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A semisupervised autoencoder-based method for anomaly detection in cutting tools

JOURNAL OF MANUFACTURING PROCESSES 2023年 93卷 315-327页

作者： Sun, Shixu Liu, Yingchao Hu, Xiaofeng Zhang, Wenjuan Shanghai Jiao Tong Univ Sch Mech Engn Shanghai 200240 Peoples R China Shanghai Aerosp Control Technol Inst Shanghai 201109 Peoples R China Shanghai Inst Precis Measurement & Test Shanghai 201109 Peoples R China Univ Warwick Warwick Business Sch Coventry CV4 7AL England

Detecting anomalies such as breakage and excessive wear of cutting tools in the machining process is crucial to prevent damage and improve productivity. Data-driven anomaly detection (AD) methods suffer from limited availability of anomaly samples, which is ineluctable in practice owing to strict reliability restrictions. Therefore, we propose a semisupervised AD approach in which only failure-free samples are required to establish an AD model. The key strategy is to learn the characteristics of failure-free samples using an improved autoencoder (AE) and discern observations by deviations from the characteristics. We rebuild the loss function of AE to impel the model to learn the common characteristics in latent space. We propose a factor that reflects the anomaly degree as the decision-making function to implement AD. The proposed approach is verified on an experimental cutting tool breakage dataset and a public cutting tool wear dataset. The experimental results demonstrate the validity of the proposed approach. The comparisons with conventional methods substantiate that the proposed approach outperforms existing AD methods.

关键词： Anomaly detection Imbalanced data learning autoencoder Cutting tool Semisupervised learning

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HyperRefiner:a refined hyperspectral pansharpening network based on the autoencoder and self-attention

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International Journal of Digital Earth 2023年第1期16卷 3268-3294页

作者： Bo Zhou Xianfeng Zhang Xiao Chen Miao Ren Ziyuan Feng Institute of Remote Sensing and Geographic Information System Peking UniversityBeiigPeople's Republic of China

Deep learning has been developed to generate promising super resolution hyperspectral imagery by fusing hyperspectral imagery with the panchromatic ***,it is still challenging to maintain edge spectral information in the necessary upsampling processes of these approaches,and diffcult to guarantee effective feature *** study proposes a pansharpening network denoted as HyperRefiner that consists of,(1)a well performing upsampling network SRNet,in which the dual attention block and refined attention block are cascaded to accomplish the extraction and fusion of features;(2)a spectral autoencoder that is embedded to perform dimensionality reduction under constrained feature extraction;and(3)the optimization module which performs self-attention at the pixel and feature levels.A comparisonwithseveral state-of-the-art models reveals that HyperRefiner can improve the quality of the fused ***,compared to the single-head HyperTransformer and with the Chikusei dataset,our network improved the Peak Signal-to-Noise Ratio,Erreur Relative Globale Adimensionnelle de Synthese and Spectral Angle Mapper by 0.86%,3.62%,and 2.09%,and reduce the total memory,floating point operations,model parameters and computation time by 41%,75%,86%and 46%,*** experimental results show that HyperRefiner outperforms several networks and demonstrates its usefulness in hyperspectral image *** code is publicly available athttps://***/zsspo/Fusion_HyperRefiner.

关键词： Image fusion hyperspectral self-attention autoencoder edge spectrum

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Integrating metadata into deep autoencoder for handling prediction task of collaborative recommender system

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第14期83卷 42125页

作者： Behara, Gopal Yannam, V. Ramanjaneyulu Nayyar, Anand Bagal, Dilip Kumar Govt Coll Engn Kalahandi Dept Comp Sci & Engn Bhawanipatna 766003 Orissa India Natl Inst Technol Dept Comp Sci & Engn Rourkela 569008 Orissa India Duy Tan Univ Fac Informat Technol Grad Sch Da Nang 550000 Vietnam Govt Coll Engn Kalahandi Dept Mech Engn Bhawanipatna 766003 Orissa India

