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Transformer Network for Remaining Useful Life Prediction of Lithium-Ion Batteries

IEEE ACCESS 2022年 10卷 19621-19628页

作者： Chen, Daoquan Hong, Weicong Zhou, Xiuze Zhejiang Inst Mech & Elect Engn Sch Intelligent Transportat Hangzhou 310053 Peoples R China Shuye Technol Co Ltd Hangzhou Peoples R China

Accurately predicting the Remaining Useful Life (RUL) of a Li-ion battery plays an important role in managing the health and estimating the state of a battery. With the rapid development of electric vehicles, there is an increasing need to develop and improve the techniques for predicting RUL. To predict RUL, we designed a Transformer-based neural network. First, battery capacity data is always full of noise, especially during battery charge/discharge regeneration. To alleviate this problem, we applied a denoising auto-encoder (DAE) to process raw data. Then, to capture temporal information and learn useful features, a reconstructed sequence was fed into a Transformer network. Finally, to bridge denoising and prediction tasks, we combined these two tasks into a unified framework. Results of extensive experiments conducted on two data sets and a comparison with some existing methods show that our proposed method performs better in predicting RUL. Our projects are all open source and are available at https://***/XiuzeZhou/RUL.

关键词： Transformers Noise reduction Task analysis Predictive models Data models Degradation Lithium-ion batteries Li-ion battery remaining useful life transformer denoising auto-encoder neural network

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Development of thermal error mapping model for the dry gear hobbing machine based on CNN-DAE integrated structure and its application

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2021年第7-8期113卷 2343-2354页

作者： Zou, Zheng Yan, Wen Ma, Wensheng Liu, Zhuang Cao, Rupeng Chen, Wei Chongqing Univ Technol Chongqing 40054 Peoples R China Chongqing Pump Ind Co Ltd Chongqing Peoples R China

The dry cutting gear hobbing machine is a specialized manufacturing equipment for small-module gears. During the continuous large-volume gear cutting process, the thermal error plays a major negative role in the quality of gear making. In this paper, based on the convolutional neural network and the denoising auto-encoder algorithm, we developed a new mapping model between features of characteristic signals and the thermal-induced deviation of the spacing between the hob and the workpiece. In the modeling, the temperature change of key thermal points, the hob spindle current signal, and the corresponding thermal-induced deviation trend of spacing between the hob and the worktable in a continuous gear cutting experiment were collected and sampled. Those samples were fed into the TensorFlow framework to train the integrated mathematical structure. Finally, the mapping model was obtained, which was capable to accurately and quickly quantify the thermal error of the YDE-type dry gear hobbing machine. Accordingly, a thermal error compensation system was developed as well. To prove the accountabilities of this newly proposed mapping model as well as the corresponding thermal error compensation system, another continuous gear cutting verification experiment was carried out on the dry gear hobbing machine equipped with this new thermal error compensation system. The experiment results showed that with the help of the newly proposed thermal error model and compensation system, the M values of machined gears, which directly reflected the effect of the thermal error on the gear hobbing accuracy, remained in a practically acceptable range (+/- 0.02 mm) in the continuous gear cutting, which proved the reliability of our proposed new mapping model.

关键词： Dry cutting hobbing machine Thermal-induced error Convolutional neural network denoising auto-encoder

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Online deep learning based on auto-encoder

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APPLIED INTELLIGENCE 2021年第8期51卷 5420-5439页

作者： Zhang, Si-si Liu, Jian-wei Zuo, Xin Lu, Run-kun Lian, Si-ming ChinaUnivers Petr Coll Informat Sci & Engn Dept Automat Beijing Campus CUP260 Mailbox China Univ Petr Beijing 102249 Peoples R China

Online learning is an important technical means for sketching massive real-time and high-speed data. Although this direction has attracted intensive attention, most of the literature in this area ignore the following three issues: (1) they think little of the underlying abstract hierarchical latent information existing in examples, even if extracting these abstract hierarchical latent representations is useful to better predict the class labels of examples;(2) the idea of preassigned model on unseen datapoints is not suitable for modeling streaming data with evolving probability distribution. This challenge is referred as "model flexibility". And so, with this in minds, the online deep learning model we need to design should have a variable underlying structure;(3) moreover, it is of utmost importance to fusion these abstract hierarchical latent representations to achieve better classification performance, and we should give different weights to different levels of implicit representation information when dealing with the data streaming where the data distribution changes. To address these issues, we propose a two-phase Online Deep Learning based on auto-encoder (ODLAE). Based on auto-encoder, considering reconstruction loss, we extract abstract hierarchical latent representations of instances;Based on predictive loss, we devise two fusion strategies: the output-level fusion strategy, which is obtained by fusing the classification results of encoder's each hidden layer;and feature-level fusion strategy, which is leveraged self-attention mechanism to fusion the every hidden layer's output. Finally, in order to improve the robustness of the algorithm, we also try to utilize the denoising auto-encoder to yield hierarchical latent representations. Experimental results on different datasets are presented to verify the validity of our proposed algorithm (ODLAE) outperforms several baselines.

