• 한국자기학회:학술대회 개요집
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• 한국자기학회 2008년도 Asian Magnetics Conference
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• pp.246.1-246.1
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• 2008

• Biotechnology and Bioprocess Engineering:BBE
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• 제8권4호
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• pp.252-256
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• 2003

In this paper we describe the status of a silicon-based microelectrode for neural recording and an advanced neural interface. We have developed a silicon neural probe, using a combination of plasma and wet etching techniques. This process enables the probe thickness to be controlled precisely. To enhance the CMOS compatibility in the fabrication process, we investigated the feasibility of the site material of the doped polycrystalline silicon with small grains of around 50 nm in size. This silicon electrode demonstrated a favorable performance with respect to impedance spectra, surface topography and acute neural recording. These results showed that the silicon neural probe can be used as an advanced microelectrode for neurological applications.

• 전자공학회논문지
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• 제50권4호
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• pp.229-235
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• 2013

본 논문은 최근 활발하게 연구되고 있는 뇌-기계 접속을 위한 완전 삽입형 마이크로 시스템의 구현에 있어서 중요한 이슈들을 고찰한다. 현재까지의 과학 기술적 연구는 신경 신호 증폭기, 무선 신호 전송 등 주로 고성능 저전력 전자기기 및 시스템을 구현하는데 집중되어 왔으나, 마이크로 시스템의 실제적인 응용은 전자 기기의 특성뿐만 아니라 밀봉 구조의 디자인에서 뇌의 생리 해부학적 특성에 이르기까지 여러 가지 요인에 의해 영향을 받게 된다. 본 논문은 특히 뇌 삽입형 마이크로 시스템의 실질적인 구현에 결정적인 영향을 주는 시스템 발열의 영향, 신경 프로브의 감지 부피, 무선 데이터 전송 및 전력 전달, 그리고 뇌의 생리 해부학적인 고려 요인에 대해 논의한다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제2권2호
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• pp.95-99
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• 2002

The Loose Part Monitoring System(LPMS) has been designed to detect. locate and evaluate detached or loosened parts and foreign objects in the reactor coolant system. In this paper, at first, we presents an application of the back propagation neural network. At the preprocessing step, the moving window average filter is adopted to reject the reject the low frequency background noise components. And then, extracting the acoustic signature such as Starting point of impact signal. Rising time. Half period. and Global time, they are used as the inputs to neural network . Secondly, we applied the neural network algorithm to LPMS in order to estimate the mass of loose parts. We trained the impact test data of YGN3 using the backpropagation method. The input parameter for training is Rising clime. Half Period amplitude. The result shored that the neural network would be applied to LPMS. Also, applying the neural network to thin practical false alarm data during startup and impact test signal at nuclear power plant, the false alarms are reduced effectively.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2022년도 춘계학술대회
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• pp.535-538
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• 2022

IoT 디바이스의 Plug & Play를 위하여 IoT 디바이스의 대표적인 유선 인터페이스인 USB의 종류를 이미지를 통하여 인식하는 모듈을 개발한다. IoT 디바이스를 구동시키기 위해서는 통신 및 디바이스 하드웨어를 구동하기 위한 드라이버가 필요하다. IoT 디바이스에 연결되는 유선 인터페이스를 스마트폰의 카메라 촬영을 통하여 얻은 이미지를 이용하여서 해당 통신 인터페이스를 인식한다. 대표적인 유선 인터페이스인 USB에 대하여 인공신경망 기반의 기계학습을 통하여 USB의 종류를 분류한다. 인공신경망의 충분한 학습을 위하여 인터넷을 통하여 USB 이미지를 수집하고, 이미지 처리를 통하여 추가적인 이미지 데이터 셋을 확보한다. 합성곱 신경망과 더불어서 다양한 심층 인공신경망으로 인식기를 구현하여서 그 성능을 비교, 평가한다.

