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Multi-Sensor Fusion and Data Analysis
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Multi-Sensor Fusion and Data Analysis

A special issue of Sensors (ISSN 1424-8220).

Deadline for manuscript submissions: closed (30 August 2019) | Viewed by 119687

Special Issue Editor

Prof. Dr. Simon X. Yang

E-Mail Website
Guest Editor
Advanced Robotics & Intelligent Systems (ARIS) Lab, School of Engineering, University of Guelph, Guelph, ON N1G 2W1, Canada
Interests: artificial intelligence; robotics; sensors and multisensor fusion; wireless sensor networks; control systems; bio-inspired intelligence; machine learning; neural networks; fuzzy systems; computational neuroscience
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Research on multi-sensor fusion and sensor data analysis have made significant progress in both theoretical investigation and practical applications, in many fields, such as monitoring, operation, planning, control, and decision making of various environmental, structural, agricultural, food processing, and manufacturing systems. Various intelligent and advanced multi-sensor fusion and data analysis algorithms and technologies have been developed for accurate information acquisition, effective monitoring, optimal decision making, and efficient operation.

This Special Issue is devoted to new advances and research results on multi-sensor fusion and data analysis for various systems, such as environmental and structural systems (e.g., lakes, rivers, dams, bridges, roads, tunnels, buildings), agricultural and food processing systems, and manufacturing systems. The topics in this issue include, but are not limited to, multi-sensor fusion for information acquisition, sensor signal processing and data analysis, machine learning for multi-sensor fusion, optimal sensor placement, intelligent multi-sensor fusion, multi-sensor-based monitoring and operation, multi-sensor based control and decision making, modelling and analysis of multi-sensors, remote sensing and monitoring, and software and hardware development for multi-sensor fusion. The applications of various multi-sensor fusion technologies and of various systems are also welcome.

Prof. Dr. Simon X. Yang
Guest Editor

Manuscript Submission Information

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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

  • Multi-sensor fusion
  • Multi-sensor based information acquisition
  • Data analysis for multi-sensor fusion
  • Modeling and analysis of multi-sensors
  • Intelligent multi-sensor fusion
  • Optimal sensor placement
  • Multi-sensor-based planning and decision making
  • Multi-sensor-based monitoring and control
  • Applications of multi-sensor fusion

Published Papers (30 papers)

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Research

22 pages, 3391 KiB  
Article
Estimating Departure Time Using Thermal Camera and Heat Traces Tracking Technique
by Ziyi Xu, Quchao Wang, Duo Li, Menghan Hu, Nan Yao and Guangtao Zhai
Sensors 2020, 20(3), 782; https://doi.org/10.3390/s20030782 - 31 Jan 2020
Cited by 10 | Viewed by 3206
Abstract
Advancement in science and technology is playing an increasingly important role in solving difficult cases at present. Thermal cameras can help the police crack difficult cases by capturing the heat trace on the ground left by perpetrators, which cannot be spotted by the [...] Read more.
