• 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.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2010년도 추계학술대회 논문집
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• pp.57-58
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• 2010

• 한국화재소방학회:학술대회논문집
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• 한국화재소방학회 2005년도 공동추계학술논문발표회 논문집
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• pp.81-85
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• 2005

Recently, fire resistance of high performance concrete for explosive spalling was issued as high performance concrete was vulnerable to the explosive spalling in initial fire. Therefore, in this study, an experiment about reduction effect to explosive spalling of high performance concrete is performed by adding several polymer fiber with various volume fraction, an then final fiber and volume fraction of that which reduce the explosive spalling of high performance concrete is presented. As the result of this study, the most fitted fiber volume fraction of reducing effect for explosive spalling at high performance concrete is under the 0.1%, as consider the flowability and efficiency.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송∙미디어공학회 2020년도 추계학술대회
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• pp.25-28
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• 2020

Automatic segmentation of brain tissues such as WM, GM, and CSF from brain MRI scans is helpful for the diagnosis of many neurological disorders. Accurate segmentation of these brain structures is a very challenging task due to low tissue contrast, bias filed, and partial volume effects. With the aim to improve brain MRI segmentation accuracy, we propose an end-to-end convolutional based U-SegNet architecture designed with multi-scale kernels, which includes cascaded dilated convolutions for the task of brain MRI segmentation. The multi-scale convolution kernels are designed to extract abundant semantic features and capture context information at different scales. Further, the cascaded dilated convolution scheme helps to alleviate the vanishing gradient problem in the proposed model. Experimental outcomes indicate that the proposed architecture is superior to the traditional deep-learning methods such as Segnet, U-net, and U-Segnet and achieves high performance with an average DSC of 93% and 86% of JI value for brain MRI segmentation.

• 의료ㆍ복지 건축 : 한국의료복지건축학회 논문집
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• 제12권2호
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• pp.69-77
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• 2006

Angiography means that a check up to know an abnormal condition in all the blood vessels include from the heart, aortae, cerobrovascular and abdonominal artery to hands and feet. Main examples of this are cerebral angiography, abdominal, liver for urinary anomaly, renovascular angiography, and artery and vein in arms and legs. Angiography uses radial rays or angiography equipment for an image output during interventional procedure and compositive diagnosis. The acts which performed in a projection room have changed drastically. In general, it is performed by using equipment which is attached one or two C-arms and the method of inserting catheter in vein after anesthesia. For this reason, some rooms that consist of angiography room units should be planned not only for expensiveness equipment and facilities also to be germ-free. Nowadays, in the angiography unit case, it is placed independently as the central part of many hospitals. It does not belong to the imaging medical department any more as considering raising filming times and the relation between C.C.U.(coronary care unit) and operation unit. This means the acts performed are diversified and well-organized rooms in support of diagnosis are required. However, it is difficult to plan the angiography room unit due to domestic researches and data on this unit are not enough. Therefore, this study aims at bringing up basic issue for architectural planning of the angiography unit in general hospital.

• KNOM Review
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• 제23권1호
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• pp.18-25
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• 2020

최근 스마트 팩토리(Smart Factory)에 대한 연구는 단순히 공장 자동화(Factory Automation, FA)의 개념에서 데이터를 수집하고 분석하는 형태로 발전하고 있다. 이것은 통신 기술의 발전(5G)과 IoT 장치(device)들이 현장 상황에 맞춰 다양하게 개발되면서 가속화 되고 있다. 또한, 기업 경쟁력 강화로 디지털트랜스포메이션(Digital Transformation)이 활발히 이루어지고 있으며, 이를 각종 IoT 장비로 부터 수신한 데이터와 자동화된 설비를 결합시켜 공정 재조정을 통한 최적화 연구가 다양하게 진행되고 있다. 따라서 본 논문에서는 관련 연구 중 하나인 예측 시스템을 활용한 설비 장애 진단 및 조치 시스템 구조 및 요소를 제안한다.

• 한국건축시공학회지
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• 제21권6호
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• pp.665-676
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• 2021

