• Journal of Bio-Environment Control
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• v.30 no.4
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• pp.419-428
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• 2021

In order to develope a mobile-based greenhouse energy calculation program, firstly, the overall thermal transmittance of 10 types of major covers and 16 types of insulation materials were measured. In addition, to estimate the overall thermal transmittance when the cover and insulation materials were installed in double or triple layers, 24 combinations of double installations and 59 combinations of triple installations were measured using the hotbox. Also, the overall thermal transmittance value for a single material and the thermal resistance value were used to calculate the overall thermal transmittance value at the time of multi-layer installation of covering and insulating materials, and the linear regression equation was derived to correct the error with the measured values. As a result of developing the model for estimating thermal transmittance when installing multiple layers of coverings and insulating materials based on the value of overall thermal transmittance of a single-material, the model evaluation index was 0.90 (good when it is 0.5 or more), indicating that the estimated value was very close to the actual value. In addition, as a result of the on-site test, it was evaluated that the estimated heat saving rate was smaller than the actual value with a relative error of 2%. Based on these results, a mobile-based greenhouse energy calculation program was developed that was implemented as an HTML5 standard web-based mobile web application and was designed to work with various mobile device and PC browsers with N-Screen support. It had functions to provides the overall thermal transmittance(heating load coefficient) for each combination of greenhouse coverings and thermal insulation materials and to evaluate the energy consumption during a specific period of the target greenhouse. It was estimated that an energy-saving greenhouse design would be possible with the optimal selection of coverings and insulation materials according to the region and shape of the greenhouse.

• Korean Journal of Food Science and Technology
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• v.53 no.4
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• pp.470-478
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• 2021

This study was conducted to monitor residual pesticides in a total of 527 school foodservice agricultural products from 2019 to 2020 in Incheon. Pesticide residues in the samples were analyzed by the multi-residue method in the Korean food code for 373 pesticides using GC-MS/MS, LC-MS/MS, GC-ECD, GC-NPD, and HPLC-UVD. By monitoring the pesticides, 12 (2.3%) of the 527 pesticides were detected, and 2 (0.4%) samples exceeded the maximum residue limit. Twelve types of pesticides were detected in the agricultural products of carrot, chard, chili pepper, chwinamul, crown daisy, parsley, perilla leaves, and spinach. As a means of risk assessment through the consumption of agricultural products detected with pesticide residues, the proportion of estimated daily intake to acceptable daily intake was estimated in the range of 0.0000-39.7425%. Results showed no particular health risk through the consumption of school foodservice agricultural products with pesticide residues.

• Journal of Korean Society of Forest Science
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• v.110 no.4
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• pp.648-657
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• 2021

This study was conducted to estimate the volume growth by forest type and major species using the national forest resource inventory and to predict the final age of maturity by deriving the mean annual increment (MAI) and the current annual increment (CAI). We estimated the volume growth using the Chapman-Richards model. In the volume estimation equations by forest type, coniferous forests exhibited the highest growth. According to the estimation formula for each major species, Larix kaempferi will grow the highest among coniferous tree species and Quercus mongolica among broad-leaved tree species. And these estimation formulas showed that the fitness index was generally low, such as 0.32 for L. kaempferi and 0.21 for Quercus variabilis. In the analysis of residual amount, which indicates the applicability of the volume estimation formula, the estimates of the estimation formula tended to be underestimated in about 30 years or more, but most of the residuals were evenly distributed around zero. Therefore, these estimation formulas have no difficulty estimating the volume of actual forest species in Korea. The maximum age attained by calculating MAI was 34 years for P. densiflora, 35 years for L. kaempferi, and 31 years for P. rigida among coniferous tree species. In broad-leaved tree species, we discovered that the maximum age was 32 years for Q. variabilis, 30 years for Q. acutissima, and 29 years for Q. mongolica. We calculated MAI and CAI to detect the point at which these two curves intersected. This point was defined by the maximum volume harvesting age. These results revealed no significant difference between the current standard cutting age in public and private forests recommended by the Korea Forest Service, supporting the reliability of forestry policy data.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1757-1766
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• 2021