Advancement in science and technology is playing an increasingly important role in solving difficult cases at present. Thermal cameras can help the police crack difficult cases by capturing the heat trace on the ground left by perpetrators, which cannot be spotted by the naked eye. Therefore, the purpose of this study is to establish a thermalfoot model using thermal imaging system to estimate the departure time. To this end, in the current work, we use a thermal camera to acquire the thermal sequence left on the floor, and convert it into the heat signal via image processing algorithm. We establish the model of thermalfoot print as we observe that the residual temperature would exponentially decrease with the departure time according to Newton’s Law of Cooling. The correlation coefficients of 107 thermalfoot models derived from the corresponding 107 heat signals are basically above 0.99. In a validation experiment, a residual analysis is conducted and the residuals between estimated departure time points and ground-truth times are almost within a certain range from −150 s to +150 s. The reverse accuracy of the thermalfoot model for estimating departure time at one-third, one-half, two-thirds, three-fourths, four-fifths, and five-sixths capture time points are 71.96%, 50.47%, 42.06%, 31.78%, 21.70%, and 11.21%, respectively. The results of comparison experiments with two subjective evaluation methods (subjective 1: we directly estimate the departure time according to obtained local curves; subjective 2: we utilize auxiliary means such as a ruler to estimate the departure time based on obtained local curves) further demonstrate the effectiveness of thermalfoot model for detecting the departure time inversely. Experimental results also demonstrated that the thermalfoot model has good performance on the departure time reversal within a short time window someone leaves, whereas it is probably only approximately 15% to accurately determine the departure time via thermalfoot model within a long time window someone leaves. The influence of outliers, ROI (Region of Interest) selection, ROI size, different capture time points and environment temperature on the performance of thermalfoot model on departure time reversal can be explored in the future work. Overall, the thermalfoot model can help the police solve crimes to some extent, which in turn brings more guarantees for people’s health, social security, and stability. Full article
(This article belongs to the Special Issue Multi-Sensor Fusion and Data Analysis)
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31 pages, 2108 KiB  
Article
Positive and Negative Evidence Accumulation Clustering for Sensor Fusion: An Application to Heartbeat Clustering
by David G. Márquez, Paulo Félix, Constantino A. García, Javier Tejedor, Ana L.N. Fred and Abraham Otero
Sensors 2019, 19(21), 4635; https://doi.org/10.3390/s19214635 - 24 Oct 2019
Cited by 8 | Viewed by 2769
Abstract
In this work, a new clustering algorithm especially geared towards merging data arising from multiple sensors is presented. The algorithm, called PN-EAC, is based on the ensemble clustering paradigm and it introduces the novel concept of negative evidence. PN-EAC combines both positive evidence, [...] Read more.
In this work, a new clustering algorithm especially geared towards merging data arising from multiple sensors is presented. The algorithm, called PN-EAC, is based on the ensemble clustering paradigm and it introduces the novel concept of negative evidence. PN-EAC combines both positive evidence, to gather information about the elements that should be grouped together in the final partition, and negative evidence, which has information about the elements that should not be grouped together. The algorithm has been validated in the electrocardiographic domain for heartbeat clustering, extracting positive evidence from the heartbeat morphology and negative evidence from the distances between heartbeats. The best result obtained on the MIT-BIH Arrhythmia database yielded an error of 1.44%. In the St. Petersburg Institute of Cardiological Technics 12-Lead Arrhythmia Database database (INCARTDB), an error of 0.601% was obtained when using two electrocardiogram (ECG) leads. When increasing the number of leads to 4, 6, 8, 10 and 12, the algorithm obtains better results (statistically significant) than with the previous number of leads, reaching an error of 0.338%. To the best of our knowledge, this is the first clustering algorithm that is able to process simultaneously any number of ECG leads. Our results support the use of PN-EAC to combine different sources of information and the value of the negative evidence. Full article
(This article belongs to the Special Issue Multi-Sensor Fusion and Data Analysis)
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22 pages, 4845 KiB  
Article
Greedy Mechanism Based Particle Swarm Optimization for Path Planning Problem of an Unmanned Surface Vehicle
by Junfeng Xin, Jiabao Zhong, Shixin Li, Jinlu Sheng and Ying Cui
Sensors 2019, 19(21), 4620; https://doi.org/10.3390/s19214620 - 24 Oct 2019
Cited by 21 | Viewed by 3086
Abstract
Recently, issues of climate change, environment abnormality, individual requirements, and national defense have caused extensive attention to the commercial, scientific, and military development of unmanned surface vehicles (USVs). In order to design high-quality routes for a multi-sensor integrated USV, this work improves the [...] Read more.
