• Proceedings of the Korea Information Processing Society Conference
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• 2017.04a
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• pp.928-931
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• 2017

네트워크 트래픽 데이터를 정제하여, Convolutional Neural Network Model 훈련에 적합한 데이터 세트로 변환하는데, 그 방법은 패킷 단위의 트래픽 데이터를 이미지 형태로 만드는 것이다. 완성된 데이터 세트를 훈련데이터로 하여 Convolutional Neural Network Model에 훈련하고, 훈련데이터의 이미지 크기를 변환해가며 훈련시킨 결과에 대해 비교 분석 및 평가를 진행한다.

• KIISE Transactions on Computing Practices
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• v.23 no.3
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• pp.194-198
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• 2017

In this paper, we propose a revised Deep Convolutional Neural Network (DCNN) model to extract Protein-Protein Interaction (PPIs) from the scientific literature. The proposed method has the merit of improving performance by applying various global features in addition to the simple lexical features used in conventional relation extraction approaches. In the experiments using AIMed, which is the most famous collection used for PPI extraction, the proposed model shows state-of-the art scores (78.0 F-score) revealing the best performance so far in this domain. Also, the paper shows that, without conducting feature engineering using complicated language processing, convolutional neural networks with embedding can achieve superior PPIE performance.

• Journal of Korea Multimedia Society
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• v.24 no.8
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• pp.1020-1025
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• 2021

Facial alignment is very important task for human life. And facial landmark detection is one of the instrumental methods in face alignment. We introduce the stacked hourglass networks with transposed convolutional layers for facial landmark detection. our method substitutes nearest neighbor upsampling for transposed convolutional layer. Our method returns better accuracy in facial landmark detection compared to stacked hourglass networks with nearest neighbor upsampling.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.359-362
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• 2016

Face is an information unique to each person such as Iris, fingerprints, etc,. Research on face recognition are in progress continuously from the past to the present. Through these research, various face recognition methods have appeared. Among these methods, there are face recognition algorithms using the face data composed in stereo. In this paper, Convolutional Neural Network with Stereo Images as input was used for face recognition. This method showed better performance than the result of stereo face recognition using PCA that is used frequently in face recognition.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.11a
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• pp.106-107
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• 2020

본 논문에서는 촬영과 동시에 유통되는 생방송 영상의 실시간 지적재산권 보호를 위한 Convolutional Neural Network를 기반으로 하는 워터마킹 프로세서의 구조를 제안한다. 제안하는 워터마킹 프로세서는 전처리 네트워크와 삽입 네트워크를 최적화하여 ASIC 칩으로 제작한다. 이는 영상을 입력으로 하는 딥 러닝 분야에서 많이 사용되는 CNN을 기반으로 하기 때문에 일반적인 딥 러닝 가속기 설계로 간주된다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.9
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• pp.1224-1230
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• 2020

Since the spread of smart phones, interest in wearable devices has increased and diversified, and is closely related to the lives of users, and has been used as a method for providing personalized services. In this paper, we propose a method to detect the user's behavior by applying information from a 3-axis acceleration sensor and a 3-axis gyro sensor embedded in a smartphone to a convolutional neural network. Human behavior differs according to the size and range of motion, starting and ending time, including the duration of the signal data constituting the motion. Therefore, there is a performance problem for accuracy when applied to a convolutional neural network as it is. Therefore, we proposed a Time-Division Feature Fusion Convolutional Neural Network (TDFFCNN) that learns the characteristics of the sensor data segmented over time. The proposed method outperformed other classifiers such as SVM, IBk, convolutional neural network, and long-term memory circulatory neural network.

• Journal of IKEEE
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• v.27 no.4
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• pp.524-530
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• 2023

This paper proposes an efficient algorithm to detect CAN (Controller Area Network) bus attack based on a lightweight CNN (Convolutional Neural Network), and an IDS(Intrusion Detection System) was designed, implemented, and verified with FPGA. Compared to conventional CNN-based IDS, the proposed IDS detects CAN bus attack on a frame-by-frame basis, enabling accurate and rapid response. Furthermore, the proposed IDS can significantly reduce hardware since it exploits only one convolutional layer, compared to conventional CNN-based IDS. Simulation and implementation results show that the proposed IDS effectively detects various attacks on the CAN bus.

• Journal of Broadcast Engineering
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• v.23 no.5
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• pp.614-621
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• 2018

This paper proposes a method of restoring corrupted depth image captured by depth camera through unsupervised learning using generative adversarial network (GAN). The proposed method generates restored face depth images using 3D morphable model convolutional neural network (3DMM CNN) with large-scale CelebFaces Attribute (CelebA) and FaceWarehouse dataset for training deep convolutional generative adversarial network (DCGAN). The generator and discriminator equip with Wasserstein distance for loss function by utilizing minimax game. Then the DCGAN restore the loss of captured facial depth images by performing another learning procedure using trained generator and new loss function.

• Journal of Korea Multimedia Society
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• v.21 no.8
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• pp.858-863
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• 2018

In this paper, we propose and implement a multi-level convolutional neural network (CNN) algorithm to identify the sexually explicit and lewdness of various image files, and verify its effectiveness by using unauthorized image files generated in the actual military. The proposed algorithm increases the accuracy by applying the convolutional artificial neural network step by step to minimize classification error between similar categories. Experimental data have categorized 20,005 images in the real field into 6 authorization categories and 11 non-authorization categories. Experimental results show that the overall detection rate is 99.51% for the image files. In particular, the excellence of the proposed algorithm is verified through reducing the identification error rate between similar categories by 64.87% compared with the general CNN algorithm.

• Proceedings of the Korean Society of Computer Information Conference
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• 2017.01a
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• pp.275-277
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• 2017

사물인터넷(Internet of things)의 발전과 함께 스마트 팩토리에 대한 관심이 증대되고 있다. 제조의 전 과정에서 발생하는 데이터를 실시간으로 수집하고 관리를 자동화하는 것이 스마트 팩토리의 목적이다. 그러나 공장에서는 현재까지도 많이 사용되는 아날로그 게이지를 관리하는 일은 사람의 노동력을 필요로 한다. 또한 아날로그 게이지는 쓰임새에 따라 모양과 형태가 매우 다양하다. 본 논문에서는 아날로그 게이지의 형태에 따라 분류하는 방법에 대해 제안한다. 제안하는 방법은 학습하기 위해 필요한 게이지 영상 데이터를 수집하고 나서 각 분류에 속하는 이미지 데이터를 CNN(Convolutional Neural Network) 딥러닝 기법으로 학습시킨 후, 각 분류에 해당하는 특징 정보를 추출하고 아날로그 게이지의 형태를 인식하는 방법을 제안한다.