• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2005년도 춘계종합학술대회
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• pp.511-514
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• 2005

최근 전자 태그를 이용한 응용 서비스가 활성화됨에 따라 전자 태그의 데이터 처리에 대한 연구가 광범위하게 진행되고 있다. 따라서, 다양한 전자 태그로부터 수집된 객체 정보를 효율적으로 처리하기 위해 웹상에서 널리 사용되고 있는 XML 기반의 데이터 처리를 요구하고 있다. 이를 위해 EPCIS(EPC Information Service)는 EPC 관련 데이터를 공유하기 위한 표준인터페이스를 정의하였다. 현재 EPCIS 1.0 스펙의 Core Event Type은 EPC 네트워크에서 필요한 핵심타입들만을 정의하였고 적용 분야에 따른 비즈니스 context와 관련된 타입들은 정의하고 있지 않다. EPC 네트워크를 특정 비즈니스 영역에 적용할 때, Core Event Type 외에 추가적으로 정의해야 할 정보에 대한 타입 이 필요하다. 이에 본 논문에서는 이러한 비즈니스 영역에서 자주 사용되는 재사용 가능한 객체 타입을 정의하고 있다. 이렇게 정의한 객체타입과 EPCIS Spec에서 정의한 Core Event Type을 활용하여 해운 물류분야의 객체 정보 데이터 모델에 따른 스키마를 설계하였다.

• 한국콘텐츠학회논문지
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• 제18권7호
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• pp.449-458
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• 2018

소셜 네트워크 서비스의 사용량이 증가함에 따라 오프라인에서 발생한 이벤트 정보가 더욱 빠르게 확산되고 있다. 이에 따라 소셜 데이터를 분석하여 이벤트를 검출하기 위한 연구들이 진행되고 있다. 본 논문에서는 소셜 네트워크 환경에서 사용자 관심도를 고려한 그래프 기반 이벤트 검출 기법을 제안한다. 제안하는 기법은 사용자들이 게시한 글을 분석하여 키워드 그래프를 구축한다. 사용자의 소셜 행위로부터 관심도를 계산하고 관심도의 변화를 고려하여 이벤트 판별에 이용한다. 따라서 의미 없이 반복 게시되어 이벤트로 검출된 결과를 제거하고 결과의 신뢰성을 향상시킬 수 있다. 제안하는 이벤트 검출 기법의 우수성을 입증하기 위해 다양한 성능평가를 수행한다.

• 전기학회논문지
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• 제64권9호
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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.

• KIEAE Journal
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• 제15권5호
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• pp.5-12
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• 2015

Purpose: Retention and infiltration of small and frequently-occurring rainfall by LID facilities account for a large proportion of the annual precipitation volume. Based on 4 standard facilities such as Porous Pavement, Infiltration Trench, Cylindrical Infiltration Well, Rectangular Infiltration Well by Seoul Metropolitan Handbook of the Prior Consultation for LID. The total retention volume of each facility was calculated according to the type and size. The Purpose of this study is to find out the quantitative relationship between Percentile Rainfall Event and Design Volume of Infiltration Facilities. Methode: For the estimation of Percentile Rainfall Event, Daily Precipitation of Seoul from 2005 to 2014 was sorted ascending and the distribution of percentile was estimated by PERCENTILE spreadsheet function. The managed Rainfall Depth and Percentile of each facility was calculated at the several sizes. In response to the rainwater charge volume of 5.5mm/hr by the Category "Private large site", the 3 types of facilities were planned for example. The calculated Rainfall Depth and Percentile were 54.4mm and 90% by the use of developed Calculation-Module based on the Spreadsheet program. Result: With this Module the existing Designed Infiltration volume which was introduced from Japan was simply converted to the Percentile-Rainfall-Event used in USA.

• 인터넷정보학회논문지
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• 제20권5호
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• pp.49-55
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• 2019

Organizations design and operate business process models to achieve their goals efficiently and systematically. With the advancement of IT technology, the number of items that computer systems can participate in and the process becomes huge and complicated. This phenomenon created a more complex and subdivide flow of business process.The process instances that contain workcase and events are larger and have more data. This is an essential resource for process mining and is used directly in model discovery, analysis, and improvement of processes. This event log is getting bigger and broader, which leads to problems such as capacity management and I / O load in management of existing row level program or management through a relational database. In this paper, as the event log becomes big data, we have found the problem of management limit based on the existing original file or relational database. Design and apply schemes to archive and analyze large event logs through Hadoop, an open source distributed file system, and HBase, a NoSQL database system.

