• Advances in Computational Design
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• 제8권3호
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• pp.255-271
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• 2023

The gear drive of a combat aircraft engine is responsible for power transmission to the different accessories necessary for the engine's operation. Incorrect power transmission can occur due to the presence of failure modes in the gears like bending fatigue, pitting, adhesive wear, scuffing, abrasive wear and polished wear etc. Fault diagnosis of the gear drive is necessary to get an early indication of failure of the gears. The present research is to develop an algorithm using different vibration signal processing techniques on industrial vibration acquisition systems to establish gear fault diagnosis architecture. The signal processing techniques have been used to extract various feature vectors in the development of the fault diagnosis architecture. An open-source dataset of other gear fault conditions is used to validate the developed architecture. The results is a basis for development of artificial intelligence based expert systems for gear fault diagnosis of a combat aircraft engine.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2000년도 추계학술대회 논문집
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• pp.233-238
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• 2000

CNC machine tool is assembled by central processor, PLC(Programmable Logic Controller), and actuator. The sequential control of machine generally controlled by a PLC. The main fault occured at PLC in 3 control parts. In LC faults, operational fault is charged over 70%. This paper describes diagnosis model and data processing for remote monitoring and diagnosis system in machine tools with open architecture controller. Two diagnostic models based on the ladder diagram. Logical Diagnosis Model(LDM), Sequential Diagnosis Model(SDM), are proposed. Data processing structure is proposed ST(Structured Text) based on IEC1131-3. The faults from CNC are received message form open architecture controller and faults from PLC are gathered by sequential data.. To do this, CNC and PLC's logical and sequential data is constructed database.

• Journal of Information Processing Systems
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• 제17권2호
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• pp.242-252
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• 2021

Following the intuition that the local information in time instances is hardly incorporated into the posterior sequence in long short-term memory (LSTM), this paper proposes an attention augmented mechanism for fault diagnosis of the complex chemical process data. Unlike conventional fault diagnosis and classification methods, an attention mechanism layer architecture is introduced to detect and focus on local temporal information. The augmented deep network results preserve each local instance's importance and contribution and allow the interpretable feature representation and classification simultaneously. The comprehensive comparative analyses demonstrate that the developed model has a high-quality fault classification rate of 95.49%, on average. The results are comparable to those obtained using various other techniques for the Tennessee Eastman benchmark process.

• 디지털콘텐츠학회 논문지
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• 제15권1호
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• pp.121-128
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• 2014

SSE-CMM은 보안엔지니어링을 공학, 보증, 위험 프로세스의 3가지 요소로 나누고 있으며 정보보호 성숙도 평가 모델과 수준을 제시하고 있다. 정보보호 성숙도 측정은 취약점 진단, 위험분석 방법론을 실무 현장에서 사용할 수 있도록 종합적으로 결론을 제시한다. 사이버거래의 일반적인 서비스는 인터넷 뱅킹, 모바일 뱅킹, 텔레뱅킹 등이다. 사이버거래 처리구조의 한 종류인 뱅킹시스템 정보보호 성숙도 측정방법론 연구 목적은 기존의 취약점 진단, 위험분석 방법론을 실무현장에서 사용할 수 있도록 종합적 결론을 제시한다. 안전성과 편리성을 확보하여 이용자들이 사이버 거래를 편리하게 이용할 수 있는 환경을 구축하는 것이 사이버 거래 활성화의 핵심이다. 특히 업무현장에서 정보보호 성숙도 측정을 통한 사이버뱅킹시스템의 안전성을 확보한다면 현장의 실무처리 결과로 많은 효과가 나타날 것으로 기대한다.

• 대한전기학회논문지:시스템및제어부문D
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• 제49권9호
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• pp.512-519
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• 2000

A Neural networks has been used for a expert system and fault diagnosis system. It is possible to nonlinear function mapping and parallel processing. Therefore It has been developing for a Diagnosis system of nuclear plower plant. In general Neural Networks is a static mapping but Dynamic Neural Network(DNN) is dynamic mapping.쪼두 a fault occur in system a state of system is changed with transient state. Because of a previous state signal is considered as a information DNN is better suited for diagnosis systems than static neural network. But a DNN has many weights so a real time implementation of diagnosis system is in need of a rapid network architecture. This paper presents a algorithm for RCP monitoring Alarm diagnosis system using Self Dynamic Neural Network(SDNN). SDNN has considerably fewer weights than a general DNN. Since there is no interlink among the hidden layer. The effectiveness of Alarm diagnosis system using the proposed algorithm is demonstrated by applying to RCP monitoring in Nuclear power plant.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 D
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• pp.2488-2491
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• 2000

