• Title/Summary/Keyword: 1차원 CNN

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Deep Learning based Frame Synchronization Using Convolutional Neural Network (합성곱 신경망을 이용한 딥러닝 기반의 프레임 동기 기법)

  • Lee, Eui-Soo;Jeong, Eui-Rim
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.24 no.4
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    • pp.501-507
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    • 2020
  • This paper proposes a new frame synchronization technique based on convolutional neural network (CNN). The conventional frame synchronizers usually find the matching instance through correlation between the received signal and the preamble. The proposed method converts the 1-dimensional correlator ouput into a 2-dimensional matrix. The 2-dimensional matrix is input to a convolutional neural network, and the convolutional neural network finds the frame arrival time. Specifically, in additive white gaussian noise (AWGN) environments, the received signals are generated with random arrival times and they are used for training data of the CNN. Through computer simulation, the false detection probabilities in various signal-to-noise ratios are investigated and compared between the proposed CNN-based technique and the conventional one. According to the results, the proposed technique shows 2dB better performance than the conventional method.

Robust Deep Learning-Based Profiling Side-Channel Analysis for Jitter (지터에 강건한 딥러닝 기반 프로파일링 부채널 분석 방안)

  • Kim, Ju-Hwan;Woo, Ji-Eun;Park, So-Yeon;Kim, Soo-Jin;Han, Dong-Guk
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.30 no.6
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    • pp.1271-1278
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    • 2020
  • Deep learning-based profiling side-channel analysis is a powerful analysis method that utilizes the neural network to profile the relationship between the side-channel information and the intermediate value. Since the neural network interprets each point of the signal in a different dimension, jitter makes it much hard that the neural network with dimension-wise weights learns the relationship. This paper shows that replacing the fully-connected layer of the traditional CNN (Convolutional Neural Network) with global average pooling (GAP) allows us to design the inherently robust neural network inherently for jitter. We experimented with the ChipWhisperer-Lite board to demonstrate the proposed method: as a result, the validation accuracy of the CNN with a fully-connected layer was only up to 1.4%; contrastively, the validation accuracy of the CNN with GAP was very high at up to 41.7%.

Power Analysis Attack of Block Cipher AES Based on Convolutional Neural Network (블록 암호 AES에 대한 CNN 기반의 전력 분석 공격)

  • Kwon, Hong-Pil;Ha, Jae-Cheol
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.21 no.5
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    • pp.14-21
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    • 2020
  • In order to provide confidential services between two communicating parties, block data encryption using a symmetric secret key is applied. A power analysis attack on a cryptosystem is a side channel-analysis method that can extract a secret key by measuring the power consumption traces of the crypto device. In this paper, we propose an attack model that can recover the secret key using a power analysis attack based on a deep learning convolutional neural network (CNN) algorithm. Considering that the CNN algorithm is suitable for image analysis, we particularly adopt the recurrence plot (RP) signal processing method, which transforms the one-dimensional power trace into two-dimensional data. As a result of executing the proposed CNN attack model on an XMEGA128 experimental board that implemented the AES-128 encryption algorithm, we recovered the secret key with 22.23% accuracy using raw power consumption traces, and obtained 97.93% accuracy using power traces on which we applied the RP processing method.

Dynamic RNN-CNN malware classifier correspond with Random Dimension Input Data (임의 차원 데이터 대응 Dynamic RNN-CNN 멀웨어 분류기)

  • Lim, Geun-Young;Cho, Young-Bok
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.23 no.5
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    • pp.533-539
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    • 2019
  • This study proposes a malware classification model that can handle arbitrary length input data using the Microsoft Malware Classification Challenge dataset. We are based on imaging existing data from malware. The proposed model generates a lot of images when malware data is large, and generates a small image of small data. The generated image is learned as time series data by Dynamic RNN. The output value of the RNN is classified into malware by using only the highest weighted output by applying the Attention technique, and learning the RNN output value by Residual CNN again. Experiments on the proposed model showed a Micro-average F1 score of 92% in the validation data set. Experimental results show that the performance of a model capable of learning and classifying arbitrary length data can be verified without special feature extraction and dimension reduction.

Deep learning-based target distance and velocity estimation technique for OFDM radars (OFDM 레이다를 위한 딥러닝 기반 표적의 거리 및 속도 추정 기법)

  • Choi, Jae-Woong;Jeong, Eui-Rim
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.26 no.1
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    • pp.104-113
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    • 2022
  • In this paper, we propose deep learning-based target distance and velocity estimation technique for OFDM radar systems. In the proposed technique, the 2D periodogram is obtained via 2D fast Fourier transform (FFT) from the reflected signal after removing the modulation effect. The periodogram is the input to the conventional and proposed estimators. The peak of the 2D periodogram represents the target, and the constant false alarm rate (CFAR) algorithm is the most popular conventional technique for the target's distance and speed estimation. In contrast, the proposed method is designed using the multiple output convolutional neural network (CNN). Unlike the conventional CFAR, the proposed estimator is easier to use because it does not require any additional information such as noise power. According to the simulation results, the proposed CNN improves the mean square error (MSE) by more than 5 times compared with the conventional CFAR, and the proposed estimator becomes more accurate as the number of transmitted OFDM symbols increases.

