• Journal of Food Hygiene and Safety
• /
• v.36 no.5
• /
• pp.447-453
• /
• 2021

In this study, the effectiveness of physical control technology, a combined light sterilization (LED, UV) and hot water treatment in reducing Aspergillus ochraceus for food production environment was investigated. In brief, 1 mL aliquot of A. ochraceus spore suspension (10^7-8 spore/mL) was inoculated onto stainless steel chips, which was then dried at 37℃, and each was subjected to different physical treatment. Treatments were performed for 0.5, 1, 2, 5, 8, and 11 hours to reduce the strains using a light-emitting diode, but no significant difference was confirmed among the treatments. However, a significant reduction was observed on the chips treated with UV-C exposure and hot water immersion. After being treated solely with 360 kJ/m² of UV-C on stainless steel chip, the fungi were significantly reduced to 1.27 log CFU/cm². Concerning the hot water treatment, the initial inoculum amount of 6.49 log CFU/cm² was entirely killed by immersion in 83℃ water for 5 minutes. Maintaining a high temperature for 5 minutes at the site is difficult. Thus, considering economic feasibility and usability, we attempted to confirm the appropriate A. ochraceus reduction conditions by combining a relatively low temperature of 60℃ and UV rays. With the combined treatments, even in lukewarm water, A. ochraceus decreased significantly through the increases in the immersion time and the amount of UV-C irradiation, and the yield was below the detection limit. Based on these results, if work tools are immersed in 60℃ lukewarm water for 3 minutes and then placed in a UV sterilization device for more than 10 minutes, the possibility of A. ochraceus cross-contamination during work is expected to be reduced.

• Korean Journal of Ecology and Environment
• /
• v.54 no.3
• /
• pp.199-208
• /
• 2021

Environmental DNA (eDNA) metabarcoding is effective method with high detection sensitivity for evaluating fish biodiversity and detecting endangered fish from natural water samples. We compared the richness of operational taxonomic units(OTUs) and composition of freshwater fishes according to filters(cellulose nitrate filter vs. glass fiber filter), extraction kits(DNeasy2^® Blood & Tissue Kit vs. DNeasy2^® PowerWater Kit), primer sets (12S rDNA vs. 16S rDNA), and PCR methods (conventional PCR vs. touchdown PCR) to determine the optimal conditions for metabarcoding analysis of Korean freshwater fish. The glass fiber filter and DNeasy2^® Blood & Tissue Kit combination showed the highest number of freshwater fish OTUs in both 12S and 16S rDNA. Among the four types, the primer sets only showed statistically significant difference in the average number of OTUs in class Actinopterygii (non-parametric Wilcoxon signed ranks test, p=0.005). However, there was no difference in the average number of OTUs in freshwater fish. The species composition also showed significant difference according to primer sets (PERMANOVA, Pseudo-F=6.9489, p=0.006), but no differences were observed in the other three types. The non-metric multidimensional scaling (NMDS) results revealed that species composition clustered together according to primer sets based on similarity of 65%; 16S rDNA primer set was mainly attributed to endangered species such as Microphysogobio koreensis and Pseudogobio brevicorpus. In contrast, the 12S rDNA primer set was mainly attributed to common species such as Zacco platypus and Coreoperca herzi. This study provides essential information on species diversity analysis using metabarcoding for environmental water samples obtained from rivers in Korea.

• Journal of IKEEE
• /
• v.25 no.3
• /
• pp.493-500
• /
• 2021

In this paper, we propose the development of deep learning structure to improve quality of polygonal containers. The deep learning structure consists of a convolution layer, a bottleneck layer, a fully connect layer, and a softmax layer. The convolution layer is a layer that obtains a feature image by performing a convolution 3x3 operation on the input image or the feature image of the previous layer with several feature filters. The bottleneck layer selects only the optimal features among the features on the feature image extracted through the convolution layer, reduces the channel to a convolution 1x1 ReLU, and performs a convolution 3x3 ReLU. The global average pooling operation performed after going through the bottleneck layer reduces the size of the feature image by selecting only the optimal features among the features of the feature image extracted through the convolution layer. The fully connect layer outputs the output data through 6 fully connect layers. The softmax layer multiplies and multiplies the value between the value of the input layer node and the target node to be calculated, and converts it into a value between 0 and 1 through an activation function. After the learning is completed, the recognition process classifies non-circular glass bottles by performing image acquisition using a camera, measuring position detection, and non-circular glass bottle classification using deep learning as in the learning process. In order to evaluate the performance of the deep learning structure to improve quality of polygonal containers, as a result of an experiment at an authorized testing institute, it was calculated to be at the same level as the world's highest level with 99% good/defective discrimination accuracy. Inspection time averaged 1.7 seconds, which was calculated within the operating time standards of production processes using non-circular machine vision systems. Therefore, the effectiveness of the performance of the deep learning structure to improve quality of polygonal containers proposed in this paper was proven.

