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检索条件"主题词=Denoising autoencoder"

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Recovery method for missing sensor data in multi-sensor based walking recognition system 8

Recovery method for missing sensor data in multi-sensor base...

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8th IEEE Annual International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (IEEE-CYBER)

作者： Xie, Cheche Bi, Sheng Dong, Min Li, Yongfa South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Guangdong Peoples R China

ISBN: (纸本)9781538670576

Missing data is a major challenge in activity recognition and becomes an increasingly important study. Most current research on activity recognition is based on multiple sensors, which may bring the problem of missing data for one or more sensors. This paper presents Multi-source denoising autoencoder (MSDA) for filling missing sensor data in walking recognition system based on previously proposed Hierarchical AdaBoost. The recovery method uses denoising autoencoder to build a neural network which output is the embedding of features that include missing sensors, so that it can predict the features for missing sensor data. The experimental results show that MSDA can not only improve the recognition accuracy, but has higher performance comparing with other missing data processing method such as EM-PCA and filling missing data with a special value.

关键词： data recovery denoising autoencoder missing sensor data walking recognition

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DCA-CLA: A scRNA-seq Classification Framework based on Deep Count autoencoder

DCA-CLA: A scRNA-seq Classification Framework based on Deep ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Wu, Yulun Guo, Yanming Li, Jian Lao, Songyang Guo, Jinlin Wang, Haoran Natl Univ Def Technol Coll Syst Engn Changsha Peoples R China

ISBN: (纸本)9780738133669

Identifying cell types is crucial for single-cell RNA sequencing (scRNA-seq) analysis and can be potentially utilized to understand high-level biological processes. Supervised models based on neural networks have recently been successfully applied in the scRNA-seq cell type classification problem and achieved promising results. While most existing works directly use the raw or transformed data, we argue that the original data are too sparse and high-dimensional, and extracting their effective low-dimensional features can better train downstream classifiers, thereby improving the cell type classification performance. In this paper, we propose a novel framework, named Deep Count autoencoder-based Classifier (DCA-CLA), to leverage the discriminative low-dimensional features for classification. Specifically, DCA-CLA first denoises the original count matrix and extracts the data features from the hidden layer using a deep count autoencoder module, then it feeds these bottleneck features into the classifier network to train the learnable parameters and test the performance. Experimental results on eight separate datasets and four pairs of datasets demonstrate that the proposed DCA-CLA framework achieves competitive performance over the state-of-the-art frameworks.

关键词： denoising autoencoder single-cell RNA sequencing classification supervised learning bioinformatics

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Predicting Unmeasured Region of the Efficiency Map of a Speed Reducer Using a denoising Auto-encoder 3

Predicting Unmeasured Region of the Efficiency Map of a Spee...

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3rd International Conference on Computational Intelligence and Applications (ICCIA)

作者： Shin, Crino Jin, Yongsik Jeong, Seunghyun Yun, Jongpil Korea Inst Ind Technol KITECH Cheonan South Korea Kyungpook Natl Univ Daegu South Korea Kyungpook Natl Univ Dept Elect Engn Daegu South Korea

ISBN: (纸本)9781538695715

This paper presents a Remaining Useful Life (RUL) prediction method for a speed reducer based on denoising auto-encoder (DAE). Constructing the efficiency map of the reducer is an important process for predicting the life span. However, due to the situational constraints that occur, unmeasured intervals hinder the completion of the efficiency map. to solve this problem, we propose a method that can predict and reconstruct an unmeasured interval effectively and reliably by using DAE. In addition, we examine the applicability of the proposed algorithm through experiments that assume various situations.

关键词： denoising autoencoder Predicting unmeasured value Remaining Useful Life

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Tactile Object Recognition using Deep Learning and Dropout 14

Tactile Object Recognition using Deep Learning and Dropout

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14th IEEE-RAS International Conference on Humanoid Robots (Humanoids)

作者： Schmitz, Alexander Bansho, Yusuke Noda, Kuniaki Iwata, Hiroyasu Ogata, Tetsuya Sugano, Shigeki Waseda Univ Sch Creat Sci & Engn Sugano Lab Shinjuku Ku Okubo 2-4-12 Tokyo 1690072 Japan Waseda Univ Sch Creat Sci & Engn Sugano Lab Shinjuku Ku Tokyo 1620044 Japan Waseda Univ Grad Sch Fundamental Sci & Engn Tokyo 1698555 Japan Waseda Univ Sch Creat Sci & Engn Dept Modern Mech Engn Tokyo Japan Waseda Univ Sch Creat Sci & Engn Dept Modern Mech Engn Shinjuku Ku Tokyo 1698555 Japan

