• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.206-211
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• 1996

A major problem in automation of turning operation is the difficulty in obtaining a sufficient and reliable chip control. Therefore it becomes desirable to find a method which can detect the chip form. In this paper, a method of the identification of chip form using output of pyrometer and neural network technique is developed. An efficiency of developed method is examined by experiments in turning and the validity of it is confirmed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.4 no.2
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• pp.505-516
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• 2000

Cellular Neural Networks(CNN) is a nonlinear information-processing system that has a locally connected characteristic and is widely used in the real-time high speed image processing. In this paper, a practical system approach of time-multiplexing CNN implementations suitable for processing large and complex images using small CNN arrays is presented and $6\times6$ CNN hardware is designed for the processing of a large image. While previous implementations are mostly suitable for black and white applications because of the thresholded outputs, our approach is especially suitable for applications in gray image processing due to the analog nature of the state node. CNN chip is designed using a 0.65${\mu}{\textrm}{m}$ 2P2M(double poly, double metal) N-Well CMOS process technology. It contains about 15,400 devices on an area of about $1.85\times1.75$ md. The designed $6\times6$ CNN is tested for the edge detection of a large image input and it's performance is verified.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.39 no.9
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• pp.37-44
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• 2002

This paper proposes efficient digital hardware design method by using fixed weight of pre-trained neural network. For this, arithmetic operations of PEs(Processing Elements) are represented with matrix-vector multiplication. The relationship of fixed weight and input data present bit-level array structure architecture which is consisted operation node. To minimize the operation node, this paper proposes node elimination method and setting common node depend on bit pattern of weight. The result of FPGA simulation shows the efficiency on hardware cost and operation speed with full precision. And proposed design method makes possibility that many PEs are implemented to on-chip.

• The Journal of Korean Institute of Next Generation Computing
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• v.15 no.2
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• pp.7-16
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• 2019

Neural networks can be implemented in variety of ways, and specialized chips is being developed for hardware improvement. In order to apply such neural networks to wearable devices, the compactness and the low power operation are essential. In this point of view, a suitable implementation method is a digital circuit design using field programmable gate array (FPGA). To implement this system, the learning algorithm which takes up a large part in neural networks must be implemented within FPGA for better performance. In this paper, a back propagation algorithm among various learning algorithms is implemented using FPGA, and this neural network is verified by OR gate operation. In addition, it is confirmed that this neural network can be used to analyze various users' bio signal measurement results by learning algorithm.

• Journal of Power System Engineering
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• v.4 no.4
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• pp.108-115
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• 2000

The continuous chip in turning operation deteriorates the precision of workpiece and can cause a hazardous condition to operator. Thus the chip form control becomes a very important task for reliable turning process. Using the difference of energy radiated from the chip, the chip form is identified using the neural network of supervised data. The feed mechanism is adjusted in order to break continuous chip according to the result of the chip form recognition and shows a good approach for precision turning operation.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.5
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• pp.987-995
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• 2022

Deep learning-based profiling side-channel analysis has been many proposed. Deep learning-based profiling analysis is a technique that trains the relationship between the side-channel information and the intermediate values to the neural network, then finds the secret key of the attack device using the trained neural network. Recently, cross-device profiling side channel analysis was proposed to consider the realistic deep learning-based profiling side channel analysis scenarios. However, it has a limitation in that attack performance is lowered if the profiling device and the attack device have not the same chips. In this paper, an environment in which the profiling device and the attack device have not the same chips is defined as the different-device, and a novel deep learning-based profiling side-channel analysis on different-device is proposed. Also, MCNN is used to well extract the characteristic of each data. We experimented with the six different boards to verify the attack performance of the proposed method; as a result, when the proposed method was used, the minimum number of attack traces was reduced by up to 25 times compared to without the proposed method.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2019.11a
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• pp.44-45
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• 2019

최근 CNN 등 인공신경망은 최근 이미지 분류, 객체 인식, 자연어 처리 등 다양한 분야에서 뛰어난 성능을 보이고 있다. 그러나, 대부분의 분야에서 보다 더 높은 성능을 얻기 위해 사용한 인공신경망 모델들은 파라미터 수 및 연산량 등이 방대하여, 모바일 및 IoT 디바이스 같은 연산량이나 메모리가 제한된 환경에서 추론하기에는 제한적이다. 따라서 연산량 및 모델 파라미터 수를 압축하기 위한 딥러닝 경량화 알고리즘이 연구되고 있다. 본 논문에서는 임베디트 보드에서의 압축된 CNN 모델의 성능을 검증한다. 인공지능 지원 맞춤형 칩인 QCS605 를 내장한 임베디드 보드에서 카메라로 입력한 영상에 대해서 원 CNN 모델과 압축된 CNN 모델의 분류 성능과 동작속도 비교 분석한다. 본 논문의 실험에서는 CNN 모델로 MobileNetV2, VGG16 을 사용했으며, 주어진 모델에서 가지치기(pruning) 기법, 양자화, 행렬 분해 등의 인공신경망 압축 기술을 적용하였을 때 원래의 모델 대비 추론 시간 및 분류의 정확도 성능을 분석하고 인공신경망 압축 기술의 유용성을 확인하였다.

• Korean Journal of Food Science and Technology
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• v.28 no.5
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• pp.916-921
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• 1996

This study was conducted to measure fat contents of potatochips by near infrared spectroscopy (NIRS). Both potatochip powder and potatochips were used to find correlations between the absorbance at certain wavelengths find the fat contents. Based on the correlation analysis, linear regression models predicting the fat contents were developed to predict the fat contents. Artificial neural network (ANN) models were also developed. Predicted values were compared to the measured ones. The regression and the ANN model predicting the fat contents of potatochip powder had determination coefficients of 0.93 and 0.92, and standard errors of prediction (SEP) of 1.29% and 1.17%, respectively. The correlation analysis of potatochips showed that the determination coefficients were low. Therefore, the fat contents of not potatochips but potatochip powder could be measured by NIRS.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.10 no.3
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• pp.75-82
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• 2001

The continuous chip in turning operation deteriorates the precision of workpiece and can cause a hazardous condition to operator. Thus the chip form becomes a very important task for reliable turning process. The chip form is identified using the neural network of supervise data Through the measurement of energy radiated from the chip. The feed mechanism os adjusted in order to break continuous chip according to the result of the chip form recognition and it shows a good approach for precision turning operation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.6A
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• pp.379-385
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• 2003

In this paper, we propose a time constant control method for sieving local minimum problem of the multiuser detector using Hopfield neural network for synchronous multi-rate code division multiple access(CDMA) system in selective fading environments and its performance is compared with that of the parallel interference cancellation(PIC). We also assume that short scrambling codes of 256 chip length are used an uplink, suggest a simple correlation estimation algorithm and circuit complexity reduction method by using cyclostationarity property of short scrambling code.It is verified that multiuser detector using Hopfield neural network more efficiently cancels multiple access interference(MAI) and obtain better bit error rate and near-far resistant than conventional detector.