• 한국멀티미디어학회논문지
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• 제21권10호
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• pp.1150-1161
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• 2018

Classification is widely used in medical images to categorize patients and non-patients. However, conventional classification requires a complex procedure, including some rigid steps such as pre-processing, segmentation, feature extraction, detection, and classification. In this paper, we propose a novel convolutional neural network (CNN), called LeukemiaNet, to specifically classify two different types of leukemia, including acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML), and non-cancerous patients. To extend the limited dataset, a PCA color augmentation process is utilized before images are input into the LeukemiaNet. This augmentation method enhances the accuracy of our proposed CNN architecture from 96.9% to 97.2% for distinguishing ALL, AML, and normal cell images.

• Journal of Electrical Engineering and Technology
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• 제13권6호
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• pp.2468-2478
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• 2018

We propose the Sparse Feature Convolutional Neural Network (SFCNN) to reduce the volume of convolutional neural networks (CNNs). Despite the superior classification performance of CNNs, their enormous network volume requires high computational cost and long processing time, making real-time applications such as online-training difficult. We propose an advanced network that reduces the volume of conventional CNNs by producing a region-based sparse feature map. To produce the sparse feature map, two complementary region-based value extraction methods, cluster max extraction and local value extraction, are proposed. Cluster max is selected as the main function based on experimental results. To evaluate SFCNN, we conduct an experiment with two conventional CNNs. The network trains 59 times faster and tests 81 times faster than the VGG network, with a 1.2% loss of accuracy in multi-class classification using the Caltech101 dataset. In vehicle classification using the GTI Vehicle Image Database, the network trains 88 times faster and tests 94 times faster than the conventional CNNs, with a 0.1% loss of accuracy.

• Journal of Multimedia Information System
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• 제3권3호
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• pp.91-96
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• 2016

Network security is an interesting area in Information Technology. It has an important role for the manager monitor and control operating of the network. There are many techniques to help us prevent anomaly or malicious activities such as firewall configuration etc. Intrusion Detection System (IDS) is one of effective method help us reduce the cost to build. The more attacks occur, the more necessary intrusion detection needs. IDS is a software or hardware systems, even though is a combination of them. Its major role is detecting malicious activity. In recently, there are many researchers proposed techniques or algorithms to build a tool in this field. In this paper, we improve the performance of IDS. We explore and analyze the impact of activation functions applying to recurrent neural network model. We use to KDD cup dataset for our experiment. By our experimental results, we verify that our new tool of IDS is really significant in this field.

• Journal of Information Processing Systems
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• 제17권4호
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• pp.707-720
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• 2021

To improve the accuracy of the recommendation system, multi-criteria recommendation systems have been widely researched. However, it is highly complicated to extract the preferred features of users and items from the data. To this end, subjective indicators, which indicate a user's priorities for personalized recommendations, should be derived. In this study, we propose a method for generating recommendation candidates by predicting multi-criteria ratings from reviews and using them to derive user priorities. Using a deep learning model based on convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM), multi-criteria prediction ratings were derived from reviews. These ratings were then aggregated to form a linear regression model to predict the overall rating. This model not only predicts the overall rating but also uses the training weights from the layers of the model as the user's priority. Based on this, a new score matrix for recommendation is derived by calculating the similarity between the user and the item according to the criteria, and an item suitable for the user is proposed. The experiment was conducted by collecting the actual "TripAdvisor" dataset. For performance evaluation, the proposed method was compared with a general recommendation system based on singular value decomposition. The results of the experiments demonstrate the high performance of the proposed method.

• 전기전자학회논문지
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• 제22권4호
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• pp.1175-1179
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• 2018

본 논문에서는 뇌파 신호를 이용하여 환자의 경련을 감지하는 순환 CNN (Convolutional Neural Networks)을 제안한다. 제안 된 방법은 뇌파 신호의 스펙트럼 특성과 전극의 위치를 보존하기 위해 영상으로 데이터를 매핑하여 처리하였다. 스펙트럼 전처리 과정을 거친 후 CNN에 입력하고 공간 및 시간 특성을 웨이블릿 변환(wavelet transform)없이 추출하여 발작을 검출하였다. 여기에 사용된 보스턴 매사추세츠 공과 대학 (Boston Massachusetts Institute of Technology, CHB-MIT) 아동 병원의 데이터셋 결과는 시간당 0.85의 민감도와 90 %의 위양성 비율 (FPR)을 보였다.

