• Journal of the Institute of Electronics Engineers of Korea TC
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• v.48 no.4
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• pp.61-68
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• 2011

Event-based service technology integrate service to detect events that occur in real time by analyzing the response. Is the core technology for real-time business and ubiquitous service environment construction. Is required event-based service technology on business processes in real-time business environment that providing rapid response to changing and custom service using a variety of information real-time monitoring and analysis in ubiquitous service environment. Last event-driven business processes can be used as a CEP(Complex Event Processing). The core of CEP technology, the event from multiple event sources analysis of events affecting and the way to handle action, is detect complex event to user. In previous studies, an event occurs that continue to perform without the need for partial operations. so many operations and spend a lot of memory is a problem. To solve these problems, event detection technique is proposed that large streams of data without processing any events, registered to configure a complex event occurs when all events in the application layer, complex event processing. The proposed method, first using a bitmap index to manage the event occurs. The complex events of the last event in response to define a trigger event. The occurrence of an event to display a bitmap index, a composite event occurrence of all event to configure the test through the point at which a trigger event occurs. Is proposed, If any event occurs to perform the operation. The proposed scheme perform operations when all event occurs without events having to perform each of the tests. As a result, avoid unnecessary operations and reducing the number of events to handle the increased efficiency of operations.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2000.10a
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• pp.292-295
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• 2000

This paper presents effective Load Balancing Algorithm by exampling EvtProc Daemon controlling the Event handling among the W/S Daemon which manages and controls Digital Cross Connect Systems. In this paper, when a lot of event occur, charging process waits regular times instead of executing consecutive exec process. so, We can reduce overheads of fork and exec process. another method of reducing the overheads is it that waiting process handles consecutives events of Op Code. And We also presents effective and stable management scheme of event handling , for the generation of event handling Child process by charging the Load Balancing to the main child process

• Korea Trade Review
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• v.45 no.3
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• pp.21-33
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• 2020

This study aims to determine if the current COVID-19 event can be admitted as an excuse for non-performance in international trade transactions. In order to do so, this study selected case study method in the analysis. Firstly, the definitions of Force Majeure addressed in CISG, UCC, Chinese Law, and Korean Law were organized. Secondly, this study reviewed the avian influenza event in 2006 and the natural disaster event occurred in Guangdong, China, in 2017. In the study, three critical evaluation factors are suggested in order to be admitted as a Force Majeure event in international transactions as following: 1) possibility of foresight of the event, 2) possibility to overcome and avoid the event, and 3) the enterprise's countermeasures of the event. As an implication, this study organized the definitions of Force Majeure that were indicated in various kinds of Laws and suggested the basic framework to analyze the possibility of admittance as a Force Majeure event.

• The Journal of the Acoustical Society of Korea
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• v.39 no.6
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• pp.600-605
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• 2020

In this paper, we propose a sound event detection method using a multi-channel multi-scale neural networks for sound sensing home monitoring for the hearing impaired. In the proposed system, two channels with high signal quality are selected from several wireless microphone sensors in home. The three features (time difference of arrival, pitch range, and outputs obtained by applying multi-scale convolutional neural network to log mel spectrogram) extracted from the sensor signals are applied to a classifier based on a bidirectional gated recurrent neural network to further improve the performance of sound event detection. The detected sound event result is converted into text along with the sensor position of the selected channel and provided to the hearing impaired. The experimental results show that the sound event detection method of the proposed system is superior to the existing method and can effectively deliver sound information to the hearing impaired.

