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检索条件"主题词=Denoising autoencoder"

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Modulation Signal denoising Based on Auto-encoder 16

Modulation Signal Denoising Based on Auto-encoder

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16th IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (IEEE BMSB)

作者： Mo, Zunyin Li, Hongli Wang, Jiao Huang, Hao Li, Jianqing Univ Elect Sci & Technol China Sch Phys Chengdu Peoples R China Univ Elect Sci & Technol China Sch Elect Sci & Engn Chengdu Peoples R China

ISBN: (纸本)9781665449083

This paper proposed a denoising method for modulated signals based on the autoencoder. The auto-encoder is a cascade structure, which is composed of multiple convolution layers and multiple pooling layers. It is mainly divided into a feature encoder and a generation decoder. We use the features of the modulated signal with noise as the input of the auto-encoder and the features of the clean signal as the label. At the same time, back-propagation algorithm and gradient descent method are used to optimize and update the parameters in the auto-encoder model to minimize the reconstruction error, so as to realize the denoising function of the modulated signal. For a variety of modulation types, this method can improve the modulation signal about 3-9 dB in different SNR environment. The denoising model can generate high-level features of different modulation signals without any artificial feature extraction and prior knowledge and has strong feature representation ability. It has the advantages of strong versatility, low complexity, good denoising effect and good stability.

关键词： denoising autoencoder Machine learning for communications Advanced signal processing for transmission convolution neural network

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OPEN SET RECOGNITION BY REGULARISING CLASSIFIER WITH FAKE DATA GENERATED BY GENERATIVE ADVERSARIAL NETWORKS

OPEN SET RECOGNITION BY REGULARISING CLASSIFIER WITH FAKE DA...

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IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Jo, Inhyuk Kim, Jungtaek Kang, Hyohyeong Kim, Yong-Deok Choi, Seungjin POSTECH Dept Comp Sci & Engn Pohang South Korea Samsung Elect Software R&D Ctr Device Solut Seoul South Korea

ISBN: (纸本)9781538646588

We present a new method to generate fake data in unknown classes in generative adversarial networks (GANs) framework. The generator in GANs is trained to generate somewhat similar to data in known classes but the different one by modelling noisy distribution on feature space of a classifier using proposed marginal denoising autoencoder. The generated data are treated as fake instances in unknown classes and given to the classifier to make it be robust to the real unknown classes. Our results show that synthetic data can act as fake unknown classes and keep down the certainty of the classifier on real unknown classes meanwhile the classification capability of known classes is not degenerated, even improved.

关键词： Generative adversarial networks denoising autoencoder Open set recognition Feature matching

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Self-supervised Siamese autoencoders 22nd

Self-supervised Siamese Autoencoders

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22nd International Symposium on Intelligent Data Analysis (IDA)

作者： Baier, Friederike Mair, Sebastian Fadel, Samuel G. Leuphana Univ Luneburg Luneburg Germany Uppsala Univ Uppsala Sweden Linkoping Univ Linkoping Sweden

ISBN: (纸本)9783031585463;9783031585470

In contrast to fully-supervised models, self-supervised representation learning only needs a fraction of data to be labeled and often achieves the same or even higher downstream performance. The goal is to pre-train deep neural networks on a self-supervised task, making them able to extract meaningful features from raw input data afterwards. Previously, autoencoders and Siamese networks have been successfully employed as feature extractors for tasks such as image classification. However, both have their individual shortcomings and benefits. In this paper, we combine their complementary strengths by proposing a new method called SidAE (Siamese denoising autoencoder). Using an image classification downstream task, we show that our model outperforms two self-supervised baselines across multiple data sets and scenarios. Crucially, this includes conditions in which only a small amount of labeled data is available. Empirically, the Siamese component has more impact, but the denoising autoencoder is nevertheless necessary to improve performance.

