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Micromachines
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Machine-Learning-Assisted Sensors
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Machine-Learning-Assisted Sensors

A special issue of Micromachines (ISSN 2072-666X). This special issue belongs to the section "E：Engineering and Technology".

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 8988

Special Issue Editor

Dr. Yuan Xu

E-Mail Website
Guest Editor
School of Electrical Engineering, University of Jinan, Jinan 250022, China
Interests: integrated navigation and robust filtering

Special Issue Information

Dear Colleagues,

Technological advancements have facilitated the manufacturing of compact, inexpensive, and low-power consuming sensors for smart devices. The data fusion filter plays an important role in the estimation of positioning and navigation. The traditional multi-sensor data fusion algorithm has effectively improved the accuracy of sensors. However, with the increase in the complexity of the sensor’s structure, accurate data fusion modeling is challenged. Machine learning (ML) brings new opportunities for the improvement of sensor accuracy. For example, ML-assisted filtering can improve the accuracy of navigation. Thus, this Special Issue aims to include research papers and review articles that focus on: (1) ML-assisted of intelligent logistics robot; (2) ML-assisted scheduling optimization of intelligent logistics equipment; (3) ML-assisted filtering for sensor data fusion and its application; (4) complex sensor model construction using the ML method; and (5) smart driving system.

We look forward to receiving your contributions.

Dr. Yuan Xu
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Micromachines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning
  • Kalman filter
  • scheduling optimization

Published Papers (6 papers)

