• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권11호
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• pp.2804-2823
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• 2013

Wireless sensor network (WSN) is a promising technology for monitoring physical phenomena at fine-grained spatial and temporal resolution. However, the typical approach of sending each sensed measurement out of the network for detailed spatial analysis of transient physical phenomena may not be an efficient or scalable solution. This paper focuses on in-network physical phenomena detection schemes, particularly the distributed computation of the boundary of physical phenomena (i.e. event), to support energy efficient spatial analysis in wireless sensor networks. In-network processing approach reduces the amount of network traffic and thus achieves network scalability and lifetime longevity. This study investigates the recent advances in distributed event detection based on in-network processing and includes a concise comparison of various existing schemes. These boundary detection schemes identify not only those sensor nodes that lie on the boundary of the physical phenomena but also the interior nodes. This constitutes an event geometry which is a basic building block of many spatial queries. In this paper, we introduce the challenges and opportunities for research in the field of in-network distributed event geometry boundary detection as well as illustrate the current status of research in this field. We also present new areas where the event geometry boundary detection can be of significant importance.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1995년도 추계학술대회 논문집
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• pp.864-867
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• 1995

Presented in the paper is a structured approach to modeling automated manufacturing system (AMS) in the form of an event graph. The proposed two-phase procedure for formal modeling is 1) reference modeling by schematic supervisory control modeling and 2) event graph transformation from supervisory control model. Also described is a formal model for a small-sized FMS in the form of an event graph.

• The Journal of Korean Physical Therapy
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• 제1권1호
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• pp.47-61
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• 1989

This paper presents the event approach to motor skill acquisition as a theoretical treatment of the learning and relearning of motor skill. 1) The use of norm-referenced developmental assesment tools and standardized qualitative assessment tool is an important component of infant movement evaluation. 2) The kinesthetic modality relaying movement and position imformation to the central nervous system is important for the detection and corretion of movement error. 3) The event approach treats the actor and the environment as inseparable in the acquisition of skills. 4) Motoy learning focuses almost entirely on how the skill is learned, contRolled and reTained. 5) Developmental assessment have needed an assessment of motor development. 6) A significant difference was found between articulation disorders children and motor coordination problem. 7) verbal ability is not essential for the learning of motor skills. 8) The Control of motor skills is a cognitive ability.

• Nuclear Engineering and Technology
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• 제29권1호
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• pp.1-6
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• 1997

This paper presents a new dynamic approach for assessing feasibility associated with the implementation of accident management strategies by the operators. This approach includes the combined use of both the concept of reliability physics and a dynamic event tree generation scheme. The reliability physics is based on the concept of a comparison between two competing variables, i.e., the requirement and the achievement parameter, while the dynamic event tree generation scheme on the continuous generation of the possible event sequences at every branch point up to the desired solution. This approach is applied to a cavity flooding strategy in a reference plant, which is to supply water into the reactor cavity using emergency fire systems in the station blackout sequence. The MAAP code and Latin Hypercube sampling technique are used to determine the uncertainty of the requirement parameter. It has been demonstrated that this combined methodology may contribute to assessing the success likelihood of the operator actions required during accidents and therefore to developing the accident management procedures.

• 제어로봇시스템학회논문지
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• 제7권5호
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• pp.375-383
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• 2001

As many industrial systems become more complex, it becomes extremely difficult to diagnose the cause of failures. This paper presents a failure diagnosis approach based on discrete event system theory. In particular, the approach is a hybrid of event-based and state-based ones leading to a simpler failure diagnoser with supervisory control capability. The design procedure is presented along with a pump-valve system as an example.

• 스마트미디어저널
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• 제6권3호
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• pp.41-48
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• 2017

Event detection using social media has been widespread since social network services have been an active communication channel for connecting with others, diffusing news message. Especially, the real-time characteristic of social media has created the opportunity for supporting for real-time applications/systems. Social network such as Twitter is the potential data source to explore useful information by mining messages posted by the user community. This paper proposed a novel system for temporal event detection by analyzing social data. As a result, this information can be used by first responders, decision makers, or news agents to gain insight of the situation. The proposed approach takes advantages of deep learning methods that play core techniques on the main tasks including informative data identifying from a noisy environment and temporal event detection. The former is the responsibility of Convolutional Neural Network model trained from labeled Twitter data. The latter is for event detection supported by Recurrent Neural Network module. We demonstrated our approach and experimental results on the case study of earthquake situations. Our system is more adaptive than other systems used traditional methods since deep learning enables to extract the features of data without spending lots of time constructing feature by hand. This benefit makes our approach adaptive to extend to a new context of practice. Moreover, the proposed system promised to respond to acceptable delay within several minutes that will helpful mean for supporting news channel agents or belief plan in case of disaster events.

