• The KIPS Transactions:PartD
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• v.16D no.2
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• pp.185-190
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• 2009

The event sequence of the same type from a set of events having time property can be summarized in one event. But if the event sequence having an interval, It is reasonable to be summarized more than one in independent sub event sequence of each other. In this paper, we suggest a method of temporal data mining that summarizes the interval events based on Allen's interval algebra and finds out interval event association rule from interval events. It provides better knowledge than others by using concept of an independent sub sequence and finding interval event association rules.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.104.4-104
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• 2001

This paper addresses a supervisory control problem to meet bounded time constraints in real-time nondeterministic discrete event systems (DESs) represented as timed transition models. For a timed language specification representing a bounded time constraint, this paper introduces the notions of trace-controllability and time-controllability. Based on the notions, this paper presents the necessary and sufficient conditions for the existence of a supervisor for a real-time nondeterministic DES to achieve the specification.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.235-235
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• 2015

This study investigates the impact of event characteristics on runoff dynamics during extreme flood events observed in a $8.5km^2$ experimental watershed located in South Korea. The 37 most extreme flood events with event rainfall in excess of 50 mm were analysed using an event-based rainfall-runoff model; the Revitalised Flood Hydrograph (ReFH) routinely used for design flood estimation in the United Kingdom. The ReFH model was fitted to each event in turn, and links were investigated between each of the two model parameters controlling runoff production and response time, respectively, and event characteristics such as rainfall depth, duration, intensity and also antecedent soil moisture. The results show that the structure of the ReFH model can effectively accommodate any nonlinearity in runoff production, but that the linear unit hydrograph fails to adequately represent a reduction in watershed response time observed for the more extreme events. By linking the unit hydrograph shape directly to rainfall depth, the consequence of the observed nonlinearity in response time is to increase design peak flow by between 50% for a 10 year return period, and up to 80% when considering the probable maximum flood (PMF).

• Journal of KIISE:Databases
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• v.37 no.6
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• pp.324-332
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• 2010

The majority of event detection in real-time embedded sensor network systems is based on data fusion that uses noisy sensor data collected from complicated real-world environments. Current research has produced several excellent low-level mechanisms to collect sensor data and perform aggregation. However, solutions that enable these systems to provide real-time data processing using readings from heterogeneous sensors and subsequently detect complex events of interest in real-time fashion need further research. We are developing real-time event detection approaches which allow light-weight data fusion and do not require significant computing resources. Underlying the event detection framework is a collection of real-time monitoring and fusion mechanisms that are invoked upon the arrival of sensor data. The combination of these mechanisms and the framework has the potential to significantly improve the timeliness and reduce the resource requirements of embedded sensor networks. In addition to that, we discuss about a privacy that is foundation technique for trusted embedded sensor network system and explain anonymization technique to ensure privacy.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.34C no.5
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• pp.93-102
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• 1997

Conventional expert systems have difficulties in the inference on time-varing situations because they don't have the structure for processing time related informations and rule representation method to describe time explicitely. Some expert systems capable of temporal reasoning are not applicable to the domain in which state changes happen by unpredictble events that cannot be represented by periodic changes of data. In this paper, an event based temporal reasoning method is proposed. It is capable of processing te unpredictable events, representing the knowledge related to event and time, and infering by that knowledge as well as infering by periodically time-varing data. The NEO/temporal, an temporal inference engine, is implemented by applying the proposed temporal reasoning situation assessment and decision supporting system is implemented to show the benefits of the proposed temporal information processing model.

• Nuclear Engineering and Technology
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• v.34 no.4
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• pp.358-369
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• 2002

Abundant information about analyses of single steam generator tube rupture (SGTR) events is available because of its importance in terms of safety. However, there are few literatures available on analyses of multiple steam generator tube rupture (MSGTR) events. In addition, knowledge of transients and consequences following a MSGTR event are very limited as there has been no occurrence of MSGTR event in the commercial operation of nuclear reactors. In this study, a postulated MSGTR event in an APR1400 is analyzed using thermal-hydraulic system code MARSI.4. The present study aims to examine the effects of affected steam generator selection. The main steam safety valve (MSSV) lift time for four cases are compared in order to examine how long operator response time is allowed depending on which steam generate. (S/G) is affected. The comparison shows that the cases where two steam generators are simultaneously affected allow longer time for operator action compared with the cases where a single steam generator is affected. Furthermore, the tube ruptures in the steam generator where a pressurizer is connected leads to the shortest operator response time.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.2
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• pp.129-137
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• 2016