关键词： Self-supervised learning representation learning Siamese networks denoising autoencoder pre-training image classification

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EFFECTIVE JOINT TRAINING OF denoising FEATURE SPACE TRANSFORMS AND NEURAL NETWORK BASED ACOUSTIC MODELS

EFFECTIVE JOINT TRAINING OF DENOISING FEATURE SPACE TRANSFOR...

引用

IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Fukuda, Takashi Ichikawa, Osamu Kurata, Gakuto Tachibana, Ryuki Thomas, Samuel Ramabhadran, Bhuvana IBM Watson Multimodal Chuo Ku Tokyo 1038510 Japan IBM Watson Multimodal Yorktown Hts NY 10598 USA

ISBN: (纸本)9781509041176

Neural Network (NN) based acoustic frontends, such as denoising autoencoders, are actively being investigated to improve the robustness of NN based acoustic models to various noise conditions. In recent work the joint training of such frontends with backend NNs has been shown to significantly improve speech recognition performance. In this paper, we propose an effective algorithm to jointly train such a denoising feature space transform and a NN based acoustic model with various kinds of data. Our proposed method first pretrains a Convolutional Neural Network (CNN) based denoising frontend and then jointly trains this frontend with a NN backend acoustic model. In the unsupervised pretraining stage, the frontend is designed to estimate clean log Mel-filterbank features from noisy log-power spectral input features. A subsequent multi-stage training of the proposed frontend, with the dropout technique applied only at the joint layer between the frontend and backend NNs, leads to significant improvements in the overall performance. On the Aurora-4 task, our proposed system achieves an average WER of 9.98%. This is a 9.0% relative improvement over one of the best reported speaker independent baseline system's performance. A final semi-supervised adaptation of the frontend NN, similar to feature space adaptation, reduces the average WER to 7.39%, a further relative WER improvement of 25%.

关键词： Speech recognition neural network CNN joint training denoising autoencoder

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Fine-Tuning Self-Supervised Multilingual Sequence-To-Sequence Models for Extremely Low-Resource NMT 7

Fine-Tuning Self-Supervised Multilingual Sequence-To-Sequenc...

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Moratuwa Engineering Research Conference (MERCon) / 7th International Multidisciplinary Engineering Research Conference

作者： Thillainathan, Sarubi Ranathunga, Surangika Jayasena, Sanath Univ Moratuwa Dept Comp Sci & Engn Katubedda Sri Lanka

ISBN: (纸本)9781665437530

Neural Machine Translation (NMT) tends to perform poorly in low-resource language settings due to the scarcity of parallel data. Instead of relying on inadequate parallel corpora, we can take advantage of monolingual data available in abundance. Training a denoising self-supervised multilingual sequence-to-sequence model by noising the available large scale monolingual corpora is one way to utilize monolingual data. For a pair of languages for which monolingual data is available in such a pre-trained multilingual denoising model, the model can be fine-tuned with a smaller amount of parallel data from this language pair. This paper presents fine-tuning self-supervised multilingual sequence-to-sequence pre-trained models for extremely low-resource domain-specific NMT settings. We choose one such pre-trained model: mBART. We are the first to implement and demonstrate the viability of non-English centric complete fine-tuning on multilingual sequence-to-sequence pretrained models. We select Sinhala, Tamil and English languages to demonstrate fine-tuning on extremely low-resource settings in the domain of official government documents. Experiments show that our fine-tuned mBART model significantly outperforms state-of-the-art Transformer based NMT models in all pairs in all six bilingual directions, where we report a 4.41 BLEU score increase on Tamil -> Sinhala and a 2.85 BLUE increase on Sinhala -> Tamil translation.

关键词： neural machine translation pre-trained models fine-tuning denoising autoencoder low-resource languages

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Online Anomaly Detection with Streaming Data based on Fine-grained Feature Forecasting 33

Online Anomaly Detection with Streaming Data based on Fine-g...