• 산업융합연구
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• 제1권1호
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• pp.127-142
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• 2003

This Study examines the implications for event studies using the daily stock data. The output present the event study results. The event period is defined from 30 days before through 30 days after the event date, and is broken into four "windows" for abnormal return cumulation: the pre-event period, days -30 through -2; dajys -1 and 0, a period commonly investigated for the immediate impact of the event; and the post-event period, days +1 through +30. It shows how firm's information offerings affect the price process and consequent issues. The Patell Z test is an examples of a standardized abnormal return approach, which estimate a separate standard error for each security-event and assumes cross-sectional independence. The generalized sign test adjusts for the fraction of positive abnormal returns in the estimation period instead of assuming 0.5.

• Nuclear Engineering and Technology
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• 제54권6호
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• pp.2084-2093
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• 2022

Probabilistic safety assessment is widely used to quantify the risks of nuclear power plants and their uncertainties. When the lognormal distribution describes the uncertainties of basic events, the uncertainty of the top event in a fault tree is approximated with the sum of lognormal random variables after minimal cutsets are obtained, and rare-event approximation is applied. As handling complicated analytic expressions for the sum of lognormal random variables is challenging, several approximation methods, especially Monte Carlo simulation, are widely used in practice for uncertainty analysis. In this study, a theoretical approach for analyzing the sum of lognormal random variables using an efficient numerical integration method is proposed for uncertainty analysis in probability safety assessments. The change of variables from correlated random variables with a complicated region of integration to independent random variables with a unit hypercube region of integration is applied to obtain an efficient numerical integration. The theoretical advantages of the proposed method over other approximation methods are shown through a benchmark problem. The proposed method provides an accurate and efficient approach to calculate the uncertainty of the top event in probabilistic safety assessment when the uncertainties of basic events are described with lognormal random variables.

• Nuclear Engineering and Technology
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• 제56권2호
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• pp.558-567
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• 2024

Diagnostic support systems are being researched to assist operators in identifying and responding to abnormal events in a nuclear power plant. Most studies to date have considered single abnormal events only, for which it is relatively straightforward to obtain data to train the deep learning model of the diagnostic support system. However, cases in which multiple abnormal events occur must also be considered, for which obtaining training data becomes difficult due to the large number of combinations of possible abnormal events. This study proposes an approach to maintain diagnostic performance for multiple abnormal events by training a deep learning model with data on single abnormal events only. The proposed approach is applied to an existing algorithm that can perform feature selection and multi-label classification. We choose an extremely randomized trees classifier to select dedicated monitoring parameters for target abnormal events. In diagnosing each event occurrence independently, two-channel convolutional neural networks are employed as sub-models. The algorithm was tested in a case study with various scenarios, including single and multiple abnormal events. Results demonstrated that the proposed approach maintained diagnostic performance for 15 single abnormal events and significantly improved performance for 105 multiple abnormal events compared to the base model.

• 한국시뮬레이션학회:학술대회논문집
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• 한국시뮬레이션학회 1998년도 추계학술대회 및 정기총회
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• pp.124-127
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• 1998

One of the eventual goals of VR research is to provide valuable experiences to the participants. In this work, we view that the content of experience is composed of a sequence of events, and develop algorithms authoring those events. Event authoring can be realized by controlling agents in VE in two different modes: (1) the autonomous mode, in which the agent exhibit autonomous behaviors based on the current world status and its own personality, and (2) the event mode, in which the behaviors generated form the autonomous mode is further controlled to meet the needs of the experiment. We define the event authoring language, so that the authors can design experiments by writing event-programs. Then the architecture of event execution manager is described, which is the heart of event-program execution. prove the effectiveness of our approach by showing results of several experiments.