• Journal of the Korea Society of Computer and Information
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• v.13 no.6
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• pp.59-68
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• 2008

In this paper, we propose a method for selecting meaningful event packets from which can receive before anything else from seismograph according to allotted priority and estimate epicenter using selected packets. Event packets which received from each station will be evaluated with their onset time, signal period and SNR by statistical method and will be selected packets related with real earthquake's P-wave. And estimated epicenters using by 'Application of epicenter estimation using first P arrivals'. With local earthquakes occurred in 2007 were announced by KMA, collected event packets on earthquake happened date and selected p-wave related packets and estimated epicenter. After result of experiment, if an earthquake occurred within seismic networks, can estimate epicenter with small misfits just after event packets arrived from above 4 stations. Considering average distance of each station, in case of using all stations' data include other organization, can estimate and alert rapidly. It show this method is useful when construct a local earthquake early warning system later.

• The Journal of the Acoustical Society of Korea
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• v.36 no.4
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• pp.267-272
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• 2017

In this paper, we propose an effective method of applying multichannel-audio feature values to GRNNs (Gated Recurrent Neural Networks) in polyphonic sound event detection. Real life sounds are often overlapped with each other, so that it is difficult to distinguish them by using a mono-channel audio features. In the proposed method, we tried to improve the performance of polyphonic sound event detection by using multi-channel audio features. In addition, we also tried to improve the performance of polyphonic sound event detection by applying a gated recurrent neural network which is simpler than LSTM (Long Short Term Memory), which shows the highest performance among the current recurrent neural networks. The experimental results show that the proposed method achieves better sound event detection performance than other existing methods.

• International journal of advanced smart convergence
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• v.9 no.2
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• pp.20-27
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• 2020

Sound event detection in real-world environments suffers from the interference of non-stationary and time-varying noise. This paper presents an adaptive noise reduction method for sound event detection based on non-negative matrix factorization (NMF). In this paper, we proposed a deep learning model that integrates Convolution Neural Network (CNN) with Non-Negative Matrix Factorization (NMF). To improve the separation quality of the NMF, it includes noise update technique that learns and adapts the characteristics of the current noise in real time. The noise update technique analyzes the sparsity and activity of the noise bias at the present time and decides the update training based on the noise candidate group obtained every frame in the previous noise reduction stage. Noise bias ranks selected as candidates for update training are updated in real time with discrimination NMF training. This NMF was applied to CNN and Hidden Markov Model(HMM) to achieve improvement for performance of sound event detection. Since CNN has a more obvious performance improvement effect, it can be widely used in sound source based CNN algorithm.

• Journal of Internet Computing and Services
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• v.17 no.6
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• pp.153-158
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• 2016

As the number of social network users increases, the information on event such as social issues and disasters receiving attention in each region is promptly posted by the bucket through social media site in real time, and its social ripple effect becomes huge. This study proposes a detection method of events that draw attention from users in specific region at specific time by using twitter data with regional information. In order to collect Twitter data, we use Twitter Streaming API. After collecting data, We implemented event detection system by analyze the frequency of a keyword which contained in a twit in a particular time and clustering the keywords that describes same event by exploiting keywords' co-occurrence graph. Finally, we evaluates the validity of our method through experiments.

• Journal of KIISE:Databases
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• v.35 no.2
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• pp.143-154
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• 2008

RFID events are large volume of stream data which come out continuously. Many studies have been done to detect a business event in RFID stream. However, the existing methods have many problems which increase unnecessary operations when business events do not satisfy minimum conditions. In this paper, to remove unnecessary operations, we define the minimum condition of business events and propose an efficient method that detects business events only when the minimum condition is satisfied. To check the minimum condition of business events, we register business queries in a query index. We detect business events using the query index and bitmap. It is shown through various experiment that the proposed method outperforms the existing methods.

• Journal of Broadcast Engineering
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• v.25 no.5
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• pp.770-775
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• 2020

Since 360-degree ERP frame structure has location-dependent distortion, existing video surveillance algorithms cannot be applied to 360-degree video. In this paper, an activated viewport based event detection method is proposed for 360-degree video. After extracting activated viewports enclosing object candidates, objects are finally detected in the viewports. These objects are tracked in 360-degree video space for region-based event detection. The proposed method is shown to improve the recall and the false negative rate more than 30% compared to the conventional method without activated viewports.