• 전기전자학회논문지
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• 제22권4호
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• pp.1131-1139
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• 2018

본 논문에서는 합성곱 신경망을 이용한 유기된 물체 인식 방법을 제안한다. 유기된 물체 인식 방법은 영상 내에서 유기 물체에 대한 영역을 먼저 검출하며 검출된 영역이 있을 경우 해당 영역에 합성곱 신경망을 적용하여 어떤 물체를 나타내는지 인식하는 과정을 거친다. 실험은 쓰레기 무단투기를 검출하는 응용 시스템을 통해 진행되었다. 실험 결과, 유기 물체에 대한 영역을 효율적으로 검출하는 것을 볼 수 있었다. 검출된 영역은 합성곱 신경망으로 들어가 쓰레기인지 아닌지 분류되는 과정을 거쳤다. 이를 위해 자체적으로 수집한 쓰레기 데이터와 오픈 데이터베이스로 합성곱 신경망을 학습시켰다. 학습 결과, 학습에 포함되지 않은 테스트셋에 대해 약 97%의 정확도를 달성하였다.

• 방송공학회논문지
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• 제24권3호
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• pp.420-429
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• 2019

최근 CNN (Convolutional Neural Network) 기반의 화질 개선 기술이 H.265/HEVC와 같은 블록 기반 영상 압축 표준을 사용하여 압축된 영상의 화질을 향상시키는 데 적극적으로 사용되어 왔다. 이 논문은 이러한 영상 압축 기술을 위한 화질 개선 연구의 추세를 요약하고 분석하는 것을 목표로 한다. 먼저, 화질 개선을 위한 CNN의 구성 요소를 살펴보고 이미지 도메인에서의 사전 연구를 요약한다. 다음으로 네트워크 구조, 데이터셋 및 학습 방법의 세 가지 측면에서 관련 연구들을 정리하고 성능 비교를 위한 구현 및 실험결과를 제시하고자 한다.

• Journal of Information Processing Systems
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• 제18권3호
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• pp.411-427
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• 2022

Digging historical and cultural information from seals in ancient books is of great significance. However, ancient Chinese seal samples are scarce and carving methods are diverse, and traditional digital image processing methods based on greyscale have difficulty achieving superior segmentation and recognition performance. Recently, some deep learning algorithms have been proposed to address this problem; however, current neural networks are difficult to train owing to the lack of datasets. To solve the afore-mentioned problems, we proposed an SEL-RefineMask which combines selector of feature pyramid network (SEL-FPN) with RefineMask to segment and recognize seals. We designed an SEL-FPN to intelligently select a specific layer which represents different scales in the FPN and reduces the number of anchor frames. We performed experiments on some instance segmentation networks as the baseline method, and the top-1 segmentation result of 64.93% is 5.73% higher than that of humans. The top-1 result of the SEL-RefineMask network reached 67.96% which surpassed the baseline results. After segmentation, a vision transformer was used to recognize the segmentation output, and the accuracy reached 91%. Furthermore, a dataset of seals in ancient Chinese books (SACB) for segmentation and small seal font (SSF) for recognition were established which are publicly available on the website.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권3호
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• pp.877-893
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• 2022

With the development of deep learning, face inpainting has been significantly enhanced in the past few years. Although image inpainting framework integrated with generative adversarial network or attention mechanism enhanced the semantic understanding among facial components, the issues of reconstruction on corrupted regions are still worthy to explore, such as blurred edge structure, excessive smoothness, unreasonable semantic understanding and visual artifacts, etc. To address these issues, we propose a Learnable Structure Knowledge of Fusion Network (LSK-FNet), which learns a prior knowledge by edge generation network for image inpainting. The architecture involves two steps: Firstly, structure information obtained by edge generation network is used as the prior knowledge for face inpainting network. Secondly, both the generated prior knowledge and the incomplete image are fed into the face inpainting network together to get the fusion information. To improve the accuracy of inpainting, both of gated convolution and region normalization are applied in our proposed model. We evaluate our LSK-FNet qualitatively and quantitatively on the CelebA-HQ dataset. The experimental results demonstrate that the edge structure and details of facial images can be improved by using LSK-FNet. Our model surpasses the compared models on L1, PSNR and SSIM metrics. When the masked region is less than 20%, L1 loss reduce by more than 4.3%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권10호
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• pp.3390-3405
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• 2022

Long Short-Term Memory (LSTM) combined with attention mechanism is extensively used to generate semantic sentences of images in image captioning models. However, features of salient regions and spatial information are not utilized sufficiently in most related works. Meanwhile, the LSTM also suffers from the problem of underutilized information in a single time step. In the paper, two innovative approaches are proposed to solve these problems. First, the Synergy-Gated Attention (SGA) method is proposed, which can process the spatial features and the salient region features of given images simultaneously. SGA establishes a gated mechanism through the global features to guide the interaction of information between these two features. Then, the Recurrent Fusion LSTM (RF-LSTM) mechanism is proposed, which can predict the next hidden vectors in one time step and improve linguistic coherence by fusing future information. Experimental results on the benchmark dataset of MSCOCO show that compared with the state-of-the-art methods, the proposed method can improve the performance of image captioning model, and achieve competitive performance on multiple evaluation indicators.