ISBN: (纸本)9781479971749

Recognizing grasped objects with tactile sensors is beneficial in many situations, as other sensor information like vision is not always reliable. In this paper, we aim for multimodal object recognition by power grasping of objects with an unknown orientation and position relation to the hand. Few robots have the necessary tactile sensors to reliably recognize objects: in this study the multifingered hand of TWENDY-ONE is used, which has distributed skin sensors covering most of the hand, 6 axis F/T sensors in each fingertip, and provides information about the joint angles. Moreover, the hand is compliant. When using tactile sensors, it is not clear what kinds of features are useful for object recognition. Recently, deep learning has shown promising results. Nevertheless, deep learning has rarely been used in robotics and to our best knowledge never for tactile sensing, probably because it is difficult to gather many samples with tactile sensors. Our results show a clear improvement when using a denoising autoencoder with dropout compared to traditional neural networks. Nevertheless, a higher number of layers did not prove to be beneficial.

关键词： control engineering computing dexterous manipulators distributed sensors learning (artificial intelligence) object recognition tactile sensors F/T sensors TWENDY-ONE deep learning denoising autoencoder distributed skin sensors dropout grasped objects recognition joint angles multifingered hand multimodal object recognition position relation power grasping tactile object recognition unknown orientation Object recognition Principal component analysis Skin Tactile sensors Training tactile sensors object recognition dexterous manipulators control engineering computing distributed sensors Machine Learning principal components analysis skin

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Compensate multiple distortions for speaker recognition systems 29

Compensate multiple distortions for speaker recognition syst...

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29th European Signal Processing Conference (EUSIPCO)

作者： Mohammadamini, Mohammad Matrouf, Driss Bonastre, Jean-Francois Serizel, Romain Dowerah, Sandipana Jouvet, Denis Avignon Univ LTA Lab Informat Avignon Avignon France Univ Lorraine CNRS INRIA Loria F-54000 Nancy France

ISBN: (纸本)9789082797060

The performance of speaker recognition systems reduces dramatically in severe conditions in the presence of additive noise and/or reverberation. In some cases, there is only one kind of domain mismatch like additive noise or reverberation, but in many cases, there are more than one distortion. Finding a solution for domain adaptation in the presence of different distortions is a challenge. In this paper we investigate the situation in which there is none, one or more of the following distortions: early reverberation, full reverberation, additive noise. We propose two configurations to compensate for these distortions. In the first one a specific denoising autoencoder is used for each distortion. In the second configuration, a denoising autoencoder is used to compensate for all of these distortions simultaneously. Our experiments show that, in the co-existence of noise and reverberation, the second configuration gives better results. For example, with the second configuration we obtained 76.6% relative improvement of EER for utterances longer than 12 seconds. For other situations in the presence of only one distortion, the second configuration gives almost the same results achieved by using a specific model for each distortion.

关键词： additive noise early reverberation full reverberation x-vector denoising autoencoder

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MULTI-TASK autoencoder FOR NOISE-ROBUST SPEECH RECOGNITION

MULTI-TASK AUTOENCODER FOR NOISE-ROBUST SPEECH RECOGNITION

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

作者： Zhang, Haoyi Liu, Conggui Inoue, Nakamasa Shinoda, Koichi Tokyo Inst Technol Tokyo Japan

ISBN: (纸本)9781538646588

For speech recognition in noisy environments, we propose a multi-task autoencoder which estimates not only clean speech features but also noise features from noisy speech. We introduce the deSpeeching autoencoder, which excludes speech signals from noisy speech, and combine it with the conventional denoising autoencoder to form a unified multi-task autoencoder (MTAE). We evaluate it using the Aurora 2 dataset and CHIME 3 dataset. It reduced WER by 15.7% from the conventional denoising autoencoder in the Aurora 2 test set A.

关键词： denoising autoencoder deSpeeching autoencoder robust speech recognition

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Mapping Between Ultrasound and Vowel Speech Using DNN Framework 9

Mapping Between Ultrasound and Vowel Speech Using DNN Framew...