The green coverage ratio is the ratio of the land area to green coverage area, and it is used as a practical urban greening index. The green coverage ratio is calculated based on the land cover map, but low spatial resolution and inconsistent production cycle of land cover map make it difficult to calculate the correct green coverage area and analyze the precise green coverage. Therefore, this study proposes a new method to calculate green coverage area using aerial images and deep neural networks. Green coverage ratio can be quickly calculated using manned aerial images acquired by local governments, but precise analysis is difficult because components of image such as acquisition date, resolution, and sensors cannot be selected and modified. This limitation can be supplemented by using an unmanned aerial vehicle that can mount various sensors and acquire high-resolution images due to low-altitude flight. In this study, we proposed a method to calculate green coverage ratio from manned or unmanned aerial images, and experimentally verified the proposed method. Aerial images enable precise analysis by high resolution and relatively constant cycles, and deep learning can automatically detect green coverage area in aerial images. Local governments acquire manned aerial images for various purposes every year and we can utilize them to calculate green coverage ratio quickly. However, acquired manned aerial images may be difficult to accurately analyze because details such as acquisition date, resolution, and sensors cannot be selected. These limitations can be supplemented by using unmanned aerial vehicles that can mount various sensors and acquire high-resolution images due to low-altitude flight. Accordingly, the green coverage ratio was calculated from the two aerial images, and as a result, it could be calculated with high accuracy from all green types. However, the green coverage ratio calculated from manned aerial images had limitations in complex environments. The unmanned aerial images used to compensate for this were able to calculate a high accuracy of green coverage ratio even in complex environments, and more precise green area detection was possible through additional band images. In the future, it is expected that the rust rate can be calculated effectively by using the newly acquired unmanned aerial imagery supplementary to the existing manned aerial imagery.

• Korean Journal of Remote Sensing
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• v.37 no.6_2
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• pp.1829-1836
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• 2021

Cloud detection means determining the presence or absence of clouds in a pixel in a satellite image, and acts as an important factor affecting the utility and accuracy of the satellite image. In this study, among the satellites of various advanced organizations that provide cloud detection data, we intend to perform quantitative and qualitative comparative analysis on the difference between the cloud detection data of GK-2A/AMI, Terra/MODIS, and Suomi-NPP/VIIRS. As a result of quantitative comparison, the Proportion Correct (PC) index values in January were 74.16% for GK-2A & MODIS, 75.39% for GK-2A & VIIRS, and 87.35% for GK-2A & MODIS in April, and GK-2A & VIIRS showed that 87.71% of clouds were detected in April compared to January without much difference by satellite. As for the qualitative comparison results, when compared with RGB images, it was confirmed that the results corresponding to April rather than January detected clouds better than the previous quantitative results. However, if thin clouds or snow cover exist, each satellite were some differences in the cloud detection results.

• Journal of Life Science
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• v.31 no.12
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• pp.1079-1087
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• 2021

To analyze gut microbiota of livestock in Korea and compare taxonomic differences, we conducted 16S rRNA metagenomic analysis through next-generation sequencing. Fecal samples from broiler chickens, pigs, and cattle were collected from domestic feedlots randomly. α-diversity results showed that significant differences in estimated species richness estimates (Chao1 and ACE, Abundance-based coverage estimators) and species richness index (OUTs, Operational taxonomic units) were identified among the three groups. However, NPShannon, Shannon, and Simpson indices revealed that abundance and evenness of the species were statistically significant only for poultry (broiler chickens) and mammals (pigs and cattle). Firmicutes was the most predominant phylum in the three groups of fecal samples. Linear discriminant (LDA) effect size (LEfSe) analysis was conducted to reveal the ranking order of abundant taxa in each of the fecal samples. A size-effect over 2.0 on the logarithmic LDA score was used as a discriminative functional biomarker. As shown by the fecal analysis at the genus level, broiler chickens were characterized by the presence of Weissella and Lactobacillus, as well as pigs were characterized by the presence of provetella and cattele were characterized by the presence of Acinetobacter. A permutational multivariate analysis of variance (PERMANOVA) showed that differences of microbial clusters among three groups were significant at the confidence level. (p=0.001). This study provides basic data that could be useful in future research on microorganisms associated with performance growth, as well as in studies on the livestock gut microbiome to increase productivity in the domestic livestock industry.

