• Journal of the Korea Safety Management & Science
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• v.12 no.2
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• pp.161-166
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• 2010

EPC Information Services (EPCIS) is an EPCglobal standard designed to enable EPC-related data sharing within and across supply chain. The EPCIS standard defines standard interfaces to enable EPC-related data to be captured and subsequently to be queried using a set of service operations and an associated data model. There are two kinds of EPCIS data: event data and master data. Event data is created in the process of carrying out business processes. Traceability of goods across supply chain is based on event data. Therefore, each company must have an event data. This study compared he difference between MS-SQL(DRDBMS) and ALTIBASE(MMDBMS) for data storage. We compared the difference between two database management in many respects such as insert time and select time. We come to a conclusion that ALTIBASE is more efficient than MS-SQL.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.14 no.6
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• pp.125-132
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• 2015

This paper presents an event data delivery scheme for wireless sensor networks that use a GTS-based channel allocation scheme. Many sensor nodes can share a GTS channel for sending their normal data to the sink node. When there is an event at a node, the node makes a temporal route to the sink node and the nodes of the route can use the GTS channel in a privileged access. This scheme controls the backoff number effectively so the data delivery priority is given to the nodes of that route. Simulation results show that the event data delivery of the proposed scheme outperforms at the end-to-end transfer delay and jitter characteristics. The proposed scheme can effectively gather the event data using the guaranteed GTS channel of the route in proposed scheme.

• 한국정보통신설비학회:학술대회논문집
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• 2009.08a
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• pp.157-161
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• 2009

In recent years, there is a remarkable progress in ICTs (Information and Communication Technologies), and then many attempts to apply ICTs to other industries are being made. In the field of disaster managements, ICTs such as RFID (Radio Frequency IDentification) and USN (Ubiquitous Sensor Network) are used to provide safe environments. Actually, various types of early warning systems using USN are now widely used to monitor natural disasters such as floods, landslides and earthquakes, and also to detect human-caused disasters such as fires, explosions and collapses. These early warning systems issue alarms rapidly when a disaster is detected or an event exceeds prescribed thresholds, and furthermore deliver alarm messages to disaster managers and citizens. In general, these systems consist of a number of various sensors and measure real-time stream data, which requires an efficient and rapid data processing technique. In this study, an event-driven architecture (EDA) is presented to collect event effectively and to provide an alert rapidly. A publish/subscribe event processing method to process simple event is introduced. Additionally, a complex event processing (CEP) technique is introduced to process complex data from various sensors and to provide prompt and reasonable decision supports when many disasters happen simultaneously. A basic concept of CEP technique is presented and the advantages of the technique in disaster management are also discussed. Then, how the main processing methods of CEP such as aggregation, correlation, and filtering can be applied to disaster management is considered. Finally, an example of flood forecasting and early alarm system in which CEP is incorporated is presented It is found that the CEP based on the EDA will provide an efficient early warning method when disaster happens.

• The KIPS Transactions:PartD
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• v.14D no.5
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• pp.483-490
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• 2007

Recently mining techniques that analyze the data stream to discover potential information, have been widely studied. However, most of the researches based on the support are concerned with the frequent event, but ignore the infrequent event even if it is crucial. In this paper, we propose SM-AF method discovering association rules to an abnormal event. In considering the window that an abnormal event is sensed, SM-AF method can discover the association rules to the critical event, even if it is occurred infrequently. Also, SM-AF method can discover the significant rare itemsets associated with abnormal event and periodic event itemsets. Through analysis and experiments, we show that SM-AF method is superior to the previous methods of mining association rules.

• 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.

• Journal of Industrial Technology
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• v.27 no.B
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• pp.235-243
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• 2007

Sensor network is used to obtain sensing data in various area. The interval to sense the events depends on the type of target application and the amounts of data generated by sensor nodes are not constant. Many applications exploit long sensing interval to enhance the life time of network but there are specific applications that requires very short interval to obtain fine-grained, high-precision sensing data. If the number of nodes in the network is increased and the interval to sense data is shortened, the amounts of generated data are greatly increased and this leads to increased amount of packets to transfer to the network. To transfer large amount of packets fast, it is necessary that the delay between successive packet transmissions should be minimized as possible. In Sensor network, since the Operating Systems are worked on the event driven, the Timer Event is used to transfer packets successively. However, since the transferring time of packet completely is varies very much, it is very hard to set appropriate interval. The longer the interval, the higher the delay and the shorter the delay, the larger the fail of transfer request. In this paper, we propose ESTEO which reduces the delay between successive packet transmissions by using SendDone Event which informs that a packet transmission has been completed.In ESTEO, the delay between successive packet transmissions is shortened very much since the transmission of next packet starts at the time when the transmission of previous packet has completed, irrespective of the transmission timee. Therefore ESTEO could provide high packet transmission rate given large amount of packets.

• 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.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.9
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• pp.1337-1346
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• 2015

In this paper, pattern classifier is designed to classify precipitation and non-precipitation events from weather radar data. The proposed classifier is based on Fuzzy Neural Network(FNN) and consists of three FNNs which operate in parallel. In the proposed network, the connection weights of the consequent part of fuzzy rules are expressed as two polynomial types such as constant or linear polynomial function, and their coefficients are learned by using Least Square Estimation(LSE). In addition, parametric as well as structural factors of the proposed classifier are optimized through Differential Evolution(DE) algorithm. After event classification between precipitation and non-precipitation echo, non-precipitation event is to get rid of all echo, while precipitation event including non-precipitation echo is to get rid of non-precipitation echo by classifier that is also based on Fuzzy Neural Network. Weather radar data obtained from meteorological office is to analysis and discuss performance of the proposed event and echo patter classifier, result of echo pattern classifier compare to QC(Quality Control) data obtained from meteorological office.

• The Korean Journal of Applied Statistics
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• v.27 no.2
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• pp.277-290
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• 2014

We analyzed binary landslide data from the Boeun area with logistic regression. Since the number of landslide occurrences is only 9 out of 5000 observations, this can be regarded as a rare event data. The main issue of logistic regression with the rare event data is a serious bias problem in regression coefficient estimates. Two bias correction methods were proposed before and we quantitatively compared them via simulation. Firth (1993)'s approach outperformed and provided the most stable results for analyzing the rare-event binary data.

• Journal of Internet Computing and Services
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• v.17 no.6
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• pp.153-158
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• 2016

As the number of social network users increases, the information on event such as social issues and disasters receiving attention in each region is promptly posted by the bucket through social media site in real time, and its social ripple effect becomes huge. This study proposes a detection method of events that draw attention from users in specific region at specific time by using twitter data with regional information. In order to collect Twitter data, we use Twitter Streaming API. After collecting data, We implemented event detection system by analyze the frequency of a keyword which contained in a twit in a particular time and clustering the keywords that describes same event by exploiting keywords' co-occurrence graph. Finally, we evaluates the validity of our method through experiments.