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IEEE International Conference on Image Processing (ICIP)

作者： Schaefer, Michael Pientka, Sophie Pfaff, Jonathan Schwarz, Heiko Marpe, Detlev Wiegand, Thomas Fraunhofer Inst Telecommun Video Commun & Applicat Dept Heinrich Hertz Inst Einsteinufer 37 D-10587 Berlin Germany

ISBN: (纸本)9781665441155

Given the capabilities of massive GPU hardware, there has been a surge of using artificial neural networks (ANN) for still image compression. These compression systems usually consist of convolutional layers and can be considered as non-linear transform coding. Notably, these ANNs are based on an end-to-end approach where the encoder determines a compressed version of the image as features. In contrast to this, existing image and video codecs employ a block-based architecture with signal-dependent encoder optimizations. A basic requirement for designing such optimizations is estimating the impact of the quantization error on the resulting bitrate and distortion. As for non-linear, multi-layered neural networks, this is a difficult problem. This paper presents a performant auto-encoder architecture for still image compression, which represents the compressed features at multiple scales. Then, we demonstrate how an algorithm, which tests multiple feature candidates, can reduce the Lagrangian cost and optimize compression efficiency. The algorithm avoids multiple network executions by pre-estimating the impact of the quantization on the distortion by a higher-order polynomial.

关键词： High Efficiency Video Coding (HEVC) Versatile Video Coding (VVC) Deep Learning auto-encoder Rate-Distortion-Optimization

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Research on textile defects detection based on improved generative adversarial network

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JOURNAL OF ENGINEERED FIBERS AND FABRICS 2022年 17卷

作者： Rui, Jin Qiang, Niu China Univ Min & Technol Sch Comp Sci & Technol Xuzhou Peoples R China Changzhou Vocat Inst Text & Garment Changzhou Peoples R China Changzhou Vocat Inst Text & Garment Changzhou 221116 Jiangsu Peoples R China

Fabric defects seriously affect the textile industry in China. Given that traditional manual detection methods have low efficiency and poor accuracy, using automatic textile defect detection methods is urgently needed. A fabric defect detection method based on an improved generative adversarial network is thus developed to address the shortage of fabric defect samples. This method learns to reconstruct the fabric image in an unsupervised manner and locates the defect areas based on the differences between the original image and the reconstruction. Afterward, the defect-related features are extracted from these areas to further recognize specific fabric defects. The central loss constraint is introduced to improve the recognition performance of this method, and lightweight processing is applied to guarantee its real-time operation in embedded systems. The application of this method is then evaluated on the publicly available Tianchi dataset. Both quantitative and qualitative results show that the proposed method can accurately detect fabric defects.

关键词： Fabric defect generative adversarial network center loss constrain lightweight processing auto-encoder

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Deep Learning-Based Computer-Aided Diagnosis (CAD): Applications for Medical Image Datasets

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SENSORS 2022年第22期22卷 8999-8999页

作者： Kadhim, Yezi Ali Khan, Muhammad Umer Mishra, Alok Atilim Univ Dept Modeling & Design Engn Syst MODES TR-06830 Ankara Turkey Atilim Univ Dept Elect & Elect Engn TR-06830 Ankara Turkey Atilim Univ Dept Mechatron Engn TR-06830 Ankara Turkey Atilim Univ Dept Software Engn TR-06830 Ankara Turkey Molde Univ Coll Specialized Univ Logist Informat & Digitalizat Grp N-6410 Molde Norway