Recently, issues of climate change, environment abnormality, individual requirements, and national defense have caused extensive attention to the commercial, scientific, and military development of unmanned surface vehicles (USVs). In order to design high-quality routes for a multi-sensor integrated USV, this work improves the conventional particle swarm optimization algorithm by introducing the greedy mechanism and the 2-opt operation, based on a combination strategy. First, a greedy black box is established for particle initialization, overcoming the randomness of the conventional method and excluding a great number of infeasible solutions. Then the greedy selection strategy and 2-opt operation are adopted together for local searches, to maintain population diversity and eliminate path crossovers. In addition, Monte-Carlo simulations of eight instances are conducted to compare the improved algorithm with other existing algorithms. The computation results indicate that the improved algorithm has the superior performance, with the shortest route and satisfactory robustness, although a fraction of computing efficiency becomes sacrificed. Moreover, the effectiveness and reliability of the improved method is also verified by its multi-sensor-based application to a USV model in real marine environments. Full article
(This article belongs to the Special Issue Multi-Sensor Fusion and Data Analysis)
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19 pages, 1459 KiB  
Article
Spectrum-Efficient Resource Allocation in Multi-Radio Multi-Hop Cognitive Radio Networks
by Bin Han, Ying Luo, Min Zeng and Hong Jiang
Sensors 2019, 19(20), 4493; https://doi.org/10.3390/s19204493 - 16 Oct 2019
Cited by 7 | Viewed by 2546
Abstract
The multi-hop cognitive radio network (CRN) has attracted much attention in industry and academia because of its seamless wireless coverage by forming multi-hop links and high spectrum utilization of cognitive radio (CR) technology. Using multi-slot statistical spectrum status information (SSI), this work investigates [...] Read more.
The multi-hop cognitive radio network (CRN) has attracted much attention in industry and academia because of its seamless wireless coverage by forming multi-hop links and high spectrum utilization of cognitive radio (CR) technology. Using multi-slot statistical spectrum status information (SSI), this work investigates the average spectrum efficiency (SE) of a multi-radio multi-hop (MRMH) CRN where each hop is permitted to use different spectra and long-distance hops can reuse the same idle primary user (PU) spectrum. Faced with the modeled SE problem, which is a complex non-convex fractional mixed integer nonlinear programming (MINLP) problem, the optimal spectrum and power allocation for multi-hop links in multi-slot and multi-channel scenarios can be obtained with the proposed successive multi-step convex approximation scheme (SMCA). As shown through computational complexity and simulation analysis, SMCA can obtain an approximate lower bound of the optimal solution for the modeled SE problem with a lower computational cost. Furthermore, some potential relationships between network performance and spectrum idle rate can be easily discussed with SMCA, which can provide some sensible deployment strategies for the MRMH CRN in future multi-slot scenarios. Full article
(This article belongs to the Special Issue Multi-Sensor Fusion and Data Analysis)
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19 pages, 2524 KiB  
Article
Self-Tuning Distributed Fusion Filter for Multi-Sensor Networked Systems with Unknown Packet Receiving Rates, Noise Variances, and Model Parameters
by Minhui Wang and Shuli Sun
Sensors 2019, 19(20), 4436; https://doi.org/10.3390/s19204436 - 13 Oct 2019
Cited by 9 | Viewed by 1962
Abstract
In this study, we researched the problem of self-tuning (ST) distributed fusion state estimation for multi-sensor networked stochastic linear discrete-time systems with unknown packet receiving rates, noise variances (NVs), and model parameters (MPs). Packet dropouts may occur when sensor data are sent to [...] Read more.
In this study, we researched the problem of self-tuning (ST) distributed fusion state estimation for multi-sensor networked stochastic linear discrete-time systems with unknown packet receiving rates, noise variances (NVs), and model parameters (MPs). Packet dropouts may occur when sensor data are sent to a local processor. A Bernoulli distributed stochastic variable is adopted to depict phenomena of packet dropouts. By model transformation, the identification problem of packet receiving rates is transformed into that of unknown MPs for a new augmented system. The recursive extended least squares (RELS) algorithm is used to simultaneously identify packet receiving rates and MPs in the original system. Then, a correlation function method is used to identify unknown NVs. Further, a ST distributed fusion state filter is achieved by applying identified packet receiving rates, NVs, and MPs to the corresponding optimal estimation algorithms. It is strictly proven that ST algorithms converge to optimal algorithms under the condition that the identifiers for parameters are consistent. Two examples verify the effectiveness of the proposed algorithms. Full article
(This article belongs to the Special Issue Multi-Sensor Fusion and Data Analysis)
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14 pages, 699 KiB  
Article
Critical Location Spatial-Temporal Coverage Optimization in Visual Sensor Network
by Yonghua Xiong, Jing Li and Manjie Lu
Sensors 2019, 19(19), 4106; https://doi.org/10.3390/s19194106 - 23 Sep 2019
Cited by 4 | Viewed by 2152
Abstract
Coverage and network lifetime are two fundamental research issues in visual sensor networks. In some surveillance scenarios, there are some critical locations that demand to be monitored within a designated period. However, with limited sensor nodes resources, it may not be possible to [...] Read more.
