Science
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• v.105 no.2
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• pp.193-201
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• 2016

This study was carried out to analyze the nutritional composition, bioactive components, antioxidant activity, and cytotoxic assay of cancer cells on Rubus crataegifolius (RC) : R. crataegifolius from Jangseong (RC-J), R. crataegifolius from Hwaseong (RC-H), R. crataegifolius from Ulsan (RC-U), R. crataegifolius from Sunchang (RC-S), and R. crataegifolius from Pohang (RC-P). The peroximate composition had the largest amount of carbohydrate content among all kinds of RC. As far as the mineral contents of RC, Calcium comprised the highest amount ($996.6{\mu}g/g{\pm}0.8%$) and Natrium the lowest ($6.2{\mu}g/g{\pm}1.0%$). A total of 26 kinds of free amino acids and 18 kinds of component amino acids were analyzed in RC. The results of electron donating were high scavenging effects of 80% in water extract (RC-UW) and 82.6% in ethanol extract (RC-UE) in $500{\mu}g/ml$ concentration from RC-U. Also, the cytotoxic effects of cancer cells B16F10 (RC-UW and RC-PE), H1299 (RC-SW and RC-PE), and MCF-7 (RC-JW and RC-SE) appeared in RC. Therefore, we confirmed that new varieties may possibly be developed with functional materials.

• Journal of Space Technology and Applications
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• v.3 no.2
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• pp.118-143
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• 2023

Space is becoming more commercialized. Despite of its delayed start-up, space activities in Korea are attracting more nation-wide supports from both investors and government. May 25, 2023, KSLV II, also called Nuri, successfully transported, and inserted seven satellites to a sun-synchronous orbit of 550 km altitude. However, Starlink has over 4,000 satellites around this altitude for its commercial activities. Hence, it is necessary for us to constantly monitor the collision risks of these satellites against resident space objects including Starlink. Here we report a quantitative research output regarding the conjunctions, particularly between the Nuri satellites and Starlink. Our calculation shows that, on average, three times everyday, the Nuri satellites encounter Starlink within 1 km distance with the probability of collision higher than 1.0E-5. A comparative study with KOMPSAT-5, also called Arirang-5, shows that its distance of closest approach distribution significantly differs from those of Nuri satellites. We also report a quantitative analysis of collision-avoiding maneuver cost of Starlink satellites and a strategy for Korea, being a delayed starter, to speed up to position itself in the space leading countries. We used the AstroOne program for analyses and compared its output with that of Socrates Plus of Celestrak. The two line element data was used for computation.

• Horticultural Science & Technology
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• v.30 no.3
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• pp.261-269
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• 2012

Soil microbial community and soil physiological parameters were investigated by analyzing phospholipid fatty acids extracted from the soils amended with chitosan powder and solution in a cucumber greenhouse. The soils were sampled at 90, 160, 200 days after treatment. Identified fatty acids were analyzed with principal component (PC) analysis. Chitosan powder soils and chitosan solution soils were separated from non-treated control soils by PC1 and PC2 90 days after treatment, respectively. And chitosan powder soils were separated from non-treated control soils by PC2 160 days after treatment. The ratio of fungi to bacteria increased significantly in chitosan solution-amended soils compared with the control soils 90 days after treatment. Microbial groups and physiological parameters were investigated 160 days after treatment: vesicular-arbuscular mycorrhizal fungi (VAM) significantly increased in soils amended with chitosan powder compared with other soils, the ratio of gram negative bacteria to gram positive bacteria and cyclo-fatty acids to precursors were significantly higher and lower in soils amended with chitosan solution and chitosan powder compared with control soils, respectively, and the ratio of fungi to bacteria were significantly lower in control soils compared with chitosan-treated soils. The chitosan powder increased the ratio of aerobic to anaerobic bacteria and lowered the ratio of saturated to unsaturated fatty acids compared with chitosan solution 200 days after soil application. In conclusion, chitosan powder changed the soil microbial community and the effects maintained up to 160 days after soil application. The effect of physiological parameters on the soil microbial community started to appear 160 days after and continued up to 200 days after soil application of chitosan.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.177-190
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• 2021

Due to the development of the fourth industrial revolution technology, efforts are being made to improve areas that humans cannot handle by utilizing artificial intelligence techniques such as machine learning. Although on-demand production companies also want to reduce corporate risks such as delays in delivery by predicting total production time for orders, they are having difficulty predicting this because the total production time is all different for each order. The Theory of Constraints (TOC) theory was developed to find the least efficient areas to increase order throughput and reduce order total cost, but failed to provide a forecast of total production time. Order production varies from order to order due to various customer needs, so the total production time of individual orders can be measured postmortem, but it is difficult to predict in advance. The total measured production time of existing orders is also different, which has limitations that cannot be used as standard time. As a result, experienced managers rely on persimmons rather than on the use of the system, while inexperienced managers use simple management indicators (e.g., 60 days total production time for raw materials, 90 days total production time for steel plates, etc.). Too fast work instructions based on imperfections or indicators cause congestion, which leads to productivity degradation, and too late leads to increased production costs or failure to meet delivery dates due to emergency processing. Failure to meet the deadline will result in compensation for delayed compensation or adversely affect business and collection sectors. In this study, to address these problems, an entity that operates an order production system seeks to find a machine learning model that estimates the total production time of new orders. It uses orders, production, and process performance for materials used for machine learning. We compared and analyzed OLS, GLM Gamma, Extra Trees, and Random Forest algorithms as the best algorithms for estimating total production time and present the results.