• The Transactions of the Korean Institute of Electrical Engineers C
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• v.52 no.1
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• pp.25-34
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• 2003

Gas insulated switchgear(GIS) is an important equipment in a substation. It is highly desirable to measure a partial discharge(PD) in GIS which is a symptom before insulation breakdown occurs. The issue is that the PD signal is weak and sensitive to external noise. In this paper, the algorithm for detection of PD in GIS using wavelet transform is proposed. The wavelet transform provides a direct quantitative measure of spectral content, "dynamic spectrum", in the time-frequency domain. The recommended mother wavelet is 'Daubechies' wavelet. 'db4', the most commonly applied mother wavelet in the power quality analysis, can be used most properly in disturbance phenomena which occurs rapidly for a short time. Through the procedure of wavelet transform, noise extraction and reconstruction, the signal is Analyzed to determine the magnitude of PD in GIS. In experimental results, we can know that partial discharge is exactly detected in combination of Dl and D2 using wavelet transform.transform.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.2
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• pp.800-816
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• 2018

In recent years, video surveillance research has been able to recognize various behaviors of pedestrians and analyze the overall situation of objects by combining image analysis technology and deep learning method. Human Activity Recognition (HAR), which is important issue in video surveillance research, is a field to detect abnormal behavior of pedestrians in CCTV environment. In order to recognize human behavior, it is necessary to detect the human in the image and to estimate the pose from the detected human. In this paper, we propose a novel approach for 2D Human Pose Estimation based on object detection using RGB-D information. By adding depth information to the RGB information that has some limitation in detecting object due to lack of topological information, we can improve the detecting accuracy. Subsequently, the rescaled region of the detected object is applied to ConVol.utional Pose Machines (CPM) which is a sequential prediction structure based on ConVol.utional Neural Network. We utilize CPM to generate belief maps to predict the positions of keypoint representing human body parts and to estimate human pose by detecting 14 key body points. From the experimental results, we can prove that the proposed method detects target objects robustly in occlusion. It is also possible to perform 2D human pose estimation by providing an accurately detected region as an input of the CPM. As for the future work, we will estimate the 3D human pose by mapping the 2D coordinate information on the body part onto the 3D space. Consequently, we can provide useful human behavior information in the research of HAR.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.3C
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• pp.112-118
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• 2005

In this paper, we propose a new nonlinear matching measure for automatic analysis of the on-off type DNA microarray images in which the hybridized spots are detected by the template matching method. The proposed measure is obtained by binary-thresholding over the whole template region and taking the number of white pixels inside the spotted area. This measure is compared with the normalized covariance in terms of the classification ability of the successfulness of the locating markers. The proposed measure is evaluated for the scanned images of HPV DNA microarrays where the marker locating is a critical issue because of the small number of spots. The targeting spots of HPV DNA chips are designed for genotyping 22 types of the human papilloma virus(HPV). The proposed measure is proven to give more discriminative response reducing the miss cases of the successful marker locating.

• Journal of Environmental Health Sciences
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• v.31 no.1
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• pp.86-93
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• 2005

Because generally large amount of sludges are generated from the process of sewage and wastewater treatment, the management and controlment of those sludge has become a important issue in many researches. In this dissertation, we conducted the research to see the physico-chemical characteristics of sludges generated from various sources. Moisture of sludges were 81.5% in textile industries, 81.4% in frame metal industries, 80.7% in 1st metal industries. Volatile solid were 22.1% in hide · rubber industries, 21.9% in coke · petroleum industries. Fixed solid were 18.5% and 17.7% in the 1st metal industries and frame metal industries. High heating value by wet base were 1,850 kcal/kg in coke · petroleum industries, 1,220 kcal/kg in hide · rubber industries, but sludges from the 1st metal industries and frame metal industries were impossible to incinerate because most of those sludges were inorganic. The leaching test showed that hazardous materials was detected in nearly every kinds of sludges. Some of sludges from hide · rubber industries and frame metal industries exceeded the leaching criteria and so they were classified as specific wastes. And other sludges generated in sewage treatment plants or other industries was below the leaching criteria.

• Journal of Sensor Science and Technology
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• v.27 no.4
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• pp.227-231
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• 2018

Indoor air pollution has become a serious issue, affecting the health and comfort of building occupants. Volatile organic compounds (VOCs) are among the most common indoor contaminants, and are released from numerous indoor emission sources. Among the VOCs, formaldehyde and toluene are toxic chemicals at low levels and are frequently detected indoors. Exposure to formaldehyde and toluene can irritate sensitive tissue and may increase the risk of cancer. Therefore, monitoring formaldehyde and toluene is critical for the health and comfort of residents. In addition, as human indoor activities can generate VOC gases, analysis of their influence on VOCs is needed. In this study, we compared electronic nose (E-Nose) data for formaldehyde and toluene with E-Nose data for indoor mixture gas with consideration for human indoor activities.

