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Efficient convolutional hierarchical autoencoder for human motion prediction

VISUAL COMPUTER 2019年第6-8期35卷 1143-1156页

作者： Li, Yanran Wang, Zhao Yang, Xiaosong Wang, Meili Poiana, Sebastian Iulian Chaudhry, Ehtzaz Zhang, Jianjun Bournemouth Univ Natl Ctr Comp Animat Bournemouth Dorset England Bournemouth Univ Natl Ctr Internet Things IoT Cyber Secur Bournemouth Dorset England Bournemouth Univ Natl Ctr Comp Animat Comp Graph Bournemouth Dorset England Nanjing Inst Adv Artificial Intelligence Nanjing Jiangsu Peoples R China Horizon Robot Nanjing Jiangsu Peoples R China Northwest A&F Univ Coll Informat Engn Xian Shaanxi Peoples R China

Human motion prediction is a challenging problem due to the complicated human body constraints and high-dimensional dynamics. Recent deep learning approaches adopt RNN, CNN or fully connected networks to learn the motion features which do not fully exploit the hierarchical structure of human anatomy. To address this problem, we propose a convolutional hierarchical autoencoder model for motion prediction with a novel encoder which incorporates 1D convolutional layers and hierarchical topology. The new network is more efficient compared to the existing deep learning models with respect to size and speed. We train the generic model on Human3.6M and CMU benchmark and conduct extensive experiments. The qualitative and quantitative results show that our model outperforms the state-of-the-art methods in both short-term prediction and long-term prediction.

关键词： Motion prediction Deep learning autoencoder Hierarchical networks

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An Efficient and Robust Underwater Image Compression Scheme Based on autoencoder

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IEEE JOURNAL OF OCEANIC ENGINEERING 2023年第3期48卷 925-945页

作者： Liu, Jiahui Yuan, Fei Xue, Chang Jia, Zhenyu Cheng, En Xiamen Univ Key Lab Underwater Acoust Commun & Marine Informat Minist Educ Xiamen 361005 Peoples R China Xiamen Univ Sch Elect Sci & Engn Xiamen 361005 Peoples R China

Images occupy a prominent place in data because they are more visually appealing than sounds or texts. Acoustic waves are the only feasible alternative for long-distance underwater transmission since seawater has a high absorption impact on lighting and electromagnetic signals. However, underwater acoustic (UWA) communication technology can only provide relatively limited bandwidth (low effectiveness) and insufficiently stable links (low reliability). As a result, it is challenging to send high-resolution underwater images via the UWA channel. This article proposes an effective and robust underwater image compression scheme. First, an autoencoder is used for underwater image extreme bit rate compression. Then, a multistep training strategy is proposed to improve the robustness of the decoder by gradually learning channel degradation features. Finally, the autoencoder encodes images in two paths to achieve efficient compression and higher image quality when reconstructing images. The main path completes the compression task with a low bit rate and high robustness, while the branching path implements the image block retransmission compensation through the feedback signal. The experimental results demonstrate that the content of the reconstructed image can still be recognized under the conditions of a compression ratio of up to 1/768 and an average bit error rate of up to 10(-1). The joint multistep training strategy and multidescription coding achieve a low bit rate and high robustness for underwater image communication, which has good application prospects.

关键词： Image coding Training Quantization (signal) Robustness OFDM Image reconstruction Channel coding autoencoder deep learning underwater communication underwater image compression

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A distributed source autoencoder of local visual descriptors for 3D reconstruction

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PATTERN RECOGNITION LETTERS 2021年 146卷 193-199页

作者： Milani, Simone Univ Padua Dept Informat Engn Via Gradenigo 6-B I-35131 Padua Italy

This paper presents a local descriptor coding scheme for multicamera surveillance and 3D reconstruction embedding an autoencoder into a traditional distributed source coding strategy. The proposed solution permits shifting most of the computational complexity at the decoder/receiver and exploiting the correlation among descriptors of different cameras (thus reducing the coded bit rate) without increasing the inter-device communication load. Experimental results show that the proposed scheme permits obtaining a satisfying accuracy with respect to the most recent solutions while generating a limited bit rate. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Distributed vision networks Distributed source coding autoencoder Local descriptor coding Structure-from-Motion