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Research

21 pages, 2015 KiB  
Article
Extreme Learning Machine/Finite Impulse Response Filter and Vision Data-Assisted Inertial Navigation System-Based Human Motion Capture
by Yuan Xu, Rui Gao, Ahong Yang, Kun Liang, Zhongwei Shi, Mingxu Sun and Tao Shen
Micromachines 2023, 14(11), 2088; https://doi.org/10.3390/mi14112088 - 12 Nov 2023
Cited by 1 | Viewed by 669
Abstract
To obtain accurate position information, herein, a one-assistant method involving the fusion of extreme learning machine (ELM)/finite impulse response (FIR) filters and vision data is proposed for inertial navigation system (INS)-based human motion capture. In the proposed method, when vision is available, the [...] Read more.
To obtain accurate position information, herein, a one-assistant method involving the fusion of extreme learning machine (ELM)/finite impulse response (FIR) filters and vision data is proposed for inertial navigation system (INS)-based human motion capture. In the proposed method, when vision is available, the vision-based human position is considered as input to an FIR filter that accurately outputs the human position. Meanwhile, another FIR filter outputs the human position using INS data. ELM is used to build mapping between the output of the FIR filter and the corresponding error. When vision data are unavailable, FIR is used to provide the human posture and ELM is used to provide its estimation error built in the abovementioned stage. In the right-arm elbow, the proposed method can improve the cumulative distribution functions (CDFs) of the position errors by about 12.71%, which shows the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Machine-Learning-Assisted Sensors)
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17 pages, 4140 KiB  
Article
WPD-Enhanced Deep Graph Contrastive Learning Data Fusion for Fault Diagnosis of Rolling Bearing
by Ruozhu Liu, Xingbing Wang, Anil Kumar, Bintao Sun and Yuqing Zhou
Micromachines 2023, 14(7), 1467; https://doi.org/10.3390/mi14071467 - 21 Jul 2023
Viewed by 955
Abstract
Rolling bearings are crucial mechanical components in the mechanical industry. Timely intervention and diagnosis of system faults are essential for reducing economic losses and ensuring product productivity. To further enhance the exploration of unlabeled time-series data and conduct a more comprehensive analysis of [...] Read more.
Rolling bearings are crucial mechanical components in the mechanical industry. Timely intervention and diagnosis of system faults are essential for reducing economic losses and ensuring product productivity. To further enhance the exploration of unlabeled time-series data and conduct a more comprehensive analysis of rolling bearing fault information, this paper proposes a fault diagnosis technique for rolling bearings based on graph node-level fault information extracted from 1D vibration signals. In this technique, 10 categories of 1D vibration signals from rolling bearings are sampled using a sliding window approach. The sampled data is then subjected to wavelet packet decomposition (WPD), and the wavelet energy from the final layer of the four-level WPD decomposition in each frequency band is used as the node feature. The weights of edges between nodes are calculated using the Pearson correlation coefficient (PCC) to construct a node graph that describes the feature information of rolling bearings under different health conditions. Data augmentation of the node graph in the dataset is performed by randomly adding nodes and edges. The graph convolutional neural network (GCN) is employed to encode the augmented node graph representation, and deep graph contrastive learning (DGCL) is utilized for the pre-training and classification of the node graph. Experimental results demonstrate that this method outperforms contrastive learning-based fault diagnosis methods for rolling bearings and enables rapid fault diagnosis, thus ensuring the normal operation of mechanical systems. The proposed WPDPCC-DGCL method offers two advantages: (1) the flexibility of wavelet packet decomposition in handling non-smooth vibration signals and combining it with the powerful multi-scale feature encoding capability of GCN for richer characterization of fault information, and (2) the construction of graph node-level fault samples to effectively capture underlying fault information. The experimental results demonstrate the superiority of this method in rolling bearing fault diagnosis over contrastive learning-based approaches, enabling fast and accurate fault diagnoses for rolling bearings and ensuring the normal operation of mechanical systems. Full article
(This article belongs to the Special Issue Machine-Learning-Assisted Sensors)
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26 pages, 9664 KiB  
Article
SiamHAS: Siamese Tracker with Hierarchical Attention Strategy for Aerial Tracking
by Faxue Liu, Jinghong Liu, Qiqi Chen, Xuan Wang and Chenglong Liu
Micromachines 2023, 14(4), 893; https://doi.org/10.3390/mi14040893 - 21 Apr 2023
Cited by 2 | Viewed by 1398
Abstract
For the Siamese network-based trackers utilizing modern deep feature extraction networks without taking full advantage of the different levels of features, tracking drift is prone to occur in aerial scenarios, such as target occlusion, scale variation, and low-resolution target tracking. Additionally, the accuracy [...] Read more.
For the Siamese network-based trackers utilizing modern deep feature extraction networks without taking full advantage of the different levels of features, tracking drift is prone to occur in aerial scenarios, such as target occlusion, scale variation, and low-resolution target tracking. Additionally, the accuracy is low in challenging scenarios of visual tracking, which is due to the imperfect utilization of features. To improve the performance of the existing Siamese tracker in the above-mentioned challenging scenes, we propose a Siamese tracker based on Transformer multi-level feature enhancement with a hierarchical attention strategy. The saliency of the extracted features is enhanced by the process of Transformer Multi-level Enhancement; the application of the hierarchical attention strategy makes the tracker adaptively notice the target region information and improve the tracking performance in challenging aerial scenarios. Meanwhile, we conducted extensive experiments and qualitative or quantitative discussions on UVA123, UAV20L, and OTB100 datasets. Finally, the experimental results show that our SiamHAS performs favorably against several state-of-the-art trackers in these challenging scenarios. Full article
