• 로봇학회논문지
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• 제17권2호
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• pp.133-141
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• 2022

This paper introduces model compression algorithms which make a deep neural network smaller and faster for embedded systems. The model compression algorithms can be largely categorized into pruning, quantization and knowledge distillation. In this study, gradual pruning, quantization aware training, and knowledge distillation which learns the activation boundary in the hidden layer of the teacher neural network are integrated. As a large deep neural network is compressed and accelerated by these algorithms, embedded computing boards can run the deep neural network much faster with less memory usage while preserving the reasonable accuracy. To evaluate the performance of the compressed neural networks, we evaluate the size, latency and accuracy of the deep neural network, DenseNet201, for image classification with CIFAR-10 dataset on the NVIDIA Jetson Xavier.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2006년도 춘계 학술발표회 논문집
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• pp.1159-1164
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• 2006

In this paper an artificial neural network model is developed to estimate the unconfined compression strength of Deep Cement Mixing(DCM) treated soil. A database which consists of a number of unconfined compression test result compiled from 9 clay sites is used to train and test of the artificial neural network model. Developed neural network model requires water content of soil, unit weight of soil, passing percent of #200 sieve, weight of cement, w-c ratio as input variables. It is found that the developed artificial neural network model can predict more precise and reliable unconfined compression strength than the conventional empirical models.

• 반도체디스플레이기술학회지
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• 제22권1호
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• pp.129-133
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• 2023

Image compression is a fundamental technique in the field of digital image processing, which will help to decrease the storage space and to transmit the files efficiently. Recently many deep learning techniques have been proposed to promise results on image compression field. Since many image compression techniques have artifact problems, this paper has compared two deep learning approaches to verify their performance experimentally to solve the problems. One of the approaches is a deep autoencoder technique, and another is a deep convolutional neural network (CNN). For those results in the performance of peak signal-to-noise and root mean square error, this paper shows that deep autoencoder method has more advantages than deep CNN approach.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송∙미디어공학회 2022년도 추계학술대회
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• pp.112-115
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• 2022

The process of deep learning usually needs to deal with massive data which has greatly limited the development of deep learning technologies today. Convolutional Neural Network (CNN) structure is often used to solve image classification problems. However, a large number of images may be required in order to train an image in CNN, which is a heavy burden for existing computer systems to handle. If the image data can be compressed under the premise that the computer hardware system remains unchanged, it is possible to train more datasets in deep learning. However, image compression usually adopts the form of lossy compression, which will lose part of the image information. If the lost information is key information, it may affect learning performance. In this paper, we will analyze the effect of image compression on deep learning performance on CNN-based cat and dog classification. Through the experiment results, we conclude that the compression of images does not have a significant impact on the accuracy of deep learning.

• Journal of Information Processing Systems
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• 제17권2호
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• pp.411-425
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• 2021

In this paper, a vehicle recognition algorithm based on deep convolutional neural network and compression dictionary is proposed. Firstly, the network structure of fine vehicle recognition based on convolutional neural network is introduced. Then, a vehicle recognition system based on multi-scale pyramid convolutional neural network is constructed. The contribution of different networks to the recognition results is adjusted by the adaptive fusion method that adjusts the network according to the recognition accuracy of a single network. The proportion of output in the network output of the entire multiscale network. Then, the compressed dictionary learning and the data dimension reduction are carried out using the effective block structure method combined with very sparse random projection matrix, which solves the computational complexity caused by high-dimensional features and shortens the dictionary learning time. Finally, the sparse representation classification method is used to realize vehicle type recognition. The experimental results show that the detection effect of the proposed algorithm is stable in sunny, cloudy and rainy weather, and it has strong adaptability to typical application scenarios such as occlusion and blurring, with an average recognition rate of more than 95%.

• 융합신호처리학회논문지
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• 제22권3호
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• pp.99-103
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• 2021

최근 CNN(Convolutional Neural Network)이 다양한 컴퓨터 비전 분야에서 우수한 성능으로 널리 사용되고 있다. 그러나 CNN은 계산 집약적이고 많은 메모리가 요구되어 한정적인 하드웨어 자원을 가지는 모바일이나 IoT(Internet of Things) 기기에 적용하기 어렵다. 이런 한계를 해결하기 위해, 기존의 학습된 모델의 성능을 최대한 유지하며 네트워크의 크기를 줄이는 인공신경망 경량화 연구가 진행되고 있다. 본 논문은 신경망 압축 기술 중 하나인 프루닝(Pruning)의 문턱값을 동적으로 조정하는 CNN 압축 기법을 제안한다. 프루닝될 가중치를 결정하는 문턱값을 실험적, 경험적으로 정하는 기존의 기술과 달리 정확도의 저하를 방지하는 최적의 문턱값을 동적으로 찾을 수 있으며, 경량화된 신경망을 얻는 시간을 단축할 수 있다. 제안 기법의 성능 검증을 위해 MNIST 데이터 셋을 사용하여 LeNet을 훈련시켰으며, 정확도 손실 없이 약 1.3 ~ 3배의 시간을 단축하여 경량화된 LeNet을 얻을 수 있었다.

