• 한국추진공학회:학술대회논문집
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• 한국추진공학회 2008년 영문 학술대회
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• pp.187-194
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• 2008

Because the types and severities of most engine faults are various and complex, it is not easy that the conventional model based fault detection approach like the GPA(Gas Path Analysis) method can monitor all engine fault conditions. Therefore this study proposed newly a diagnostic algorithm for isolating and diagnosing effectively the faulted components of the smart UAV propulsion system, which has been developed by KARI(Korea Aerospace Research Institute), using the fuzzy logic and the neural network algorithms. A precise performance model should be needed to perform the model-based diagnostics. The based engine performance model was developed using SIMULINK. For the work and mass flow matching between components of the steady-state simulation, the state-flow library was applied. The proposed steady-state performance model can simulate off-design point performance at various flight conditions and part loads, and in order to evaluate the steady-state performance model their simulation results were compared with manufacturer's performance deck data. According to comparison results, it was confirm that the steady-state model well agreed with the deck data within 3% in all flight envelop. The diagnosis procedure of the proposed diagnostic system has the following steps. Firstly after obtaining database of fault patterns through performance simulation, then secondly the diagnostic system was trained by the FFBP networks. Thirdly after analyzing the trend of the measuring parameters due to fault patterns, then fourthly faulted components were isolated using the fuzzy logic. Finally magnitudes of the detected faults were obtained by the trained neural networks. Because the detected faults have almost same as degradation values of the implanted fault pattern, it was confirmed that the proposed diagnostic system can detect well the engine faults.

• 한국통신학회논문지
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• 제16권1호
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• pp.46-56
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• 1991

본 논문에서는 16위도 X 8비트 Content Addressable and Reentrant Memory(CARM)를 설계하였다. CARM은 읽기, 저장, 매칭, 리엔트린트(Reentrant)의 4가지 동작 모드를 수행한다. CARM의 읽기와 저장 동작은 기존의 스태틱 RAM과 같다.CARM은 집 장에서 레영역 회수(Garbate collection)를 조건적으로 수행할 수 있는 리엔트런트 동작을 가지고 있다. 이러한 기능은 다이내믹 데이타 플로우 컴퓨터의 고속 매칭 유닛에 사용될 수 있다. CARM은 또한 매칭어드레스를 그들의 우선권에 따라 순차적으로 인코딩을 할 수 있는 기능을 가지고 있다. 이러한 CARM은 전체적으로 메모리 셀, 순차적 어드레스 인코더(Sequential Address Encoer, S.A.E), 리엔트런트 동작, 읽기/저장 제어, 데이타/마스크 레지스터, 감지 증폭기, 인코더, 디코더 등의 8개의 블럭으로 구성된다.CARM은 데이타 플로우 컴퓨터, 패턴 인식,테이블 룩업(Table look-up), 영상처리 등에 응용될 수 있을 것이다. 설계된 회로에 대해 각 동작별로 Apollo 워크스테이션의 QUICKSIM을 이용하여 논리 시물레이션을 하였고, 각 블럭별 회로의 SPICE 시뮬레이션을 하였다. 시뮬레이션결과 액세스 타임은 26ns였고, 매치 동작을 수행하는 데에는 4lns의 자연시간이 소요됐다. 결체 레이아웃은 3{\;}\mu\textrm{m} n well CMOS 공정에 따른 설계 규칙을 이용하여 수행하였다.

• Journal of Information Technology Applications and Management
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• 제21권4_spc호
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• pp.309-329
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• 2014

This paper proposes an integrative framework for determining a dominant design in the mobile operation systems. A dominant design has emerged as a de facto standard from traditional industries to ICT. ICT-related industries have been reorganized around the mobile industry in which the mobile operation system can be seen as a strategical keystone. In this paper, we develop a holistic approach for the emergence of a dominant design in the mobile operation systems. Firstly, we combined an integrative framework based on previous research findings with new determinants derived from cases of the mobile operation systems. Secondly, we categorized all determinants according to technological, firm-level and environmental factors within our proposed framework. Finally, we compared this framework to patterns coded from cases which include Android and iOS. This results show that a dominant design is likely to emerge as a result of the interaction of the determinants, not the influence of a single determinant. Furthermore, our proposed framework may extend the existing literature on dominant designs in both researchers and practitioners and provide implications to actors for establishing a competition strategy in the mobile telecommunication ecosystems.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2005년도 춘계학술대회 논문집
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• pp.1131-1135
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• 2005