Coverage and network lifetime are two fundamental research issues in visual sensor networks. In some surveillance scenarios, there are some critical locations that demand to be monitored within a designated period. However, with limited sensor nodes resources, it may not be possible to meet both coverage and network lifetime requirements. Therefore, in order to satisfy the network lifetime constraint, sometimes the coverage needs to be traded for network lifetime. In this paper, we study how to schedule sensor nodes to maximize the spatial-temporal coverage of the critical locations under the constraint of network lifetime. First, we analyze the sensor node scheduling problem for the spatial-temporal coverage of the critical locations and establish a mathematical model of the node scheduling. Next, by analyzing the characteristics of the model, we propose a Two-phase Spatial-temporal Coverage-enhancing Method (TSCM). In phase one, a Particle Swarm Optimization (PSO) algorithm is employed to organize the directions of sensor nodes to maximize the number of covered critical locations. In the second phase, we apply a Genetic Algorithm (GA) to get the optimal working time sequence of each sensor node. New coding and decoding strategies are devised to make GA suitable for this scheduling problem. Finally, simulations are conducted and the results show that TSCM has better performance than other approaches. Full article
(This article belongs to the Special Issue Multi-Sensor Fusion and Data Analysis)
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14 pages, 2266 KiB  
Article
Prediction of pH Value by Multi-Classification in the Weizhou Island Area
by Haocai Huang, Rendong Feng, Jiang Zhu and Peiliang Li
Sensors 2019, 19(18), 3875; https://doi.org/10.3390/s19183875 - 08 Sep 2019
Cited by 3 | Viewed by 2733
Abstract
Ocean acidification is changing the chemical environment on which marine life depends. It causes a decrease in seawater pH and changes the water quality parameters of seawater. Changes in water quality parameters may affect pH, a key indicator for assessing ocean acidification. Therefore, [...] Read more.
Ocean acidification is changing the chemical environment on which marine life depends. It causes a decrease in seawater pH and changes the water quality parameters of seawater. Changes in water quality parameters may affect pH, a key indicator for assessing ocean acidification. Therefore, it is particularly important to study the correlation between pH and various water quality parameters. In this paper, several water quality parameters with potential correlation with pH are investigated, and multiple linear regression, softmax regression, and support vector machine are used to perform multi-classification. Most importantly, experimental data were collected from Weizhou Island, China. The classification results show that the pH has a strong correlation with salinity, temperature, and dissolved oxygen. The prediction accuracy of the classification is good, and the correlation with dissolved oxygen is the most significant. The prediction accuracies of the three methods for multi-classifiers based on the above three factors reach 87.01%, 87.77%, and 89.04%, respectively. Full article
(This article belongs to the Special Issue Multi-Sensor Fusion and Data Analysis)
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15 pages, 2160 KiB  
Article
Robust Stereo Visual-Inertial Odometry Using Nonlinear Optimization
by Shujun Ma, Xinhui Bai, Yinglei Wang and Rui Fang
Sensors 2019, 19(17), 3747; https://doi.org/10.3390/s19173747 - 29 Aug 2019
Cited by 18 | Viewed by 3977
Abstract
The fusion of visual and inertial odometry has matured greatly due to the complementarity of the two sensors. However, the use of high-quality sensors and powerful processors in some applications is difficult due to size and cost limitations, and there are also many [...] Read more.
