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5th International Conference on Computing, Networks and Internet of Things (CNIOT)

作者： Li, Jinlong Li, Ying Lanzhou Inst Technol Coll Elect Informat Engn Lanzhou Peoples R China

ISBN: (纸本)9798400716751

The behavioral symptoms of patients with mild to moderate depression (MMD) are usually not obvious enough, which poses a challenge to MMD recognition research. A three-level feature construction strategy for facial expression was proposed to fully characterize the differences in facial activity between MMD patients and healthy control groups (HCs). Level 1: Construct geometric features to describe facial activity preliminarily. Level 2: Input geometric features into a denoising autoencoder (DAE) to generate a new hidden layer representation. Level 3: Gaussian mixture model (GMM) further characterizes the hidden layer features. Meanwhile, a speech feature set was constructed based on the fundamental frequency (F0), pitch intensity, mel frequency cepstral coefficients (MFCCs), and syllable pauses of speech. Finally, facial expression and speech were fused at the feature layer, and MMD recognition was carried out based on four classic classification algorithms. The experimental results show that the MMD recognition accuracy of the male and female groups can reach 70.3% and 68.3%, respectively.

关键词： Depression recognition Facial expression Speech denoising autoencoder

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Harmless Overfitting: Using denoising autoencoders in Estimation of Distribution Algorithms

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JOURNAL OF MACHINE LEARNING RESEARCH 2020年第1期21卷 1-31页

作者： Probst, Malte Rothlauf, Franz Honda Res Inst EU Carl Legien Str 30 D-63073 Offenbach Germany Johannes Gutenberg Univ Mainz Informat Syst & Business Adm Jakob Welder Weg 9 D-55128 Mainz Germany Johannes Gutenberg Univ Mainz Mainz Germany

Estimation of Distribution Algorithms (EDAs) are metaheuristics where learning a model and sampling new solutions replaces the variation operators recombination and mutation used in standard Genetic Algorithms. The choice of these models as well as the corresponding training processes are subject to the bias/variance tradeoff, also known as under- and overfitting: simple models cannot capture complex interactions between problem variables, whereas complex models are susceptible to modeling random noise. This paper suggests using denoising autoencoders (DAEs) as generative models within EDAs (DAE-EDA). The resulting DAE-EDA is able to model complex probability distributions. Furthermore, overfitting is less harmful, since DAEs overfit by learning the identity function. This overfitting behavior introduces unbiased random noise into the samples, which is no major problem for the EDA but just leads to higher population diversity. As a result, DAE-EDA runs for more generations before convergence and searches promising parts of the solution space more thoroughly. We study the performance of DAE-EDA on several combinatorial single-objective optimization problems. In comparison to the Bayesian Optimization Algorithm, DAE-EDA requires a similar number of evaluations of the objective function but is much faster and can be parallelized efficiently, making it the preferred choice especially for large and difficult optimization problems.

关键词： denoising autoencoder estimation of distribution algorithm overfitting combinatorial optimization neural networks

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Intelligent denoise laser ultrasonic imaging for inspection of selective laser melting components with rough surface

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NDT & E INTERNATIONAL 2022年 125卷 102548-102548页

作者： Xu, Wanli Zhang, Jun Li, Xiaohong Yuan, Shuxian Ma, Guanbing Xue, Zhixiang Jing, Xuechao Cao, Jiancheng Wuhan Univ Sch Power & Mech Engn Wuhan 430072 Hubei Peoples R China CGN Inspect Technol Co Ltd Suzhou 215100 Jiangsu Peoples R China Southeast Univ Sch Mat Sci & Engn Nanjing 211189 Jiangsu Peoples R China