• Land and Housing Review
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• v.12 no.2
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• pp.47-59
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• 2021

This study aims to review the trends of the population outflows in the metropolitan area of Busan and to investigate the factors that affect population out-migration to the Seoul metropolitan area. The following variables are considered for analysis: traditional population movement variables and quality of life variables, such as population, society, employment, housing, culture, safety, medical care, greenery, education, and childcare. The 'domestic population movement data', provided by the MDIS of the National Statistical Office, was used for this research. Out of the total of 57 million population movement data in the period 2012 - 2017, population outmigration from Busan to the Seoul metropolitan area was extracted. Independent variables were drawn from public data sources in accordance with the temporal and spatial settings of the study. The multiple linear regression model was specified based on the dataset, and the fit of the model was measured by the p-value, and the values of Adjusted R2, Durbin-Watson analysis, and F-statistics. The results of the analysis showed that the variables that have a significant effect on population movement from Busan to the Seoul metropolitan area were as follows: 'single-person households', 'the elderly population', 'the total birth rate', 'the number of companies', 'the number of employees', 'the housing sales price index', 'cultural facilities', and 'the number of students per teacher'. More positive (+) influences of the population out-movement were observed in areas with higher numbers of single-person households, lowers proportions of the elderly, lower numbers of businesses, higher numbers of employees, higher numbers of housing sales, lower numbers of cultural facilities, and lower numbers of students. The findings suggest that policies should enhance the environments such as quality jobs, culture, and welfare that can retain young people within Busan. Improvements in the quality of life and job creation are critical factors that can mitigate the outflows of the Busan residents to the Seoul metropolitan area.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.7
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• pp.271-278
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• 2021

As the current cyber attacks become more intelligent, the existing Intrusion Detection System is difficult for detecting intelligent attacks that deviate from the existing stored patterns. In an attempt to solve this, a model of a deep learning-based intrusion detection system that analyzes the pattern of intelligent attacks through data learning has emerged. Intrusion detection systems are divided into host-based and network-based depending on the installation location. Unlike network-based intrusion detection systems, host-based intrusion detection systems have the disadvantage of having to observe the inside and outside of the system as a whole. However, it has the advantage of being able to detect intrusions that cannot be detected by a network-based intrusion detection system. Therefore, in this study, we conducted a study on a host-based intrusion detection system. In order to evaluate and improve the performance of the host-based intrusion detection system model, we used the host-based Leipzig Intrusion Detection-Data Set (LID-DS) published in 2018. In the performance evaluation of the model using that data set, in order to confirm the similarity of each data and reconstructed to identify whether it is normal data or abnormal data, 1D vector data is converted to 3D image data. Also, the deep learning model has the drawback of having to re-learn every time a new cyber attack method is seen. In other words, it is not efficient because it takes a long time to learn a large amount of data. To solve this problem, this paper proposes the Siamese Convolutional Neural Network (Siamese-CNN) to use the Few-Shot Learning method that shows excellent performance by learning the little amount of data. Siamese-CNN determines whether the attacks are of the same type by the similarity score of each sample of cyber attacks converted into images. The accuracy was calculated using Few-Shot Learning technique, and the performance of Vanilla Convolutional Neural Network (Vanilla-CNN) and Siamese-CNN was compared to confirm the performance of Siamese-CNN. As a result of measuring Accuracy, Precision, Recall and F1-Score index, it was confirmed that the recall of the Siamese-CNN model proposed in this study was increased by about 6% from the Vanilla-CNN model.