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Shared autoencoder Gaussian Process Latent Variable Model for Visual Classification

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2018年第9期29卷 4272-4286页

作者： Li, Jinxing Zhang, Bob Zhang, David Hong Kong Polytech Univ Dept Comp Hong Kong Hong Kong Peoples R China Univ Macau Dept Comp & Informat Sci Zhuhai 999078 Peoples R China

Multiview learning reveals the latent correlation among different modalities and utilizes the complementary information to achieve a better performance in many applications. In this paper, we propose a novel multiview learning model based on the Gaussian process latent variable model (GPLVM) to learn a set of nonlinear and nonparametric mapping functions and obtain a shared latent variable in the manifold space. Different from the previous work on the GPLVM, the proposed shared autoencoder Gaussian process (SAGP) latent variable model assumes that there is an additional mapping from the observed data to the shared manifold space. Due to the introduction of the autoencoder framework, both nonlinear projections from and to the observation are considered simultaneously. Additionally, instead of fully connecting used in the conventional autoencoder, the SAGP achieves the mappings utilizing the GP, which remarkably reduces the number of estimated parameters and avoids the phenomenon of overfitting. To make the proposed method adaptive for classification, a discriminative regularization is embedded into the proposed method. In the optimization process, an efficient algorithm based on the alternating direction method and gradient decent techniques is designed to solve the encoder and decoder parts alternatively. Experimental results on three real-world data sets substantiate the effectiveness and superiority of the proposed approach as compared with the state of the art.

关键词： autoencoder discriminative Gaussian process (GP) kernel latent variable model multiview

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Enhancing Estimation Performance in Distributed Sensing Through autoencoder-Based Sensor Array Feature Extraction

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IEEE SENSORS JOURNAL 2024年第20期24卷 33647-33655页

作者： Wang, Junming Shu, Jing Li, Zheng Tong, Raymond Kai-Yu Chinese Univ Hong Kong Dept Biomed Engn Hong Kong Peoples R China

In the distributed sensing method, various sensor arrays encompass both sensor readings and potential spatial information. While increasing the number of sensors can enhance accuracy, an alternative approach to achieve this lies in extracting spatial knowledge from combinations of horizontal and vertical sensor arrays. By adopting a scaffold-based conceptualization of sensor distribution, this study introduces a novel input preprocessing approach termed the "scaffold-based preprocessing autoencoder" (SPA) to augment distributed sensing methods. This approach leverages autoencoders to extract informative features from diverse combinations of sensor arrays arranged in vertical columns and horizontal planes. The effectiveness of the proposed approach is empirically validated in the posture sensing of a vacuum-powered bellow-shaped fluidic elastomer actuator (FEA), employing nine distributed flexible bending sensors. The results demonstrate that the neural network incorporating features extracted from sensor combinations surpasses the performance of conventional long short-term memory (LSTM) neural networks. Comparative investigations validate the distinct advantages of these arrangements, with horizontal combinations yielding improved estimation accuracy in the X- and Y-axes, and vertical combinations enhancing accuracy in the Z-axis. Collectively, these combinations yield reductions in root mean square error (RMSE) of 9.51-6.93 mm in the X-axis, 6.77-5.43 mm in the Y-axis, and 7.72-4.38 mm in the Z-axis. Besides, the application of SPA to the gated recurrent unit (GRU) and bidirectional LSTM (BiLSTM) models also demonstrated significant improvements. Both models exhibited a substantial reduction in the sum of RMSE in each axis, with decreases of 20% and 27%, respectively.

