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20th International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Zhang, Qingshuo Tsang, Eric C. C. Hu, Meng He, Qiang Chen, Degang Macau Univ Sci & Technol Fac Informat Technol Taipa Macao Peoples R China Beijing Univ Civil Engn & Architecture Sch Sci Beijing 100044 Peoples R China North China Elect Power Univ Dept Math & Phys Beijing 102206 Peoples R China

ISBN: (纸本)9781665466080

The multi-label learning algorithm based on an extreme learning machine has the advantage of high efficiency and generalization ability, but its classification ability is weak due to ignoring the correlation between features and labels. Accordingly, in this paper, the fuzzy set-based kernel extreme learning machine autoencoder for multi-label classification (KELM-AE-fuzzy) is proposed. Firstly, the correlation between features and labels is analyzed based on fuzzy set theory, and the correlation label membership matrix and label completion matrix are constructed. Then, the kernel extreme learning machine autoencoder is used to fuse the correlation label membership matrix with the original feature space and generate the reconstructed feature space. Eventually, kernel extreme learning machine (KELM) is used as a classifier, where the label matrix is used with the label completion matrix. Comparative experiments on several multi-label datasets demonstrate that KELM-AE-fuzzy outperforms other multi-label algorithms, and the effectiveness of the proposed algorithm is verified.

关键词： Multi-label Learning Fuzzy Set Kernel Extreme Learning Machine autoencoder

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Sparse autoencoder for Sparse Code Multiple Access 3

Sparse Autoencoder for Sparse Code Multiple Access

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3rd International Conference on Artificial Intelligence in Information and Communication (IEEE ICAIIC)

作者： Singh, Medini Mishra, Deepak Vanidevi, M. Indian Inst Space Sci & Technol Dept Avionics Thiruvananthapuram Kerala India

ISBN: (纸本)9781728176383

In the forthcoming 5G technology, Sparse Code Multiple Access (SCMA) is the most promising scheme that aims at improving spectral efficiency further and providing massive connectivity. The challenge behind implementing SCMA scheme is: constructing optimized codebooks in order to obtain minimum BER while keeping the receiver complexity minimum. To address this problem, we resort to the usage of an efficient deep learning technique, autoencoders, that club the encoder and the decoder part and automatically learn the most optimum codeword that could give the least BER. In this paper, SCMA sparse autoencoder, which is a variant of the autoencoder, is proposed. that has better BER performance than a conventional autoencoder, without paying in terms of computational complexity.

关键词： 5G SCMA autoencoder sparse autoencoder BER

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CONVOLUTIONAL autoencoder-BASED IMAGE RECONSTRUCTION FOR UNSUPERVISED MULTIMODAL CHANGE DETECTION

CONVOLUTIONAL AUTOENCODER-BASED IMAGE RECONSTRUCTION FOR UNS...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Radoi, Anamaria Univ Politehn Bucuresti Bucharest Romania

ISBN: (纸本)9781665403696

Due to the numerous technological developments, the last years have witnessed an increase in the diversity of remote sensing data, whereas the need to interpret multimodal remote sensing data emerged. In this article, we propose a new multimodal unsupervised change detection strategy that projects a pre-event image in the post-event imaging modality. From a reconstruction perspective, the blocks in the pre-event scene are rebuilt from denoised versions of post-event blocks by means of convolutional denoising autoencoders. In order to perform this reconstruction, a dictionary of locations of similar blocks is learned from the pre-event image by analyzing compressed representations. The experiments, conducted over remote sensing images acquired by different sensors, show the effectiveness and the reliability of the proposed approach in various scenarios reflecting diverse types of changes.

关键词： Multimodal change detection image reconstruction autoencoder convolutional neural networks

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A Human Ear Reconstruction autoencoder 16

A Human Ear Reconstruction Autoencoder

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16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) / 16th International Conference on Computer Vision Theory and Applications (VISAPP)

作者： Sun, Hao Pears, Nick Dai, Hang Univ York Dept Comp Sci York N Yorkshire England Mohamed bin Zayed Univ Artificial Intelligence Abu Dhabi U Arab Emirates

ISBN: (纸本)9789897584886

The ear, as an important part of the human head, has received much less attention compared to the human face in the area of computer vision. Inspired by previous work on monocular 3D face reconstruction using an autoencoder structure to achieve self-supervised learning, we aim to utilise such a framework to tackle the 3D ear reconstruction task, where more subtle and difficult curves and features are present on the 2D ear input images. Our Human Ear Reconstruction autoencoder (HERA) system predicts 3D ear poses and shape parameters for 3D ear meshes, without any supervision to these parameters. To make our approach cover the variance for in-the-wild images, even grayscale images, we propose an in-the-wild ear colour model. The constructed end-to-end self-supervised model is then evaluated both with 2D landmark localisation performance and the appearance of the reconstructed 3D ears.

关键词： Ear 3D Ear Model 3D Morphable Model 3D Reconstruction Self-supervised Learning autoencoder

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Whisper Speech Enhancement Using Joint Variational autoencoder for Improved Speech Recognition 22

Whisper Speech Enhancement Using Joint Variational Autoencod...