Nowadays, Deep learning (DL) techniques have been proven successful as learning techniques in various research fields ranging from computer vision to social networks. The approach of DL is flourishing in the field of recommender systems (RS). Researchers have deployed metadata or auxiliary information using DL approaches in diverse applications in the last decade to achieve better recommendation accuracy. Thus, the metadata plays a vital role in obtaining a better user-item interaction. At the same time, existing techniques are based on fixed user and item factors. Therefore, the model does not correctly identify actual latent factors representation, resulting in a high prediction error. To handle this problem, a user metadata embedding using a deep autoencoder RS model called "Metadata Embedding Deep autoencoder (MEDAE)" based collaborative filtering is proposed. MEDAE model takes embeds user metadata such as demographics along with the rating data. The MEDAE model consists of an embedding layer, Encoder, and Decoder. The embedding layer generates embedding or latent features of the users, items, and metadata;Encoder receives concatenated features of the user, item, and metadata, then encodes the inputs and passes them to the decoder;and the decoder reconstructs the output. To test the effectiveness of proposed model Root Mean Squared Error and Mean Absolute Error measures are used. Different architectures (like Big-Small-Big (BSB) (5), BSB (3), Small-Big-Small (3), and SBS (5)) of the MEDAE model are evaluated on MovieLens datasets along with different parameters such as activation functions (ELU and SELU) and regularization and results concluded that the MEDAE with SBS (3) and ELU + SELU component improves 4% of RMSE and 2% MAE over the baseline methods.

关键词： Collaborative Filtering Recommender System Deep Learning Metadata autoencoder

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autoencoder based Physical Layer Authentication for UAV Communications 97

Autoencoder based Physical Layer Authentication for UAV Comm...

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97th IEEE Vehicular Technology Conference (VTC-Spring)

作者： Senigagliesi, Linda Ciattaglia, Gianluca Gambi, Ennio Univ Politecn Marche Dept Informat Engn Ancona Italy

ISBN: (纸本)9798350311143

The use of flying Unmanned Aerial Vehicles (UAVs) for communications is becoming more and more widespread, especially in 5G and beyond networks. In such a context, detection and authentication of UAVs is assuming an increasingly important role. In this paper we show that it is possible to distinguish different drones which communicate with a fixed ground base station (BS) on the basis of their channel characteristics and of the micro-Doppler signature associated to the specific features of each UAV. An urban scenario is simulated where UAVs fly at a constant height and channels are affected by Additive White Gaussian Noise (AWGN) and fading. With the aim of helping the BS in its authentication task, we take advantage of a sparse autoencoder trained on the channel of the legitimate transmitter, while data coming from possible attackers are classified as anomalies. We prove that, with proper network training, low levels of false alarm and missed detection can be achieved, especially if the attacker has no line-of-sight link, and that the presence of micro-Doppler actually contribute to enhance the authentication performance.

关键词： autoencoder machine learning micro-Doppler PLA UAV communications

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autoencoder-BASED RADIO FREQUENCY INTERFERENCE MITIGATION FOR SMAP PASSIVE RADIOMETER

AUTOENCODER-BASED RADIO FREQUENCY INTERFERENCE MITIGATION FO...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Owfi, Ali Afghah, Fatemeh Clemson Univ Holcombe Dept Elect & Comp Engn Clemson SC 29634 USA

ISBN: (纸本)9798350320107

Passive space-borne radiometers operating in the 1400-1427 MHz protected frequency band face radio frequency interference (RFI) from terrestrial sources. With the growth of wireless devices and the appearance of new technologies, the possibility of sharing this spectrum with other technologies would introduce more RFI to these radiometers. This band could be an ideal mid-band frequency for 5G and Beyond, as it offers high capacity and good coverage. Current RFI detection and mitigation techniques at SMAP (Soil Moisture Active Passive) depend on correctly detecting and discarding or filtering the contaminated data leading to the loss of valuable information, especially in severe RFI cases. In this paper, we propose an autoencoder-based RFI mitigation method to remove the dominant RFI caused by potential coexistent terrestrial users (i.e., 5G base station) from the received contaminated signal at the passive receiver side, potentially preserving valuable information and preventing the contaminated data from being discarded (1).