关键词： Sensors Sensor arrays Feature extraction Sensor phenomena and characterization Long short term memory Estimation Accuracy autoencoder deep learning distributed sensing soft robotics soft sensor

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Missing Data Imputation With OLS-Based autoencoder for Intelligent Manufacturing

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2019年第6期55卷 7219-7229页

作者： Wang, Yanxia Li, Kang Gan, Shaojun Cameron, Che Univ Leeds Sch Elect & Elect Engn Leeds LS2 9JT W Yorkshire England Queens Univ Belfast Sch Elect Elect Engn & Comp Sci Belfast BT7 1NN Antrim North Ireland

Motivated by the global economy that is greatly shaped by the landscape changes in energy and manufacturing where more and more devices and systems are interconnected, intelligent manufacturing in which data mining is of great importance is studied. In this article, an energy monitoring platform for small- and medium-sized enterprises developed by the point energy team (***) is first introduced, which monitors and records the energy consumption of manufacturing processes at various levels of granularity. In processing the collected data, the incompleteness in the data due to various factors needs to be addressed first otherwise it may lead to the inaccurate portrayal of the system and poor generalization of the resultant model trained by the data. Hence, a novel orthogonal-least-square-based autoencoder is proposed to generate new samples for the imputation of missing values. This approach is to learn the representative code from the original samples by constructing an improved encoder network in which the hidden neurons are orthogonal with each other. The new samples are then generated through the decoder network. The proposed approach selects the hidden neurons one by one based on the OLS estimation until an adequate network is built. The classical techniques and other generative models are compared to verify the effectiveness of the proposed algorithm. For these methods, the optimal parameters are estimated based on the performance metric of the cross-validation mean square error. In the experiment, two real industrial datasets from a baking process and a polymer extrusion process are adopted and the percentage of missing values varies from 0.02 to 0.25. The experimental results confirm that the proposed method offers stable performance in the presence of different missing ratios, and it outperforms significantly alternative approaches while the missing ratio is greater than 0.05.

关键词： autoencoder missing data imputation monitoring platform orthogonal least square (OLS)

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P- and S-Wave Separation in Complex Geological Structures via Knowledge-Guided autoencoder

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷 1页

作者： Lu, Cai Zou, Xuyang Wang, Yunchen Zong, Jingjing Su, Qin Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Sch Resources & Environm Chengdu 611731 Peoples R China

The separation of P- and S-waves is pivotal in the processing of multicomponent seismic data. The complexity of geological structures often leads to intricate P- and S-wavefields, which poses challenges for identifying and separating waves using conventional signal features, such as the F-K domain or tau-p domain distributions, while data-driven machine learning methods overly rely on the quality of samples. This article proposes a novel approach for P- and S-wave separation in complex geological structures based on a knowledge-guided autoencoder network. First, the existing full waveform inversion (FWI) can obtain relatively accurate knowledge representations of P- and S-waves. A recurrent neural network (RNN) was employed for elastic wave FWI to acquire a knowledge representation of intricate P- and S-waves in complex structures. Subsequently, a dual-branch autoencoder network was constructed based on the obtained knowledge representation of the complex P- and S-waves. One branch was guided by the knowledge representation of P-waves for P-wave separation, whereas the other branch was guided by the knowledge representation of S-waves for S-wave separation. Finally, a comprehensive autoencoder network architecture was devised that incorporates waveform reconstruction loss, P-wave knowledge guidance loss, and S-wave knowledge guidance loss for effective P- and S-wave separation. Theoretical analyses and numerical simulations were performed, and they demonstrated the effectiveness of the proposed method for achieving P- and S-wave separation in complex geological structures.

关键词： Geology Propagation Data mining Mathematical models Knowledge representation Feature extraction Transforms autoencoder complex signal knowledge wave separation

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autoencoder-based OFDM for Agricultural Image Transmission 10

Autoencoder-based OFDM for Agricultural Image Transmission

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10th International Conference on Advanced Cloud and Big Data (CBD)

作者： Li, Dongbo Liu, Xiangyu Shao, Yuxuan Sun, Yuchen Cheng, Siyao Liu, Jie Harbin Inst Technol Fac Comp IoT & Ambient Intelligence Res Ctr Harbin Peoples R China Harbin Inst Technol Fac Comp Harbin Peoples R China Harbin Inst Technol Shenzhen Int Res Inst AI Shenzhen Peoples R China