• Korean Journal of Mineralogy and Petrology
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• v.34 no.2
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• pp.133-146
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• 2021

Aragonite is one of common polymorphs of calcium carbonate (CaCO₃) and formed via biological or physical processes through precipitation in many different environments including marine ecosystems. It is noted that aragonite formation and growth as well as the substitution of trace elements such as strontium (Sr) in the aragonite structure would be dependant on several key parameters such as concentrations of chemical species and temperature. In this study, properties of the incorporation of Sr into aragonite were investigated over a wide range of various saturation conditions and temperatures similar to the marine ecosystem. All pure aragonite samples were inorganically synthesized through a constant-addition method with varying concentrations of the reactive species ([Ca]=[CO₃] 0.01-1 M), injection rates of the reaction solution (0.085-17 mL/min), and solution temperatures (5-40 ℃). Pure aragonite was also formed even under the Sr incorporation conditions (0.02-0.5 M, 15-40 ℃). When temperature and saturation index (SI) with respect to aragonite increased, the crystallinity and the crystal size of aragonite increased indicating the growth of aragonite crystal. However, it was difficult to interpret the crystal growth rate because the crystal growth rate calculated using BET-specific surface area was significantly influenced by the crystal morphology. The distribution coefficient of Sr (K_Sr) into aragonite decreased from 2.37 to 1.57 with increasing concentrations of species (Ca²⁺ and CO₃^2-) at a range of 0.02-0.5 M. Similarly, it was also found that K_Sr decreased 1.90 to 1.54 at a range of 15-40 ℃. All K_Sr values are greater than 1, and the inverse correlation between the K_Sr and the crystal growth rate indicate that Sr incorporation into aragonite is in a compatible relationship.

• The Korean Journal of Gastroenterology
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• v.72 no.6
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• pp.325-328
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• 2018