关键词： Online deep learning auto-encoder Output-level fusion Feature-level fusion denoising auto-encoder

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Generalizing Projected Gradient Descent for Deep-Learning-Aided Massive MIMO Detection

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2024年第3期23卷 1827-1839页

作者： He, Lanxin Wang, Zheng Yang, Shaoshi Liu, Tao Huang, Yongming Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Nanjing 210000 Peoples R China Southeast Univ Sch Informat Sci & Engn Nanjing 210096 Peoples R China Beijing Univ Posts & Telecommun BUPT Sch Informat & Commun Engn Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Key Lab Universal Wireless Commun Minist Educ Beijing 100876 Peoples R China

In this paper, the projected-gradient-descent (PGD) -based detector for massive MIMO system, which consists of two basic operations - projection and gradient descent (GD), is studied to achieve the performance improvement. Since the projection and GD step have different loss functions, necessary compromise has to be made to balance them during iterations. For this reason, the generalized PGD (GPGD) method is proposed with flexible choices of projection and GD. Different from performing projection and GD alternatively, we show that implementing projection after every multiple GD steps is a better solution. Meanwhile, the step-size of GD is also investigated for convergence efficiency. After that, by unfolding this proposed GPGD method with deep neural networks (DNN), the self-corrected auto-detector (SAD) is established to achieve better decoding performance, where enhancement by attention mechanism and extension by another iterative method are also given for performance improvement and efficiency.

关键词： Detectors Massive MIMO Noise reduction Wireless communication Uplink Optimization Decoding Massive MIMO detection projected gradient descent denoising auto-encoder attention mechanism

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Improving phoneme recognition of throat microphone speech recordings using transfer learning

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SPEECH COMMUNICATION 2021年 129卷 25-32页

作者： Turan, M. A. Tugtekin Erzin, Engin Koc Univ Coll Engn KUIS AI Lab Istanbul Turkey

Throat microphones (TM) are a type of skin-attached non-acoustic sensors, which are robust to environmental noise but carry a lower signal bandwidth characterization than the traditional close-talk microphones (CM). Attaining high-performance phoneme recognition is a challenging task when the training data from a degrading channel, such as TM, is limited. In this paper, we address this challenge for the TM speech recordings using a transfer learning approach based on the stacked denoising auto-encoders (SDA). The proposed transfer learning approach defines an SDA-based domain adaptation framework to map the source domain CM representations and the target domain TM representations into a common latent space, where the mismatch across TM and CM is eliminated to better train an acoustic model and to improve the TM phoneme recognition. For the phoneme recognition task, we use the convolutional neural network (CNN) and the hidden Markov model (HMM) based CNN/HMM hybrid system, which delivers better acoustic modeling performance compared to the conventional Gaussian mixture model (GMM) based models. In the experimental evaluations, we observed more than 12% relative phoneme error rate (PER) improvement for the TM recordings with the proposed transfer learning approach compared to baseline performances.

关键词： Phoneme recognition Feature augmentation Transfer learning Throat microphone denoising auto-encoder

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Intelligent fault diagnosis of rotating machinery based on deep learning with feature selection

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JOURNAL OF LOW FREQUENCY NOISE VIBRATION AND ACTIVE CONTROL 2020年第4期39卷 939-953页

作者： Han, Dongying Liang, Kai Shi, Peiming Yanshan Univ Sch Vehicles & Energy Qinhuangdao Hebei Peoples R China Yanshan Univ Sch Elect Engn Qinhuangdao 006004 Hebei Peoples R China