Computer-aided diagnosis (CAD) has proved to be an effective and accurate method for diagnostic prediction over the years. This article focuses on the development of an automated CAD system with the intent to perform diagnosis as accurately as possible. Deep learning methods have been able to produce impressive results on medical image datasets. This study employs deep learning methods in conjunction with meta-heuristic algorithms and supervised machine-learning algorithms to perform an accurate diagnosis. Pre-trained convolutional neural networks (CNNs) or auto-encoder are used for feature extraction, whereas feature selection is performed using an ant colony optimization (ACO) algorithm. Ant colony optimization helps to search for the best optimal features while reducing the amount of data. Lastly, diagnosis prediction (classification) is achieved using learnable classifiers. The novel framework for the extraction and selection of features is based on deep learning, auto-encoder, and ACO. The performance of the proposed approach is evaluated using two medical image datasets: chest X-ray (CXR) and magnetic resonance imaging (MRI) for the prediction of the existence of COVID-19 and brain tumors. Accuracy is used as the main measure to compare the performance of the proposed approach with existing state-of-the-art methods. The proposed system achieves an average accuracy of 99.61% and 99.18%, outperforming all other methods in diagnosing the presence of COVID-19 and brain tumors, respectively. Based on the achieved results, it can be claimed that physicians or radiologists can confidently utilize the proposed approach for diagnosing COVID-19 patients and patients with specific brain tumors.

关键词： deep learning CNN auto-encoder ant colony optimization COVID-19 brain tumor

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Physics-Informed Learning for High Impedance Faults Detection

Physics-Informed Learning for High Impedance Faults Detectio...

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14th IEEE Madrid PowerTech Conference (IEEE POWERTECH)

作者： Li, Wenting Deka, Deepjyoti Los Alamos Natl Lab Ctr Nonlinear Studies CNLS Los Alamos NM 87545 USA Los Alamos Natl Lab Theoret Div Los Alamos NM USA

ISBN: (纸本)9781665435970

High impedance faults (HIFs) in distribution grids may cause wildfires and threaten human lives. Conventional protection relays at substations fail to detect more than 10% HIFs since over-currents are low and the signatures of HIFs are local. With more mu PMU being installed in the distribution system, high-resolution mu PMU datasets provide the opportunity of detecting HIFs from multiple points. Still, the main obstacle in applying the mu PMU datasets is the lack of labels. To address this issue, we construct a physics-informed convolutional auto-encoder (PICAE) to detect HIFs without labeled HIFs for training. The significance of our PICAE is a physical regularization, derived from the elliptical trajectory of voltages-current characteristics, to distinguish HIFs from other abnormal events even in highly noisy situations. We formulate a system-wide detection framework that merges multiple nodes' local detection results to improve the detection accuracy and reliability. The proposed approaches are validated in the IEEE 34-node test feeder simulated through PSCAD/EMTDC. Our PICAE outperforms the existing works in various scenarios and is robust to different observability and noise.

关键词： High impedance faults Detection Convolutional neural networks auto-encoder mu PMU Physics informed

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ADVERSARIAL auto-ENCODING FOR PACKET LOSS CONCEALMENT

ADVERSARIAL AUTO-ENCODING FOR PACKET LOSS CONCEALMENT

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IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)

作者： Pascual, Santiago Serra, Joan Pons, Jordi Dolby Labs London England

ISBN: (纸本)9781665448703

Communication technologies like voice over IP operate under constrained real-time conditions, with voice packets being subject to delays and losses from the network. In such cases, the packet loss concealment (PLC) algorithm reconstructs missing frames until a new real packet is received. Recently, autoregressive deep neural networks have been shown to surpass the quality of signal processing methods for PLC, specially for long-term predictions beyond 60 ms. In this work, we propose a non-autoregressive adversarial auto-encoder, named PLAAE, to perform real-time PLC in the waveform domain. PLAAE has a causal convolutional structure, and it learns in an auto-encoder fashion to reconstruct signals with gaps, with the help of an adversarial loss. During inference, it is able to predict smooth and coherent continuations of such gaps in a single feed-forward step, as opposed to autoregressive models. Our evaluation highlights the superiority of PLAAE over two classic PLCs and two deep autoregressive models in terms of spectral and intonation reconstruction, perceptual quality, and intelligibility.