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Interspeech Conference

作者： Agrawal, Vikas Kumar, Shashi Rath, Shakti P. Samsung R&D Inst India Bangalore Karnataka India Reverie Language Technol Bangalore Karnataka India

ISBN: (纸本)9781713836902

Whispering is the natural choice of communication when one wants to interact quietly and privately. Due to vast differences in acoustic characteristics of whisper and natural speech, there is drastic degradation in the performance of whisper speech when decoded by the Automatic Speech Recognition (ASR) system trained on neutral speech. Recently, to handle this mismatched train and test scenario Denoising autoencoders (DA) are used which gives some improvement. To improve over DA performance we propose another method to map speech from whisper domain to neutral speech domain via Joint Variational Auto-Encoder (JVAE). The proposed method requires time-aligned parallel data which is not available, so we developed an algorithm to convert parallel data to time-aligned parallel data. JVAE jointly learns the characteristics of whisper and neutral speech in a common latent space which significantly improves whisper recognition accuracy and outperforms traditional autoencoder based techniques. We benchmarked our method against two baselines, first being ASR trained on neutral speech and tested on whisper dataset and second being whisper test set mapped using DA and tested on same neutral ASR. We achieved an absolute improvement of 22.31% in Word Error Rate (WER) over the first baseline and an absolute 5.52% improvement over DA.

关键词： whisper speech recognition autoencoder wTIMIT Variational autoencoder jointVAE

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Real-driving-implementable drowsy driving detection method using heart rate variability based on long short-term memory and autoencoder 11th

Real-driving-implementable drowsy driving detection method u...

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11th IFAC Symposium on Biological and Medical Systems (BMS)

作者： Fujiwara, Koichi Hori, Kentaro Fujiwara, Koichi Kano, Manabu Kyoto Univ Dept Syst Sci Kyoto Japan Quadlytics Inc Kyoto Japan Nagoya Univ Dept Mat Proc Engn Nagoya Aichi Japan

Drowsy driving is a fatal problem that may cause traffic accidents. Although many driver drowsiness detection methods have been proposed, most of them have problems in input data availability, robustness to real driving environments, or detection precision. A drowsiness detection method based on heart rate variability (HRV), which is an R-R interval (RRI) fluctuation obtained from an electrocardiogram (ECG), has been proposed since ECG is easy to measure by using a wearable sensor. HRV is related to the autonomic nervous system (ANS) and is affected by drowsiness. However, its drowsiness detection performance was not always satisfactory. This study proposes a new drowsiness detection method using raw RRI data instead of HRV to improve the drowsiness detection performance. The proposed method uses raw RRI time series as inputs, and a drowsiness detection model is trained based on long short-term memory (LSTM) and autoencoder (AE), which are types of neural networks. RRI data during driving were collected from 25 participants using a driving simulator. The drowsiness detection model was trained following the proposed method. The experimental result showed that the proposed method achieved an AUC of 0.88, a sensitivity of 81%, and a specificity of 91%, which was higher than the HRV-based method. The result suggests that it is better to use raw RRIs as inputs than HRV features. Copyright (C) 2021 The Authors.

关键词： Driver drowsiness Anomaly detection Heart rate variability Long short-term memory autoencoder

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Complex autoencoder Approach to Constant Envelope Waveform Coding 18

Complex Autoencoder Approach to Constant Envelope Waveform C...

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IEEE 18th Annual Consumer Communications and Networking Conference (CCNC)

作者： Gorday, Paul Erdol, Nurgun Zhuang, Hanqi Florida Atlantic Univ Dept Comp & Elect Engn & Comp Sci Boca Raton FL 33431 USA

ISBN: (纸本)9781728197944

This paper proposes a new complex autoencoder suitable for learning spectrally efficient, constant envelope waveform coding. In contrast to prior work, we model the encoder output layer as a phase modulation layer with a complex exponential activation function. In addition, we model the decoder with a complex-valued feature detection layer that may be coherent or noncoherent. The complex topology leads to noncoherent waveform coding methods not obtained in prior studies. The paper provides a mathematical framework for training the proposed autoencoder along with illustrative examples that demonstrate its ability to learn improved spectral efficiency relative to traditional orthogonal and biorthogonal modulations.

关键词： Machine Learning autoencoder Complex-valued Waveform Coding Modulation Communications

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Recurrent autoencoder for Weld Discontinuity Prediction

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JOURNAL OF ADVANCED JOINING PROCESSES 2024年 9卷

作者： Walther, Dominik Junger, Christina Schmidt, Leander Schricker, Klaus Notni, Gunther Bergmann, Jean Pierre Maeder, Patrick Tech Univ Ilmenau Data Intens Syst & Visualizat Grp dAI SY Max Planck Ring 14 D-98693 Ilmenau Germany Tech Univ Ilmenau Dept Qual Assurance & Ind Image Proc Gustav Kirchhoff Pl 2 D-98693 Ilmenau Germany Tech Univ Ilmenau Prod Technol Grp Gustav Kirchhoff Pl 2 D-98693 Ilmenau Germany Fraunhofer Inst Appl Opt & Precis Engn IOF Jena Einstein Str 7 D-07745 Jena Germany Friedrich Schiller Univ Fac Biol Sci Furstengraben 1 D-07743 Jena Germany

