• Journal of KIISE:Databases
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• v.36 no.4
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• pp.292-305
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• 2009

Wireless sensor networks have a lot of application areas such as industrial process control, machine and resource management, environment and habitat monitoring. One of the main objects of using wireless sensor networks in these areas is the event detection. To detect events at a user's request, we need a join processing between sensor data and the predicates of the events. If there are too many predicates of events compared with a node's capacity, it is impossible to store them in a node and to do an in-network join with the generated sensor data This paper proposes a predicate-merge based in-network join approach to efficiently detect multiple events, considering the limited capacity of a sensor node and many predicates of events. It reduces the number of the original predicates of events by substituting some pairs of original predicates with some merged predicates. We create an estimation model of a message transmission cost and apply it to the selection algorithm of targets for merged predicates. The experiments validate the cost estimation model and show the superior performance of the proposed approach compared with the existing approaches.

• Proceedings of the Korea Information Processing Society Conference
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• 2005.05a
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• pp.149-152
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• 2005

워크플로우 시스템은 비즈니스 환경에서 프로세스의 자동화 수행을 통해 업무 처리의 효율성 및 성능을 극대화시켜주는 미들웨어 시스템이며 워크플로우 엔진은 이러한 비즈니스 서비스의 실질적인 수행을 컨트롤 및 관리해주는 역할을 한다. 워크플로우 클라이언트로부터의 서비스 요청에 대한 처리를 위해 워크플로우 엔진은 엔진 내부의 핵심 컴포넌트들의 연동에 의해 생성되는 서비스 인스턴스들의 처리 행위를 통해 서비스를 수행하며, 서비스 처리를 하면서 발생되는 이벤트들에 대해서 로그를 기록한다. 이러한 로그 데이터들은 워크플로우 모니터링 분석에 중요한 근거 자료로서 사용되며, 워크플로우 웨어하우징 및 마이닝등의 분야에서 주요 근간 데이터로서 사용될 수 있다. 본 논문에서는 자체 제작된 e-chautauqua 초대형 워크플로우 시스템을 배경으로 초대형 워크플로우 라는 환경에서 대용량의 로그를 어떻게 구성하는지에 대해서도 살펴볼 것이며, 워크플로우 엔진을 구성하는 핵심 컴포넌트들의 연동에 의해 수행되는 서비스 인스턴스들의 이벤트들이 어떠한 모습으로 로그 메시지를 구성하게 되는지에 대한 로그 메시지 포맷에 대한 전반적인 워크플로우 로깅 메커니즘에 대해 기술하고자 한다.

• Geophysics and Geophysical Exploration
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• v.25 no.4
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• pp.177-188
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• 2022

Recently, the application of distributed acoustic sensors (DAS), which can replace geophones and seismometers, has significantly increased along with interest in micro-seismic monitoring technique, which is one of the CO₂ storage monitoring techniques. A significant amount of temporally and spatially continuous data is recorded in a DAS monitoring system, thereby necessitating fast and accurate data processing techniques. Because event detection and seismic phase picking are the most basic data processing techniques, they should be performed on all data. In this study, a machine learning-based P, S wave phase picking algorithm was developed to compensate for the limitations of conventional phase picking algorithms, and it was modified using a transfer learning technique for the application of DAS data consisting of a single component with a low signal-to-noise ratio. Our model was constructed by modifying the convolution-based EQTransformer, which performs well in phase picking, to the ResUNet structure. Not only the global earthquake dataset, STEAD but also the augmented dataset was used as training datasets to enhance the prediction performance on the unseen characteristics of the target dataset. The performance of the developed algorithm was verified using K-net and KiK-net data with characteristics different from the training data. Additionally, after modifying the trained model to suit DAS data using the transfer learning technique, the performance was verified by applying it to the DAS field data measured in the Pohang Janggi basin.

• Journal of Internet Computing and Services
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• v.21 no.3
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• pp.83-92
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• 2020

Recently, many companies and organizations are interested in predictive process monitoring for the efficient operation of business process models. Traditional process monitoring focused on the elapsed execution state of a particular process instance. On the other hand, predictive process monitoring focuses on predicting the future execution status of a particular process instance. In this paper, we implement the function of the business process remaining time prediction, which is one of the predictive process monitoring functions. In order to effectively model the remaining time, normalization by activity is proposed and applied to the predictive model by taking into account the difference in the distribution of time feature values according to the properties of each activity. In order to demonstrate the superiority of the predictive performance of the proposed model in this paper, it is compared with previous studies through event log data of actual companies provided by 4TU.Centre for Research Data.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.6
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• pp.1255-1261
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• 2020