关键词： packet loss concealment adversarial networks auto-encoder speech enhancement

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Unsupervised deep clustering via contractive feature representation and focal loss

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PATTERN RECOGNITION 2022年 123卷 108386-108386页

作者： Cai, Jinyu Wang, Shiping Xu, Chaoyang Guo, Wenzhong Fuzhou Univ Coll Comp & Data Sci Fuzhou 350116 Peoples R China Fuzhou Univ Network Comp & Intelligent Informat Proc Lab Fuzhou 350116 Peoples R China Putian Univ Sch Informat Engn Putian 351100 Peoples R China

Deep clustering aims to promote clustering tasks by combining deep learning and clustering together to learn the clustering-oriented representation, and many approaches have shown their validity. How-ever, the feature learning modules in existing methods hardly learn a discriminative representation. In addition, the label assignment mechanism becomes inefficient when dealing with some hard samples. To address these issues, a new joint optimization clustering framework is proposed through introducing the contractive representation in feature learning and utilizing focal loss in the clustering layer. The con-tractive penalty term added in feature learning would cause the local feature space contraction, resulting in learning more discriminative features. To our certain knowledge, this is also the first work to utilize the focal loss to improve the label assignment in deep clustering method. Moreover, the construction of the joint optimization framework enables the proposed method to learn feature representation and la -bel assignment simultaneously in an end-to-end way. Finally, we comprehensively compare with some state-of-the-art clustering approaches on several clustering tasks to demonstrate the effectiveness of the proposed method. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Unsupervised learning Clustering Contractive feature representation Focal loss auto-encoder

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Improving neural topic modeling via Sinkhorn divergence

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INFORMATION PROCESSING & MANAGEMENT 2022年第3期59卷 102864-102864页

作者： Liu, Luyang Huang, Heyan Gao, Yang Zhang, Yongfeng Beijing Inst Technol Sch Comp Sci & Technol Beijing 100081 Peoples R China Beijing Inst Technol Southeast Acad Informat Technol Putian 351100 Fujian Peoples R China Beijing Engn Res Ctr High Volume Language Informa Beijing Peoples R China Rutgers State Univ Dept Comp Sci 110 Frelinghuysen Rd Piscataway NJ 08854 USA

Textual data have been a major form to convey internet users' content. How to effectively and efficiently discover latent topics among them has essential theoretical and practical value. Recently, neural topic models(NTMs), especially Variational auto-encoder-based NTMs, proved to be a successful approach for mining meaningful and interpretable topics. However, they usually suffer from two major issues:(1)Posterior collapse: KL divergence will rapidly reach zeros resulting in low-quality representation in latent distribution;(2)Unconstrained topic generative models: Topic generative models are always unconstrained, which potentially leads to discovering redundant topics. To address these issues, we propose autoencoding Sinkhorn Topic Model based on Sinkhorn auto-encoder(SAE) and Sinkhorn divergence. SAE utilizes Sinkhorn divergence rather than problematic KL divergence to optimize the difference between posterior and prior, which is free of posterior collapse. Then, to reduce topic redundancy, Sinkhorn Topic Diversity Regularization(STDR) is presented. STDR leverages the proposed Salient Topic Layer and Sinkhorn divergence for measuring distance between salient topic features and serves as a penalty term in loss function facilitating discovering diversified topics in training. Several experiments have been conducted on 2 popular datasets to verify our contribution. Experiment results demonstrate the effectiveness of the proposed model.

关键词： Deep learning Topic model Sinkhorn divergence auto-encoder

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An intelligent and efficient network intrusion detection system using deep learning

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COMPUTERS & ELECTRICAL ENGINEERING 2022年 99卷 107764-107764页

作者： Qazi, Emad-ul-Haq Imran, Muhammad Haider, Noman Shoaib, Muhammad Razzak, Imran Naif Arab Univ Secur Sci NAUSS Ctr Excellence Cybercrime & Digital Forens CoECDF Riyadh Saudi Arabia King Saud Univ Coll Comp & Informat Sci Riyadh Saudi Arabia Federat Univ Sch Engn Informat Technol & Phys Sci Ballarat Australia Victoria Univ Coll Engn & Sci Sydney Australia Deakin Univ Sch Informat Technol Melbourne Australia

