• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.11a
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• pp.252-255
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• 2003

This article concerns the event management of the transportation and delivery system in the real time environment. Until now mainstream researches in the transportation and delivery area have largely focused how to create optimized vehicle routing schedule fast and stably, although the execution and event management area has a lot of opportunities which could make logistics efficient and especially customers satisfied. This paper suggests to-be model processes how to execute vehicle routing schedule correctly and when events are occurred how to cope with them effectively. To embody such ideas GIS, GPS and Mobile technologies are attached on optimizing scheduler engine, TMS, WMS, ATP and Monitoring system.

• Nuclear Engineering and Technology
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• v.49 no.2
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• pp.360-372
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• 2017

Many advanced reactor designs rely on passive systems to fulfill safety functions during accident sequences. These systems depend heavily on boundary conditions to induce a motive force, meaning the system can fail to operate as intended because of deviations in boundary conditions, rather than as the result of physical failures. Furthermore, passive systems may operate in intermediate or degraded modes. These factors make passive system operation difficult to characterize within a traditional probabilistic framework that only recognizes discrete operating modes and does not allow for the explicit consideration of time-dependent boundary conditions. Argonne National Laboratory has been examining various methodologies for assessing passive system reliability within a probabilistic risk assessment for a station blackout event at an advanced small modular reactor. This paper provides an overview of a passive system reliability demonstration analysis for an external event. Considering an earthquake with the possibility of site flooding, the analysis focuses on the behavior of the passive Reactor Cavity Cooling System following potential physical damage and system flooding. The assessment approach seeks to combine mechanistic and simulation-based methods to leverage the benefits of the simulation-based approach without the need to substantially deviate from conventional probabilistic risk assessment techniques. Although this study is presented as only an example analysis, the results appear to demonstrate a high level of reliability of the Reactor Cavity Cooling System (and the reactor system in general) for the postulated transient event.

• The KIPS Transactions:PartD
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• v.9D no.2
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• pp.251-258
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• 2002

There has been much research on data mining techniques for applying more advanced applications. However, most of those techniques has focused on transaction data rather than time series data. In this paper, we introduce a approach to convert time series data into virtual transaction data for more useful data mining applications. A virtual transaction is defined to be a collection of events that occur relatively close to each other. A virtual transaction generator uses time window or event window methods. Our approach based on time series data can be used with most conventional transaction algorithms without further modification.

• Proceedings of the IEEK Conference
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• summer
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• pp.237-243
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• 2004

A multichannel smart sound sensor capable to detect and identify sound events in noisy conditions is presented in this paper. Sound information extraction is a complex task and the main difficulty consists is the extraction of high­level information from an one-dimensional signal. The input of smart sound sensor is composed of data collected by 5 microphones and its output data is sent through a network. For a real time working purpose, the sound analysis is divided in three steps: sound event detection for each sound channel, fusion between simultaneously events and sound identification. The event detection module find impulsive signals in the noise and extracts them from the signal flow. Our smart sensor must be capable to identify impulsive signals but also speech presence too, in a noisy environment. The classification module is launched in a parallel task on the channel chosen by data fusion process. It looks to identify the event sound between seven predefined sound classes and uses a Gaussian Mixture Model (GMM) method. Mel Frequency Cepstral Coefficients are used in combination with new ones like zero crossing rate, centroid and roll-off point. This smart sound sensor is a part of a medical telemonitoring project with the aim of detecting serious accidents.

• Structural Engineering and Mechanics
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• v.20 no.1
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• pp.1-20
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• 2005

The present paper deals with the seismic response analysis and the evaluation of most likely failure modes for a water storage structure. For the stress analysis, a 3-D mathematical model has been adopted to represent the structure appropriately. The structure has been analyzed for both static and seismic loads. Seismic analysis has been carried out considering the hydrodynamic effects of the contained water. Based on the stress analyses results, the most likely failure modes viz. tensile cracking and compressive crushing of concrete for the various structural elements; caused by the seismic event have been investigated. Further an attempt has also been made to quantify the initial leakage rate and average emptying time for the structure during seismic event after evaluating the various crack parameters viz. crack-width and crack-spacing at the locations of interest. The results are presented with reference to peak ground acceleration (PGA) of the seismic event. It has been observed that, an increase in PGA would result in significant increase in stresses and crack width in the various structural members. Significant increase in initial leakage rate and decrease in average emptying time for the structure has also been observed with the increase in PGA.