The random microdefects are inevitable during the Selective laser melting (SLM) process due to the principle of discrete-stacking. The rough surface induced strong background noise reduces the probability of detection of traditional laser ultrasonic testing system. In this study, an intelligent denoise laser ultrasonic imaging method was developed to inspect the micro defects on the rough surface of SLM components: (1) a non-contact laser ultrasonic scanning setup was established for data acquisition of multiple ultrasonic signals;(2) a denoising algorithm based on unsupervised machine learning was designed and trained by abundant ultrasonic data to enhance the signal to noise ratio (SNR);(3) a signal matching algorithm based on the cross-correlation and selfnormalized method was established to match the amplitude and arrival time of Rayleigh waves from different scanning points. The performance of developed method was verified using micro hole defects on the rough surface of a SLM part. The results indicated that the established denoising algorithm could increase the average SNR from 27.0 dB to 35.2 dB. All holes with diameter of 50 mu m and 100 mu m can be detected and sized based on the high SNR image without removing the rough surface. The conclusion can be drawn that the proposed intelligent denoise laser ultrasonic imaging method is a very potential way for the online inspection of SLM.

关键词： Laser ultrasonic testing Selective laser melting Surface roughness denoising autoencoder

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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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Deep Learning for OFDM Channel Estimation in Impulsive Noise Environments

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WIRELESS PERSONAL COMMUNICATIONS 2022年第3期125卷 2947-2964页

作者： Li, Xinbin Han, Zhaoxing Yu, Haifeng Yan, Lei Han, Song Yanshan Univ Inst Elect Engn Qinhuangdao 066004 Hebei Peoples R China

Impulsive noise suppression is essential in orthogonal frequency division multiplexing (OFDM) systems, since impulsive noise may cause a serious decline in channel estimation performance. To solve this problem, a channel estimator based on denoising autoencoder-deep neural network (DAE-DNN) is proposed in this paper. The proposed method is based on a data-driven deep learning framework. Firstly, DAE preprocesses signals to learn damaged data and recover the complete signal are used in the presence of impulsive noise. Then, the transmitted data processed by DAE are used to train the DNN in the offline training process. Finally, the estimated channel state information (CSI) is offered by the proposed DNN model in the online working process. The simulation results demonstrate that the proposed method improves OFDM channel estimation performance significantly. As expected, the proposed method has a better performance than existing ones, such as least squares, minimum mean square error and orthogonal matching pursuit algorithms. Moreover, the proposed method is robust under impulsive noise environments.

关键词： Deep learning OFDM channel estimation denoising autoencoder Impulsive noise cancellation

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Deep learning-based motion artifact removal in functional near-infrared spectroscopy

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NEUROPHOTONICS 2022年第4期9卷 41406页

作者： Gao, Yuanyuan Chao, Hanqing Cavuoto, Lora Yan, Pingkun Kruger, Uwe Norfleet, Jack E. Makled, Basiel A. Schwaitzberg, Steven De, Suvranu Intes, Xavier Rensselaer Polytech Inst Ctr Modeling Simulat & Imaging Med Troy NY 12180 USA Rensselaer Polytech Inst Dept Biomed Engn Troy NY 12180 USA Univ Buffalo Dept Ind & Syst Engn Buffalo NY USA US Army Combat Capabil Dev Command Soldier Ctr Orlando FL USA SFC Paul Ray Smith Simulat & Training Technol Ctr Orlando FL USA Med Simulat Res Branch Orlando FL USA Univ Buffalo Dept Surg Buffalo NY USA

Significance: Functional near-infrared spectroscopy (fNIRS), a well-established neuroimaging technique, enables monitoring cortical activation while subjects are unconstrained. However, motion artifact is a common type of noise that can hamper the interpretation of fNIRS data. Current methods that have been proposed to mitigate motion artifacts in fNIRS data are still dependent on expert-based knowledge and the post hoc tuning of parameters. Aim: Here, we report a deep learning method that aims at motion artifact removal from fNIRS data while being assumption free. To the best of our knowledge, this is the first investigation to report on the use of a denoising autoencoder (DAE) architecture for motion artifact removal. Approach: To facilitate the training of this deep learning architecture, we (i) designed a specific loss function and (ii) generated data to mimic the properties of recorded fNIRS sequences. Results: The DAE model outperformed conventional methods in lowering residual motion artifacts, decreasing mean squared error, and increasing computational efficiency. Conclusion: Overall, this work demonstrates the potential of deep learning models for accurate and fast motion artifact removal in fNIRS data. (c) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

关键词： functional near-infrared spectroscopy motion artifact deep learning denoising autoencoder

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3D Reconstruction from 2D Cerebral Angiograms as a Volumetric denoising Problem 18th

3D Reconstruction from 2D Cerebral Angiograms as a Volumetri...