In the absence of a priori knowledge, manual feature selection is too blind to find the sensitive features which can effectively classify the different fault features. And it is difficult to obtain a large number of typical fault samples in practice to train the intelligent classifier. A novel intelligent fault diagnosis method based on feature selection and deep learning is proposed for rotating machine mechanical in the paper. In this method, the deep neural network is not only used for feature extraction but also for fault diagnosis. First, the deep neural network 1 is used to extract feature from the spectral signal of the original signal. In addition, the original vibration signal is decomposed to a series of intrinsic mode function components by empirical mode decomposition, and the statistical features of each intrinsic mode function component are extracted by the deep neural network 2 in time domain and frequency domain. Second, the extraction features of the original signal spectrum and the extraction features of each intrinsic mode function component are evaluated, respectively. After features evaluation, the selected sensitive features are combined together to construct a joint feature. Finally, the joint feature is put into the deep neural network 3 to realize the automatic recognition of different fault states of rotating machinery. The experimental results show that the method proposed in this paper which integrated time-domain, frequency-domain statistical characteristics, empirical mode decomposition, feature selection, and deep learning methods can obtain the fault information in detail and can select sensitive features from a large number of fault features. The method can reduce the network size, improve the mechanical fault diagnosis classification accuracy, and has strong robustness.

关键词： Intelligent fault diagnosis empirical mode decomposition feature selection denoising auto-encoder deep learning

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Progressive Shape-Distribution- encoder for Learning 3D Shape Representation

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2017年第3期26卷 1231-1242页

作者： Xie, Jin Zhu, Fan Dai, Guoxian Shao, Ling Fang, Yi New York Univ Abu Dhabi NYU Multimedia & Visual Comp Lab Dept Elect & Comp Engn Abu Dhabi U Arab Emirates NYU Tandon Sch Engn Dept Elect & Comp Engn New York NY 10003 USA Univ East Anglia Sch Comp Sci Norwich NR4 7TJ Norfolk England

Since there are complex geometric variations with 3D shapes, extracting efficient 3D shape features is one of the most challenging tasks in shape matching and retrieval. In this paper, we propose a deep shape descriptor by learning shape distributions at different diffusion time via a progressive shape-distribution-encoder (PSDE). First, we develop a shape distribution representation with the kernel density estimator to characterize the intrinsic geometry structures of 3D shapes. Then, we propose to learn a deep shape feature through an unsupervised PSDE. Specially, the unsupervised PSDE aims at modeling the complex non-linear transform of the estimated shape distributions between consecutive diffusion time. In order to characterize the intrinsic structures of 3D shapes more efficiently, we stack multiple PSDEs to form a network structure. Finally, we concatenate all neurons in the middle hidden layers of the unsupervised PSDE network to form an unsupervised shape descriptor for retrieval. Furthermore, by imposing an additional constraint on the outputs of all hidden layers, we propose a supervised PSDE to form a supervised shape descriptor. For each hidden layer, the similarity between a pair of outputs from the same class is as large as possible and the similarity between a pair of outputs from different classes is as small as possible. The proposed method is evaluated on three benchmark 3D shape data sets with large geometric variations, i.e., McGill, SHREC' 10 ShapeGoogle, and SHREC' 14 Human data sets, and the experimental results demonstrate the superiority of the proposed method to the existing approaches.

关键词： 3D shape retrieval shape descriptor denoising auto-encoder heat kernel signature heat diffusion

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A dual encoder DAE neural network for imbalanced binary classification based on NSGA-III and GAN

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PATTERN ANALYSIS AND APPLICATIONS 2022年第1期25卷 17-34页

作者： Qu, Jiantao Liu, Feng Ma, Yuxiang Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing 100044 Peoples R China Minist Educ Engn Res Ctr Network Management Technol High Spee Beijing 100044 Peoples R China Henan Univ Sch Comp & Informat Engn Kaifeng 475004 Peoples R China

In real-world datasets, the number of samples in each class is often imbalanced, which results in the classifier's suboptimal performance. Presently, the imbalanced binary classification approach based on deep learning has achieved good results and gets more attention constantly. In this study, we present a dual encoder (denoising auto-encoder) DAE neural network based on non-dominated sorting genetic algorithm (NSGA-III) and generative adversarial network (GAN) to address the imbalanced binary classification problem. The primary aim of our approach is to increase the separability between the reconstruction error of minority class latent features and the reconstruction error of majority class latent features. For this purpose, we first create a dual encoder DAE network to obtain the reconstruction error of latent features of training data. Second, when training the neural network, we introduced GAN to perform a layer-wise training which can improve the training effect of the model. Third, in order to increase the separability of the reconstruction error of minority class and majority class, we utilize NSGA-III to optimize the parameters of the second encoder. Then, we can obtain a set of non-dominated solutions. Finally, based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method, we can get the best solution, which is the most appropriate parameter set of the second encoder to distinguish the minority class and the majority class. The experiment results on both benchmark datasets and a real-world dataset for communication anomaly detection demonstrate the superiority of the proposed approach in imbalanced binary classification problem.