• 융합보안논문지
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• 제20권5호
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• pp.41-52
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• 2020

본 논문은 보안 이벤트 위험성에 따른 능동적 대응을 위해서 우선순위 알고리즘을 구현하는 것이며, 이를 기반으로 효율적인 이벤트 처리를 수행하는 이벤트 스케줄러를 구현하고자 한다. CVE나 CVSS 같이 세계적으로 표준을 가지고 있는 기준에 따라, 보안 이벤트를 실행시켰을 때 점수를 매길 수 있는 기준을 마련하고, 정형화 하여 보다 객관적으로 우선순위를 정할 수 있도록 한다. 그래서 이를 바탕으로 보안 이벤트 데이터베이스를 구축하고, 이를 이용하여 스케줄링을 할 수 있도록 한다. 또한 보안 이벤트 스케줄링 우선순위 알고리즘을 우리나라 보안 이벤트 실정에 맞게 개발하고 적용함으로써 국내 기관 및 기업의 정보보호에 대한 신뢰성 확보와 산업 발전에 기여하게 될 것이다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권2호
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• pp.609-631
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• 2022

The main goal of VANETs is to improve the safety of all road users. Therefore, the accuracy and trustworthiness of messages transmitted in VANETs are essential, given that life may rely on them. VANETs are provided with basic security services through the use of public key infrastructure-based authentication. However, the trust of users is still an open issue in VANETs. It is important to prevent bogus message attacks from internal vehicles as well as protect vehicle privacy. In this paper, we propose a trust management scheme that ensures trust in VANETs while maintaining vehicle privacy. The trust scheme establishes trust between vehicles where a trust value is assigned to every vehicle based on its behavior and messages are accepted only from vehicles whose trust value is greater than a threshold, therefore, protecting VANETs from malicious vehicles and eliminating bogus messages. If a traffic event happens, vehicles upload event messages to the reachable roadside unit (RSU). Once the RSU has confirmed that the event happened, it announces the event to vehicles in its vicinity and records it into the blockchain. Using this mechanism, RSUs are prevented from sending fake or unverified event notifications. Simulations are carried out in the context of bogus message attacks to evaluate the trust scheme's reliability and efficiency. The results of the simulation indicate that the proposed scheme outperforms the compared schemes and is highly resistant to bogus message attacks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권10호
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• pp.3965-3982
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• 2015

Event detection is one of the key issues in many wireless sensor network (WSN) applications. The uncertainties that are derived from the instability of sensor node, measurement noise and incomplete sampling would influence the performance of event detection to a large degree. Many of the present researches described the sensor readings with crisp values, which cannot adequately handle the uncertainties inhered in the imprecise sensor readings. In this paper, a fault-tolerant event detection algorithm is proposed based on Dempster-Shafer (D-S) theory (also called evidence theory). Instead of crisp values, all possible states of the event are represented by the Basic Probability Assignment (BPA) functions, with which the output of each sensor node are characterized as weighted evidences. The combination rule was subsequently applied on each sensor node to fuse the evidences gathered from the neighboring nodes to make the final decision on whether the event occurs. Simulation results show that even 20% nodes are faulty, the accuracy of the proposed algorithm is around 80% for event region detection. Moreover, 97% of the error readings have been corrected, and an improved detection capability at the boundary of the event region is gained by 75%. The proposed algorithm can enhance the detection accuracy of the event region even in high error-rate environment, which reflects good reliability and robustness. The proposed algorithm is also applicable to boundary detection as it performs well at the boundary of the event.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1997년도 춘계학술대회
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• pp.145-149
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• 1997

This paper describes a methodology for the development of models of discrete event system. The methodology is based on transformation of continuous state space into discrete one to homomorphically represent dynamics of continuous processes in discrete events. This paper proposes a formal structure which can coupled discrete event system models within a framework. The structure employs the discrete event specification formalism for the discrete event system models. The proposed formal structure has been applied to develop a discrete event specification model for the complex spectral density analysis of strip for urin analyzer system. For this, spectral density data of strip is partitioned into a set of Phases based on events identified through urine spectrophotometry. For each phase, a continuous system of the continuous model for the urine spectral density analysis has been simulated by programmed C++. To validate this model, first develop the discrets event specification model, then simulate the model in the DEVSIM++ environment. It has the similar simulation results for the data obtained from the continuous system simulation. The comparison shows that the discrete event specification model represents dynamics of the urine spectral density at each phase.

• 산업경영시스템학회지
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• 제39권2호
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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.