• Korean Journal of Veterinary Research
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• v.33 no.2
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• pp.301-308
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• 1993

Three or 7day old piglets were infected experimentally with different encephalomyocarditis virus isolates to detect the viral antigen by the immunoperoxidase technique and to observe strain difference in their pathogenecity in newborn pigs by comparing clinical signs and pathologic lesions. Clinical signs of the infected pigs were different depending on the virus strain, pig age and infection route. Encephalomyocarditis virus(EMCV) NVSL-PR isolate was more pathogenic than MN-25 and MN-30 isolate. Three day old piglets showed more severe illness than 7 day old piglets. Predominant clinical signs were sudden death without noticeable clinical signs and dyspnea manifested as heavy abdominal breathing. Contact-infection from infected piglets to controls was observed in the oro-nasally infected group but not the intramuscular group. Common necropsy findings of dead piglets in both age groups infected with MN-25 and NVSL-PR were accumulation of excessive fluid in the body cavities and mild to diffuse necrotic areas observed in the hearts and occasionally in the livers. Microscopically, myocarditis with inflammatory cell infiltration, necrosis of the myocardial muscle fibers and occasional mineralization were observed along with interstitial pneumonia and centrolobular necrosis in the liver. Using an immunoperoxidase technique, viral antigen was detected in myocardial muscle fibers of piglets infected with EMCV.

• Journal of Digital Contents Society
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• v.12 no.3
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• pp.279-289
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• 2011

The latest hardware and software techniques are combined to give safe walking guidance and convenient service of realtime walking assistance system for visually impaired person. This system consists of obstacle detection and perception, place recognition, and sign recognition for pedestrian can safely walking to arrive at their destination. In this paper, we exploit the sign object detection system in subway station for sign recognition that one of the important factors of walking assistance system. This paper suggest the adaptive feature map that can be robustly extract the sign object region from complexed environment with light and noise. And recognize a sign using fast zernike moment features which is invariant under translation, rotation and scale of object during walking. We considered three types of signs as arrow, restroom, and exit number and perform the training and recognizing steps through adaboost classifier. The experimental results prove that our method can be suitable and stable for real-time system through yields on the average 87.16% stable detection rate and 20 frame/sec of operation time for three types of signs in 5000 images of sign database.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.2
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• pp.692-712
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• 2022

Buoys used for Aid to Navigation systems are widely used to guide the sea paths and are powered by batteries, requiring continuous battery replacement. However, since human labor is required to replace the batteries, humans can be exposed to dangerous situation, including even collision with shipping vessels. In addition, Maritime sensors are installed on the route signs, so that these are often damaged by collisions with small and medium-sized ships, resulting in significant financial loss. In order to prevent these accidents, maritime object detection technology is essential to alert ships approaching buoys. Existing studies apply a number of filters to eliminate noise and to detect objects within the sea image. For this process, most studies directly access the pixels and process the images. However, this approach typically takes a long time to process because of its complexity and the requirements of significant amounts of computational power. In an emergent situation, it is important to alarm the vessel's rapid approach to buoys in real time to avoid collisions between vessels and route signs, therefore minimizing computation and speeding up processes are critical operations. Therefore, we propose Fast Connected Component Labeling (FCCL) which can reduce computation to minimize the processing time of filter applications, while maintaining the detection performance of existing methods. The results show that the detection performance of the FCCL is close to 30 FPS - approximately 2-5 times faster, when compared to the existing methods - while the average throughput is the same as existing methods.

• Journal of Broadcast Engineering
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• v.26 no.2
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• pp.132-142
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• 2021

The purpose of this study is to present an effective methodology that can measure heart rate, heart rate variability, oxygen saturation, respiration rate, mental stress level, and blood pressure using mobile front camera that can be accessed most in real life. Face recognition was performed in real-time using Blaze Face to acquire facial image data, and the forehead was designated as ROI (Region Of Interest) using feature points of the eyes, nose, and mouth, and ears. Representative values for each channel of the ROI were generated and aligned on the time axis to measure vital signs. The vital signs measurement method was based on Fourier transform, and noise was removed and filtered according to the desired vital signs to increase the accuracy of the measurement. To verify the results, vital signs measured using facial image data were compared with pulse oximeter contact sensor, and TI non-contact sensor. As a result of this work, the possibility of extracting a total of six vital signs (heart rate, heart rate variability, oxygen saturation, respiratory rate, stress, and blood pressure) was confirmed through facial images.

