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

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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Multi-local Collaborative autoencoder

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KNOWLEDGE-BASED SYSTEMS 2022年 239卷 107844-107844页

作者： Chu, Jielei Wang, Hongjun Liu, Jing Gong, Zhiguo Li, Tianrui Southwest Jiaotong Univ Sch Comp & Artificial Intelligence Chengdu 611756 Peoples R China Southwest Jiaotong Univ Inst Artificial Intelligence Chengdu 611756 Peoples R China Sichuan Univ Sch Business Chengdu 610065 Sichuan Peoples R China Univ Macau Dept Comp & Informat Sci State Key Lab Internet Things Smart City Macau Peoples R China

The excellent performance of representation learning of autoencoders have attracted considerable interest in various applications. However, the structure and multi-local collaborative relationships of unlabeled data are ignored in their encoding procedure that limits the capability of feature extraction. This paper presents a Multi-local Collaborative autoencoder (MC-AE), which consists of novel multi local collaborative representation RBM (mcrRBM) and multi-local collaborative representation GRBM (mcrGRBM) models. Here, the Locality Sensitive Hashing (LSH) method is used to divide the input data into multi-local cross blocks which contains multi-local collaborative relationships of the unlabeled data and features since the similar multi-local instances and features of the input data are divided into the same block. In mcrRBM and mcrGRBM models, the structure and multi-local collaborative relationships of unlabeled data are integrated into their encoding procedure. Then, the local hidden features converges on the center of each local collaborative block. Under the collaborative joint influence of each local block, the proposed MC-AE has powerful capability of representation learning for unsupervised clustering. However, our MC-AE model perhaps perform training process for a long time on the large-scale and high-dimensional datasets because more local collaborative blocks are integrate into it. Five most related deep models are compared with our MC-AE. The experimental results show that the proposed MC-AE has more excellent capabilities of collaborative representation and generalization than the contrastive deep models. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Restricted Boltzmann machine autoencoder Deep collaborative representation Feature learning Unsupervised clustering

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Graph Laplacian-Improved Convolutional Residual autoencoder for Unsupervised Human Action and Emotion Recognition

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IEEE ACCESS 2022年 10卷 131128-131143页

作者： Paoletti, Giancarlo Beyan, Cigdem Del Bue, Alessio Fdn Ist Italiano Tecnol IIT Pattern Anal & Comp Vis PAVIS Res Line Genoa Italy Fdn Ist Italiano Tecnol IIT Pattern Anal & Comp Vis PAVIS Res Line Genoa Italy Fdn Ist Italiano Tecnol IIT Pattern Anal & Comp Vis PAVIS Res Line Genoa Italy

This paper presents an unsupervised feature learning approach based on 3D-skeleton data for human action and human discrete emotion recognition. Relying on the time series of skeleton data analysis to perform such tasks is effective and important to preserve the individual's privacy better. Besides, such methods can represent a viable alternative to emotion recognition applications, in which most works use frontal or profile facial images disclosing the subject's appearance. On the other hand, current unsupervised methods are able to encode the high variety of contexts and nature of the data, but often at the expense of a higher model complexity or longer computational time. To lessen these shortcomings, this paper proposes a convolutional residual autoencoder that models the skeletal geometry across the temporal dynamics of the data without relying on computationally expensive recurrent architectures. Our approach also implements a Graph Laplacian Regularization leveraging upon the implicit skeleton joints connectivity, further improving the robustness of the feature embeddings learned without using action or emotion labels. It was validated on large-scale datasets, having variability in the domain, the input skeleton data (e.g. the number of joints, adjacency matrices), and sensor technology. The results show its effectiveness by notably surpassing the performance of the state-of-the-art unsupervised methods while also achieving better recognition scores compared to the several fully supervised approaches. Extensive experimental analysis proves the usefulness of the proposed method under various evaluation protocols with observed higher-quality feature representations, even if when it is trained with fewer data. The results highlight the proposed method's remarkable transfer-ability across various domains, and its faster inference time.

