• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.4
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• pp.500-506
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• 2012

A method to model left ventricular assist device (LVAD) and detect suction occurrence for safe LVAD operation is presented. An axial flow blood pump as a LVAD has been used to assist patient with heart problems. While an axial flow blood pump, a kind of a non-pulsatile pump, has relative advantages of small size and efficiency compared to pulsatile devices, it has a difficulty in determining a safe pump operating condition. It can show different pump operating statuses such as a normal status and a suction status whether suction occurs in left ventricle or not. A fuzzy subtractive clustering method is used to determine a model of the axial flow blood pump with this pump operating characteristic and the developed pump model can provide blood flow estimates before and after suction occurrence in left ventricle. Also, a fuzzy subtractive clustering method is utilized to develop a suction detection model which can identify whether suction occurs in left ventricle or not.

• Tunnel and Underground Space
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• v.27 no.5
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• pp.282-294
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• 2017

This study analyzed the development status and applications of collision avoidance systems for mine safety management. The definitions of collision avoidance system used in Australia and USA were compared. Sensing technologies utilized in the collision avoidance systems were reviewed. In addition, several collision avoidance systems developed in oversea mining company, such as $MineAlert^{TM}$ Collision Awareness System, Cat $MineStar^{TM}$, and Intelligent Proximity Detection, were reviewed. In the domestic mining industry, no collision avoidance system was used. However, similar systems were utilized in the construction and railroad industry. Collision avoidance system can prevent unexpected collision accident and thus improve worker's safety in mine. Therefore, it is necessary to analyze and apply sensors and system appropriate for the domestic mining environment via review of overseas collision avoidance system.

• Journal of Veterinary Science
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• v.21 no.5
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• pp.71.1-71.10
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• 2020

Background: Feline calicivirus (FCV) is a major and highly infectious pathogen in cats worldwide. However, there have been limited studies about the status of FCV infections in Korea. Objectives: To investigate the current status of FCV infections in stray cats in Korea. Methods: A novel reverse transcription polymerase chain reaction (RT-PCR) assay was developed based on the conserved nucleotide sequences of reported FCV strains. Field swab samples were collected from 122 cats (2 hospital admitted cats and 120 stray cats) in 2016 and 2017. All the samples were tested by virus isolation and 2 different RT-PCRs, including the novel RT-PCR, for the detection of FCV. Results: The novel RT-PCR assay showed no cross-reactivity to the nucleic acids of the other feline pathogens tested, and the limit of detection was calculated as 10⁰ TCID₅₀/mL based on an in vitro assessment. The novel RT-PCR assay detected 5 positive samples from the 122 field samples, which showed perfect agreement with the results of the virus isolation method. In contrast, another RT-PCR assay used in a previous study in Korea detected no positive samples. The prevalence of FCV infection in stray cats was 2.5% (3/120) based on the results of virus isolation and the novel RT-PCR assays. Conclusions: The current study is the first report of the detection and prevalence of FCV in stray cats in Korea. The novel RT-PCR assay developed in this study showed high sensitivity and specificity, which indicates a useful diagnostic assay to identify FCV infection in cats.

• Journal of Software Assessment and Valuation
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• v.16 no.2
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• pp.143-151
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• 2020

The digital content's security and authentication are important in the field of digital content application. There are some methods to perform the authentication. The digital watermarking is one of authentication methods. Paper presents a digital watermark authentication method that works in the application of digital image. The proposed watermark has the triple status information and performs the embedding and the detection without original Content. When authenticating the owner information of digital content, an autocorrelation function is used. In addition, a spread spectrum method is used to be adaptive to the signal of the original content in the frequency domain(DWT Domain). Therefore, the possibility of errors occurring in the detection of hidden information was reduced. it also has a advantage what Watermarking in DWT has faster embedding and detection time than other transformation domains(DFT, DCT, etc.). if it has a an image of size N=mXm, the computational amount can be reduced from O(N·logN) to O(N). The particular advantage is that it can hide more information(bits) per bit.

• Journal of the Korea Organic Resources Recycling Association
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• v.31 no.2
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• pp.5-18
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• 2023

As a result of the survey on the emission status of fluorine-based chemicals in Korea, 13 kinds of substances, including hydrogen fluoride (91%) and perfluorocarbons (5%), were emitted in workplaces. By regional groups, about 98% was emitted in the Gyeongbuk, Gyeonggi and Chungcheong regions, and about 98% in three sectors of industry related to manufacture of electronic parts, chemicals and non-metallic mineral products. The detection status of PFOS and PFOA in domestic main rivers was continuously detected in the Nakdong River, the Geum River and the Anseong Stream estuary with high fluorine-related chemical emissions, and four sites of PFOS and two sites of PFOA were detected for the first time in 2021. PFOS and PFOA were continuously detected in relatively high concentrations in the rivers where there were many semiconductor and display related sectors of industry.