The fusion of visual and inertial odometry has matured greatly due to the complementarity of the two sensors. However, the use of high-quality sensors and powerful processors in some applications is difficult due to size and cost limitations, and there are also many challenges in terms of robustness of the algorithm and computational efficiency. In this work, we present VIO-Stereo, a stereo visual-inertial odometry (VIO), which jointly combines the measurements of the stereo cameras and an inexpensive inertial measurement unit (IMU). We use nonlinear optimization to integrate visual measurements with IMU readings in VIO tightly. To decrease the cost of computation, we use the FAST feature detector to improve its efficiency and track features by the KLT sparse optical flow algorithm. We also incorporate accelerometer bias into the measurement model and optimize it together with other variables. Additionally, we perform circular matching between the previous and current stereo image pairs in order to remove outliers in the stereo matching and feature tracking steps, thus reducing the mismatch of feature points and improving the robustness and accuracy of the system. Finally, this work contributes to the experimental comparison of monocular visual-inertial odometry and stereo visual-inertial odometry by evaluating our method using the public EuRoC dataset. Experimental results demonstrate that our method exhibits competitive performance with the most advanced techniques. Full article
(This article belongs to the Special Issue Multi-Sensor Fusion and Data Analysis)
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20 pages, 14274 KiB  
Article
DMS-SLAM: A General Visual SLAM System for Dynamic Scenes with Multiple Sensors
by Guihua Liu, Weilin Zeng, Bo Feng and Feng Xu
Sensors 2019, 19(17), 3714; https://doi.org/10.3390/s19173714 - 27 Aug 2019
Cited by 28 | Viewed by 5248
Abstract
Presently, although many impressed SLAM systems have achieved exceptional accuracy in a real environment, most of them are verified in the static environment. However, for mobile robots and autonomous driving, the dynamic objects in the scene can result in tracking failure or large [...] Read more.
Presently, although many impressed SLAM systems have achieved exceptional accuracy in a real environment, most of them are verified in the static environment. However, for mobile robots and autonomous driving, the dynamic objects in the scene can result in tracking failure or large deviation during pose estimation. In this paper, a general visual SLAM system for dynamic scenes with multiple sensors called DMS-SLAM is proposed. First, the combination of GMS and sliding window is used to achieve the initialization of the system, which can eliminate the influence of dynamic objects and construct a static initialization 3D map. Then, the corresponding 3D points of the current frame in the local map are obtained by reprojection. These points are combined with the constant speed model or reference frame model to achieve the position estimation of the current frame and the update of the 3D map points in the local map. Finally, the keyframes selected by the tracking module are combined with the GMS feature matching algorithm to add static 3D map points to the local map. DMS-SLAM implements pose tracking, closed-loop detection and relocalization based on static 3D map points of the local map and supports monocular, stereo and RGB-D visual sensors in dynamic scenes. Exhaustive evaluation in public TUM and KITTI datasets demonstrates that DMS-SLAM outperforms state-of-the-art visual SLAM systems in accuracy and speed in dynamic scenes. Full article
(This article belongs to the Special Issue Multi-Sensor Fusion and Data Analysis)
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21 pages, 4142 KiB  
Article
Application of Improved Particle Swarm Optimization for Navigation of Unmanned Surface Vehicles
by Junfeng Xin, Shixin Li, Jinlu Sheng, Yongbo Zhang and Ying Cui
Sensors 2019, 19(14), 3096; https://doi.org/10.3390/s19143096 - 13 Jul 2019
Cited by 36 | Viewed by 4647
Abstract
Multi-sensor fusion for unmanned surface vehicles (USVs) is an important issue for autonomous navigation of USVs. In this paper, an improved particle swarm optimization (PSO) is proposed for real-time autonomous navigation of a USV in real maritime environment. To overcome the conventional PSO’s [...] Read more.