A Neural network is possible to nonlinear function mapping and parallel processing. Therefore It has been developing for a Diagnosis system of nuclear plower plant. In general Neural Networks is a static mapping but Dynamic Neural Network(DNN) is dynamic mapping. When a fault occur in system, a state of system is changed with transient state. Because of a previous state signal is considered as a information. DNN is better suited for diagnosis systems than static neural network. But a DNN has many weights, so a real time implementation of diagnosis system is in need of a rapid network architecture. This paper presents a algorithm for RCP monitoring Alarm diagnosis system using Self Dynamic Neural Network(SDNN). SDNN has considerably fewer weights than a general DNN. Since there is no interlink among the hidden layer. The effectiveness of Alarm diagnosis system using the proposed algorithm is demonstrated by applying to RCP monitoring in Nuclear power plant.

• 한국의학물리학회지:의학물리
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• 제30권2호
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• pp.49-58
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• 2019

Purpose: Automated analytical systems have begun to emerge as a database system that enables the scanning of medical images to be performed on computers and the construction of big data. Deep-learning artificial intelligence (AI) architectures have been developed and applied to medical images, making high-precision diagnosis possible. Materials and Methods: For diagnosis, the medical images need to be labeled and standardized. After pre-processing the data and entering them into the deep-learning architecture, the final diagnosis results can be obtained quickly and accurately. To solve the problem of overfitting because of an insufficient amount of labeled data, data augmentation is performed through rotation, using left and right flips to artificially increase the amount of data. Because various deep-learning architectures have been developed and publicized over the past few years, the results of the diagnosis can be obtained by entering a medical image. Results: Classification and regression are performed by a supervised machine-learning method and clustering and generation are performed by an unsupervised machine-learning method. When the convolutional neural network (CNN) method is applied to the deep-learning layer, feature extraction can be used to classify diseases very efficiently and thus to diagnose various diseases. Conclusions: AI, using a deep-learning architecture, has expertise in medical image analysis of the nerves, retina, lungs, digital pathology, breast, heart, abdomen, and musculo-skeletal system.

• 융합신호처리학회논문지
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• 제5권4호
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• pp.263-270
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• 2004

최근 웨이블릿 변환은 많은 분야에서 다양하게 적용된다. 본 논문에서 tasks뇌파의 중요한 몇가지 특성파 검출을 위한 다비치 웨이블릿은 뇌파분석에 필요하다. 우리가 제안한 시스템은 다른 방법보다는 특성파 검출에 높은 성능을 가졌다. 본 연구의 뉴럴시스템의 구조는 하나의 은닉층과 3계층 피드포워드층은 오류 BP 학습알고리즘을 적용하였다. 4명의 피험자에게 알고리즘을 적용하여 92% 분류율을 보였다. 제안된 시스템은 웨이블릿과 신경망으로 tasks 뇌파의 보다 정확하게 분석함을 보였다. 모의실험결과 tasks 뇌파는 의사의 노동력을 줄일수 있고 정량적 해석이 가능함을 보였다.

• 대한의용생체공학회:의공학회지
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• 제5권1호
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• pp.85-92
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• 1984

A new architecture of the Digital Scan Converter (DSC) for the linear-scan ultrasound medical imaging systems is proposed and its hardware implementation is reported. While the conventional DSC merely displays the acquisited data and does nor allow access to the frame memory, it is possible, in the new system, to access to the frame memory for further imaging processing so as to obtain useful information for medical diagnosis. Image processing can be performed either by a special pupose processor, or by VAX 11/780. The system is made to operate asyncronously to increase the frame rate with tags assigned to the data. The proposed DSC was designed to be used without much modification for the sector scan system as well.

• Journal of Information Processing Systems
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• 제4권3호
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• pp.103-112
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• 2008

This paper proposes a policy adjuster-driven Grid workflow management system for collaborative healthcare platform, which supports collaborative heart disease diagnosis applications. To select policies according to service level agreement of users and dynamic resource status, we devised a policy adjuster to handle workflow management polices and resource management policies using policy decision scheme. We implemented this new architecture with workflow management functions based on policy quorum based resource management system for providing poincare geometrycharacterized ECG analysis and virtual heart simulation service. To evaluate our proposed system, we executed a heart disease identification application in our system and compared the performance to that of the general workflow system and PQRM system under different types of SLA.