• Journal of Internet Computing and Services
• /
• v.22 no.1
• /
• pp.41-50
• /
• 2021

Cyber Kill Chain is derived from Kill chain of traditional military terms. Kill chain means "a continuous and cyclical process from detection to destruction of military targets requiring destruction, or dividing it into several distinct actions." The kill chain has evolved the existing operational procedures to effectively deal with time-limited emergency targets that require immediate response due to changes in location and increased risk, such as nuclear weapons and missiles. It began with the military concept of incapacitating the attacker's intended purpose by preventing it from functioning at any one stage of the process of reaching it. Thus the basic concept of the cyber kill chain is that the attack performed by a cyber attacker consists of each stage, and the cyber attacker can achieve the attack goal only when each stage is successfully performed, and from a defense point of view, each stage is detailed. It is believed that if a response procedure is prepared and responded, the chain of attacks is broken, and the attack of the attacker can be neutralized or delayed. Also, from the point of view of an attack, if a specific response procedure is prepared at each stage, the chain of attacks can be successful and the target of the attack can be neutralized. The cyber command and control system is a system that is applied to both defense and attack, and should present defensive countermeasures and offensive countermeasures to neutralize the enemy's kill chain during defense, and each step-by-step procedure to neutralize the enemy when attacking. Therefore, thist paper proposed a cyber kill chain model from the perspective of defense and attack of the cyber command and control system, and also researched and presented the threat classification/analysis/prediction framework of the cyber command and control system from the defense aspect

• Korean Journal of Remote Sensing
• /
• v.37 no.6_1
• /
• pp.1757-1766
• /
• 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
• /
• v.37 no.6_1
• /
• pp.1773-1784
• /
• 2021

In order to minimize damage to oil spill accidents in the ocean, it is essential to collect a spilled area as soon as possible. Thus satellite-based remote sensing is a powerful source to detect oil spills in the ocean. With the recent rapid increase in the number of available satellites, it has become possible to generate a status report of marine oil spills soon after the accident. In this study, the oil spill area was calculated using various satellite images for the Symphony oil spill accident that occurred off the coast of Qingdao Port, China, on April 27, 2021. In particular, improving the accuracy of oil spill area determination was applied using high-resolution commercial satellite images with a spatial resolution of 2m. Sentinel-1, Sentinel-2, LANDSAT-8, GEO-KOMPSAT-2B (GOCI-II) and Skysat satellite images were collected from April 27 to May 13, but five images were available considering the weather conditions. The spilled oil had spread northeastward, bound for coastal region of China. This trend was confirmed in the Skysat image and also similar to the movement prediction of oil particles from the accident location. From this result, the look-alike patch observed in the north area from the Sentinel-1A (2021.05.01) image was discriminated as a false alarm. Through the survey period, the spilled oil area tends to increase linearly after the accident. This study showed that high-resolution optical satellites can be used to calculate more accurately the distribution area of spilled oil and contribute to establishing efficient response strategies for oil spill accidents.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.25 no.6
• /
• pp.245-253
• /
• 2021

RC slab bridges account for the largest portion of deteriorated bridges in Korea. However, most RC slabs are not included in the first and second classes of bridges, which are subject to bridge safety management and maintenance. The highest damaged components in highway bridges are the subsidiary facilities including expansion joints and bearings. In particular, leakage through expansion joints causes deterioration and cracks of concrete and exposure of reinforced bars. Therefore, this study analyzed the effect of adhesion damage at expansion joints on the response of the deck in RC slab bridges. When the spacing between the expansion joints at both ends was closely adhered, cracks occurred in the concrete at both ends of the deck due to the resistance rigidity at the expansion joints. Based on the response results, the correlation analysis between displacements in the longitudinal direction of the expansion joint and concrete stress at both ends of the deck for each damage scenario was performed to investigate the effect of the occurrence of damage on the bridge behavior. When expansion joint devices at both sides were damaged, the correlation between displacement and stress showed a low correlation of 0.18 when the vehicles proceeded along all the lanes. Compared with those in the intact state, the deflections of the deck in the damaged case at both sides showed a low correlation of 0.34 to 0.53 while the vehicle passed and 0.17 to 0.43 after the vehicle passed. This means that the occurrence of cracks in the ends of concrete changed the behavior of the deck. Therefore, data-deriven damage detection could be developed to manage the damage to expansion joints that cause damage and deterioration of the deck.