Laser beam butt welding is often the technique of choice for a wide range of industrial tasks. To achieve high quality welds, manufacturers often rely on heavy and expensive clamping systems to limit the sheet movement during the welding process, which can affect quality. Jiggless welding offers a cost-effective and highly flexible alternative to common clamping systems. In laser butt welding, the process-induced joint gap has to be monitored in order to counteract the effect by means of an active position control of the sheet metal. Various studies have shown that sheet metal displacement can be detected using inductive probes, allowing the prediction of weld quality by ML-based data analysis. The probes are dependent on the sheet metal geometry and are limited in their applicability to complex geometric structures. Camera systems such as long-wave infrared (LWIR) cameras can instead be mounted directly behind the laser to overcome a geometry dependent limitation of the jiggles system. In this study we will propose a deep learning approach that utilizes LWIR camera recordings to predict the remaining welding process to enable an early detection of weld interruptions. Our approach reaches 93.33% accuracy for time-wise prediction of the point of failure during the weld.

关键词： autoencoder GRU MLP Laser Beam Butt Welding Weld Discontinuity Detection LWIR camera

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Data-Driven Approach for Credit Card Fraud Detection with autoencoder and One-Class Classification Techniques 1

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International-Federation-of-Information-Processing-Working-Group-5.7 (IFIP WG 5.7) International Conference on Advances in Production Management Systems (APMS)

作者： Ouedraogo, Abdoul-Fatao Heuchenne, Cedric Nguyen, Quoc-Thong Tran, Hien Dong A Univ Inst Artificial Intelligence & Data Sci Da Nang Vietnam Univ Liege HEC Management Sch B-4000 Liege Belgium Univ Lille GEMTEX ENSAIT F-59000 Lille France

ISBN: (数字)9783030858742

ISBN: (纸本)9783030858742;9783030858735

With the development of e-commerce, payment by credit card has become an essential means for the purchases of goods and services online. Especially, the Manufacturing Sector faces a high risk of fraud online payment. Its high turnover is the reason making this sector is lucrative with fraud. This gave rise to fraudulent activity on the accounts of private users, banks, and other services. For this reason, in recent years, many studies have been carried out using machine learning techniques to detect and block fraudulent transactions. This article aims to present a new approach based on real-time data combining two methods for the detection of credit card fraud. We first use the variational autoencoder(VAE) to obtain representations of normal transactions, and then we train a support vector data description (SVDD) model with these representations. The advantage of the representation learned automatically by the variational autoencoder is that it makes the data smoother, which makes it possible to increase the detection performance of one-class classification methods. The performance evaluation of the proposed model is done on real data from European credit cardholders. Our experiments show that our approach has obtained good results with a very high fraud detection rate.

关键词： Anomalies detection Outliers autoencoder Variational autoencoder One class classification Credit card fraud

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A Color Vision Approach Considering Weather Conditions Based on autoencoder Techniques Using Deep Neural Networks

A Color Vision Approach Considering Weather Conditions Based...

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8th IEEE Asia-Pacific Conference on Computer Science and Data Engineering (IEEE CSDE)

作者： Raj, Mohammad Mainuddin Tasdid, Samaul Haque Nidra, Maliha Ahmed Noor, Jobaer Ria, Sanjana Amin Alam, Md Ashraful Brac Univ Dept Comp Sci & Engn Dhaka Bangladesh

ISBN: (纸本)9781665495523

Color machine vision is a riveting technology crucial in pioneering innovations like autonomous vehicles, autonomous drone deliveries, automated stores, robots, infrastructure and surveillance monitoring programs for security, manufacturing defect monitoring and more. When it comes to real life applications of automated machines, safety is a major concern and to ensure utmost safety the unpredictable has to be taken into consideration. We propose and demonstrate a color vision approach that allows image normalization hinged on autoencoder techniques employing deep neural networks. The model is composed of image preprocessing, encoding and decoding. The images are resized in preprocessing portion the images go through a cognitive operation where the input image becomes suitable to enter the autoencoding technique section. The autoencoder is comprised of two core components - encoder and decoder. To employ this system deep neural network is applied which generates a code of an image in the encoding process. Sequentially, the code changes over to decoding. Decoder portion decodes it and regenerates the initial image extracting it from the code of the encoder portion. It allows normalizing color images under different weather conditions such as images captured during rainy or foggy weather conditions. We devise it such that rainy and foggy images are normalized concurrently and in real time. The autoencoder is trained with numerous rainy and foggy datasets utilizing CNN. In this research we investigate the model normalizing images in two different weather conditions - rainy and foggy conditions in real time. We used SSIM and PSNR to verify the accuracy of the model and confirm its capability reconstructing images in real time for advanced real life color vision implementations.

关键词： Color Vision Deep Neural Network CNN autoencoder

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