• 천문학회보
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• 제46권2호
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• pp.46.2-46.2
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• 2021

We present solar event auto detection using deep-learning-based object detection algorithms and DeepSDO event dataset. DeepSDO event dataset is a new detection dataset with bounding boxed as ground-truth for three solar event (coronal holes, sunspots and prominences) features using Solar Dynamics Observatory data. To access the reliability of DeepSDO event dataset, we compared to HEK data. We train two representative object detection models, the Single Shot MultiBox Detector (SSD) and the Faster Region-based Convolutional Neural Network (R-CNN) with DeepSDO event dataset. We compared the performance of the two models for three solar events and this study demonstrates that deep learning-based object detection can successfully detect multiple types of solar events. In addition, we provide DeepSDO event dataset for further achievements event detection in solar physics.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제4권6호
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• pp.1237-1252
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• 2010

This paper proposes an intelligent technique for data sharing and event transition among personal devices including smart phones, laptops, and desktops. We implemented the PES (Personal Event Service) system that proactively executes appropriate tasks across multiple devices without explicit user requests by sharing the data used by the user and recognizing user intention based on the observed actions of the user for specific devices. The client module of PES installed on each device monitors the user actions and recognizes the intention of the user. The server provides data sharing and maintenance for clients. The connection between client and server is established by Java RMI (Remote Method Invocation). A series of experiments were performed to evaluate user satisfaction and system accuracy, and the results showed that the PES system can proactively provide appropriate, personalized services with a high degree of satisfaction to the user in an effective and efficient manner.

• 한국철도학회:학술대회논문집
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• 한국철도학회 2009년도 춘계학술대회 논문집 특별세미나,특별/일반세션
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• pp.341-347
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• 2009

In high speed railway, event recorder system stores a train speed and the related data for train operation in real time. Using those information, we can analysis the train operation and the reason of train accident. Analysis software gets the stored data from Event Recorder and shows the status of various signals related with train operation. Using it, also we can analysis the train operation before and after the given time. In this paper we propose the analysis software to show and analysis the operation of high speed train. The method of transferring the stored data from Event Recorder into Analysis Software is proposed. We develop the efficient procedure to store the transferred data into analysis system. Also the effective method to show the store data and to analysis them is studied for finding the cause of train accident.

• The Journal of Asian Finance, Economics and Business
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• 제8권12호
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• pp.1-7
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• 2021

The study examines the influence of COVID-19 on the stock market returns of Saudi Arabia. The data was analyzed through event study methodology using daily price data of Tadawul All Share Index (TASI). The study examines the behavior pattern of the Saudi Arabian stock market in different phases during the event period by selecting six-event windows with a range of 10 days. The results report a negative Abnormal Return (AR) of -0.003 on the event date, while the abnormal returns reversed the next day to 0.005 positively. The result of Cumulative Abnormal Return (CAR) is negative and significant at the 1 percent level in all the six-event windows starting from the event date to day 59 after the event for the TASI index. Even though the influence of the COVID-19 pandemic decreased after 30 days of the event date, it increased during the last ten days of the event window. The stock market volatility of Saudi Arabia increased during the post-event period compared to the pre-event period with a negative mean return of -0.326 and a greater standard deviation. In a conclusion, the study found a significant influence of the COVID-19 pandemic on the stock market returns of TASI.

• 유통과학연구
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• 제13권9호
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• pp.5-11
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• 2015

Purpose - The purpose of this research is to provide insights that can be used for deliberate decision making around challenging big data investments by measuring the economic value of such big data implementations. Research design, data, and methodology - We perform empirical research through an event study. To this end, we measure actual abnormal returns of companies that are triggered by their investment announcements in big data, or firm size information, during the three-year research period. The research period targets a timeframe after the introduction of big data at Korean firms listed on the Korea stock markets. Results - Our empirical findings discover that on the event day and the day after, the abnormal returns are significantly positive. In addition, our further examination of firm size impacts on the abnormal returns does not show any evidence of an effect. Conclusions - Our research suggests that an event study can be useful as an alternative means to measure the return on investment (ROI) for big data in order to lessen the difficulties or decision making around big data investments.

• 정보처리학회논문지D
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• 제16D권5호
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• pp.691-700
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• 2009

이벤트는 환자의 증상과 같이 시간 속성을 갖는 하나의 흐름을 의미하며 인터벌 이벤트는 시작과 종료 시점에 대한 시간 간격을 갖는다. 그리고 시간 데이터마이닝에 대한 많은 연구가 있었지만 환자 이력, 구매자 이력, 로그 이력과 같은 인터벌 이벤트에 대한 지식 탐사 방법에 대한 연구는 미흡하다. 이 논문에서는 이벤트들의 인과 관계에 대한 연관 규칙을 탐사하고 이 규칙에 기반하여 결과 이벤트 발생을 예측하는 시간 데이터마이닝 방법을 제안한다. 제안 방법은 이벤트 시간 속성을 사용하여 인터벌 이벤트로 요약하고 이벤트들의 인과 관계를 탐사하여 이벤트 발생을 예측한다. 성능평가를 통하여 제안 방법은 다양한 지지도를 적용하여 발생 빈도에 상관없이 이벤트 발생에 높은 영향을 주는 의미있는 희소 관계를 발견함으로써 기존의 데이터마이닝 기법에 비하여 보다 우수한 정보를 탐사할 수 있다.