关键词： Action recognition autoencoder emotion recognition full-body movement graph Laplacian skeletal data unsupervised feature learning

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Deep Self-Supervised Graph Attention Convolution autoencoder for Networks Clustering

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 2023年第4期69卷 974-983页

作者： Chen, Chao Lu, Hu Hong, Haotian Wang, Hai Wan, Shaohua Jiangsu Univ Sch Comp Sci & Commun Engn Zhenjiang 212013 Peoples R China Jiangsu Univ Sch Automot & Traff Engn Zhenjiang 212013 Peoples R China Univ Elect Sci & Technol China Shenzhen Inst Adv Study Shenzhen 518110 Peoples R China

In recent years, graph-based deep learning algorithms have attracted widespread attention in the field of consumer electronics. Still, most of the current graph neural networks are based on supervised learning or semi-supervised learning, which often relies on the true labels of the given samples as auxiliary information. To solve this problem, we propose a Deep Self-Supervised Attention Convolution autoencoder Graph Clustering (DSAGC) model and use it for social networks clustering. We divide the proposed model into two parts: a pretext task and a downstream task. In the pretext task, we obtain pseudo-labels of samples by graph attention autoencoder and clustering, then adopt the proposed reliable sample selection mechanism to gain a high confidential sample. These samples and corresponding pseudo-labels are selected as input of downstream task for helping the downstream task to finish training. The model obtains the predictive labels of the sample from the downstream task. This model does not depend on the true label of the sample for training and uses the pseudo-labels produced by unsupervised learning to improve the clustering performance. We apply the proposed model to three commonly used public social network datasets to test its performance. Compared with the presented unsupervised clustering algorithms and other deep graph neural network algorithms, various metrics have shown that the proposed model consistently outperforms the state-of-the-art. The proposed method can be used for consumer behavior analysis. We release the source code at https://***/hulu88/DSAGC.

关键词： Self-supervised autoencoder reliable sample selection social networks clustering

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A dual linear autoencoder approach for vessel trajectory prediction using historical AIS data

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OCEAN ENGINEERING 2020年 209卷 107478-107478页

作者： Murray, Brian Perera, Lokukaluge Prasad UiT Arctic Univ Norway Tromso Norway

Advances in artificial intelligence are driving the development of intelligent transportation systems, with the purpose of enhancing the safety and efficiency of such systems. One of the most important aspects of maritime safety is effective collision avoidance. In this study, a novel dual linear autoencoder approach is suggested to predict the future trajectory of a selected vessel. Such predictions can serve as a decision support tool to evaluate the future risk of ship collisions. Inspired by generative models, the method suggests to predict the future trajectory of a vessel based on historical AIS data. Using unsupervised learning to facilitate trajectory clustering and classification, the method utilizes a cluster of historical AIS trajectories to predict the trajectory of a selected vessel. Similar methods predict future states iteratively, where states are dependent upon the prior predictions. The method in this study, however, suggests predicting an entire trajectory, where all states are predicted jointly. Further, the method estimates a latent distribution of the possible future trajectories of the selected vessel. By sampling from this distribution, multiple trajectories are predicted. The uncertainties of the predicted vessel positions are also quantified in this study.

关键词： Maritime situation awareness Trajectory prediction Collision avoidance Machine learning autoencoder AIS

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Deep feature extraction, dimensionality reduction, and classification of medical images using combined deep learning architectures, autoencoder, and multiple machine learning models

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2023年第6期31卷 1113-1128页

作者： Kiraz, Ahmet Hidayet Djibrillah, Fatime Oumar Yuksel, Mehmet Emin Erciyes Univ Grad Sch Nat & Appl Sci Dept Biomed Engn Kayseri Turkiye Erciyes Univ Fac Engn Dept Biomed Engn Kayseri Turkiye

Accurate analysis and classification of medical images are essential factors in clinical decision-making and patient care. A novel comparative approach for medical image classification is proposed in this study. This new approach involves several steps: deep feature extraction, which extracts the informative features from medical images;concatenation, which concatenates the extracted deep features to form a robust feature vector;dimensionality reduction with autoencoder, which reduces the dimensionality of the feature vector by transforming it into a different feature space with a lower dimension;and finally, these features obtained from all these steps were fed into multiple machine learning classifiers (SVM, KNN, linear DA, and ANN) for the classification purpose. The study is performed to conduct a comparative analysis, aiming to evaluate the individual impact of each step within the proposed methodology and also assess the performance of each implemented classifier in order to find a best pipeline for medical image classification. The effectiveness of the proposed approach is assessed using two different medical image datasets. The performance assessment for the classifiers implemented is achieved using overall accuracy, sensitivity, and specificity metrics. The findings show that the linear DA classifier preceded by deep feature extraction, concatenation, and dimensionality reduction reveals itself to be a very efficient pipeline for accurate classification of medical images by utilizing a very small number of features.

关键词： Medical images classification deep feature extraction concatenation dimensionality reduction autoencoder

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