• The Journal of the Korea Contents Association
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• v.10 no.1
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• pp.114-121
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• 2010

Based on a complex event processing technique, an abuse pattern monitoring method is developed to provide an real-time detection. CEP is a technique to find complex event pattern in a massive information system. In this study, the events occurred by game-play are observed to be against the rules using CEP. User abuse patterns are pre-registered in CEP engine. And CEP engine monitors user abuse after aggregating the game data transferred by game logging server.

• Journal of the Korean Ceramic Society
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• v.35 no.6
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• pp.551-558
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• 1998

Detection of microcrack in {{{{ {Al }_{2 } {O }_{3 } }} ceramics were studided by AE(acoustic emission) technique with 4-point bending test in order to evaluate the fracture process and formation of microcrack. Fully-dense alu-mina ceramics having a different grain size were fabricated by varing the hot-pressing temperature. The grain size of alumina increased with increasing the hot-pressing temperature whereas the bending strength decreasd. The microcracks were observed by SEM and TEM. The generation of AE event increased with increasing the applied load and many AE event was generated at maximum applied load. Alumina with smaller grain size shows the generation of many AE event resulting in an increase of microcrack formation. An intergranular fracture is predominantly observed in fine-grained alumina whereas intragranular fracture occurs predominantly in coarse-grained alumina,. Analysis of micorstructure and AE prove that primary mi-crocracks occur within grain-boundaries of alumina. The larger microcracking were formed by the growth and/or coalesence of primary microcracks. Then the materials become to fracuture by main crack gen-eration at the maximum applied load.

• Journal of the Korea Society of Computer and Information
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• v.24 no.5
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• pp.59-66
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• 2019

According to the KT telecommunication statistics, people stayed inside their houses on an average of 11.9 hours a day. As well as, according to NSC statistics in the united states, people regardless of age are injured for a variety of reasons in their houses. For purposes of this research, we have investigated an abnormal event detection algorithm to classify infrequently occurring behaviors as accidents, health emergencies, etc. in their daily lives. We propose a fusion method that combines three classification algorithms with vision pattern, audio pattern, and activity pattern to detect unusual user events. The vision pattern algorithm identifies people and objects based on video data collected through home CCTV. The audio and activity pattern algorithms classify user audio and activity behaviors using the data collected from built-in sensors on their smartphones in their houses. We evaluated the proposed individual pattern algorithm and fusion method based on multiple scenarios.

• Proceedings of the KSRS Conference
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• v.2
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• pp.698-701
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• 2006

Remotely sensed data have been utilized efficiently for damage detection immediately after the natural disaster since they provide valuable information on land cover change due to spatial synchronization and multitemporal observation over large areas. Damage information obtained at an early stage is important for rapid emergency response and recovery works. Many useful techniques to analyze the characteristics of the pre- and post-event satellite images in large-scale damage detection have been successfully investigated for emergency management. Since high-resolution satellite images provide a wealth of information on damage occurred in urban areas, they are successfully utilized for damage detection in urban areas. In this research, a method to perform automated damage detection is proposed based on the differences of the textural characteristics in pre- and post- high resolution satellite images.

• 한국정보통신설비학회:학술대회논문집
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• 2009.08a
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• pp.127-130
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• 2009

We propose a method for detecting DDoS (Distributed Denial of Service) traffic in real-time inside the backbone network. For this purpose, we borrow the concepts of MACD (Moving Average Convergence Divergence) and RoC (Rate of Change), which are used for technical analysis in the stock market Due to the fact that the method is based on a quantitative, rather than a heuristic, detection level, DDoS traffic can be detected with greater accuracy (by reducing the false alarm ratio). Through simulation results, we show how the detection level is determined and demonstrate how much the accuracy of detection is enhanced.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.1027-1028
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• 2008

In this paper, we proposed image-processing technique for automatic real-time fire and smoke detection in tunnel fire environment. To minimize false detection of fire in tunnel we used motion information of video sequence. And this makes it possible to detect exact position of event in early stage with detection, test, and verification procedures. In addition, by comparing false detection elimination results of each step, we have proved the validity and efficiency of proposed algorithm.