• Smart Structures and Systems
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• 제32권3호
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• pp.179-193
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• 2023

This study explores an alternative to the existing centralized process for data anomaly detection in modern Internet of Things (IoT)-based structural health monitoring (SHM) systems. An edge intelligence framework is proposed for the early detection and classification of various data anomalies facilitating quality enhancement of acquired data before transmitting to a central system. State-of-the-art deep neural network pruning techniques are investigated and compared aiming to significantly reduce the network size so that it can run efficiently on resource-constrained edge devices such as wireless smart sensors. Further, depthwise separable convolution (DSC) is invoked, the integration of which with advanced structural pruning methods exhibited superior compression capability. Last but not least, quantization-aware training (QAT) is adopted for faster processing and lower memory and power consumption. The proposed edge intelligence framework will eventually lead to reduced network overload and latency. This will enable intelligent self-adaptation strategies to be employed to timely deal with a faulty sensor, minimizing the wasteful use of power, memory, and other resources in wireless smart sensors, increasing efficiency, and reducing maintenance costs for modern smart SHM systems. This study presents a theoretical foundation for the proposed framework, the validation of which through actual field trials is a scope for future work.

• 방송공학회논문지
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• 제23권3호
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• pp.383-394
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• 2018

본 논문에서는 최근 활발히 연구되고 있는 딥 러닝 기반의 이미지와 비디오 압축 기술에 대해 살펴본다. 딥 러닝 기반의 이미지 압축 기술은 심층 신경망에 압축 대상 이미지를 입력하고 반복적 또는 일괄적 방식으로 은닉 벡터를 추출하여 부호화한다. 이미지 압축 효율을 높이기 위해 심층 신경망은 복원 이미지의 화질은 높이면서 부호화된 은닉 벡터가 보다 적은 비트로 표현될 수 있도록 학습된다. 이러한 기술들은 특히 저 비트율에서 기존의 이미지 압축 기술에 비해 뛰어난 화질의 이미지를 생성할 수 있다. 한편, 딥 러닝 기반의 비디오 압축 기술은 압축 대상 비디오를 직접 입력하여 처리하기 보다는 기존 비디오 코덱의 압축 툴 성능을 개선하는 접근법을 취하고 있다. 본 논문에서 소개하는 심층 신경망 기술들은 최신 비디오 코덱의 인루프 필터를 대체하거나 추가적인 후처리 필터로 사용되어 복원 영상의 화질 개선을 통해 압축 효율을 향상시킨다. 마찬가지로, 화면 내 예측 및 부호화에 적용된 심층 신경망 기술들은 기존 화면 내 예측 툴과 함께 사용되어 예측 정확도를 높이거나 새로운 화면 내 부호화 과정을 추가함으로써 압축 효율을 향상 시킨다.

• International Journal of Computer Science & Network Security
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• 제23권11호
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• pp.73-76
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• 2023

We introduce DCNN and DRAE appraoches for compression of medical videos, in order to decrease file size and storage requirements, there is an increasing need for medical video compression nowadays. Using a lossy compression technique, a higher compression ratio can be attained, but information will be lost and possible diagnostic mistakes may follow. The requirement to store medical video in lossless format results from this. The aim of utilizing a lossless compression tool is to maximize compression because the traditional lossless compression technique yields a poor compression ratio. The temporal and spatial redundancy seen in video sequences can be successfully utilized by the proposed DCNN and DRAE encoding. This paper describes the lossless encoding mode and shows how a compression ratio greater than 2 (2:1) can be achieved.

• 한국음향학회지
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• 제37권6호
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• pp.475-482
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• 2018

본 논문에서는 DNN(Deep Neural Network) 기반 오디오 표식을 위한 데이터 증강 방법을 연구한다. 본 시스템에서는 오디오 신호를 멜-스펙트로그램으로 변환하여 오디오 표식을 위한 심층신경망의 입력으로 사용한다. 적은 수의 훈련 데이터를 사용하는 경우 발생하는 문제를 해결하기 위해, 타임 스트레칭, 피치 변화, 동적 영역 압축, 블록 혼합 등의 방법을 사용하여 훈련 데이터를 증강시켰다. 사용된 데이터 증강 기법의 최적 파라미터와 최적 조합을 오디오 표식 시뮬레이션을 통해 확인하였다.