• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.15 no.8
• /
• pp.5256-5262
• /
• 2014

Recently, Distributed Denial of Service (DDoS) attacks, such as spreading malicious code, cyber-terrorism, have occurred in government agencies, the press and the financial sector. DDoS attacks are the simplest Internet-based infringement attacks techniques that have fatal consequences. DDoS attacks have caused bandwidth consumption at the network layer. These attacks are difficult to detect defend against because the attack packets are not significantly different from normal traffic. Abnormal traffic is threatening the stability of the network. Therefore, the abnormal traffic by generating indications will need to be detected in advance. This study examined the abnormal traffic detection technique using a forecasting model-based trend model.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2017.05a
• /
• pp.424-424
• /
• 2017

Several studies of the world have analyzed the regional rainfall trends in large data sets. However, it reported that the long-term behavior of rainfall was different on spatial and temporal scales. The objective of this study is to determine the local trends of rainfall indices in the Yom River Basin, Thailand. The rainfall indices consist of the annual total precipitation (PRCTPOP), number of heavy rainfall days ($R_{10}$), number of very heavy rainfall days ($R_{20}$), consecutive of dry days (CDD), consecutive of wet days (CWD), daily maximum rainfall ($R_{x1}$), five-days maximum rainfall ($R_{x5}$), and total of annual rainy day ($R_{annual}$). The rainfall data from twelve hydrological stations during the period 1965-2015 were used to analysis rainfall trend. The Mann-Kendall test, which is non-parametric test was adopted to detect trend at 95 percent confident level. The results of these data were found that there is only one station an increasing significantly trend in PRCTPOP index. CWD, which the index is expresses longest annual wet days, was exhibited significant negative trend in three locations. Meanwhile, the significant positive trend of CDD that represents longest annual dry spell was exhibited four locations. Three out of thirteen stations had significant decreasing trend in $R_{annual}$ index. In contrast, there is a station statistically significant increasing trend. The analysis of $R_{x1}$ was showed a station significant decreasing trend at located in the middle of basin, while the $R_{x5}$ of the most locations an insignificant decreasing trend. The heavy rainfall index indicated significant decreasing trend in two rainfall stations, whereas was not notice the increase or decrease trends in very heavy rainfall index. The results of this study suggest that the trend signal in the Yom River Basin in the half twentieth century showed the decreasing tendency in both of intensity and frequency of rainfall.

• The Transactions of the Korean Institute of Electrical Engineers
• /
• v.45 no.3
• /
• pp.432-437
• /
• 1996

This paper describes a diagnostic technique of power transformers by on-line detection of ultrasonic signals. A trend of partial discharge variation in a real transformer was estimated by counting the number of ultrasonic signals until insulation paper in the point-to-plane electrode is punctured. The number of electrical signals is closely related to the ultrasonic signals generated by partial discharge. The trend of the ultrasonic signal number could easily be distinguished by taking moving average. The insulation failure due to partial discharge in transformers can be predicted based on the trend analysis of ultrasonic signal number caused by partial discharge. (author). 14 refs., 10 figs.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.50 no.3
• /
• pp.61-69
• /
• 2008

Land-use in northern Cheongju region is changing rapidly because of the increased interactions of human activities with the environment as population increases. Land-use change detection is considered essential for monitoring the growth of an urban complex. The analysis was undertaken mainly on the basis of the multi-temporal Landsat images (1991, 1992 and 2000) and DEM data in a post-classification analysis with GIS to map land-use distribution and to analyse factors influencing the land-use changes for Cheongju city. The area of each land-use category was also calculated for monitoring land-use changes. Land-use statistics revealed that substantial land-use changes have taken place and that the built-up areas have expanded by about $17.57km^2$ (11.47%) over the study period (1991 - 2000). This study illustrated an increasing trend of urban and barren lands areas with a decreasing trend of agricultural and forest areas. Land-use changes from one category to others have been clearly represented by the NDVI composite images, which were found suitable for delineating the development of urban areas and land use changes in northern Cheongju region. Rapid economic developments together with the increasing population were noted to be the major factors influencing rapid land use changes. Urban expansion has replaced urban and barren lands.

• Vacuum Magazine
• /
• v.2 no.1
• /
• pp.4-9
• /
• 2015

A nanopore is a very small hole that can be used as single-molecule detector. The detection principle is based on monitoring the ionic current reduced by passage of a molecule through the nanopore as a voltage is applied across the nanopore. Here, we introduce biological nanopores and solid-state nanopores. Then, research and current trend about nanopore-based DNA biosensor and protein analysis are reviewed.