• Journal of Computing Science and Engineering
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• v.7 no.4
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• pp.272-284
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• 2013

In this paper, we propose a new framework for anomaly detection in medical wireless sensor networks, which are used for remote monitoring of patient vital signs. The proposed framework performs sequential data analysis on a mini gateway used as a base station to detect abnormal changes and to cope with unreliable measurements in collected data without prior knowledge of anomalous events or normal data patterns. The proposed approach is based on the Mahalanobis distance for spatial analysis, and a kernel density estimator for the identification of abnormal temporal patterns. Our main objective is to distinguish between faulty measurements and clinical emergencies in order to reduce false alarms triggered by faulty measurements or ill-behaved sensors. Our experimental results on both real and synthetic medical datasets show that the proposed approach can achieve good detection accuracy with a low false alarm rate (less than 5.5%).

• Proceedings of the Korean Information Science Society Conference
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• 2011.06a
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• pp.491-493
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• 2011

Traffic sign detection is the domain of automatic driver assistant systems. There are literatures for traffic sign detection using color information, however, color-based method contains ill-posed condition and to extract the region of interest is difficult. In our work, we propose a method for traffic sign detection using k-means clustering method, back-propagation neural network, and projection histogram features that yields the robustness for ill-posed condition. Using the color information of traffic signs enables k-means algorithm to cluster the region of interest for the detection efficiently. In each step of clustering, a cluster is verified by the neural network so that the cluster exactly represents the location of a traffic sign. Proposed method is practical, and yields robustness for the unexpected region of interest or for multiple detections.

• 한국지형공간정보학회:학술대회논문집
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• 2000.12a
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• pp.10-17
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• 2000

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.4
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• pp.190-211
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• 2022

This study investigated the effect of the shape and height of road signs on detection performance when detecting road signs with LiDAR, which is recognized as an essential sensor for autonomous vehicles. For the study, four types of road signs with the same area and material and different shapes were produced, and a road driving test was performed by installing a 32Ch rotating LiDAR on the upper part of the vehicle. As a result of comparing the shape of the point cloud and the NPC according to the shape of the road sign, It is expected that a distance of less than 40m is required to recognize the overall shape of a road sign using 32Ch LiDAR, and shapes such as triangles and rectangles are more advantageous than squares in securing the maximum point cloud from a long distance. As a result of the study according to the height of the road sign, At short distances (within 20m), if the height of the sign is raised to more than 2m, it deviates from the vertical viewing angle of the LiDAR and cannot express the complete point cloud shape. However, it showed a negligible effect compared to the near-field height change. These research results are expected to be utilized in the development of road facilities dedicated to LiDAR for the commercialization of autonomous cooperative driving technology.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.1
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• pp.70-79
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• 2017

This study proposes a method to quickly and accurately acquire distance information on direction signs. The proposed method is composed of the recognition of the sign, pre-processing to facilitate the acquisition of the road sign distance, and the acquisition of the distance data. The road sign recognition uses color detection including gamma correction in order to mitigate various noise issues. In order to facilitate the acquisition of distance data, this study applied tilt correction using linear factors, and resolution correction using Fourier transform. To acquire the distance data, morphological operation was used to highlight the area, along with labeling and template matching. By acquiring the distance information on the direction sign through such a processes, the proposed system can be output the distance remaining to the next junction. As a result, when the proposed method is applied to system it can process the data in real-time using the fast calculation speed, average speed was shown to be 0.46 second per frame, with accuracy of 0.65 in similarity value.

• Korean Journal of Veterinary Service
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• v.45 no.3
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• pp.145-153
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• 2022

Pathogens such as feline herpesvirus, feline calicivirus, Bordetella bronchiseptica, Chlamydia felis, Mycoplasma felis and Pasteurella multocida usually cause feline upper respiratory tract disease (URTD). Real-time PCR was used to analyze the detection and prevalence of the most common respiratory pathogens in cats with (n=69) and without respiratory signs (n=31). Pathogens were detected in 53 cats, divided into 37 (69.8%) with a single pathogen, 15 (28.3%) with two pathogens, and 1 (1.9%) with three pathogens. M. felis had the highest detection rate in 29 (42.0%) cats, P. multocida was detected in 18 (26.1%), FHV in 10 (14.5%), FCV in 7 (10.1%), B. bronchiseptica in 3 (4.3%), and C. felis in 2 (2.9%). M. felis was the most frequently detected pathogen in cats living outdoors without vaccination. Of the 37 cats infected with single pathogen, nasal discharge was observed in 13 (35.1%), ocular signs in 6 (16.2%), drooling in 5 (13.5%), dyspnea in 3 (8.1%), and asymptomatic in 10 (27.0%). In 51 outdoor and 49 indoor cats, pathogens were detected in 35 (68.6%) and 18 (36.7%) cats, respectively. Of the 29 cats infected with M. felis, 22 (75.9%) showed respiratory signs, and 7 (24.1%) were healthy. In the age of the 53 positive cats, 10 (18.9%) were under the age of 1 year, 26 (49.1%) were aged 1~3 years, and 17 (32.1%) were aged 3 years or older. Although the number of cats in the study was small, the results can provide valuable data on the prevalence of URTD in Korean cats.