• Korean Journal of Breeding Science
• /
• v.40 no.4
• /
• pp.408-413
• /
• 2008

Rice blast resistance is considered one of the most important traits in rice breeding and the disease, caused by Magnaporthe grisea Barr, has brought significant crop losses annually. Moreover, breakdown of resistance normally occurs in two to five years after cultivar release, thus a more durable resistance is needed for better control of this disease. We developed a new variety, Keumo3, which showed strong resistance to leaf blast. It was tested in 2003 to 2007 at fourteen blast nursery sites covering entire rice-growing regions of South Korea. It showed resistance reactions in 12 regions and moderate in 2 regions without showing susceptible reactions. Durability test by sequential planting method indicated that this variety had better resistance. Results showed that Keumo3 was incompatible against 19 blast isolates with the exception of KI101 by artificial inoculation. To understand the genetic control of blast resistance in rice cultivar Keumo3 and facilitate its utilization, recombinant inbred lines (RIL) consisting of 290 F5 lines derived from Akidagomachi/Keumo3 were analyzed and genotyped with Pizt InDel marker zt56591. The recombination value between the marker allele of zt56591 and bioassay data of blast nursery test was 1.1%. These results indicated that MAS can be applied in selecting breeding populations for blast resistance using zt56591 as DNA marker.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.20 no.3
• /
• pp.562-569
• /
• 2019

Raman spectroscopy has been receiving increased attention as a standoff explosive detection technique. In addition, there is a growing need for a fast search method that can identify raman spectrum for measured chemical substances compared to known raman spectra in large database. By far the most simple and widely used method is to calculate and compare the Euclidean distance between the given spectrum and the spectra in a database. But it is non-trivial problem because of the inherent high dimensionality of the data. One of the most serious problems is the high computational complexity of searching for the closet spectra. To overcome this problem, we presented the MPS Sort with Sorted Variance+PDS method for the fast algorithm to search for the closet spectra in the last paper. the proposed algorithm uses two significant features of a vector, mean values and variance, to reject many unlikely spectra and save a great deal of computation time. In this paper, we present two new methods for the fast algorithm to search for the closet spectra. the PCA+PDS algorithm reduces the amount of computation by reducing the dimension of the data through PCA transformation with the same result as the distance calculation using the whole data. the Hierarchical Cluster Tree algorithm makes a binary hierarchical tree using PCA transformed spectra data. then it start searching from the clusters closest to the input spectrum and do not calculate many spectra that can not be candidates, which save a great deal of computation time. As the Experiment results, PCA+PDS shows about 60.06% performance improvement for the MPS Sort with Sorted Variance+PDS. also, Hierarchical Tree shows about 17.74% performance improvement for the PCA+PDS. The results obtained confirm the effectiveness of the proposed algorithm.

• Korean Journal of Environmental Agriculture
• /
• v.37 no.4
• /
• pp.283-290
• /
• 2018

BACKGROUND: Pesticide residue analysis is an essential activity in order to establish the food safety of agricultural products. Analytical approaches to the food safety are required to meet internationally the guideline of Codex (Codex Alimentarius Commission, CAC/GL 40). In this study, we developed a liquid chromatograph-tandem mass spectrometer (LC-MS/MS) method to determine the herbicide clopyralid in food matrixes. METHODS AND RESULTS: Clopyralid was extracted with aqueous acetonitrile containing formic acid and the extracts were mixed in a citrate buffer consisted of magnesium sulfate anhydrous, NaCl, sodium citrate dihydrate and disodium hydrogencitrate sesquihydrate followed by centrifugation. The supernatants were filtered through a nylon membrane filter and used for the analysis of clopyralid. The method was validated by accuracy and precision experiments on the samples fortified at 3 different levels of clopyralid. LC-MS/MS in positive mode was employed to quantitatively determine clopyralid in the food samples. Matrix-matched calibration curves were inearranged from 0.001 to 0.25 mg/kg with r2 > 0.994. The limits of detection and quantification were determined to be 0.001 and 0.01 mg/kg, respectively. There covery values of clopyralid for tified at 0.01 mg/kg in the control samples ranged from approximately 82 to 106% with relative standard deviations below 2 0%. CONCLUSION: The method developed in this study meets successfully the Codex guideline for pesticide residue analysis in food samples. This, the method could be applicable to determine pesticides in foods produced domestically and internationally.