引用

33rd Chinese Control and Decision Conference (CCDC)

作者： Liu, Keying Mao, Wentao Shi, Huadong Wu, Chao Chen, Jiaxian Henan Normal Univ Sch Comp & Informat Engn Xinxiang 453007 Henan Peoples R China Engn Lab Intelligence Business & Internet Things Xinxiang 453007 Henan Peoples R China

ISBN: (纸本)9781665440899

In the industrial applications like fault diagnosis and health management, monitoring data generally reaches sequentially in a streaming form. To recognize fault occurrence in real time without system halt, it is necessary to improve the accuracy and stability of anomaly detection with streaming data. To solve this problem, a new online anomaly detection method with streaming data is proposed based on fine-grained feature forecasting. First, to conduct fine-grained decomposition of features, a denoising autoencoder network is run to extract multiple-dimensional deep features of online data in the initial period of normal state. Second, a forecasting model with tensor Tucker decomposition and ARIMA is conducted to predict the fluctuation trend of all feature sequences. Finally, the deviation degree between the prediction values and sequentially-arrived data is calculated, and an alarm threshold is built according to the 95% confidence interval of the maximum deviation. Then the anomalous state data can be detected in real time. This paper adopts the problem of bearing early fault online detection as an example, and run comparative experiments on the IEEE PHM Challenge 2012 bearing dataset. The results show that the proposed method has good detection accuracy and is with no false alarm, while the model training does not rely on any offline data. Then the proposed method is applicable to the problem of online anomaly detection.

关键词： Streaming data denoising autoencoder Anomaly detection Alarm threshold ARIMA model

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Understanding Chromatin Remodeling Through Physics-Based Machine Learning Approaches

Understanding Chromatin Remodeling Through Physics-Based Mac...

引用

作者： Alvarado, Walter The University of Chicago

学位级别：Ph.D., Doctor of Philosophy

The packing of nucleosomes regulates gene expression through genome condensation and expansion, but the specific structures and their thermodynamic stabilities remain unresolved. In this work, we employ the use of a meso-scale model of chromatin, referred to as "1-Cylinder-per-Nucleosome," or 1CPN, in combination with nonlinear manifold learning to identify and characterize the structure and free energy of metastable states of short chromatin segments. Our results reveal the intrinsic formation of two previously characterized tetranucleosomal conformations, the "α-tetrahedron" and the "β-rhombus," which have been suggested to play a role in inducing chromatin compaction or elongation, respectively. Building upon these findings, we leverage convolutional neural networks and molecular dynamics simulations, to design a deep convolutional denoising autoencoder (DAE) capable of providing nucleosome-level resolution of scanning transmission electron microscopy images of chromatin (ChromSTEM). Our DAE is trained on synthetic images generated from simulations of the chromatin fiber using the 1CPN model of chromatin, thereby learning structural features driven by the physics of chromatin folding. We find that our DAE outperforms other well-known denoising algorithms without degradation of structural features and allows for the resolution of individual nucleosomes and organized domains within chromatin-dense regions. Lastly, we investigate how post-translational modifications (PTMs), modeled as modifications to the nucleosome interaction potential, affect the construction of these motifs and, consequently, the chromatin fiber as a whole. Our study provides important insight into chromatin folding and highlights the value of interdisciplinary approaches in this field.

关键词： Chromatin denoising autoencoder Machine learning Molecular dynamics Nonlinear manifold learning Tetranucleosome

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DAE-Transformer-based Remaining Useful Life Prediction for Lithium-Ion Batteries in Energy Storages 3

DAE-Transformer-based Remaining Useful Life Prediction for L...