(This article belongs to the Special Issue Machine-Learning-Assisted Sensors)
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16 pages, 4246 KiB  
Article
Event-Based Optical Flow Estimation with Spatio-Temporal Backpropagation Trained Spiking Neural Network
by Yisa Zhang, Hengyi Lv, Yuchen Zhao, Yang Feng, Hailong Liu and Guoling Bi
Micromachines 2023, 14(1), 203; https://doi.org/10.3390/mi14010203 - 13 Jan 2023
Cited by 4 | Viewed by 2206
Abstract
The advantages of an event camera, such as low power consumption, large dynamic range, and low data redundancy, enable it to shine in extreme environments where traditional image sensors are not competent, especially in high-speed moving target capture and extreme lighting conditions. Optical [...] Read more.
The advantages of an event camera, such as low power consumption, large dynamic range, and low data redundancy, enable it to shine in extreme environments where traditional image sensors are not competent, especially in high-speed moving target capture and extreme lighting conditions. Optical flow reflects the target’s movement information, and the target’s detailed movement can be obtained using the event camera’s optical flow information. However, the existing neural network methods for optical flow prediction of event cameras has the problems of extensive computation and high energy consumption in hardware implementation. The spike neural network has spatiotemporal coding characteristics, so it can be compatible with the spatiotemporal data of an event camera. Moreover, the sparse coding characteristic of the spike neural network makes it run with ultra-low power consumption on neuromorphic hardware. However, because of the algorithmic and training complexity, the spike neural network has not been applied in the prediction of the optical flow for the event camera. For this case, this paper proposes an end-to-end spike neural network to predict the optical flow of the discrete spatiotemporal data stream for the event camera. The network is trained with the spatio-temporal backpropagation method in a self-supervised way, which fully combines the spatiotemporal characteristics of the event camera while improving the network performance. Compared with the existing methods on the public dataset, the experimental results show that the method proposed in this paper is equivalent to the best existing methods in terms of optical flow prediction accuracy, and it can save 99% more power consumption than the existing algorithm, which is greatly beneficial to the hardware implementation of the event camera optical flow prediction., laying the groundwork for future low-power hardware implementation of optical flow prediction for event cameras. Full article
(This article belongs to the Special Issue Machine-Learning-Assisted Sensors)
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16 pages, 5624 KiB  
Article
A Systematic Calibration Modeling Method for Redundant INS with Multi-Sensors Non-Orthogonal Configuration
by Chunfeng Gao, Guo Wei, Lin Wang, Qi Wang and Zhikun Liao
Micromachines 2022, 13(10), 1684; https://doi.org/10.3390/mi13101684 - 07 Oct 2022
Cited by 1 | Viewed by 1378
Abstract
Because of the non-orthogonal configuration of multi-sensors, the redundant inertial navigation system (INS) has a more complex error model compared with the traditional orthogonal INS, and the complexity of sensors configuration also increases the difficulty of error separation. Based on sufficient analysis of [...] Read more.
Because of the non-orthogonal configuration of multi-sensors, the redundant inertial navigation system (INS) has a more complex error model compared with the traditional orthogonal INS, and the complexity of sensors configuration also increases the difficulty of error separation. Based on sufficient analysis of the error principle of redundant IMUs, a generalized high-accuracy calibration modeling method which is suitable for filtering method systematic calibration is summarized in this paper, and it has been applied to an RIMU prototype consisting of four ring laser gyros (RLGs) and four quartz accelerometers. Through the rotational excitation of the three-axis turntable in the laboratory, the high-precision filtering method systematic calibration of the RIMU is achieved, and static navigation and dynamic vehicle test experiments are also carried out. The experimental results reflect that the positioning accuracy can be obviously improved by using this new systematic calibration error model and the validity of this modeling method is also verified. Full article
(This article belongs to the Special Issue Machine-Learning-Assisted Sensors)
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9 pages, 2231 KiB  
Article
Modeling the Heating Dynamics of a Semiconductor Bridge Initiator with Deep Neural Network
by Jianbing Xu, Jimin Tan, Hanshi Li, Yinghua Ye and Di Chen
Micromachines 2022, 13(10), 1611; https://doi.org/10.3390/mi13101611 - 27 Sep 2022
Viewed by 1391
Abstract
A semiconductor bridge (SCB) is an ignition device that provides a safe and efficient method widely used in civilian and military fields. The heating process of an SCB under electrical stimulation has a wide range of applications owing to its unique energy release [...] Read more.
A semiconductor bridge (SCB) is an ignition device that provides a safe and efficient method widely used in civilian and military fields. The heating process of an SCB under electrical stimulation has a wide range of applications owing to its unique energy release process. However, the temperature variation of an SCB is challenging to obtain, both experimentally because of the rapid reaction on a microscale and with simulation due to its high demand in nonlinear calculations. In this study, we propose deep learning (DL) approach to study the electrothermal-coupled multi-physical heating process of the SCB initiator. We generated training data with multi-physics simulation (MPS), producing surface temperature distributions of SCBs under different voltages. The model was then trained with partial data in this database and evaluated on a separate test set. A generative adversarial network (GAN) with a customized loss function was used for modeling point-wise temperature dynamics. In the test set, our proposed method can predict the temperature distribution of an SCB under different voltages with high accuracy of over 0.9 during the heating process. We reduced the computation time by several orders of magnitude by replacing MPS with a deep neural network. Full article
(This article belongs to the Special Issue Machine-Learning-Assisted Sensors)
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