关键词： Imbalanced data GAN NSGA-III denoising auto-encoder Deep learning

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Estimating gene expression from DNA methylation and copy number variation: A deep learning regression model for multi-omics integration

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GENOMICS 2020年第4期112卷 2833-2841页

作者： Seal, Dibyendu Bikash Das, Vivek Goswami, Saptarsi De, Rajat K. Univ Calcutta AK Choudhury Sch Informat Technol JD-2Sect 3 Kolkata 700106 India Novo Nordisk Res Ctr Seattle Inc 530 Fairview Ave N 5000 Seattle WA 98109 USA Bangabasi Morning Coll 35 Rajkumar Chakraborty SaraniScott Ln Kolkata 700009 India Indian Stat Inst Machine Intelligence Unit 203 Barrackpore Trunk Rd Kolkata 700108 India

Gene expression analysis plays a significant role for providing molecular insights in cancer. Various genetic and epigenetic factors (being dealt under multi-omics) affect gene expression giving rise to cancer phenotypes. A recent growth in understanding of multi-omics seems to provide a resource for integration in interdisciplinary biology since they altogether can draw the comprehensive picture of an organism's developmental and disease biology in cancers. Such large scale multi-omics data can be obtained from public consortium like The Cancer Genome Atlas (TCGA) and several other platforms. Integrating these multi-omics data from varied platforms is still challenging due to high noise and sensitivity of the platforms used. Currently, a robust integrative predictive model to estimate gene expression from these genetic and epigenetic data is lacking. In this study, we have developed a deep learning-based predictive model using Deep denoising auto-encoder (DDAE) and Multi-layer Perceptron (MLP) that can quantitatively capture how genetic and epigenetic alterations correlate with directionality of gene expression for liver hepatocellular carcinoma (LIHC). The DDAE used in the study has been trained to extract significant features from the input omics data to estimate the gene expression. These features have then been used for back-propagation learning by the multilayer perceptron for the task of regression and classification. We have benchmarked the proposed model against state-of-the-art regression models. Finally, the deep learning-based integration model has been evaluated for its disease classification capability, where an accuracy of 95.1% has been obtained.

关键词： Gene expression denoising auto-encoder Multilayer perceptron Regression Multi-omics integration DNA methylation Copy number variation

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scDCCA: deep contrastive clustering for single-cell RNA-seq data based on auto-encoder network

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BRIEFINGS IN BIOINFORMATICS 2023年第1期24卷 bbac625-bbac625页

作者： Wang, Jing Xia, Junfeng Wang, Haiyun Su, Yansen Zheng, Chun-Hou Anhui Univ Sch Comp Sci & Technol Hefei Anhui Peoples R China Anhui Univ Inst Phys Sci & Informat Technol Hefei Anhui Peoples R China Xinjiang Univ Sch Math & Syst Sci Urumqi Peoples R China Anhui Univ Sch Artificial Intelligence Hefei Anhui Peoples R China

The advances in single-cell ribonucleic acid sequencing (scRNA-seq) allow researchers to explore cellular heterogeneity and human diseases at cell resolution. Cell clustering is a prerequisite in scRNA-seq analysis since it can recognize cell identities. However, the high dimensionality, noises and significant sparsity of scRNA-seq data have made it a big challenge. Although many methods have emerged, they still fail to fully explore the intrinsic properties of cells and the relationship among cells, which seriously affects the downstream clustering performance. Here, we propose a new deep contrastive clustering algorithm called scDCCA. It integrates a denoising auto encoder and a dual contrastive learning module into a deep clustering framework to extract valuable features and realize cell clustering. Specifically, to better characterize and learn data representations robustly, scDCCA utilizes a denoising Zero-Inflated Negative Binomial model-based auto-encoder to extract low-dimensional features. Meanwhile, scDCCA incorporates a dual contrastive learning module to capture the pairwise proximity of cells. By increasing the similarities between positive pairs and the differences between negative ones, the contrasts at both the instance and the cluster level help the model learn more discriminative features and achieve better cell segregation. Furthermore, scDCCA joins feature learning with clustering, which realizes representation learning and cell clustering in an end-to-end manner. Experimental results of 14 real datasets validate that scDCCA outperforms eight state-of-the-art methods in terms of accuracy, generalizability, scalability and efficiency. Cell visualization and biological analysis demonstrate that scDCCA significantly improves clustering and facilitates downstream analysis for scRNA-seq data. The code is available at https://***/WJ319/scDCCA.

关键词： scRNA-seq data deep clustering contrastive learning ZINB model denoising auto-encoder

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