• International Journal of Advanced Culture Technology
• /
• v.8 no.4
• /
• pp.119-128
• /
• 2020

Object detection is an important field of computer vision and is applied to applications such as security, autonomous driving, and face recognition. Recently, as the application of artificial intelligence technology including deep learning has been applied in various fields, it has become a more powerful tool that can learn meaningful high-level, deeper features, solving difficult problems that have not been solved. Therefore, deep learning techniques are also being studied in the field of object detection, and algorithms with excellent performance are being introduced. In this paper, a deep learning-based object detection algorithm used to detect multiple objects in an image is investigated, and future development directions are presented.

• Journal of Institute of Control, Robotics and Systems
• /
• v.21 no.11
• /
• pp.989-995
• /
• 2015

Due to the recent increase of the importance and demand of security services, the importance of a surveillance monitor system that makes an automatic security system possible is increasing. As the market for surveillance monitor systems is growing, price competitiveness is becoming important. As a result of this trend, surveillance monitor systems based on an embedded system are widely used. In this paper, an object detection algorithm based on an embedded system for a surveillance monitor system is introduced. To apply the object detection algorithm to the embedded system, the most important issue is the efficient use of resources, such as memory and processors. Therefore, designing an appropriate algorithm considering the limit of resources is required. The proposed algorithm uses two background models; therefore, the embedded system is designed to have two independent processors. One processor checks the sub-background models for if there are any changes with high update frequency, and another processor makes the main background model, which is used for object detection. In this way, a background model will be made with images that have no objects to detect and improve the object detection performance. The object detection algorithm utilizes one-dimensional histogram distribution, which makes the detection faster. The proposed object detection algorithm works fast and accurately even in a low-priced embedded system.

• Journal of KIISE
• /
• v.41 no.12
• /
• pp.1066-1074
• /
• 2014

In this paper, we present a visual analytics system that uses serial-correlation to detect an abnormal event in spatio-temporal data. Our approach extracts the topic-model from spatio-temporal tweets and then filters the abnormal event candidates using a seasonal-trend decomposition procedure based on Loess smoothing (STL). We re-extract the topic from the candidates, and then, we apply STL to the second candidate. Finally, we analyze the serial-correlation between the first candidates and the second candidate in order to detect abnormal events. We have used a visual analytic approach to detect the abnormal events, and therefore, the users can intuitively analyze abnormal event trends and cyclical patterns. For the case study, we have verified our visual analytics system by analyzing information related to two different events: the 'Gyeongju Mauna Resort collapse' and the 'Jindo-ferry sinking'.

• Journal of Korea Multimedia Society
• /
• v.22 no.4
• /
• pp.432-442
• /
• 2019

Most current human action recognition methods based on deep learning methods. It is required, however, a very high computational cost. In this paper, we propose an action change detection method to reduce repetitive human action recognition tasks. In reality, simple actions are often repeated and it is time consuming process to apply high cost action recognition methods on repeated actions. The proposed method decides whether action has changed. The action recognition is executed only when it has detected action change. The action change detection process is as follows. First, extract the number of non-zero pixel from motion history image and generate one-dimensional time-series data. Second, detecting action change by comparison of difference between current time trend and local extremum of time-series data and threshold. Experiments on the proposed method achieved 89% balanced accuracy on action change data and 61% reduced action recognition repetition.

• The Journal of the Korea Contents Association
• /
• v.19 no.2
• /
• pp.259-268
• /
• 2019

Dengue virus is a typical mosquito-borne virus, and the half of the world's population is exposed to infection. Dengue virus causes relatively mild symptoms such as dengue fever. However, when not treated properly, it is known to cause severe symptoms such as dengue hemorrhagic fever and dengue shock syndrome with a mortality rate of over 20%. Development of dengue virus detection technology is very important because it is reported that early diagnosis of dengue fever can lower the mortality rate to less than 1%. In this study, patent search related to dengue virus detection technology was conducted in Korea, USA, Europe, Japan, and China. The quantitative analysis of 69 validated patents from the searched patents was conducted by country, year, and patent holder. In addition, in-depth analysis was carried out by classifying into three categories: molecular diagnostics, immuno-diagnostics, and cell culture-based diagnostics from all validated patents. From these results, we analyzed the patent trend related to dengue virus detection and dengue fever diagnosis technology and discussed the features and limitations of molecular diagnostics and immuno-diagnostics at present level. Furthermore, we discussed the direction of technology development and future prospects to overcome limitations.