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9th International Symposium on Chinese Spoken Language Processing (ISCSLP)

作者： Zheng, Xinyuan Wei, Jianguo Lu, Wenhuan Fang, Qiang Dang, Jianwu Tianjin Univ Sch Comp Sci & Technol Tianjin Peoples R China Tianjin Univ Sch Comp Software Tianjin Peoples R China Chinese Acad Sci Beijing 100864 Peoples R China JAIST Sch Informat Tokyo Japan

ISBN: (纸本)9781479942190

Building up the mapping between articulatory movements and corresponding speech could great facility the speech training and speech aid for voiceless patients. In this paper, we propose a deep learning framework for building up a mapping between articulatory information and corresponding speech, which were recorded by ultrasound system. The dataset includes six Chinese vowels. We use Bimodal Deep autoencoder algorithm based on RBM to learn the relationship between speech and articulation, the weights matrix of representation of them. Speech and ultrasound images have been reconstructed using the extracted features. The reconstruction error of articulation by our method is less than that of PCA based approach. The reconstructed speech is similar to the original one. We propose a mapping from ultrasound tongue image to acoustic signal with a revised denoising autoencoder, the results show that it is a promising approach. In contrast, another experiment is conducted to synthesize the ultrasound tongue image from the speech, but the result should be improved.

关键词： DBN articulatory-acoustic mapping denoising autoencoder

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Align-Denoise: Single-Pass Non-Autoregressive Speech Recognition 22

Align-Denoise: Single-Pass Non-Autoregressive Speech Recogni...

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Interspeech Conference

作者： Chen, Nanxin Zelasko, Piotr Moro-Velazquez, Laureano Villalba, Jesus Dehak, Najim Johns Hopkins Univ Ctr Language & Speech Proc Baltimore MD 21218 USA Johns Hopkins Univ Human Language Technol Ctr Excellence Baltimore MD 21218 USA

ISBN: (纸本)9781713836902

Deep autoregressive models start to become comparable or superior to the conventional systems for automatic speech recognition. However, for the inference computation, they still suffer from inference speed issue due to their token-by-token decoding characteristic. Non-autoregressive models greatly improve decoding speed by supporting decoding within a constant number of iterations. For example, Align-Refine was proposed to improve the performance of the non-autoregressive system by refining the alignment iteratively. In this work, we propose a new perspective to connect Align-Refine and denoising autoencoder. We introduce a novel noisy distribution to sample the alignment directly instead of obtaining it from the decoder output. The experimental results reveal that the proposed Align-Denoise speeds up both training and inference with performance improvement up to 5% relatively using single-pass decoding.

关键词： speech recognition non-autoregressive model deep learning denoising autoencoder iterative refinement

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Reading Imagined Letter Shapes from the Mind's Eye Using Real-time 7 Tesla fMRI 10

Reading Imagined Letter Shapes from the Mind's Eye Using Rea...

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10th International Winter Conference on Brain-Computer Interface (BCI)

作者： Goebel, Rainer van Hoof, Rick Bhat, Salil Luhrs, Michael Senden, Mario Maastricht Univ Dept Cognit Neurosci Maastricht Netherlands

ISBN: (纸本)9781665413374

We present a 7 Tesla fMRI proof-of-concept study of the first letter speller BCI that decodes imagined letter shapes from activity patterns in early visual cortical areas. New tools are developed to enable real-time population receptive field retinotopic mapping for encoding and decoding. Using two different letter shapes (H and T), classification performance of generated activity patterns during imagery reaches 80% accuracy in each individual. Using a denoising autoencoder, recognizable letter shapes could be reconstructed and displayed as feedback to participants in the scanner.

关键词： fMRI letter speller imagery population receptive fields denoising autoencoder

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Research on Anomaly Detection of Offshore Buoy Water Temperature Data Based on LSTM-DAE Model 5

Research on Anomaly Detection of Offshore Buoy Water Tempera...

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5th International Conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2023

作者： Gao, Saiyu Song, Miaomiao Hu, Wei Li, Wenqing Zheng, Shanshan Zhuo, Lei Liu, Ru Huang, Jiuzhang Song, Haihui Cheng, Kaiyu Institute of Oceanographic Instrumentation Qilu University of Technology Shandong Academy of Sciences Qingdao266001 China National Marine Monitoring Equipment Engineering Technology Research Center China

ISBN: (纸本)9798350357950

At present, there are many quality problems in the hydrological data of ocean buoys, and it is urgent to carry out scientific and standardized data quality control to ensure the accuracy and rationality of the buoy data. This paper focuses on water temperature data collected by offshore buoys, and an LSTM-DAE abnormal value detection model is proposed. In this model, both the encoder and decoder of the denoising autoencoder adopt a long short-term memory network (LSTM) as the core component of the neural network layer. Experimental results show that compared with the traditional LSTM model for outlier detection, the proposed model can better deal with the long-term series characteristics in the buoy data and reconstruct the input data more accurately, so as to improve the effect of outlier detection. © 2023 IEEE.

关键词： abnormal value denoising autoencoder LSTM ocean buoys quality control

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