• Journal of information and communication convergence engineering
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• v.9 no.1
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• pp.69-73
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• 2011

Twitter is a microblogging service that allows its user to share short messages called tweets with each other. All the tweets are visible on a public timeline. These tweets have the valuable geospatial component and particularly time critical events. In this paper, our interest is in the rapid detection of disaster events such as tsunami, tornadoes, forest fires, and earthquakes. We describe the detection system of disaster events and show the way to detect a target event from Twitter data. This research examines the three disasters during the same time period and compares Twitter activity and Internet news on Google. A significant result from this research is that emergency detection could begin using microblogging service.

• Journal of the Korean Society for Library and Information Science
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• v.51 no.4
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• pp.77-97
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• 2017

A recent sharp increase of the biomedical literature causes researchers to struggle to grasp the current research trends and conduct creative studies based on the previous results. In order to alleviate their difficulties in keeping up with the latest scholarly trends, numerous attempts have been made to develop specialized analytic services that can provide direct, intuitive and formalized scholarly information by using various text mining technologies such as information extraction and event detection. This paper introduces and evaluates total 8 Convolutional Neural Network (CNN) models for extracting biomedical events from academic abstracts by applying various feature utilization approaches. Also, this paper conducts performance comparison evaluation for the proposed models. As a result of the comparison, we confirmed that the Entity-Type-Fully-Connected model, one of the introduced models in the paper, showed the most promising performance (72.09% in F-score) in the event classification task while it achieved a relatively low but comparable result (21.81%) in the entire event extraction process due to the imbalance problem of the training collections and event identify model's low performance.

• IEMEK Journal of Embedded Systems and Applications
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• v.14 no.2
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• pp.87-96
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• 2019

Low power issue is one of the most critical problems in the Internet of Things (IoT), which are powered by battery. To solve this problem, various approaches have been presented so far. In this paper, we propose a method to reduce the power consumption by reducing the numbers of accesses into the flash memory consuming a large amount of power for on-chip software execution. Our approach is based on using cooperative logging structure to distribute the sampling overhead in single sensor node to adjacent nodes in case of rare-event applications. The proposed algorithm to identify event occurrence is newly introduced with negative feedback method by observing difference between past data and recent data coming from the sensor. When an event with need of flash access is determined, the proposed approach only allows access to write the sampled data in flash memory. The proposed event detection algorithm (EDA) result in 30% reduction of power consumption compared to the conventional flash write scheme for all cases of event. The sampled data from the sensor is first traced into the random access memory (RAM), and write access to the flash memory is delayed until the page buffer of the on-chip flash memory controller in the micro controller unit (MCU) is full of the numbers of the traced data, thereby reducing the frequency of accessing flash memory. This technique additionally reduces power consumption by 40% compared to flash-write all data. By sharing the sampling information via LoRa channel, the overhead in sampling data is distributed, to reduce the sampling load on each node, so that the 66% reduction of total power consumption is achieved in several IoT edge nodes by removing the sampling operation of duplicated data.

• Nuclear Engineering and Technology
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• v.55 no.10
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• pp.3822-3830
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• 2023

The large-area Compton camera (LACC), featuring significantly high detection sensitivity, was developed for high-speed localization of gamma-ray sources. Due to the high gamma-ray interaction event rate induced by the high sensitivity, however, the multiplexer-based data acquisition system (DAQ) rapidly saturated, leading to deteriorated energy and imaging resolution at event rates higher than 4.7 × 10³ s^-1. In the present study, a new simultaneous multi-channel DAQ was developed to improve the energy and imaging resolution of the LACC even under high event rate conditions (10⁴-10⁶ s^-1). The performance of the DAQ was evaluated with several point sources under different event rate conditions. The results indicated that the new DAQ offers significantly better performance than the existing DAQ over the entire energy and event rate ranges. Especially, the new DAQ showed high energy resolution under very high event rate conditions, i.e., 6.9% and 8.6% (for 662 keV) at 1.3 × 10⁵ and 1.2 × 10⁶ s^-1, respectively. Furthermore, the new DAQ successfully acquired Compton images under those event rates, i.e., imaging resolutions of 13.8° and 19.3° at 8.7 × 10⁴ and 10⁶ s^-1, which correspond to 1.8 and 73 μSv/hr or about 18 and 730 times the background level, respectively.