To identify the cause of the error and maintain the health of system, an administrator usually analyzes event log data since it contains useful information to infer the cause of the error. However, because today's systems are huge and complex, it is almost impossible for administrators to manually analyze event log files to identify the cause of an error. In particular, as OpenStack, which is being widely used as cloud management system, operates with various service modules being linked to multiple servers, it is hard to access each node and analyze event log messages for each service module in the case of an error. For this, in this paper, we propose a novel message-based log analysis method that enables the administrator to find the cause of an error quickly. Specifically, the proposed method 1) consolidates event log data generated from system level and application service level, 2) clusters the consolidated data based on messages, and 3) analyzes interrelations among message groups in order to promptly identify the cause of a system error. This study has great significance in the following three aspects. First, the root cause of the error can be identified by collecting event logs of both system level and application service level and analyzing interrelations among the logs. Second, administrators do not need to classify messages for training since unsupervised learning of event log messages is applied. Third, using Dynamic Time Warping, an algorithm for measuring similarity of dynamic patterns over time increases accuracy of analysis on patterns generated from distributed system in which time synchronization is not exactly consistent.

• The Korean Journal of Archival Studies
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• no.51
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• pp.175-208
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• 2017

This study aims to establish the definition of "event archive," which collects and manages records that focus on a specific event, in order to present a new perspective onto the collection and management of private records. As such, this study defines "event archive" and then identifies its characteristics by comparing it with community archives and locality archives. An event archive consists of person, spatiality, and temporality. As it puts an emphasis on temporality in particular, this study suggests a spiral model, which grafts person and spatiality onto a time axis. Moreover, this study suggests three considerations for the construction of an event archive. First, it presents different construction methods for different types of event archives depending on the time an event occurred. Second, it suggests records collection and management areas considering the three components of an event archive-person, spatiality, and temporality. Third, it supports a digital archive for an event archive to become an open archive.

• Smart Media Journal
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• v.6 no.3
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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.

• Journal of Korean Neurosurgical Society
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• v.63 no.5
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• pp.598-606
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• 2020

Objective : The purpose of this study was to evaluate the impact of time interval between index event and stenting on the periprocedural risk of stenting for symptomatic carotid stenosis and to determine the optimal timing of stenting. Methods : This retrospective study included 491 (322 symptomatic [65.6%] and 169 asymptomatic [34.4%]) patients undergoing carotid stenting. The symptomatic patients were categorized into Day 0-3, 4-7, 8-10, 11-14, 15-21, and >21 groups according to the time interval between index event and stenting. Periprocedural (≤30 days) risk for clinical (any neurological deterioration) and radiological (new infarction on postprocedural diffusion-weighted imaging) events of stenting in each time interval versus asymptomatic stenosis was calculated with logistic regression analysis adjusted for confounders, and provided as odds ratio (OR) and 95% confidence interval (CI). Results : Overall clinical event rate (4.3%) of stenting for symptomatic carotid stenosis was higher than that for asymptomatic stenosis (1.2%; OR, 3.979 [95% CI, 1.093-14.489]; p=0.036). Stenting in Day 0-3 (13.2%; OR, 10.997 [95% CI, 2.333-51.826]; p=0.002) and Day 4-7 (8.3%; OR, 6.775 [95% CI, 1.382-33.227]; p=0.018) was associated with high risk for clinical events. However, the clinical event rates in stenting after 7 days from index event (Day 8-10, 1.8%; Day 11-14, 2.5%; Day 15-21, 0%; Day >21, 2.9%) were not different from that in stenting for asymptomatic stenosis. Overall radiological event rate (55.6%) in symptomatic stenosis was also higher than that in asymptomatic stenosis (35.5%; OR, 2.274 [95% CI, 1.553-3.352]; p<0.001). The high risk for radiological events was maintained in all time intervals (Day 0-3 : 55.3%; OR, 2.224 [95% CI, 1.103-4.627]; p=0.026; Day 4-7 : 58.3%; OR, 2.543 [95% CI, 1.329-4.949]; p=0.005; Day 8-10 : 53.6%; OR, 2.096 [95% CI, 1.138-3.889]; p=0.018; Day 11-14 : 57.5%; OR, 2.458 [95% CI, 1.225-5.021]; p=0.012; Day 15-21 : 55.6%; OR, 2.271 [95% CI, 1.099-4.764]; p=0.028; Day >21 : 54.8%; OR, 2.203 [95% CI, 1.342-3.641]; p=0.002). Conclusion : This study showed that as stenting was delayed, the periprocedural risk for clinical events decreased. The clinical event risk was high only in stenting within 7 days and comparable with that for asymptomatic stenosis in stenting after 7 days from index event, although the radiological event risk was not affected by stenting timing. Therefore, our results suggest that delayed stenting after 7 days from symptom onset is a safe strategy for symptomatic stenosis.