위마비증(gastroparesis)이나 만성 구역 구토 증후군(chronic unexplained nausea and vomiting)으로 인한 구역과 구토에 대한 치료는 일반적으로 사용하는 위장관 촉진제에 만족스럽지 못한 경우가 많고, 여러 부작용으로 인하여 장기적으로 사용하기 어려워 보다 효과적인 치료 방법이 필요하다. 최근 미국에서 발표된 본 연구는 위마비증이나 연관 증후군 환자에서 구역과 구토 증상을 줄이기 위한 aprepitant(neurokinin-1 receptor antagonist) 치료의 효과를 분석한 것으로, 향후 일반적인 치료에 불응성 위마비증 환자에서 새로운 약제 사용을 시도해볼 수 있어 소개하고자 한다. 본 Aprepitant for the Relief of Nausea (APRON) 연구는 기질적 질환을 배제하기 위하여 최근 2년 이내 위내시경이 정상이며, 적어도 6개월 이상 조기 포만감(early satiety), 식후 만복감(postprandial fullness), 팽만감(bloating) 그리고 명치부 통증(epigastric pain)을 유발하는 증상과 함께 만성적인 구역이 있는 18세 이상의 성인 중 4시간의 위배출 검사를 시행받은 환자를 대상으로 하였다. 객관적인 지표로 0점에서 45점까지 보이는 9-증상 Gastroparesis Cardinal Symptom Index(GCSI)가 2주 이상 총 21점 이상이며, 0-100 mm의 visual analog scale (VAS)의 7일간의 구역 증상 평균 25 mm 이상인 환자를 대상으로 하였다. 일주일에 3일 이상 narcotics를 사용하였거나 와파린이나 pimozide, terfenadien, astemizole, cisapride를 복용하였던 환자, 2배 이상으로 간 효소 수치상승을 보이거나 Child-Pugh score 10점 이상, aprepitant에 알레르기를 보이는 환자는 제외되었다. 그렇지만 metoclopramide나 erythromycin을 안정적으로 사용 중인 환자는 제외되지 않았다. 위배출 검사는 2시간에서 60% 이상 남아 있거나 4시간에서 10% 이상 남아 있는 경우에 지연된 것으로 정의되었으며, 지연된 위배출 검사 결과 자체는 환자의 등록 기준에 포함되진 않았다. 등록 기준에 포함된 환자는 1:1로 무작위 배정되어 하루 한 번 125 mg의 aprepitant 복용군과 위약군으로 나누어져 연구가 진행되었으며, 약제 복용 4주간 2주 간격으로, 그리고 복용 후 2주 뒤까지 구역 증상의 호전 정도와 약제 안전성을 확인하였다. 이러한 효과를 판정하기 위하여 환자가 방문하는 동안 GCSI를 포함한 Patient Assessment of Upper GI Symptoms (PAGI-SYM), Gastrointestinal Symptom Rating Scale, daily VAS, daily diary version of the GCSI 그리고 정신 측정 도구와 삶의 질 도구인 Patient health Questionnaire 15와 Short Form 36 version이 측정되었다. 구역에 대한 aprepitant와 위약의 치료 효과의 일차적 판정은 이전 항암 요법에 대한 aprepitant 연구와 같이 28일 평균 VAS 25 mm 미만이거나 치료 전 7일간의 VAS와 비교하여 28일 치료 기간 동안 25 mm 이상 감소한 경우로 정의하였고, 이차 결과는 구역의 매일 시간, 치료 중 구역이 없는 날짜의 퍼센트, PAGI-SYM score의 개선 등으로 확인하였다. 2013년 4월부터 2015년 7월까지 총 126명의 환자가 등록되어 aprepitant군 63명, 위약군 63명으로 무작위 배정되었다. 전체의 57%인 72명에서 위배출 지연이 보였으며, 나머지 43%에서는 정상 또는 빠른 위배출 소견을 보여 만성적으로 설명할 수 없는 구역과 구토에 포함된 환자군으로 확인되었다. 또한 29%에서 당뇨를 가지고 있었으며, 8%에서 수면제를 사용하고 있었다. 최종적으로 aprepitant군은 59명, 위약군은 63명이 연구를 끝까지 종료하였다. 일차 결과에서 aprepitant 군 46%, 위약군 40%의 구역 호전을 보여 두 치료군 간에 통계적으로 의미 있는 차이는 보이지 않았다(상대 위험도 1.2, 95% CI: 0.8-1.7; p=0.43). 그러나 일차 분석의 두 가지 척도(28일 평균 VAS 25 mm 미만과 기저 VAS보다 평균 28일 VAS의 25 mm 이상 감소)를 모두 함께 고려한 민감도 분석에서는 aprepitant군이 37% (22/59)로 위약군의 17%(11/63)에 비하여 통계적으로 의미 있는 구역의 호전을 보였다(상대 위험도 2.1, 95% CI: 1.1-4.1; p=0.01). 또한 이차 분석을 살펴보면 aprepitant군에서 PAGI-SYM 중증도 지수로 확인하였을 때, 구역(1.8 vs. 1.0; p=0.005)과 구토(1.6 vs. 0.5; p=0.001)의 중증도 및 매일 구역 시간의 감소를 보였고, 28일 동안 구역이 없는 날짜의 퍼센트 증가 소견을 보였다. 다른 이차 결과 분석에서 aprepitant군이 PAGI-SYM 중증도 지수의 GCSI 종합 점수(1.3 vs. 0.7; p=0.001), 상당한 증상호전, 구역 구토의 세부 점수, 팽만감 세부 점수 그리고 위식도 역류 증상 점수에서 호전을 보였고, 매일 일기로 표현한 daily diary version of the GCSI에 상복부 통증 중증도, 전체 증상 그리고 Gastrointestinal Symptom Rating Scale의 종합 점수에서 호전을 보였다. 연구 중 발생한 부작용은 주로 경증과 중등도 정도의 부작용이 주로 발생하였지만, aprepitant군(35% vs. 17% 위약군, p=0.04)에서 더 많이 발생하였다. 결론적으로 위마비증 또는 위마비증 유사 증후군으로 인한 만성 구역 및 구토 환자의 무작위 시험에서 aprepitant는 VAS 점수를 통한 주요 결과를 분석하였을 때는 구역의 중증도를 호전시키지 못하였지만 다른 이차적 결과에 대해서는 위약군에 대하여 호전 소견을 보였다. 따라서 aprepitant에 효과적인 반응을 보이는 위마비증 환자를 감별하는 추가 임상시험이 필요할 것으로 판단된다.