• 제어로봇시스템학회논문지
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• 제13권9호
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• pp.825-831
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• 2007

In this paper, an interface between a human and a computer is presented. The human and computer interface(HCI) serves as another area of human and machine interfaces. Methods for the HCI we used are voice recognition and image recognition for detecting human's emotional feelings. The idea is that the computer can recognize the present emotional state of the human operator, and amuses him/her in various ways such as turning on musics, searching webs, and talking. For the image recognition process, the human face is captured, and eye and mouth are selected from the facial image for recognition. To train images of the mouth, we use the Hopfield Net. The results show 88%$\sim$92% recognition of the emotion. For the vocal recognition, neural network shows 80%$\sim$98% recognition of voice.

• 한국IT서비스학회지
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• 제7권2호
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• pp.149-164
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• 2008

As the computing landscape is shifting to ubiquitous computing environments, there is increasingly growing the demand for a variety of device controls that react to user's implicit activities without excessively drawing user attentions. We developed an EMG-based car interface that enables the physically handicapped to drive a car using their functioning peripheral nerves. Our method extracts electromyogram signals caused by wrist movements from four places in the user's forearm and then infers the user's intent from the signals using multi-layered neural nets. By doing so, it makes it possible for the user to control the operation of car equipments and thus to drive the car. It also allows the user to enter inputs into the embedded computer through a user interface like an instrument LCD panel. We validated the effectiveness of our method through experimental use in a car built with the EMG-based interface.

• IEIE Transactions on Smart Processing and Computing
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• 제4권3호
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• pp.133-140
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• 2015

Neural stimulating implantable medical devices (IMDs) have been widely used to treat neurological diseases or interface with sensory feedback for amputees or patients suffering from severe paralysis. More recent IMDs, such as retinal implants or brain-computer interfaces, demand higher performance to enable sophisticated therapies, while consuming power at higher orders of magnitude to handle more functions on a larger scale at higher rates, which limits the ability to supply the IMDs with primary batteries. Inductive power transmission across the skin is a viable solution to power up an IMD, while it demands high power efficiencies at every power delivery stage for safe and effective stimulation without increasing the surrounding tissue's temperature. This paper reviews various wireless neural stimulating systems and their power management techniques to maximize IMD power efficiency. We also explore both wireless electrical and optical stimulation mechanisms and their power requirements in implantable neural interface applications.

• 전기전자학회논문지
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• 제25권4호
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• pp.650-663
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• 2021

Subsurface topology estimation is an important factor in the geophysical survey. Electrical impedance tomography is one of the popular methods used for subsurface imaging. The EIT inverse problem is highly nonlinear and ill-posed; therefore, reconstructed conductivity distribution suffers from low spatial resolution. The subsurface region can be approximated as piece-wise separate regions with constant conductivity in each region; therefore, the conductivity estimation problem is transformed to estimate the shape and location of the layer boundary interface. Each layer interface boundary is treated as an open boundary that is described using front points. The subsurface domain contains multi-layers with very complex configurations, and, in such situations, conventional methods such as the modified Newton Raphson method fail to provide the desired solution. Therefore, in this work, we have implemented a 7-layer artificial neural network (ANN) as an inverse problem algorithm to estimate the front points that describe the multi-layer interface boundaries. An ANN model consisting of input, output, and five fully connected hidden layers are trained for interlayer boundary reconstruction using training data that consists of pairs of voltage measurements of the subsurface domain with three-layer configuration and the corresponding front points of interface boundaries. The results from the proposed ANN model are compared with the gravitational search algorithm (GSA) for interlayer boundary estimation, and the results show that ANN is successful in estimating the layer boundaries with good accuracy.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2007년도 하계학술대회 논문집
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• pp.394-396
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• 2007

In this paper proposed to neural network PI self-tuning direct controller using Error back propagation algorithm. Proposed controller applies to speed controller and current controller. Also, this built up the interface environment to drive it simply and exactly in any kind of reference, environment fluent and parameter transaction of PMSM. Neural network PI self-tuning simulator using Visual C++ and Matlab Simulation is organized to construct this environment, Built up interface has it's own purpose that even the user who don't have the accurate knowledge of Neural network can embody operation characteristic rapidly and easily.