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10th RSI International Conference on Robotics and Mechatronics (ICRoM)

作者： Daryakenari, F. H. Mollahossein, M. Taheri, A. Vossoughi, G. R. Sharif Univ Technol Dept Mech Engn Mech Res Lab Ctr Excellence Design Robot & Automat Tehran Iran

ISBN: (纸本)9781665454520

In this paper we aim to propose an Artificial Neural Network (ANN) model in order to classify lower limb surface Electromyographical signals, without applying any pre-processing to the inputs. To this end, first three different autoencoder models have been proposed in order to choose the autoencoder architecture with the best performance. Then encoder layers of the best autoencoder model transferred to a classification network. This network aims to classify raw input sEMG signals into three categories, namely;sitting, standing and walking. After training the classification network, we were able to successfully classify input sEMG signals and reach to 81.51% accuracy for all the classes and 91% and 94% accuracy for the first two classes, with the unseen subjects. The major difference of this research with previous ones lies in fact that evaluation of our network occurs in a realistic scenario, where data from test and validation subjects is not seen by the network before and also our transferred network uses unlabeled raw sEMG signals for extracting features from signal, which gives us the ability to transfer this model in situations where labeled data is not available.

关键词： sEMG autoencoder Transfer learning Deep neural network Classification

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Evolutive Adversarially-Trained Bayesian Network autoencoder for Interpretable Anomaly Detection 11

Evolutive Adversarially-Trained Bayesian Network Autoencoder...

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International Conference on Probabilistic Graphical Models

作者： Casajus-Setien, Jorge Bielza, Concha Larranaga, Pedro Univ Politecn Madrid ETS Ingenieros Informat Boadilla Del Monte 28660 Spain

Semi-supervised detection of outliers with only positive and unlabeled data, which is among the most frequent forms of the anomaly detection (AD) problem in real scenarios, requires for a model to capture the normal behaviour of data from a training set exclusively comprised of normal-labelled data, so new unseen data can be afterwards compared to the induced notion of normality to be flagged -or not- as anomalous. In modelling a certain pattern of behaviour, generative models such as generative-adversarial networks (GANs) have proved to have great performance. Thus, numerous AD algorithms with GANs at its core have been proposed, most of them powered by deep neural networks and relying on an autoencoder for the AD task. In the present work, a novel approach to semi-supervised AD with Bayesian networks using generative-adversarial training and an evolutive strategy is proposed, which aims to palliate the intrinsic lack of interpretability of deep neural networks. The proposed model is tested on a real-world AD problem in cybersecurity.

关键词： Bayesian networks Generative models Adversarial training Evolutive algorithms autoencoder Anomaly detection Cybersecurity

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Unsupervised Anomaly Detection and Root Cause Analysis for an Industrial Press Machine based on Skip-Connected autoencoder 21

Unsupervised Anomaly Detection and Root Cause Analysis for a...

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21st IEEE International Conference on Machine Learning and Applications (IEEE ICMLA)

作者： Sun, Chenwei Trat, Martin Bender, Janek Ovtcharova, Jivka Jeppesen, George Baer, Jan FZI Res Ctr Informat Technol Intelligent Syst & Prod Engn Karlsruhe Germany Dieffenbacher GmbH Maschinen & Anlagenbau Eppingen Germany

ISBN: (纸本)9781665462839

We propose an unsupervised-learning-based method for anomaly detection and root cause analysis for an industrial press machine. A skip-connected autoencoder with 55% performance improvement measured by reconstruction root mean square error to vanilla variant in average is used to train the collected multivariant time series data in different schemes. We then conduct a stacked evaluation method for both machinelevel anomalies with the root cause localization and anomaly on specific cylinder tracks. Both real-world and synthetic anomalies embedded in real data are used for evaluation. The result shows that the multi-models training scheme and the relatively short window length can gain better performance, i.e., fewer anomaly false alarms and misses.

关键词： autoencoder skip connection multivariant time series anomaly detection root cause analysis

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A Deep Convolutional autoencoder-Based Approach for Parkinson's Disease Diagnosis Through Speech Signals 18th

A Deep Convolutional Autoencoder-Based Approach for Parkinso...

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18th International Conference on Advanced Data Mining and Applications (ADMA)

作者： Khaskhoussy, Rania Ben Ayed, Yassine Univ Sfax Natl Engn Sch Sfax ENIS MIRACL Multimedia Informat Syst & Adv Comp Lab BP 1173 Sfax 3038 Tunisia

ISBN: (纸本)9783031220630;9783031220647

Parkinson's Disease (PD) is a neurodegenerative disease that primarily manifests through cognitive, motor and speech disorders. But it has been proven that voice changes in Parkinson's patients are among the symptoms that appear early. In this research paper, we propose a speech processing based approach for early Parkinson disease detection. Our approach evaluate the use of a deep convolutional autoencoder to extract the deep features from raw speech of PD patients and healthy subjects. Then, a classification step with MultiLayer Perceptron (MLP) which uses these deep features to build up a PD discriminant model. For an evaluation step we use a UCI dataset, our proposed approach achieve an accuracy of 95.52%, which is better than the related works accuracies using the same dataset. This prove that our system can be strongly recommended to monitor the progression of PD.