With continuously escalating threats and attacks, accurate and timely intrusion detection in communication networks is challenging. Many approaches have already been proposed recently on network intrusion detection. However, they face critical challenges due to the continuous increase of new threats that current systems do not understand. Motivated by the outstanding performance of deep learning (DL) in many detection and recognition tasks, we introduce an intelligent and efficient network intrusion detection system (NIDS) based on DL. This study proposes a non-symmetric deep auto-encoder for network intrusion detection problems and presents its detailed functionality and performance. We validate the robustness and effectiveness of the proposed NIDS using a benchmark dataset, i.e., KDD CUP'99. Our DL-based method is implemented in the TensorFlow library and GPU framework, and it achieves an accuracy of 99.65%. The proposed system can be used in network security research domains and DL-based detection and classification systems.

关键词： Network security Intrusion detection Deep learning auto-encoder SVM

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Fusion of transformer and ML-CNN-BiLSTM for network intrusion detection

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EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING 2023年第1期2023卷 71-71页

作者： Xiang, Zelin Li, Xuwei Sichuan Int Studies Univ Chengdu Inst Publ Infrastruct Dept Dujiangyan 611844 Sichuan Peoples R China Sichuan Univ Coll Comp Sci Chengdu 610065 Sichuan Peoples R China

Network intrusion detection system (NIDS) can effectively sense network attacks, which is of great significance for maintaining the security of cyberspace. To meet the requirements of efficient and accurate network status monitoring, a NIDS model using Transformer-based fusion deep learning architecture is proposed. Firstly, GAN-Cross is used to expand minority class sample data, thereby alleviating the issues of imbalanced minority class about the original dataset. Then, the Transformer module is used to adjust the ML-CNN-BiLSTM model to enhance the feature encoding ability of the intrusion model. Finally, the data enhancement model and feature enhancement model are integrated into the NIDS model, the detection model is optimized, the features of network state data are extracted at a deeper level, and the generalization ability of the detection model is enhanced. Some simulation experiments using UNSW-NB15 datasets show that the proposed fusion architecture can achieve efficient analysis of complex network traffic datasets, with an accuracy of 0.903, effectively improving the detection accuracy of NIDS and its ability to detect unknown attacks. The proposed model has good application value in ensuring the stable operation of network systems.

关键词： Network intrusion detection Data enhancement auto-encoder Transformer module BiLSTM GAN-Cross

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Shallow and deep learning of an artificial neural network model describing a hot flow stress Evolution: A comparative study

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MATERIALS & DESIGN 2022年 220卷

作者： Opela, Petr Schindler, Ivo Kawulok, Petr Kawulok, Rostislav Rusz, Stanislav Sauer, Michal VSBTech Univ Ostrava Fac Mat Sci & Technol 17 Listopadu 2172-15 Ostrava 70800 Czech Republic

In recent years, the utilization of artificial neural networks (ANNs) as regression models to solve the issue of hot flow stress forecasting has become a standard approach. In a connection with this kind of regression issue, employed ANNs are usually learned via a shallow learning technique while only limited attention has been paid to a deep learning method. In the frame of the submitted research, the shallow learning approach is thoroughly compared to the deep learning techniques which are based on the use of a Restricted Boltzmann Machine (RBM) and an auto-encoder (AE). To do so, these learning techniques are applied on a feed-forward multi-layer ANN describing the experimental hot flow curve dataset of micro-alloyed medium carbon steel. In comparison with the shallow learning method, both deep learning approaches provided higher accuracy in the network response - especially in the case of a higher number of hidden layers. The results have also shown that neither the RBM-based deep learning method nor the AE-based method had a significant effect on the duration of the necessary calculations. However, it turned out that the RBM-based method can, under certain conditions, lead to a more reliable network performance. (C) 2022 The Author(s). Published by Elsevier Ltd.

关键词： Artificial neural network Deep learning techniques Deep belief network Restricted Boltzmann machine auto-encoder Hot deformation behavior

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