Multi-sensor fusion for unmanned surface vehicles (USVs) is an important issue for autonomous navigation of USVs. In this paper, an improved particle swarm optimization (PSO) is proposed for real-time autonomous navigation of a USV in real maritime environment. To overcome the conventional PSO’s inherent shortcomings, such as easy occurrence of premature convergence and human experience-determined parameters, and to enhance the precision and algorithm robustness of the solution, this work proposes three optimization strategies: linearly descending inertia weight, adaptively controlled acceleration coefficients, and random grouping inversion. Their respective or combinational effects on the effectiveness of path planning are investigated by Monte Carlo simulations for five TSPLIB instances and application tests for the navigation of a self-developed unmanned surface vehicle on the basis of multi-sensor data. Comparative results show that the adaptively controlled acceleration coefficients play a substantial role in reducing the path length and the linearly descending inertia weight help improve the algorithm robustness. Meanwhile, the random grouping inversion optimizes the capacity of local search and maintains the population diversity by stochastically dividing the single swarm into several subgroups. Moreover, the PSO combined with all three strategies shows the best performance with the shortest trajectory and the superior robustness, although retaining solution precision and avoiding being trapped in local optima require more time consumption. The experimental results of our USV demonstrate the effectiveness and efficiency of the proposed method for real-time navigation based on multi-sensor fusion. Full article
(This article belongs to the Special Issue Multi-Sensor Fusion and Data Analysis)
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23 pages, 6966 KiB  
Article
An Improved Genetic Algorithm for Path-Planning of Unmanned Surface Vehicle
by Junfeng Xin, Jiabao Zhong, Fengru Yang, Ying Cui and Jinlu Sheng
Sensors 2019, 19(11), 2640; https://doi.org/10.3390/s19112640 - 11 Jun 2019
Cited by 88 | Viewed by 6157
Abstract
The genetic algorithm (GA) is an effective method to solve the path-planning problem and help realize the autonomous navigation for and control of unmanned surface vehicles. In order to overcome the inherent shortcomings of conventional GA such as population premature and slow convergence [...] Read more.
The genetic algorithm (GA) is an effective method to solve the path-planning problem and help realize the autonomous navigation for and control of unmanned surface vehicles. In order to overcome the inherent shortcomings of conventional GA such as population premature and slow convergence speed, this paper proposes the strategy of increasing the number of offsprings by using the multi-domain inversion. Meanwhile, a second fitness evaluation was conducted to eliminate undesirable offsprings and reserve the most advantageous individuals. The improvement could help enhance the capability of local search effectively and increase the probability of generating excellent individuals. Monte-Carlo simulations for five examples from the library for the travelling salesman problem were first conducted to assess the effectiveness of algorithms. Furthermore, the improved algorithms were applied to the navigation, guidance, and control system of an unmanned surface vehicle in a real maritime environment. Comparative study reveals that the algorithm with multi-domain inversion is superior with a desirable balance between the path length and time-cost, and has a shorter optimal path, a faster convergence speed, and better robustness than the others. Full article
(This article belongs to the Special Issue Multi-Sensor Fusion and Data Analysis)
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19 pages, 10414 KiB  
Article
Intelligent Control of Bulk Tobacco Curing Schedule Using LS-SVM- and ANFIS-Based Multi-Sensor Data Fusion Approaches
by Juan Wu and Simon X. Yang
Sensors 2019, 19(8), 1778; https://doi.org/10.3390/s19081778 - 13 Apr 2019
Cited by 10 | Viewed by 4982
Abstract
The bulk tobacco flue-curing process is followed by a bulk tobacco curing schedule, which is typically pre-set at the beginning and might be adjusted by the curer to accommodate the need for tobacco leaves during curing. In this study, the controlled parameters of [...] Read more.
The bulk tobacco flue-curing process is followed by a bulk tobacco curing schedule, which is typically pre-set at the beginning and might be adjusted by the curer to accommodate the need for tobacco leaves during curing. In this study, the controlled parameters of a bulk tobacco curing schedule were presented, which is significant for the systematic modelling of an intelligent tobacco flue-curing process. To fully imitate the curer’s control of the bulk tobacco curing schedule, three types of sensors were applied, namely, a gas sensor, image sensor, and moisture sensor. Feature extraction methods were given forward to extract the odor, image, and moisture features of the tobacco leaves individually. Three multi-sensor data fusion schemes were applied, where a least squares support vector machines (LS-SVM) regression model and adaptive neuro-fuzzy inference system (ANFIS) decision model were used. Four experiments were conducted from July to September 2014, with a total of 603 measurement points, ensuring the results’ robustness and validness. The results demonstrate that a hybrid fusion scheme achieves a superior prediction performance with the coefficients of determination of the controlled parameters, reaching 0.9991, 0.9589, and 0.9479, respectively. The high prediction accuracy made the proposed hybrid fusion scheme a feasible, reliable, and effective method to intelligently control over the tobacco curing schedule. Full article
(This article belongs to the Special Issue Multi-Sensor Fusion and Data Analysis)
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18 pages, 2963 KiB  
Article
Measuring Acoustic Roughness of a Longitudinal Railhead Profile Using a Multi-Sensor Integration Technique
by Dahae Jeong, Han Shin Choi, Yong Je Choi and Wootae Jeong
Sensors 2019, 19(7), 1610; https://doi.org/10.3390/s19071610 - 03 Apr 2019
Cited by 16 | Viewed by 3423
Abstract
It is necessary to measure accurately the rolling noise generated by the friction between wheels and rails in railway transport systems. Although many systems have recently been developed to measure the surface roughness of wheels and rails, there exist large deviations in measurements [...] Read more.