ISBN: (纸本)9798350309713

With the rapid development of the Internet of Things (IoT), smart agricultural puts forward higher demands on the transmission of agricultural big data. This paper proposes an end-to-end learning communication with autoencoderbased orthogonal frequency division multiplexing (OFDM-AE) for agricultural image transmission, which solves the problems of delay, congestion and high complexity caused by the processing method to information of independent modularization for the conventional OFDM. It is proposed to construct AE based on convolutional neural network (CNN) to realize global joint optimization of end-to-end communication system. In this paper, the network architecture of OFDM-AE is designed and trained on massive agricultural image data. We analyze the performance of the proposed OFDM-AE in different signal-to-noise ratio (SNR) cases. The experimental results show that the OFDM-AE can retain the image feature information and has a very advantageous complexity performance compared to the conventional OFDM with various modulation methods.

关键词： Internet of Things big data autoencoder OFDM agricultural image transmission

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autoencoder FOR VIBROTACTILE SIGNAL COMPRESSION

AUTOENCODER FOR VIBROTACTILE SIGNAL COMPRESSION

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

作者： Li, Zhuoran Hassen, Rania Wang, Zhou Univ Waterloo Dept Elect & Comp Engn Waterloo ON Canada HUAWEI Technol Canada CO Ltd Human Machine Interact HMI Lab Toronto Res Ctr Markham ON Canada Assiut Univ Comp Sci Dept Assiut Egypt

ISBN: (纸本)9781728176055

Vibrotactile signals contain rich haptic information about textured surfaces but their large data volume makes it a challenging task to transmit such signals to remote locations to create immersive and realistic user experiences. Inspired by the recent success of deep neural network (DNN) based autoencoder, we make the first attempt to apply autoencoder for lossy compression of haptic vibrotactile signals, where a convolutional neural network (CNN) and a rate-distortion (RD) function are used as the transform and cost functions, respectively. Performance comparisons with state-of-the-art methods using both peak signal-to-noise ratio (PSNR) and perceptually motivated spectral temporal similarity (ST-SIM) measures show that the proposed end-to-end vibrotactile autoencoder (EVA) is highly competitive at preserving signal quality while keeping the data rate low.

关键词： haptic communication vibrotactile signals compression autoencoder deep neural network BD-rate

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autoencoder-K-means Algorithm for Efficient Anomaly Detection to Improve Space Operations

Autoencoder-K-means Algorithm for Efficient Anomaly Detectio...

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International Conference on Smart Applications, Communications and Networking (SmartNets)

作者： Akbarian, Hamid Mahgoub, Imadeldin Williams, Andre Florida Atlantic Univ Dept Comp Engn Boca Raton FL 33431 USA

ISBN: (纸本)9798350385328;9798350385335

As we venture further into space, the need for Artificial Intelligence (AI) based anomaly detection algorithms for space operations significantly escalates. These algorithms are essential for ground-based operators to issue accurate corrective commands that prevent potential disasters and spacecraft failure and reduce operation costs. To address this issue, this paper explores integrating two Deep Unsupervised Learning methods such, as autoencoder (AE) and K-means. This approach integrates the reconstruction and clustering methods to detect anomalies. The AE method learns deep temporal dynamics by reducing the dimensions and denoising the data. K-means then clusters this compressed output from the AE. Performance of AE-K-means measures by calculating precision, recall, and F1-score parameters. Precision is the percentage of detected anomalies that are true positives (TP);recall is the percentage of actual anomalies correctly detected;and the F1-score is used to evaluate the algorithm's performance by applying precision and recall to a single value. The datasets used for this research comprise telemetry data expertly labeled and sourced from the Soil Moisture Active Passive (SMAP) satellite. Our results show that AE-K-means improves anomaly detection in complex multivariate time series datasets, like satellite telemetry, and it can provide an enhanced and smart solution to the challenges of detecting anomalies in space operations and systems.

关键词： autoencoder K-means NearestNeighbor F1-score Tanh ReLu

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