• 대한임베디드공학회논문지
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• 제14권2호
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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.

• 대한전자공학회논문지TC
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• 제48권4호
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• pp.61-68
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• 2011

이벤트 기반 서비스 기술은 실시간으로 발생하는 이벤트를 감지하고 분석하여 이에 대한 반응으로 서비스가 연동되는 기술로, 실시간 기업 환경 구축이나 유비쿼터스 서비스 환경 구축을 위한 핵심 기반 기술이다. 실시간 기업 환경에서 요구되고 있는 기업 내 업무 프로세스에서 발생하는 다양한 정보를 실시간 모니터링, 분석하여 변화에 대한 신속한 대응을 제공하거나, 유비쿼터스 서비스 환경에서 상황에 맞게 적시에 맞춤형서비스를 제공하기 위해서는 이벤트 기반의 서비스 기술이 요구된다. 최근 이벤트 중심의 비즈니스 프로세스로 복합 이벤트 처리(CEP : Complex Event Processing) 방식이 사용된다. 복합 이벤트 처리 방식은 여러 이벤트 소스로부터 발생한 이벤트를 대상으로 이벤트들의 영향을 분석하여 대응되는 액션을 처리하는 방식으로 가장 핵심이 되는 기술은 어떻게 사용자에게 의미있는 이벤트(복합 이벤트)를 검출하는가이다. 기존의 연구에서는 복합 이벤트를 구성하는 모든 이벤트가 발생하지 않아도 부분적으로 발생하는 이벤트에 대해 계속적으로 연산을 수행하여 많은 연산과 많은 메모리를 소비하는 문제점이 있다. 이러한 문제를 해결하기 위해 본 논문에서는 대용량의 스트림 데이터에서 발생한 모든 이벤트를 처리하지 않고 응용 계층에서 등록한 복합 이벤트를 구성하는 모든 이벤트가 발생하면, 복합 이벤트를 처리하는 이벤트 검출 기법을 제안한다. 제안 하는 기법은 먼저 비트맵 인덱스를 이용하여 이벤트의 발생 유/무를 관리한다. 또한 복합 이벤트 질이의 마지막 이벤트를 트리거 이벤트로 정의하며, 이 트리거 이벤트가 발생하는 시점을 통해 이벤트의 발생을 표시한 비트맵 인덱스에 복합 이벤트를 구성하는 모든 이벤트의 발생 유/무를 검사하여 모든 이벤트가 발생하였다면, 연산을 수행할 수 있도록 제안한다. 제안하는 기법은 실험을 통해 복합 이벤트를 구성하는 이벤트의 검사를 매번 수행하지 않고 모든 이벤트가 발생하였을 때에만 연산을 수행함으로 불필요한 연산을 방지하고, 처리하는 이벤트의 수를 감소시켜 연산의 효율성을 증가 시켰다.

• 상하수도학회지
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• 제22권1호
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• pp.65-71
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• 2008

급속 혼화공정에서 응집제의 동력학적 수화반응 특성을 고려하여 1초 이내의 순간혼합을 제시하고 있으며, 이러한 이론에 근거하여 설치된 Pump Diffusion Mixer(PDM)의 관내 응집제 확산 분포특성을 조사하였다. D=1,200mm 관경에서 압력수 유량비에 따라 응집제 주입지점으로부터 4.5D되는 지점에서 관 단면의 지점별 제타 전위를 측정하여 평가한 결과, 압력수의 유량비가 2%에서는 분사속도가 낮아 관 단면에 응집제가 골고루 분사되지 못하는 것으로 조사되었다. 그러나 압력수 유량비가 4% 이상이 되면 비교적 균등하게 혼합되며, 8%에서는 관 단면 전체에 균등하게 확산 분포되는 것으로 나타났다.

• Journal of Astronomy and Space Sciences
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• 제34권2호
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• pp.139-151
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• 2017

This paper presents an overview of deep space orbit determination software (DSODS), as well as validation and verification results on its event prediction capabilities. DSODS was developed in the MATLAB object-oriented programming environment to support the Korea Pathfinder Lunar Orbiter (KPLO) mission. DSODS has three major capabilities: celestial event prediction for spacecraft, orbit determination with deep space network (DSN) tracking data, and DSN tracking data simulation. To achieve its functionality requirements, DSODS consists of four modules: orbit propagation (OP), event prediction (EP), data simulation (DS), and orbit determination (OD) modules. This paper explains the highest-level data flows between modules in event prediction, orbit determination, and tracking data simulation processes. Furthermore, to address the event prediction capability of DSODS, this paper introduces OP and EP modules. The role of the OP module is to handle time and coordinate system conversions, to propagate spacecraft trajectories, and to handle the ephemerides of spacecraft and celestial bodies. Currently, the OP module utilizes the General Mission Analysis Tool (GMAT) as a third-party software component for high-fidelity deep space propagation, as well as time and coordinate system conversions. The role of the EP module is to predict celestial events, including eclipses, and ground station visibilities, and this paper presents the functionality requirements of the EP module. The validation and verification results show that, for most cases, event prediction errors were less than 10 millisec when compared with flight proven mission analysis tools such as GMAT and Systems Tool Kit (STK). Thus, we conclude that DSODS is capable of predicting events for the KPLO in real mission applications.