It is necessary to measure accurately the rolling noise generated by the friction between wheels and rails in railway transport systems. Although many systems have recently been developed to measure the surface roughness of wheels and rails, there exist large deviations in measurements between each system whose measuring mechanism is based on a single sensor. To correct the structural problems in existing systems, we developed an automatic mobile measurement platform, named the Automatic Rail Checker (ARCer), which measures the acoustic roughness of a longitudinal railhead profile maintaining a constant speed. In addition, a new chord offset synchronization algorithm has been developed. This uses three displacement sensors to improve the measuring accuracy of the acoustic roughness of a longitudinal railhead profile, thereby minimizing the limitations of mobile platform measurement systems and measurement deviation. We then verified the accuracy of the measurement system and the algorithm through field tests on rails with different surface wear conditions. Full article
(This article belongs to the Special Issue Multi-Sensor Fusion and Data Analysis)
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21 pages, 511 KiB  
Article
An Area Coverage and Energy Consumption Optimization Approach Based on Improved Adaptive Particle Swarm Optimization for Directional Sensor Networks
by Song Peng and Yonghua Xiong
Sensors 2019, 19(5), 1192; https://doi.org/10.3390/s19051192 - 08 Mar 2019
Cited by 19 | Viewed by 3119
Abstract
Coverage is a vital indicator which reflects the performance of directional sensor networks (DSNs). The random deployment of directional sensor nodes will lead to many covergae blind areas and overlapping areas. Besides, the premature death of nodes will also directly affect the service [...] Read more.
Coverage is a vital indicator which reflects the performance of directional sensor networks (DSNs). The random deployment of directional sensor nodes will lead to many covergae blind areas and overlapping areas. Besides, the premature death of nodes will also directly affect the service quality of network due to limited energy. To address these problems, this paper proposes a new area coverage and energy consumption optimization approach based on improved adaptive particle swarm optimization (IAPSO). For area coverage problem, we set up a multi-objective optimization model in order to improve coverage ratio and reduce redundancy ratio by sensing direction rotation. For energy consumption optimization, we make energy consumption evenly distribute on each sensor node by clustering network. We set up a cluster head selection optimization model which considers the total residual energy ratio and energy consumption balance degree of cluster head candidates. We also propose a cluster formation algorithm in which member nodes choose their cluster heads by weight function. We next utilize an IAPSO to solve two optimization models to achieve high coverage ratio, low redundancy ratio and energy consumption balance. Extensive simulation results demonstrate the our proposed approach performs better than other ones. Full article
(This article belongs to the Special Issue Multi-Sensor Fusion and Data Analysis)
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15 pages, 2209 KiB  
Article
Improved Convolutional Pose Machines for Human Pose Estimation Using Image Sensor Data
by Baohua Qiang, Shihao Zhang, Yongsong Zhan, Wu Xie and Tian Zhao
Sensors 2019, 19(3), 718; https://doi.org/10.3390/s19030718 - 10 Feb 2019
Cited by 19 | Viewed by 4685
Abstract
In recent years, increasing human data comes from image sensors. In this paper, a novel approach combining convolutional pose machines (CPMs) with GoogLeNet is proposed for human pose estimation using image sensor data. The first stage of the CPMs directly generates a response [...] Read more.