• Journal of the Korea Institute of Military Science and Technology
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• v.15 no.4
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• pp.417-423
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• 2012

Radar Cross Section(RCS) is a measure of how detectable an object is with a radar. A larger RCS indicates that an object is more easily detected. Informally, the RCS of an object is the cross-sectional area of a perfectly reflecting sphere that would produce the same amount of reflection strength as the object in question would. In order to estimate RCS of aircraft weapons the external surface is modeled as a collection of simple shape elements. And the overall RCS is estimated as a vector sum of configuring elements' cross-sections which are well known given by analytic formulae. A RCS estimation code is developed for a typical shape of Air-To-Surface bombs and missiles. Size of weapons and location of fins are implemented in the code in addition to the presence of canards. The ability to predict radar return from flying vehicles becomes a critical technology issue in the development of stealth configurations. This simplified method of RCS estimation is known to be fast and accurate enough in an optical region of high frequency incident radio wave.

• Genomics & Informatics
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• v.18 no.3
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• pp.29.1-29.11
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• 2020

Maize seed pigmentation is one of the important issue to develop maize seed breeding. The differently gene expression was characterized and compared for three inbred lines, such as the pigment accumulated seed (CM22) and non-pigmented seed (CM5 and CM19) at 10 days after pollination. We obtained a total of 63,870, 82,496, and 54,555 contigs by de novo assembly to identify gene expression in the CM22, CM5, and CM19, respectably. In differentially expressed gene analysis, it was revealed that 7,044 genes were differentially expressed by at least two-fold, with 4,067 upregulated in colored maize inbred lines and 2,977 upregulated in colorless maize inbred lines. Of them,18 genes were included to the anthocyanin biosynthesis pathways, while 15 genes were upregulated in both CM22/5 and CM22/19. Additionally, 37 genes were detected in the metabolic pathway concern to the seed pigmentation by BINs analysis using MAPMAN software. Finally, these differently expressed genes may aid in the research on seed pigmentation in maize breeding programs.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.4
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• pp.135-144
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• 2018

The domestic pigsty environment is highly vulnerable to the spread of respiratory diseases such as foot-and-mouth disease because of the small space. In order to manage this issue, a variety of studies have been conducted to automatically analyze behavior of individual pigs in a pig pen through a video surveillance system using a camera. Even though it is required to correctly segment touching pigs for tracking each pig in complex situations such as aggressive behavior, detecting the correct boundaries among touching pigs using Kinect's depth information of lower accuracy is a challenging issue. In this paper, we propose a segmentation method using motion information of the touching pigs. In addition, our proposed method can be applied for detecting tracking errors in case of tracking individual pigs in the complex environment. In the experimental results, we confirmed that the touching pigs in a pig farm were separated with the accuracy of 86%, and also confirmed that the tracking errors were detected accurately.

• Smart Structures and Systems
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• v.15 no.6
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• pp.1583-1600
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• 2015

When natural disasters occur, including earthquakes, tsunamis, and debris flows, they are often accompanied by various types of damages such as the collapse of buildings, broken bridges and roads, and the destruction of natural scenery. Natural disaster detection and warning is an important issue which could help to reduce the incidence of serious damage to life and property as well as provide information for search and rescue afterwards. In this study, we propose a novel computer vision technique for debris flow detection which is feature-based that can be used to construct a debris flow event warning system. The landscape is composed of various elements, including trees, rocks, and buildings which are characterized by their features, shapes, positions, and colors. Unlike the traditional methods, our analysis relies on changes in the natural scenery which influence changes to the features. The "background module" and "monitoring module" procedures are designed and used to detect debris flows and construct an event warning system. The multi-criteria decision-making method used to construct an event warring system includes gradient information and the percentage of variation of the features. To prove the feasibility of the proposed method for detecting debris flows, some real cases of debris flows are analyzed. The natural environment is simulated and an event warning system is constructed to warn of debris flows. Debris flows are successfully detected using these two procedures, by analyzing the variation in the detected features and the matched feature. The feasibility of the event warning system is proven using the simulation method. Therefore, the feature based method is found to be useful for detecting debris flows and the event warning system is triggered when debris flows occur.

• Journal of Microbiology and Biotechnology
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• v.31 no.12
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• pp.1701-1708
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• 2021

Food safety is the most important global health issue due to foodborne pathogens after consumption of contaminated food. Foodborne bacteria such as Escherichia coli, Salmonella enterica, Staphylococcus aureus, Campylobacter spp., Bacillus cereus, Vibrio spp., Yersinia enterocolitica and Clostridium perfringens are leading causes of the majority of foodborne illnesses and deaths. These foodborne pathogens often come from the livestock feces, thus, we analyzed fecal microbial communities of three different livestock species to investigate the prevalence of foodborne pathogens in livestock feces using metagenomics analysis. Our data showed that alpha diversities of microbial communities were different according to livestock species. The microbial diversity of cattle feces was higher than that of chicken or pig feces. Moreover, microbial communities were significantly different among these three livestock species (cattle, chicken, and pig). At the genus level, Staphylococcus and Clostridium were found in all livestock feces, with chicken feces having higher relative abundances of Staphylococcus and Clostridium than cattle and pig feces. Genera Bacillus, Campylobacter, and Vibrio were detected in cattle feces. Chicken samples contained Bacillus, Listeria, and Salmonella with low relative abundance. Other genera such as Corynebacterium, Streptococcus, Neisseria, Helicobacter, Enterobacter, Klebsiella, and Pseudomonas known to be opportunistic pathogens were also detected in cattle, chicken, and pig feces. Results of this study might be useful for controlling the spread of foodborne pathogens in farm environments known to provide natural sources of these microorganisms.