Cache side-channel attack is a class of attacks to retrieve sensitive information from a system by exploiting shared cache resources in CPUs. As the attacks are delivered to wide range of environments from mobile systems to cloud systems recently, many detection strategies have been proposed. Since the conventional cache side-channel attacks are likely to incur tremendous number of cache events, most of the previous detection mechanisms were designed to carefully monitor mostly cache events. However, recently proposed attacks tend to incur less cache events during the attack. PRIME+ABORT attack, for example, leverages the Intel TSX instead of accessing cache to measure access time. Because of the characteristic, attack detection mechanisms based on cache events may hardly detect the attack. In this paper, we conduct an in-depth analysis of the PRIME+ABORT attack to identify the other useful hardware events for detection rather than cache events. Based on our finding, we present a novel mechanism called PRIME+ABORT Detector to detect the PRIME+ABORT attack and demonstrate that the detection mechanism can achieve 99.5% success rates with 0.3% performance overhead.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11b
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• pp.622-624
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• 2005

발전소 시뮬레이터는 발전소 설계의 검증, 제어 시스템의 성능 시험, 제어 시스템 상수 최적화, 새로운 제어 기법의 사전 검증 및 운전요원 훈련과 같은 다양한 목적으로 사용되어진다. 본 논문에서는 발전소 운전요원들의 운영 능력 향상을 위하여 사용자의 이벤트들을 저장하여 사용자 요구 시에 일정 시간 주기로 도면에 설계하는 스크립트 기능과 그래픽 기반의 시뮬레이션을 위한 두 종류의 결과 모니터링 윈도우 그리고 사용자의 다양한 시뮬레이션을 위한 시뮬레이션 속도 변경 기능을 기존의 발전소 시뮬레이터에 설계, 구현한다. 또한 시뮬레이션 속도 변경 기능 확장에 따른 시뮬레이션의 정확성을 판단하기 위하여 수행 결과 데이터를 추출하여 시뮬레이션의 정확성을 검증한다.

• 한국항공운항학회:학술대회논문집
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• 2015.11a
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• pp.183-185
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• 2015

A-SMGCS는 외부로부터 비행계획정보, 레이더 정보 및 기상정보를 수신하여 경로, 안내, 감시 및 항공등화 제어 기능을 수행하는데, 이러한 기능을 검정하기 위한 시뮬레이터는 기초자료 준비, 기상 시나리오 생성, 이벤트 시나리오 생성 및 항적자료 생성 등의 4가지 기능으로 구성되어 가상의 정보를 자동으로 생성하게 된다. 생성된 정보는 A-SMGCS에서 수신하여 그 정보를 바탕으로 각각의 기능들을 실행하게 되고, 그 실행 상태는 A-SMGCS HMI에 모니터링 된다. 그러므로 시뮬레이터에서 생성되는 가상의 시나리오들은 실제 공항에서 발생하거나 예측되는 모든 경우의 상황을 A-SMGCS에 제공하여 정확한 검정을 할 수 있도록 지원함으로써 A-SMGCS의 성능을 한층 더 향상시킬 수 있을 것이다. 따라서 본 논문에서는 이러한 효과가 기대되는 시뮬레이터의 기능과 연동관계를 정리하여 최적의 시뮬레이터를 제작할 수 있는 방법에 대한 연구를 실시하였다.

• The KIPS Transactions:PartD
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• v.11D no.7 s.96
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• pp.1375-1384
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• 2004

Streaming data processing is an area of interest with much research under way. There has been increasing attention on the demands for efficient processing of streaming data produced in the application areas such as monitoring and sensor network. We have developed a continuous query processing system for streaming data and evaluated its performance in this paper. XML, the standard for data exchange on the web, is used as the model for the streaming data and the XQuery appended with a time interval is adopted as the query language for expressing con-tinuous queries. In the proposed system, the result is produced through background processing and materialized for reute in subsequent query processing. Through a detailed set of performance experiments, we shoed the effectiveness of the proposed system.

• Journal of KIISE
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• v.42 no.7
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• pp.840-845
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• 2015

In recent years, Big Data has been a topic of great interest for the production and operation work of offshore plants as well as for enterprise resource planning. The ability to predict future equipment performance based on historical results can be useful to shuttling assets to more productive areas. Specifically, a centrifugal compressor is one of the major piece of equipment in offshore plants. This machinery is very dangerous because it can explode due to failure, so it is necessary to monitor its performance in real time. In this paper, we present stream data processing architecture that can be used to compute the performance of the centrifugal compressor. Our system consists of two major components: a virtual tag stream generator and a real-time data stream manager. In order to provide scalability for our system, we exploit a parallel programming approach to use multi-core CPUs to process the massive amount of stream data. In addition, we provide experimental evidence that demonstrates improvements in the stream data processing for the centrifugal compressor.

• The Journal of the Korea institute of electronic communication sciences
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• v.5 no.1
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• pp.44-50
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• 2010

Wireless sensor network is consist of multiple sensor nodes and performs a shared tasks through the coordination of sensor nodes. Traffic in WSN is categorized as periodical monitoring traffic, event-driven traffic and query-based traffic. Periodic traffic takes significant proportion of the whole traffic processing because multiple sensor nodes generate traffic in a steady interval although the generation frequency of periodic traffic is low. In this paper, we propose a traffic control algorithm of network protocol for periodic traffic in terms of energy efficiency and conduct performance analysis of the algorithm.