노후 건축물의 수가 증가함에 따라, 건물의 안전진단, 유지 보수에 대한 중요성이 증가하고 있다. 기존 외관 조사는 점검자의 주관적인 판단이 수반되어 평가 결과가 다르고 객관성과 신뢰성이 떨어진다. 따라서 본 연구는 기존 연구를 통해 기실시된 외관 조사 및 상태 평가 프로세스의 한계를 제시하였으며, UAV, Laser Scanner를 통해 3D Point Cloud 데이터를 수집하였다. 또한, Reverse Engineering 기술을 이용하여 3D 모델을 생성한 후 객관적인 상태평가 데이터를 취득하였다. 이후 기존의 정밀검사 데이터와 정밀 안전진단 데이터를 활용하여 DNN 구조를 생성하고, 고정밀도 측정 장치를 이용하여 얻은 상태평가 데이터를 적용하여 객관적인 건물안전등급을 산출하였다. 자동화된 프로세스는 20개의 노후된 건축물에 적용되며 동일 면적 건축물 기준 수작업으로 실시되는 안전진단의 시간에 비해 약 50% 감소하였다. 이후 본 연구에서는 안전등급 결과값과 기존값을 비교하여 안전등급 산출과정의 정확성을 검증하고 약 90%의 높은 정확도를 가진 DNN을 구축하였다. 이는 향후 노후 건물의 안전등급 산정의 신뢰성이 향상되고 비용과 시간을 절약해 경제성이 향상될 것으로 기대된다.

• International Journal of Naval Architecture and Ocean Engineering
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• 제8권3호
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• pp.243-251
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• 2016

Autonomous Underwater Vehicles (AUVs) generally work in complex marine environments. Any fault in AUVs may cause significant losses. Thus, system reliability and automatic fault diagnosis are important. To address the actuator failure of AUVs, a fault diagnosis method based on the Gaussian particle filter is proposed in this study. Six free-space motion equation mathematical models are established in accordance with the actuator configuration of AUVs. The value of the control (moment) loss parameter is adopted on the basis of these models to represent underwater vehicle malfunction, and an actuator failure model is established. An improved Gaussian particle filtering algorithm is proposed and is used to estimate the AUV failure model and motion state. Bayes algorithm is employed to perform robot fault detection. The sliding window method is adopted for fault magnitude estimation. The feasibility and validity of the proposed method are verified through simulation experiments and experimental data.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2015년도 추계학술대회
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• pp.224-227
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• 2015

일반적으로 자동차드라이버의 스마트폰을 통한 데이터전송은 자동차운전자의 핸드폰은 데이터를 실시간으로 원격데이터 센터에 전송하는 경우에 용량 의존적인 순위를 가지고 있다. 생성되는 진단보드 데이터들은 드라이버의 폰에서의 모바일 진단 어플리케이션에 임시적으로 저장하고, 인터넷에 연결 되었을 때 데이터 센터에 전송한다. 클라우드에서 실행에 방해하는 다른 태스크들이 없는 원격 자동차 어플리케이션 사용방법을 위한 node.js는 모바일 네트워크을 통한 클라우드에서 데이터 저장업무를 다루기 위하여 적합하다. 우리는 외부 어플리케이션으로부터 driver inputs and delivers output을 패스하는 원격 유저와 운용하는 스마트폰 어플리케이션에서 자동차와의 어플리케이션 interface 방법을 사용하는 실시간 분석 안드로이드 어플리케이션 반응을 시뮬레이션 통해 제안된 아키텍쳐의 유효성을 입증한다. 이 논문에서, 우리는 이벤트 루프 접근을 기반으로 하는 이것은 웹서버 구조를 특징으로 하는 원격 자동차 결함 진단 시스템 연구를 제안한다.

• BioChip Journal
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• 제12권4호
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• pp.332-339
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• 2018

The future neuro-prosthetic devices would be required spikes data monitoring through sub-nanoscale transistors that enables to neuroscientists and clinicals for scalable, wireless and implantable applications. This research investigates the spikes monitoring through integrated CNT front-end amplifier for neuro-prosthetic diagnosis. The proposed carbon nanotube-based architecture consists of front-end amplifier (FEA), integrate fire neuron and pseudo resistor technique that observed high electrical performance through neural activity. A pseudo resistor technique ensures large input impedance for integrated FEA by compensating the input leakage current. While carbon nanotube based FEA provides low-voltage operation with directly impacts on the power consumption and also give detector size that demonstrates fidelity of the neural signals. The observed neural activity shows amplitude of spiking in terms of action potential up to $80{\mu}V$ while local field potentials up to 40 mV by using proposed architecture. This fully integrated architecture is implemented in Analog cadence virtuoso using design kit of CNT process. The fabricated chip consumes less power consumption of $2{\mu}W$ under the supply voltage of 0.7 V. The experimental and simulated results of the integrated FEA achieves $60G{\Omega}$ of input impedance and input referred noise of $8.5nv/{\sqrt{Hz}}$ over the wide bandwidth. Moreover, measured gain of the amplifier achieves 75 dB midband from range of 1 KHz to 35 KHz. The proposed research provides refreshing neural recording data through nanotube integrated circuit and which could be beneficial for the next generation neuroscientists.