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18th International Symposium on Visual Computing (ISVC)

作者： Wu, Sean Kaneko, Naoki Mendoza, Steve Liebeskind, David S. Scalzo, Fabien Pepperdine Univ Keck Data Sci Inst Malibu CA 90265 USA UCLA Dept Intervent Neuroradiol Los Angeles CA 90095 USA UCLA Dept Neurol Los Angeles CA 90095 USA

ISBN: (纸本)9783031479687;9783031479694

Accurately capturing the 3D geometry of the brain's blood vessels is critical in helping neuro-interventionalists to identify and treat neurovascular disorders, such as stroke and aneurysms. Currently, the gold standard for obtaining a 3D representation of angiograms is through the process of 3D rotational angiography, a timely process requiring expensive machinery, which is also associated with high radiation exposure to the patient. In this research, we propose a new technique for reconstructing 3D volumes from 2D angiographic images, thereby reducing harmful X-ray radiation exposure. Our approach involves parameterizing the input data as a back-projected noisy volume from the images, which is then fed into a 3D denoising autoencoder. Through this method, we have achieved clinically relevant reconstructions with varying amounts of 2D projections from 49 patients. Additionally, our 3D denoising autoencoder outperformed previous generative models in biplane reconstruction by 15.51% for intersection over union (IOU) and 3.5% in pixel accuracy due to keeping a semi-accurate input with back projection. This research highlights the significant role of back-projection in achieving relative visual correspondence in the input space to reconstruct 3D volumes from 2D angiograms. This approach has the potential to be deployed in future neurovascular surgery, where 3D volumes of the patient's brain blood vessels can be visualized with less X-ray radiation exposure time.

关键词： 3D Reconstruction Deep Learning denoising autoencoder Rotational Angiography Cerebral Angiograms

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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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Hyperparameter Tuning to Optimize Implementations of denoising autoencoders for Imputation of Missing Spatio-temporal Data

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Procedia Computer Science 2021年 184卷 107-114页

作者： Muhammad Danial Siddiqi Boyuan Jiang Reza Asadi Amelia Regan Donald Bren School of Information and Computer Sciences (ICS) University of California Irvine Irvine CA 92697 USA Institute of Transportation Studies (ITS) University of California Irvine Irvine CA 92697 USA

Spatio-temporal data collected from sensors can sometimes have gaps where data is missing. Transportation planners and engineers use such data to perform various different types of analyses, but the gaps in the data make it difficult to make accurate predictions. denoising autoencoders are used to generate a cleaner version of the input signals. This includes generating data for missing or dropped input signals as well. We used three different deep learning models to implement denoising autoencoders to measure each model’s accuracy. Since tuning the hyperparameters can influence the accuracy of the predictions of a model, each model was optimized by focusing on four main parameters. Each of the hyperparameters had a different effect on each of the models. The simplest of the models yielded much better results than anticipated after optimizing. The most complex of the models was still better, but only slightly. That slight improvement required one hundred times the computational cost required by the simplest model. Tuning the hyperparameters to optimize a simpler model can prove more beneficial than creating a more complex model that is slower and difficult to optimize.

关键词： Transportation data analysis Spatio-temporal problem denoising autoencoder Missing data imputation

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Understanding Chromatin Remodeling Through Physics-Based Machine Learning Approaches

Understanding Chromatin Remodeling Through Physics-Based Mac...

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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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