引用

3rd International Conference on New Energy and Power Engineering, ICNEPE 2023

作者： Huang, Bowen Zeng, Zihao Zhou, Yamin Liu, Jiang Zheng, Qian Wang, Luting Feng, Zihao Sun, Shukai Pan, Zheng Energy Saving Management Branch of State Grid Hunan Electric Power Co. Ltd Changsha China Hunan University College of Electrical and Information Engineering Changsha China

ISBN: (纸本)9798350393224

To improve the operation stability and reliability of energy storage stations (ESSs), it's significance to ensure high-precision battery remaining useful life (RUL) prediction. Recently, the raw capacity of batteries in ESSs are affected by noise and long-term dependence on time series, which negatively impact the accuracy of the RUL prediction model. To address this issue, this paper proposes a RUL prediction model based on denoising autoencoder (DAE) and Transformer for batteries in ESSs. Firstly, the DAE is used to reduce the noise of the original battery capacity to reduce the influence of data noise on the accuracy of the RUL prediction model. Then, the Transformer network is used to learn to normalize the time-series long-term dependencies of the capacity to generate high-precision RUL predictions. The proposed model is verified by experiments based on the battery degradation dataset in the NASA Center for Prediction Excellence data repository. © 2023 IEEE.

关键词： component denoising autoencoder Lithium-ion battery Remaining useful life Transformer

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MIDC-Net: Medical Image denoising and Disease Classification Network for Chest X-rays 3

MIDC-Net: Medical Image Denoising and Disease Classification...

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3rd International Conference on Electronic Information Engineering and Computer Science, EIECS 2023

作者： Li, Jiatu University of California San Diego Halicioǧlu Data Science Institute Department of Mathematics San DiegoCA United States

ISBN: (纸本)9798350316674

Accurate medical imaging is vital for precise disease diagnosis and effective treatment. However, X-ray images may be subject to varying degrees of noise due to factors such as patient health conditions requiring reduced X-ray radiation. In this paper, the Medical Image denoising and Disease Classification Network (MIDC-Net) is proposed to solve the issue of noisy chest X-rays that negatively affects thorax disease classification by performing image denoising and disease classification simultaneously. The MIDC-Net integrates a Convolutional Neural Network (CNN) for classification, and a denoising autoencoder with convolutional layers and residual connections for image denoising. It comprises three components, namely an encoder, a decoder, and a classifier, where the input of the encoder is noisy images with varying levels of Gaussian noise, and the output of the decoder and the classifier are reconstructed images and predicted diseases. The loss of the MIDC-Net is a linear combination of Mean Square Error (MSE) loss used for denoising and Binary Cross-Entropy (BCE) loss used for multi-label classification. Experimental outcomes on the NIH chest X-ray dataset illustrated that the reconstructed images resembled the original X-rays, and the network achieved a low MSE loss, but the quality of denoised images exhibited an inverse relationship with noise levels. Regarding multi-label thorax classification results, the test accuracies all exceed 95% regardless of noise levels of the input images, demonstrating efficient and accurate prediction in the scenario of noisy inputs. © 2023 IEEE.

关键词： convolutional neural network denoising autoencoder Image denoising multi-label classification

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autoencoder: Issues, Challenges and Future Prospect 3rd

Autoencoder: Issues, Challenges and Future Prospect

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3rd International Conference on Recent Innovations and Technological Development in Mechanical Engineering, ICRITDME 2020

作者： Maheshwari, Anega Mitra, Priyanka Sharma, Bhavna Department of Computer Science Jaipur Engineering College and Research Centre Rajasthan Jaipur India

ISBN: (纸本)9789811692352

As of more recently, deep learning-based models have demonstrated considerable potential, as they have outperformed all traditional practices. When data becomes high dimensional, extraction of features and compression of data become progressively significant. In this paper, we describe the autoencoder deep learning algorithm. autoencoder is primarily a neural network-based feature extraction methodology that accomplishes outstanding victory in producing highlights of high-dimensional data. autoencoder assumes a principal job in unsupervised learning which targets to rework inputs into outputs with minimal reconstruction error. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： autoencoder Convolutional autoencoder denoising autoencoder Unsupervised learning

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