An Artificial Intelligence Game Agent Using CNN Based Records Learning and Reinforcement Learning (CNN 기반 기보학습 및 강화학습을 이용한 인공지능 게임 에이전트)

  • Jeon, Youngjin;Cho, Youngwan
    • Journal of IKEEE
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    • v.23 no.4
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    • pp.1187-1194
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    • 2019
  • This paper proposes a CNN architecture as value function network of an artificial intelligence Othello game agent and its learning scheme using reinforcement learning algorithm. We propose an approach to construct the value function network by using CNN to learn the records of professional players' real game and an approach to enhance the network parameter by learning from self-play using reinforcement learning algorithm. The performance of value function network CNN was compared with existing ANN by letting two agents using each network to play games each other. As a result, the winning rate of the CNN agent was 69.7% and 72.1% as black and white, respectively. In addition, as a result of applying the reinforcement learning, the performance of the agent was improved by showing 100% and 78% winning rate, respectively, compared with the network-based agent without the reinforcement learning.

Efficient One-dimensional Current Configuration and Encoding Method for ITSC Diagnosis of 3-Phase Induction Motor using CNN (CNN을 이용한 3상 유도전동기 ITSC 진단의 효율적인 1차원 전류 신호 구성 및 Encoding방법)

  • Yeong-Jin Goh
    • Journal of IKEEE
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    • v.28 no.2
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    • pp.180-186
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    • 2024
  • This paper proposes an efficient fault diagnosis method for ITSC(Inter-Turn Short Circuit) in three-phase induction motors using CNN. By utilizing only the D-axis component of the D-Q synchronous coordinate system, it compares SWM(Slide Window Method) and GAF(Gramian Angular Field) methods for image encoding. Results show GAF achieving ~74% accuracy, while SWM achieves ~65%, indicating GAF's superiority by 9%. Learning time (~14.74s) remains consistent, particularly with epochs ≤ 100, showcasing faster learning.

Combining 2D CNN and Bidirectional LSTM to Consider Spatio-Temporal Features in Crop Classification (작물 분류에서 시공간 특징을 고려하기 위한 2D CNN과 양방향 LSTM의 결합)

  • Kwak, Geun-Ho;Park, Min-Gyu;Park, Chan-Won;Lee, Kyung-Do;Na, Sang-Il;Ahn, Ho-Yong;Park, No-Wook
    • Korean Journal of Remote Sensing
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    • v.35 no.5_1
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    • pp.681-692
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    • 2019
  • In this paper, a hybrid deep learning model, called 2D convolution with bidirectional long short-term memory (2DCBLSTM), is presented that can effectively combine both spatial and temporal features for crop classification. In the proposed model, 2D convolution operators are first applied to extract spatial features of crops and the extracted spatial features are then used as inputs for a bidirectional LSTM model that can effectively process temporal features. To evaluate the classification performance of the proposed model, a case study of crop classification was carried out using multi-temporal unmanned aerial vehicle images acquired in Anbandegi, Korea. For comparison purposes, we applied conventional deep learning models including two-dimensional convolutional neural network (CNN) using spatial features, LSTM using temporal features, and three-dimensional CNN using spatio-temporal features. Through the impact analysis of hyper-parameters on the classification performance, the use of both spatial and temporal features greatly reduced misclassification patterns of crops and the proposed hybrid model showed the best classification accuracy, compared to the conventional deep learning models that considered either spatial features or temporal features. Therefore, it is expected that the proposed model can be effectively applied to crop classification owing to its ability to consider spatio-temporal features of crops.

Secure Communication using N-double scroll in HyperChaos circuit. (N-double scroll을 이용한 하이퍼카오스 회로에서의 암호 통신)

  • 배영철;김주완
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2001.10a
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    • pp.701-704
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    • 2001
  • Nowadays there are being done many researches on chaos phenomenon among an assortment of group. Currently, already many applications has been developed, applying this phenomenon to engineering problem. now we are to show how we achieved secure communication through hyperchaotic synchronization system using 1-dimensional CNN(Cellular Neural Network). we focused on materializing secure communication.

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Secure Communication using N-double scroll in Hyperchaos circuit. (N-double scroll을 이용한 하이퍼카오스 회로에서의 카오스 동기화)

  • 배영철;김주완
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2001.10a
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    • pp.705-708
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    • 2001
  • Nowadays there are being done many researches on chaos phenomenon among many kinds of people. Currently, already many applications has been developed, and they applied this phenomenon to engineering problem. now we have tried to make it possible to use secure communication through hyperchaotic synchronization system using 1-dimensional CNN(Cellular Neural Network). we focused on materializing secure communication.

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