关键词： Parkinson's disease autoencoder Deep features Raw speech MLP

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17th International Workshop on Acoustic Signal Enhancement (IWAENC)

作者： Ito, Yuki Nakamura, Tomohiko Koyama, Shoichi Saruwatari, Hiroshi Univ Tokyo Grad Sch Informat Sci & Technol Bunkyo Ku 7-3-1 Hongo Tokyo 1138656 Japan

ISBN: (数字)9781665468671

ISBN: (纸本)9781665468671

We propose a method of head-related transfer function (HRTF) interpolation from sparsely measured HRTFs using an autoencoder with source position conditioning. The proposed method is drawn from an analogy between an HRTF interpolation method based on regularized linear regression (RLR) and an autoencoder. Through this analogy, we found the key feature of the RLR-based method that HRTFs are decomposed into source-position-dependent and source-position-independent factors. On the basis of this finding, we design the encoder and decoder so that their weights and biases are generated from source positions. Furthermore, we introduce an aggregation module that reduces the dependence of latent variables on source position for obtaining a source-position-independent representation of each subject. Numerical experiments show that the proposed method can work well for unseen subjects and achieve an interpolation performance with only one-eighth measurements comparable to that of the RLR-based method.

关键词： head-related transfer functions deep neural networks autoencoder hypernetworks spatial audio

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Channel Prediction over Irregular Terrains: Deep autoencoder with Random Forest 23

Channel Prediction over Irregular Terrains: Deep Autoencoder...

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23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)

作者： Wang, Yuyang Iyer, Shiva R. Chizhik, Dmitry Du, Jinfeng Valenzuela, Reinaldo UT Austin Austin TX 78712 USA NYU Brooklyn NY USA Nokia Bell Labs Murray Hill NJ USA

ISBN: (数字)9781665494557

ISBN: (纸本)9781665494557

Channel modeling is critical for coverage prediction, system level simulations, and wireless propagation characterization. Industry practice applies linear fit to the pathloss in decibels against the logarithm of the distance. Simple linear fit, however, cannot fully capture the shadowing effects in the channel, especially for a link with rich scatterings such as nonline-of-sight (NLOS) links in a complex propagation environment. In this paper, we propose an interpretable hybrid learning model with expert knowledge to predict the channel pathloss in desertlike environment using terrain profiles. We apply an autoencoder to extract compressed information from terrain profiles. The compressed representation of terrain, combined with features selected based on expert knowledge such as LOS/NLOS indicator and curvature of the terrain, are used to predict the pathloss. We show that a Random Forest regression model outperforms CNN/DNN models in generalizability of predicting unseen data by training and testing in disjoint sectors of the measured areas.

关键词： pathloss prediction autoencoder random forest

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NiaNet: A framework for constructing autoencoder architectures using nature-inspired algorithms 17

NiaNet: A framework for constructing Autoencoder architectur...

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17th Conference on Computer Science and Intelligence Systems (FedCSIS)

作者： Pavlic, Saso Karakatic, Saso Fister, Iztok, Jr. Univ Maribor Fac Elect Engn & Comp Sci Koroska Cesta 46 SLO-2000 Maribor Slovenia

ISBN: (数字)9788396242396

ISBN: (纸本)9788396242396

autoencoder, an hourly glass-shaped deep neural network capable of learning data representation in a lower dimension, has performed well in various applications. However, developing a high-quality AE system for a specific task heavily relies on human expertise, limiting its widespread application. On the other hand, there has been a gradual increase in automated machine learning for developing deep learning systems without human intervention. However, there is a shortage of automatically designing particular deep neural networks such as AE. This study presents the NiaNet method and corresponding software framework for designing AE topology and hyper-parameter settings. Our findings show that it is possible to discover the optimal AE architecture for a specific dataset without the requirement for human expert assistance. The future potential of the proposed method is also discussed in this paper.

