• Parasites, Hosts and Diseases
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• v.48 no.3
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• pp.213-217
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• 2010

Sarcocystis sp. infection was investigated in 20 necropsied captive wild mammals and 20 birds in 2 petting zoos in Malaysia. The gross post-mortem lesions in mammals showed marbling of the liver with uniform congestion of the intestine, and for birds, there was atrophy of the sternal muscles with hemorrhage and edema of the lungs in 2 birds. Naked eye examination was used for detection of macroscopic sarcocysts, and muscle squash for microscopic type. Only microscopically visible cysts were detected in 8 animals and species identification was not possible. Histological examination of the sections of infected skeletal muscles showed more than 5 sarcocysts in each specimen. No leukocytic infiltration was seen in affected organs. The shape of the cysts was elongated or Circular, and the mean size reached $254{\times}24.5{\mu}m$ and the thickness of the wall up to $2.5{\mu}m$. Two stages were recognized in the cysts, the peripheral metrocytes and large numbers of crescent shaped merozoites. Out of 40 animals examined, 3 mammals and 5 birds were positive (20%). The infection rate was 15% and 25% in mammals and birds, respectively. Regarding the organs, the infection rate was 50% in the skeletal muscles followed by tongue and heart (37.5%), diaphragm (25%), and esophagus (12.5%). Further ultrastructural studies are required to identify the species of Sarcocystis that infect captive wild animals and their possible role in zoonosis.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.2 no.3
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• pp.219-230
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• 2004

Most of the low-level liquid radioactive wastes generated from PWR plants are classified into high or low total suspended solid(HTDS or LTDS), and into radiochemical and radioactive laundry waste. Although the evaporation process has a high decontami- nation ability, it has several problems such as corrosion, foam, and congestion. A new liquid waste disposal process using the ion-exchange demineralizer(IED), instead of the current evaporation process, has been introduced into the Yonggwang NPP #5 and 6. These two methods have been compared to understand the differences in this study. Aspects compared here were the released radioactivity amount of the liquid radioactive wastes, the dose of off-site residents, the decontamination factor, and the amount of the solid radioactive wastes. The IED system is designed to discharge higher radioactivity about 20% than the evaporating system, and the actual radioactivity released from the evaporating and IED system were 0.473mCi and 1.098mCi, respectively. The radioactivity released from the IED was 2.32 times higher than that of the evaporating system. The dose of off-site residents was $2.97{\times}10^{-6}$mSv for the evaporating system, and $6.47{\times}10^{-6}$mSv for IED. The decontamination factor(DF) of the evaporator is, in most cases, far lower than the lower limits of detection(LLD) with the Ge-Li detector. Due to the low concentration of the liquid wastes collected from the liquid waste system, the decontamination factor of IED is very low. Since there is not enough data on the amount of solid radioactive wastes generated by the evaporation system, the comparison on these two systems has been conducted on the basis of the design, and the comparison result was that the evaporating system generated more wastes about 40% than IED.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.8 no.5
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• pp.1-11
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• 2009

TCS Data imply characteristics of traffic conditions. However, there are outliers in TCS data, which can not represent the travel time of the pertinent section, if these outliers are not eliminated, travel time may be distorted owing to these outliers. Various travel time can be distributed under the same section and time because the variation of the travel time is increase as the section distance is increase, which make difficult to calculate the representative of travel time. Accordingly, it is important to grasp travel time characteristics in order to compute the representative of travel time using TCS Data. In this study, after analyzing the variation ratio of the travel time according to the link distance and the level of congestion, the outlier elimination model and the smoothing model for TCS data were proposed. The results show that the proposed model can be utilized for estimating a reliable travel time for a long-distance path in which there are a variation of travel times from the same departure time, the intervals are large and the change in the representative travel time is irregular for a short period.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.1
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• pp.81-87
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• 2020

Recently, the network configuration is being rapidly changed to enable easy and free network service configuration based on SDN/NFV. Despite the many advantages and applications of SDN, many security issues such as Distributed Denial of Service (DDoS) attacks are being constantly raised as research issues. In particular, the effectiveness of DDoS attacks is much faster, SDN is causing more and more fatal damage. In this paper, we propose an entropy-based technique to detect and mitigate DDoS attacks in SDN, and prove it through experiments. The proposed scheme is designed to mitigate these attacks by detecting DDoS attacks on single and multiple victim systems and using time - specific techniques. We confirmed the effectiveness of the proposed scheme to reduce packet loss rate by 20(19.86)% while generating 3.21% network congestion.