• Journal of the Korea Society of Computer and Information
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• v.26 no.8
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• pp.1-11
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• 2021

This paper proposes a novel mechanism called Event-driven Uncore Frequency Scaler (eUFS) to improve the energy efficiency of the HPC systems. UFS exploits the hardware events such as LAPI (Last-level Cache Accesses Per Instructions) and CPI (Clock Cycles Per Instruction) to dynamically adjusts the uncore frequency. Hardware events are collected at a reference time period, and the target uncore frequency is determined using the collected event and the previous uncore frequency. Experiments with the NPB benchmarks demonstrate that the eUFS reduces the energy consumption by 6% on average for class C and D NPB benchmarks while it only increases the execution time by 2% on average.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.9
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• pp.1409-1415
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• 2017

The navigation and EDR(Event Data Recorder) have a different roles but also a lot of similar or common functions and elements. A representative element is a GPS. The GPS of navigation is a more higher than the that of EDR in performance. The high performance GPS has a very short initialization time and more accurate than the GPS of EDR. And the informations of OBD(On Board Diagnostics) is able to be shared in navigation and EDR. But these inter-working product between the navigation and EDR has been not yet shown. So, this paper is aimed to show how to interwork in two devices. We utilize USB bulk communication in two devices for inter-working. It is very simple ways and it could interwork in GPS, event video and ODB II data. It could be effective in cost competence and convenience. These was implemented by hardware and software and proved by the standard test.

• The Korean Journal of Financial Management
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• v.26 no.1
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• pp.65-94
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• 2009

The event of change in KOSPI 200 Index composition is one of the main subjects for the test of EMH. According to EMH, when a certain event is not related with firm's fundamental value, stock price should not change after the announcement of news. This hypothesis leads us to the conclusion of horizontal demand curve of stock. This logic was questioned by Shleifer(1986) and argued that downward sloping demand curve hypothesis was supported. But Harris and Gruel(1986) found a different empirical evidence that price reversal occurs in the long run, which is called price pressure hypothesis. They argued that short term price effect by large block trading (price pressure) is offset in the long run because these event is unrelated to fundamental value. Therefor, they argued that EMH can not be rejected in the long run. Until now, there are two empirical studies with Korean market data in this area. Using a data with same time period of $1996{\sim}1999$, Kweon and Park(2000) and Ahn and Park(2005) showed that stock price or beta is not significantly affected by change in index composition. This study retested this event expanding sample period from 1996 to 2006, and analyzed why this event was considered an uninformative events in the preceding studies. We analyzed a market impact by separating samples according to firm size and market condition. In case of newly enlisted firm, we found the evidence supporting price pressure hypothesis on average. However, we found the long run price effect in the sample of large firms under bearish markets. At the same time, we know that the number of samples under the category of large firms under bearish markets is relatively small, which drives the same result of supporting the hypothesis that change in index composition is a non-informative event on average. Also, the long run price effect of large size firms under bearish markets was supported by the analyses using trading volumes. On the other hand, in case of delisting from the index, we found the long run price effect but that was not supported by trading volume analyses.

• Korean Journal of Computational Design and Engineering
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• v.17 no.2
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• pp.97-110
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• 2012

High performance sensors and modern data logging technology with real-time telemetry facilitate system fault diagnosis in a very precise manner. Fault detection, isolation and identification in fault diagnosis systems are typical steps to analyze the root cause of failures. This systematic failure analysis provides not only useful clues to rectify the abnormal behaviors of a system, but also key information to redesign the current system for retrofit. The main barriers to effective failure analysis are: (i) the gathered data (event) logs are too large in general, and further (ii) they usually contain noise and redundant data that make precise analysis difficult. This paper therefore applies suitable pre-processing techniques to data reduction and feature extraction, and then converts the reduced data log into a new format of event sequence information. Finally the event sequence information is decoded to investigate the correlation between specific event patterns and various system faults. The efficiency of the developed pre-filtering procedure is examined with a terminal box data log of a marine diesel engine.

• International Commerce and Information Review
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• v.17 no.1
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• pp.289-307
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• 2015

This study was to analyze dynamics of host regional effect in accordance with Mega-events. Yeosu Expo, 2012 was the Mega-event, and dynamic changes in economic indicators such as number of tourists, GRDP, employment rate, and real estate price were analyzed before and after the event. The Mega-event affected positively and increased on the number of tourists. While GRDP affected positively only right before the event, and the employment rate was not significantly affected by the event. The real estate price was increased from the announced time of hosting to the event held, but later decreased. This study suggested the comprehensive method for analyzing the effect of Mega-event and there was a cyclical causality among the result variables.