关键词： AutoML autoencoder deep learning natureinspired algorithms optimization

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Advanced Flame front Detection in Combustion Processes Using autoencoder Approach

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ENERGIES 2024年第7期17卷 1759页

作者： Ricci, Federico Mariani, Francesco Univ Perugia Engn Dept Via Goffredo Duranti 93 I-06125 Perugia Italy

This research explores the detection of flame front evolution in spark-ignition engines using an innovative neural network, the autoencoder. High-speed camera images from an optical access engine were analyzed under different air excess coefficient lambda conditions to evaluate the autoencoder's performance. This study compared this new approach (AE) with an established method used by the same research group (BR) across multiple combustion cycles. Results revealed that the AE method outperformed the BR in accurately identifying flame pixels and significantly reducing overestimations outside the flame boundary. AE exhibited higher sensitivity levels, indicating its superior ability to identify pixels and minimize errors compared to the BR method. Additionally, AE's accuracy in representing combustion evolution was notably improved, offering a more detailed depiction of the process. AE's strength lies in its independence from specific threshold searches, a requirement in the BR method. By relying on learned representations within its latent space, AE eliminates laborious threshold exploration, ensuring reliability and reducing workload pressures. Comparative analyses consistently confirmed AE's superior performance in accurately reproducing and delineating combustion evolution compared to BR. This study highlights AE's potential as a promising technique for precise flame front detection in combustion processes. Its ability to autonomously extract features, minimize errors, and enhance overall accuracy signifies a significant step forward in analyzing flame fronts. AE's reliability, reduced need for manual intervention, and adaptability across various conditions suggest a promising future for improving combustion analysis techniques in spark-ignition engines with optical access.

关键词： machine learning autoencoder imaging flame combustion ultra lean

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Rapid scanning method for SICM based on autoencoder network

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MICRON 2024年 177卷 103579页

作者： Wu, Wenlin Liao, Xiaobo Wang, Lei Chen, Siyu Zhuang, Jian Zheng, Qiangqiang Southwest Univ Sci & Technol Key Lab Testing Technol Mfg Proc Minist Educ Mianyang 621010 Peoples R China Xi An Jiao Tong Univ Sch Mech Engn Xian 710049 Peoples R China

Scanning Ion Conductance Microscopy (SICM) enables non-destructive imaging of living cells, which makes it highly valuable in life sciences, medicine, pharmacology, and many other fields. However, because of the uncertainty retrace height of SICM hopping mode, the time resolution of SICM is relatively low, which makes the device fail to meet the demands of dynamic scanning. To address above issues, we propose a fast-scanning method for SICM based on an autoencoder network. Firstly, we cut under-sampled images into small image lists. Secondly, we feed them into a self-constructed primitive-autoencoder super-resolution network to compute high-resolution images. Finally, the inferred scanning path is determined using the computed images to reconstruct the real high-resolution scanning path. The results demonstrate that the proposed network can reconstruct higher-resolution images in various super-resolution tasks of low-resolution scanned images. Compared to existing traditional interpolation methods, the average peak signal-to-noise ratio improvement is greater than 7.5823 dB, and the average structural similarity index improvement is greater than 0.2372. At the same time, using the proposed method for high-resolution image scanning leads to a 156.25% speed improvement compared to traditional methods. It opens up possibilities for achieving high-time resolution imaging of dynamic samples in SICM and further promotes the widespread application of SICM in the future.

关键词： Temporal resolution SICM, Temporal resolution autoencoder

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TRANSFERABILITY OF CONVOLUTIONAL autoencoder MODEL FOR LOSSY COMPRESSION TO UNKNOWN HYPERSPECTRAL PRISMA DATA 12

TRANSFERABILITY OF CONVOLUTIONAL AUTOENCODER MODEL FOR LOSSY...

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12th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)

作者： Kuester, Jannick Gross, Wolfgang Schreiner, Simon Heizmann, Michael Middelmann, Wolfgang Fraunhofer IOSB Fraunhofer Ctr Machine Learning Ettlingen Germany Karlsruhe Inst Technol KIT Inst Ind Informat Technol IIIT Karlsruhe Germany

ISBN: (数字)9781665470698

ISBN: (纸本)9781665470698

This work addresses the challenge of the portability of autoencoder models for the lossy compression of different spatially independent and unknown hyperspectral satellite data. We propose an advanced 1D-Convolutional autoencoder architecture for lossy hyperspectral data compression with high transferability to unknown spectral signatures. In the first experiment, the model is trained on a single PRISMA data set, and in the second experiment it is trained on five PRISMA data sets from all over the world. The abstraction ability of the two models is verified by processing six spatially independent hyperspectral PRISMA satellite data sets. The evaluation is based on the reconstruction accuracy using the SNR and SA metrics and compares it to other learning-based lossy compression techniques. We demonstrate the high transferability and generalization of our 1D-Convolutional autoencoder for a fixed compression ratio on each PRISMA satellite data set, which results in superior reconstruction accuracy compared to state-of-the-art methods.

关键词： hyperspectral data compression feature extraction dimensionality reduction autoencoder hyperspectral imaging spectral analysis

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