关键词： Interference Mitigation SMAP satellite Spectrum Coexistence RFI autoencoder

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Development of Automatic Hard Landing Detection Model Using autoencoder

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INTERNATIONAL JOURNAL OF AERONAUTICAL AND SPACE SCIENCES 2023年第3期24卷 778-791页

作者： Jeong, Seon Ho Park, Eun Gyo Cho, Jin Yeon Kim, Jeong Ho Korea Aerosp Ind LTD Adv SW Technol Team Teheran Ro Seoul 06151 South Korea Inha Univ Dept Aerosp Engn 36 Gaetbeol Ro Incheon 21999 South Korea

A hard landing is a typical accident that occurs during the landing of an aircraft. Because hard landings can cause stress buildup in the aircraft structure that can lead to fatal accidents if not properly identified, it is necessary to clearly determine whether a hard landing has occurred. However, erroneous judgments may occur because of the limitations of the existing methods of identifying hard landings. Although many studies have been conducted to reduce misjudgments, most existing approaches require the selection of proper key (flight) parameters or predefined thresholds, which require a great deal of experience and high-level professional knowledge. Therefore, in this study, a new model for identifying hard landings without explicit selection of key parameters or manual determination of thresholds is proposed by introducing an outlier detection technique. An autoencoder, an artificial neural network model, is applied to the detection of outliers from landing data obtained through high-fidelity landing simulation. The training of the autoencoder and performance analysis is conducted to demonstrate the validity of the proposed method. Normal landing data are used as the training dataset for the autoencoder, and the percentage of abnormal landing data are gradually increased to the training dataset to check the robustness of the proposed method. The performance analysis results showed that the proposed method applying the autoencoder can be successfully used to identify hard landing situations such as late flares, even if a small amount of abnormal data are included in the training dataset, as is the case for actual landing data.

关键词： Hard landing Late flare Neural networks autoencoder Outlier detection

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Design of a Maritime autoencoder Communication System Based on Attention Mechanisms and DenseBlock

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TSINGHUA SCIENCE AND TECHNOLOGY 2025年第4期30卷 1496-1510页

作者： Han, Xiaoling Lin, Bin Shao, Shuai Wu, Nan Wang, Haocheng Qian, Liping Wu, Yuan Dalian Maritime Univ Coll Informat & Sci Technol Dalian 116026 Peoples R China Zhejiang Univ Technol Coll Informat Engn Hangzhou 310014 Peoples R China Univ Macau State Key Lab Internet Things Smart City Macau 999078 Peoples R China

As the maritime industry continues to thrive and maritime services diversify, the demand for highly reliable maritime communication systems has become increasingly prominent. However, harsh marine conditions pose significant challenges to communication systems. In this work, we propose a Maritime autoencoder (MAE) communication system based on Attention Mechanisms (AMs) and DenseBlock (namely AM-Dense-MAE). AM-Dense-MAE utilizes DenseBlock and long short-term memory to extract deep features and capture spatio-temporal relationships, addressing the issue of "long-term dependency". Furthermore, the decoder incorporates spatial attention modules and convolutional block attention module to enhance the preservation of crucial information and suppress irrelevant data. We employ the Rician fading channel model to simulate maritime communication channels. A substantial volume of data is utilized for model training and parameter optimization. Simulation results demonstrate that, in comparison to the benchmarks, the proposed AM-Dense-MAE exhibits better block error rate performance under various signal-to-noise ratio conditions and showcases generalization capabilities across diverse parameter settings.

关键词： maritime communication system autoencoder DenseBlock Attention Mechanisms (AMs) maritime communication system autoencoder DenseBlock Attention Mechanisms (AMs)

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Asymmetrical Attention Networks Fused autoencoder for Debiased Recommendation

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ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY 2023年第6期14卷 1-24页

作者： Zhang, Yihao Zhao, Chu Liao, Weiwen Zhou, Wei Yuan, Meng Chongqing Univ Technol 69 Hongguang Ave Chongqing 400054 Peoples R China Chongqing Univ 174 Shazheng St Chongqing Peoples R China Beihang Univ 37 Xueyuan Rd Beijing Peoples R China