• Journal of Veterinary Science
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• v.24 no.5
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• pp.70.1-70.14
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• 2023

Background: Mycobacterium avium subspecies paratuberculosis (MAP) causes a chronic and progressive granulomatous enteritis and economic losses in dairy cattle in subclinical stages. Subclinical infection in cattle can be detected using serum MAP antibody enzyme-linked immunosorbent assay (ELISA) and fecal polymerase chain reaction (PCR) tests. Objectives: To investigate the differences in blood parameters, according to the detection of MAP using serum antibody ELISA and fecal PCR tests. Methods: We divided 33 subclinically infected adult cattle into three groups: seronegative and fecal-positive (SNFP, n = 5), seropositive and fecal-negative (SPFN, n = 10), and seropositive and fecal-positive (SPFP, n = 18). Hematological and serum biochemical analyses were performed. Results: Although the cows were clinically healthy without any manifestations, the SNFP and SPFP groups had higher platelet counts, mean platelet volumes, plateletcrit, lactate dehydrogenase levels, lactate levels, and calcium levels but lower mean corpuscular volume concentration than the SPFN group (p < 0.017). The red blood cell count, hematocrit, monocyte count, glucose level, and calprotectin level were different according to the detection method (p < 0.05). The SNFP and SPFP groups had higher red blood cell counts, hematocrit and calprotectin levels, but lower monocyte counts and glucose levels than the SPFN group, although there were no significant differences (p > 0.017). Conclusions: The cows with fecal-positive MAP status had different blood parameters from those with fecal-negative MAP status, although they were subclinically infected. These findings provide new insights into understanding the mechanism of MAP infection in subclinically infected cattle.

• The Journal of Society for e-Business Studies
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• v.22 no.2
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• pp.1-18
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• 2017

Recently, identification of the extremely stressed condition of children is an essential skill for real-time recognition of a dangerous situation because incidents of children have been dramatically increased. In this paper, therefore, we present a model based on machine learning techniques for stress status identification of a child by using bio-signals such as voice and heart rate that are major factors for presenting a child's emotion. In addition, a smart band for collecting such bio-signals and a mobile application for monitoring child's stress status are also suggested. Specifically, the proposed method utilizes stress patterns of children that are obtained in advance for the purpose of training stress status identification model. Then, the model is used to predict the current stress status for a child and is designed based on conventional machine learning algorithms. The experiment results conducted by using a real-world dataset showed that the possibility of automated detection of a child's stress status with a satisfactory level of accuracy. Furthermore, the research results are expected to be used for preventing child's dangerous situations.

• Korean Journal of Soil Science and Fertilizer
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• v.49 no.5
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• pp.469-479
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• 2016

Kimchi cabbage is one of the most important vegetables in Korea and a target crop for market stabilization as well. In particular Kimchi cabbages in a highland area are very sensitive to the fluctuations in supply and demand. Yield variability due to growth conditions dictates the market fluctuations of Kimchi cabbage price. This study was carried out to understand the distribution of the highland Kimchi cabbage growth status in Anbandeok. Anbandeok area in Gangneung, Gangwon-do, Korea is one of the main producing districts of highland Kimchi cabbage. The highland Kimchi cabbage status map of each growth factor was obtained from unmanned aerial vehicle (UAV) imagery and field survey data. Six status maps include UAVRGB image map, normalized difference vegetation index (NDVI) distribution/anomaly map, Crop distribution map, Planting/Harvest distribution map, Growth parameter map and Growth disorder map. As a result, the highland Kimchi cabbage status maps from May 31 to Sep. 6 in 2016 were presented to show spatial variability in the field. The benefits of the highland Kimchi cabbage status map can be summarized as follows: crop growth monitoring, reference for field observations and survey, the relative comparison of the growth condition in field scale, evaluation of growth in comparison of average year, change detection of annual crops or planting areas, abandoned fields monitoring, prediction of harvest season etc.

• Korean Journal of Remote Sensing
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• v.38 no.1
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• pp.73-82
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• 2022

Marine fisheries resources face major anthropogenic threat from unregulated fishing activities; thus require precise detection for protection through marine surveillance. Korea developed an efficient land-based small fishing vessel monitoring system using real-time V-Pass data. However, those data directly do not provide information on fishing activities, thus further efforts are necessary to differentiate their activity status. In Korea, especially in Busan, longlining is practiced by many small fishing vessels to catch several types of fishes that need to be identified for proper monitoring. Therefore, in this study we have improved the existing fishing status classification method by applying Hidden Markov Model (HMM) on V-Pass data in order to further classify their fishing status into three groups, viz. non-fishing, longlining and other types of fishing. Data from 206 fishing vessels at Busan on 05 February, 2021 were used for this purpose. Two tiered HMM was applied that first differentiates non-fishing status from the fishing status, and finally classifies that fishing status into longlining and other types of fishing. Data from 193 and 13 ships were used as training and test datasets, respectively. Using this model 90.45% accuracy in classifying into fishing and non-fishing status and 88.23% overall accuracy in classifying all into three types of fishing statuses were achieved. Thus, this method is recommended for monitoring the activities of small fishing vessels equipped with V-Pass, especially for detecting longlining.

• Journal of Korean Tunnelling and Underground Space Association
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• v.20 no.6
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• pp.1161-1175
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• 2018

An unexpected event could be easily followed by a large secondary accident due to the limitation in sight of drivers in road tunnels. Therefore, a series of automated incident detection systems have been under operation, which, however, appear in very low detection rates due to very low image qualities on CCTVs in tunnels. In order to overcome that limit, deep learning based tunnel incident detection system was developed, which already showed high detection rates in November of 2017. However, since the object detection process could deal with only still images, moving direction and speed of moving vehicles could not be identified. Furthermore it was hard to detect stopping and reverse the status of moving vehicles. Therefore, apart from the object detection, an object tracking method has been introduced and combined with the detection algorithm to track the moving vehicles. Also, stopping-reverse discrimination algorithm was proposed, thereby implementing into the combined incident detection processes. Each performance on detection of stopping, reverse driving and fire incident state were evaluated with showing 100% detection rate. But the detection for 'person' object appears relatively low success rate to 78.5%. Nevertheless, it is believed that the enlarged richness of image big-data could dramatically enhance the detection capacity of the automatic incident detection system.