Since human is vulnerable to emotional, physical and environmental distractions, most human inspectors cannot sustain a consistent 8-hour inspection in a day specifically for small components like door locking levers. As an alternative for human inspection, presented in this study is the development of a machine vision inspection system (MVIS) purposely for door locking levers. Comprises the development is the structure of the MVIS components, designed to meet the demands, features and specifications of door locking lever manufacturing companies in increasing their production throughput upon keeping the quality assured. This computer-based MVIS is designed to perform quality measures of detecting missing portions and defects like burr on every door locking lever. NI Vision Builder software for Automatic Inspection (AI) is found to be the optimum solution in configuring the needed quality measures. The proposed software has measurement techniques such as edge detecting and pattern-matching which are capable of gauging, detecting missing portion and checking alignment. Furthermore, this study exemplifies the incorporation of the optimized NI Builder inspection environment to the pre-inspection and post-inspection subsystems.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제8권4호
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• pp.316-321
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• 2008

Advanced computer network technology enables computers to be connected in an open network environment. Despite the growing numbers of security threats to networks, most intrusion detection identifies security attacks mainly by detecting misuse using a set of rules based on past hacking patterns. This pattern matching has a high rate of false positives and can not detect new hacking patterns, which makes it vulnerable to previously unidentified attack patterns and variations in attack and increases false negatives. Intrusion detection and analysis technologies are thus required. This paper investigates the asymmetric costs of false errors to enhance the performances the detection systems. The proposed method utilizes the network model to consider the cost ratio of false errors. By comparing false positive errors with false negative errors, this scheme achieved better performance on the view point of both security and system performance objectives. The results of our empirical experiment show that the network model provides high accuracy in detection. In addition, the simulation results show that effectiveness of anomaly traffic detection is enhanced by considering the costs of false errors.

• 국제학술발표논문집
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• The 8th International Conference on Construction Engineering and Project Management
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• pp.313-322
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• 2020

The recognition of the risk hazards is a vital step to effectively prevent accidents on a construction site. The advanced development in computer vision systems and the availability of the large visual database related to construction site made it possible to take quick action in the event of human error and disaster situations that may occur during management supervision. Therefore, it is necessary to analyze the risk factors that need to be managed at the construction site and review appropriate and effective technical methods for each risk factor. This research focuses on analyzing Occupational Safety and Health Agency (OSHA) related to risk zone identification rules that can be adopted by the image recognition technology and classify their risk factors depending on the effective technical method. Therefore, this research developed a pattern-oriented classification of OSHA rules that can employ a large scale of safety hazard recognition. This research uses joint reasoning of risk zone Identification and numeric input by utilizing a stereo camera integrated with an image detection algorithm such as (YOLOv3) and Pyramid Stereo Matching Network (PSMNet). The research result identifies risk zones and raises alarm if a target object enters this zone. It also determines numerical information of a target, which recognizes the length, spacing, and angle of the target. Applying image detection joint logic algorithms might leverage the speed and accuracy of hazard detection due to merging more than one factor to prevent accidents in the job site.

• 정보과학회 논문지
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• 제44권1호
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• pp.1-12
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• 2017

지속적으로 증가하는 인터넷 서비스 요구사항을 만족하기 위하여 인터넷 서비스를 제공하는 시스템은 웹 서버와 DB(database) 서버로 구성된 multitier 구조로 변화되어왔다. 이러한 multitier 웹 어플리케이션 환경에서 기존의 IDS(intrusion detection system)는 웹 서버와 DB 서버에서 misused traffic pattern들이나 signature들을 매칭하여 이미 알려진 공격을 검출하고 해당 접속을 차단하는 방식으로 동작한다. 하지만 이러한 방식의 IDS는 정상적인 HTTP(hypertext transfer protocol) request를 이용하여 악의적으로 DB 서버의 내용의 변조를 시도하는 attacker의 공격을 DB 서버단에서 제대로 검출하지 못한다. 그 이유는 DB 서버는 웹 서버로부터 받은 SQL(structured query language) query가 어떤 사용자의 HTTP request에 의해 발생한 것인지 알지 못하는 상태에서 처리하며, 웹 서버는 SQL query 처리 결과 중 어떤 것이 악의적으로 DB 서버 변조를 시도한 SQL query에 의한 결과인지 알 수 없기 때문이다. 이런 공격을 검출하기 위해서는 HTTP request와 SQL query 사이의 상호작용관계를 명확히 파악하고, 이를 이용하여 악의적인 SQL query를 발생시킨 사용자를 추적해야 한다. 이를 위해서는 해당 시스템의 소스코드를 분석하거나 application logic을 완벽하게 파악해야 하므로 현실적으로 불가능하다. 본 논문에서는 웹 서버와 DB 서버에서 제공하는 로그만을 이용하여 모든 HTTP request와 SQL query간의 mapping 관계를 찾아내고, 이를 이용하여 특정 SQL query를 발생시킨 HTTP request를 추정하는 기법을 제안한다. 모의실험을 통하여 94%의 정확도로 HTTP request를 추정할 수 있음을 확인하였다.