Popularity bias is a massive challenge for autoencoder-based models, which decreases the level of personalization and hurts the fairness of recommendations. User reviews reflect their preferences and help mitigate bias or unfairness in the recommendation. However, most existing works typically incorporate user (item) reviews into a long document and then use the same module to process the document in parallel. Actually, the set of user reviews is completely different from the set of item reviews. User reviews are heterogeneous in that they reflect a variety of items purchased by users, while item reviews are only related to the item itself and are thus typically homogeneous. In this article, a novel asymmetric attention network fused with autoencoders is proposed, which jointly learns representations from the user and item reviews and implicit feedback to perform recommendations. Specifically, we design an asymmetric attentive module to capture rich representations from user and item reviews, respectively, which solves data sparsity and explainable problems. Furthermore, to further address popularity bias, we apply a noise-contrastive estimation objective to learn high-quality "de-popularity" embedding via the decoder structure. A series of extensive experiments are conducted on four benchmark datasets to show that leveraging user review information can eliminate popularity bias and improve performance compared to various state-of-the-art recommendation techniques.

关键词： Popularity bias review-aware Recommendation asymmetrical hierarchical network autoencoder

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autoencoder with Fitting Network for Terahertz Wireless Communications:A Deep Learning Approach

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China Communications 2022年第3期19卷 172-180页

作者： Zhaohui Huang Dongxuan He Jiaxuan Chen Zhaocheng Wang Sheng Chen Beijing National Research Center for Information Science and Technology Department of Electronic EngineeringTsinghua UniversityBeijing 100084China Shenzhen International Graduate School Tsinghua UniveristyShenzhen 518055China School of Electronics and Computer Science University of SouthamptonSouthampton SO171BJU.K.

Terahertz wireless communication has been regarded as an emerging technology to satisfy the ever-increasing demand of ultra-high-speed wireless ***,affected by the imperfections of cheap and energy-efficient Terahertz devices,Terahertz signals suffer from serve hybrid distortions,including in-phase/quadrature imbalance,phase noise and nonlinearity,which degrade the demodulation performance *** improve the robustness against these hybrid distortions,an improved autoencoder is proposed,which includes coding the transmitted symbols at the transmitter and decoding the corresponding signals at the ***,due to the lack of information of Terahertz channel during the training of the autoencoder,a fitting network is proposed to approximate the characteristics of Terahertz channel,which provides an approximation of the gradients of *** results show that our proposed autoencoder with fitting network can recover the transmitted symbols under serious hybrid distortions,and improves the demodulation performance significantly.

关键词： Terahertz wireless communication hybrid distortion signal demodulation autoencoder

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OIAE: Overall Improved autoencoder with Powerful Image Reconstruction and Discriminative Feature Extraction

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COGNITIVE COMPUTATION 2023年第4期15卷 1334-1341页

作者： Zhao, Haifeng Wu, Haiting Wang, Xin Anhui Univ Sch Comp Sci & Technol Minist Educ Key Lab Intelligent Comp & Signal Proc Shenzhen Peoples R China PKU Shenzhen Inst Shenzhen Peoples R China Anhui Univ Sch Comp Sci & Technol Hefei Peoples R China Shenzhen Raixun Informat Technol Co Ltd PKU Shenzhen Inst Shenzhen Peoples R China

As an unsupervised learning method, the autoencoder (AE) plays a very important role in model pre-training. However, the current AEs pre-training methods are still faced with the problems of not being able to reconstruct pictures better and mining deeper features. In this paper, we come up with a new AE, overall improved autoencoder (OIAE). Its main contribution is twofold: Wasserstein Generative Adversarial Networks (WGAN) is used to study the relationship between AEs reconstruction ability and pre-training performance and a regularization method is proposed to enable the autoencoder to learn discriminative features. We set up ablation experiments to prove the effectiveness of our two improvements and OIAE and compare them with baseline. The classification accuracy of the OIAE pre-trained classification network improved by 0.74% on the basic dataset and 16.44% on the more difficult dataset. These promising results demonstrate the effectiveness of our method in AEs pre-training tasks.

关键词： autoencoder Image reconstruction Regularization Discriminative feature

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