• Journal of KIISE:Software and Applications
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• v.34 no.11
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• pp.991-1000
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• 2007

A new robust approach to detect vehicles in a single frame of traffic scenes is presented. The method is based on the multi-level shadow classification, which has been shown to have the capability of extracting correct shadow shapes regardless of the operating conditions. The rationale of this classification is supported by the fact that shadow regions underneath vehicles usually exhibit darker gray level regardless of the vehicle brightness and illuminating conditions. Classified shadows provide string clues on the presence of vehicles. Unlike other schemes, neither background nor temporal information is utilized; thereby the performance is robust to the abrupt change of weather and the traffic congestion. By a simple evidential reasoning, the shadow evidences are combined with bright evidences to locate correct position of vehicles. Experimental results show the missing rate ranges form 0.9% to 7.2%, while the false alarm rate is below 4% for six traffic scenes sets under different operating conditions. The processing speed for more than 70 frames per second could be obtained for nominal image size, which makes the real-time implementation of measuring the traffic parameters possible.

• Journal of Internet Computing and Services
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• v.23 no.5
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• pp.1-7
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• 2022

IoT technology that utilizes wireless body area networks (WBAN) and biosensors is an important field in the health industry to minimize resources and monitor patients. In order to integrate IoT and WBAN, a cooperative protocol that constitutes WBAN's limited sensor nodes and rapid routing for efficient data transmission is required. In this paper we propose an we propose an energy efficient and cooperative link energy-efficient routing strategy(LEERS) to solve the problems of redundant data transmission detection and limited network sensor lifetime extention. The proposed scheme considers the hop count node congestion level towards the residual energy sink and bandwidth and parameters. In addition, by determining the path cost function and providing effective multi-hop routing, it is shown that the existing method is improved in terms of residual energy and throughput

• Journal of Digital Convergence
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• v.19 no.5
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• pp.197-205
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• 2021

Recently, the retail industry has been increasingly demanding information technology convergence and utilization to respond to various external environmental threats such as COVID-19 and to be competitive using AI technologies, but there is a very lack of research and application services. This study is a CCTV video data-driven AI application case study, using CCTV image data collection in retail space, object detection and tracking AI model, time series database to store real-time tracked objects and tracking data, heatmap to analyze congestion and interest in retail space, social access zone.We present the orientation and verify its usability in the direction designed through practical implementation.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.3D
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• pp.349-352
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• 2006

To cope with traffic congestion, traffic accidents and lack of parking facilities, caused by dramatic increase in total vehicle number, vigorous researches on managing vehicles efficiently are done, both domestically and internationally. The vehicle license plate recognition makes effective management of traffic possible, with its wide application in many fields, covering from speed enforcement, collecting toll, stolen vehicle detection to parking management. The vehicle license plate recognition system causes high cost for collecting training data. Many researches are done by using the virtual sample method, which can be effective for utilizing limited number of training data by generating virtual sample. This paper investigates techniques to improve the performance of vehicle license plate recognition by using the training data's annexation. Also, popular methods for virtual sample creation used for text recognition algorithm are analyzed and their effectiveness is verified.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.239-241
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• 2022

Currently, in Korea, a multi-seater lane (HOV) and a designated lane system are being implemented to solve traffic congestion. However, in both systems, it is difficult to crack down on cases of violations without permission, so people are required to be assigned to areas that want to crack down. In this process, manpower and budget are inefficiently consumed. To compensate for these shortcomings, we propose the development of an integrated enforcement system through YOLO, a deep learning object recognition model. If the two systems are implemented and integrated using YOLO, they will have advantages in terms of manpower and budget over existing systems because only data learning and system maintenance are considered. In addition, in the case of violations in which it is difficult for the existing unmanned system to crack down, the effect of increasing the crackdown rate through continuous learning can be expected.

• Advanced Industrial SCIence
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• v.2 no.4
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• pp.11-16
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• 2023

The subway is a means of public transportation that plays an important role in the transportation system of modern cities. However, congestion often occurs due to sudden breakdowns and system outages, causing inconvenience. Therefore, in this paper, we conducted a study on failure prediction and prevention using machine learning to efficiently operate the subway system. Using UC Irvine's MetroPT-3 dataset, we built a subway breakdown prediction model using logistic regression. The model predicted the non-failure state with a high accuracy of 0.991. However, precision and recall are relatively low, suggesting the possibility of error in failure prediction. The ROC_AUC value is 0.901, indicating that the model can classify better than random guessing. The constructed model is useful for stable operation of the subway system, but additional research is needed to improve performance. Therefore, in the future, if there is a lot of learning data and the data is well purified, failure can be prevented by pre-inspection through prediction.