• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2004.03a
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• pp.380-384
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• 2004

A fuel spiking test was performed to measure the surge margin of the compressor in a gas turbine engine. During the test, fuel spiking signal was superimposed on the engine controller demand and the mixed signals were used to control a fuel line servo-valve. For the superimposition, a subsystem composed of a fuel controller and a function generator was used. During the fuel spiking test, the original scheduled fuel signals and the modified signals were compared to guarantee the consistency excluding the spiking signals. The spiking signals were carefully selected to maintain the engine speed constant. The fuel spiking effects were checked by three dynamic pressure sensors. Sensors were placed before the servo-valve, after the servo-valve, and after the compressor location, respectively. The modulations of the spiking signal duration and fuel flow rate were examined to make the- operating point approach the surge region. The real engine test was performed at the Altitude Engine Test Facility (AETF) in Korea Aerospace Research Institute (KARI). In the real engine test, fuel spiking signals with 25~50 ㎳ of spiking signal time and 17~46 % of base fuel flow rate condition were used. The dithering signal was 5~6 ㎃ at 490 Hz. The test results showed good agreement between the fuel spiking signals and the fuel line pressure signals. Also, the compressor discharge pressure signals showed fuel spiking effects and the changes of the operating point on the compressor characteristic map could be traced.

• 유체기계공업학회:학술대회논문집
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• 2003.12a
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• pp.88-91
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• 2003

A fuel spiking test was performed to measure the surge margin of gas turbine engines. The surge marin was mainly determined by the compressors and fuel spiking was used to change the operating point in the compressor characteristic map while speed remained constant. To access the surge margin region different spiking signals were applied by modulations of time(frequency) and fuel flow rate(amplitude). The test results showed good agreements with expected fuel spiking patterns and possibility of further studies.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.11a
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• pp.15-17
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• 2011

DVS 카메라는 인간의 눈을 모델링하여 만들어져서 화면의 변화에 반응하여 Address - Event - Representation 데이터를 생성하고 이 데이터는 jAER Viwer를 통해 확인할 수 있다. 이렇게 생성된 DVS 카메라의 데이터를 Spiking Neural Network의 입력으로 주기 위해 GPU를 이용한 Spiking Neural Network 시뮬레이터인 GPUSNN과 jAER 사이에 인터페이스가 필요하다. 이 인터페이스를 이용하면 GPUSNN을 통해 비전 알고리즘을 빠르고 효과적으로 Spiking Neural Network 시뮬레이션을 할 수 있을 것이다.

• Journal of the Korean Society of Propulsion Engineers
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• v.8 no.2
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• pp.18-24
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• 2004

A fuel spiking test was performed to measure the surge margin of the compressor in a gas turbine engine. During the test, fuel spiking signal is superposed on the engine controller demand signals and the combined signals are used to control a fuel control valve. For the superposition, a subsystem composed of a fuel controller and a function generator is used. The real engine test was performed at the Altitude Engine Test Facility (AETF) in Korea Aerospace Research Institute (KARI). In the preliminary test, the fuel spiking signals are in good agreement with the dynamic pressure at the fuel line and at the compressor discharge point. After the preliminary test, a fuel spiking test to measure the surge point at a specific engine speed was performed. The test results show that the fuel spiking test is very effective in the measurement of surge.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.2
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• pp.165-170
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• 2020

The spiking neural network operates in a different mechanism than the existing neural network. The existing neural network transfers the output value to the next neuron via an activation function that does not take into account the biological mechanism for the input value to the neuron that makes up the neural network. In addition, there have been good results using deep structures such as VGGNet, ResNet, SSD and YOLO. spiking neural networks, on the other hand, operate more like the biological mechanism of real neurons than the existing activation function, but studies of deep structures using spiking neurons have not been actively conducted compared to in-depth neural networks using conventional neurons. This paper proposes the method of loading an deep neural network model made from existing neurons into a conversion tool and converting it into a spiking deep neural network through the method of replacing an existing neuron with a spiking neuron.

• Bulletin of the Korean Chemical Society
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• v.27 no.8
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• pp.1145-1148
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• 2006

Excitability is one of the basic and fundamental mechanisms utilized for signal transmission in living organisms. With reference to the condition by Marek and the condition by Schneider, we found a condition in which excitability with similar shapes can appear by chemical and electric perturbation. Our condition is constructed with 3 chemical channels and 1 electric channel, and can be used as a condition for a chemical spiking neuron and as a unit of a chemical spiking neural network.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.05a
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• pp.347-350
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• 2021

본 논문에서는 지도학습(Supervised Learning)알고리즘을 사용하는 모듈러 스파이킹 신경회로망(Modular Spiking Neural Networks)에서 학습의 진행 상황에 맞추어 학습용 데이터를 사용하는 다중 단계 학습알고리즘을 제안한다. 또한 컴퓨터 시뮬레이션에 의하여 항공영상 클러스터링 문제에 적용한 결과를 보임으로써 실제적인 문제에서의 적용 타당성과 가능성을 보인다.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.793-795
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• 2020

본 논문에서는 스파이킹 뉴론(Spiking Neuron)들이 쿨롱에너지 포텐셜 (Coulomb Energy Potential)을 가지는 스파이킹 신경회로망에서의 학습알고리즘을 일반화하여 구간연산(Interval Arithmetic)의 학습이 가능한 학습알고리즘을 제안한다. 제안하는 학습알고리즘은 입력 데이터로서 구간(Interval) 데이터와 포인트(Point) 데이터를 모두 학습 할 수 있는 일반화된 학습알고리즘으로서 간단한 컴퓨터 시뮬레이션을 통하여 범위(Lower bound & Upper bound)를 가지는 구간데이터와 포인트데이터의 통합적인 학습이 가능하고 전문가시스템(expert system)에서의 "don't care attributes"의 학습 등에도 활용이 가능함을 보인다.

• Current Optics and Photonics
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• v.6 no.4
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• pp.400-406
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• 2022

We numerically demonstrate that the inherent spiking behavior in the quasi-continuous-wave (QCW) operation of an Nd:YAG laser can be suppressed by adopting bias pumping. After spiking suppression, the output QCW pulses from a bias-pumped Nd:YAG laser are very stable, and they can maintain nearly the same temporal shape as that of pump pulse under different pump repetition rates and peak powers. Our study implies that bias pumping is an alternative method of spiking suppression in solid-state lasers, and the application areas of an Nd:YAG laser may be extended by bias pumping.

• Smart Media Journal
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• v.11 no.3
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• pp.26-30
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• 2022

Spiking neural network is a neural network that applies the working principle of real brain neurons. Due to the biological mechanism of neurons, it consumes less power for training and reasoning than conventional neural networks. Recently, as deep learning models become huge and operating costs increase exponentially, the spiking neural network is attracting attention as a third-generation neural network that connects convolution neural networks and recurrent neural networks, and related research is being actively conducted. However, in order to apply the spiking neural network model to the industry, a lot of research still needs to be done, and the problem of model retraining to apply a new model must also be solved. In this paper, we propose a method to minimize the cost of model retraining by extracting the weights of the existing trained deep learning model and converting them into the weights of the spiking neural network model. In addition, it was found that weight conversion worked correctly by comparing the results of inference using the converted weights with the results of the existing model.