In recent years, increasing human data comes from image sensors. In this paper, a novel approach combining convolutional pose machines (CPMs) with GoogLeNet is proposed for human pose estimation using image sensor data. The first stage of the CPMs directly generates a response map of each human skeleton’s key points from images, in which we introduce some layers from the GoogLeNet. On the one hand, the improved model uses deeper network layers and more complex network structures to enhance the ability of low level feature extraction. On the other hand, the improved model applies a fine-tuning strategy, which benefits the estimation accuracy. Moreover, we introduce the inception structure to greatly reduce parameters of the model, which reduces the convergence time significantly. Extensive experiments on several datasets show that the improved model outperforms most mainstream models in accuracy and training time. The prediction efficiency of the improved model is improved by 1.023 times compared with the CPMs. At the same time, the training time of the improved model is reduced 3.414 times. This paper presents a new idea for future research. Full article
(This article belongs to the Special Issue Multi-Sensor Fusion and Data Analysis)
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22 pages, 4546 KiB  
Article
Automatic Recognition of Ripening Tomatoes by Combining Multi-Feature Fusion with a Bi-Layer Classification Strategy for Harvesting Robots
by Jingui Wu, Baohua Zhang, Jun Zhou, Yingjun Xiong, Baoxing Gu and Xiaolong Yang
Sensors 2019, 19(3), 612; https://doi.org/10.3390/s19030612 - 01 Feb 2019
Cited by 41 | Viewed by 6622
Abstract
Automatic recognition of ripening tomatoes is a main hurdle precluding the replacement of manual labour by robotic harvesting. In this paper, we present a novel automatic algorithm for recognition of ripening tomatoes using an improved method that combines multiple features, feature analysis and [...] Read more.
Automatic recognition of ripening tomatoes is a main hurdle precluding the replacement of manual labour by robotic harvesting. In this paper, we present a novel automatic algorithm for recognition of ripening tomatoes using an improved method that combines multiple features, feature analysis and selection, a weighted relevance vector machine (RVM) classifier, and a bi-layer classification strategy. The algorithm operates using a two-layer strategy. The first-layer classification strategy aims to identify tomato-containing regions in images using the colour difference information. The second classification strategy is based on a classifier that is trained on multi-medium features. In our proposed algorithm, to simplify the calculation and to improve the recognition efficiency, the processed images are divided into 9 × 9 pixel blocks, and these blocks, rather than single pixels, are considered as the basic units in the classification task. Six colour-related features, namely the Red (R), Green (G), Blue (B), Hue (H), Saturation (S) and Intensity (I) components, respectively, colour components, and five textural features (entropy, energy, correlation, inertial moment and local smoothing) were extracted from pixel blocks. Relevant features and their weights were analysed using the iterative RELIEF (I-RELIEF) algorithm. The image blocks were classified into different categories using a weighted RVM classifier based on the selected relevant features. The final results of tomato recognition were determined by combining the block classification results and the bi-layer classification strategy. The algorithm demonstrated the detection accuracy of 94.90% on 120 images, this suggests that the proposed algorithm is effective and suitable for tomato detection Full article
(This article belongs to the Special Issue Multi-Sensor Fusion and Data Analysis)
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13 pages, 2717 KiB  
Article
Deep Belief Network for Spectral–Spatial Classification of Hyperspectral Remote Sensor Data
by Chenming Li, Yongchang Wang, Xiaoke Zhang, Hongmin Gao, Yao Yang and Jiawei Wang
Sensors 2019, 19(1), 204; https://doi.org/10.3390/s19010204 - 08 Jan 2019
Cited by 48 | Viewed by 5884
Abstract
With the development of high-resolution optical sensors, the classification of ground objects combined with multivariate optical sensors is a hot topic at present. Deep learning methods, such as convolutional neural networks, are applied to feature extraction and classification. In this work, a novel [...] Read more.
With the development of high-resolution optical sensors, the classification of ground objects combined with multivariate optical sensors is a hot topic at present. Deep learning methods, such as convolutional neural networks, are applied to feature extraction and classification. In this work, a novel deep belief network (DBN) hyperspectral image classification method based on multivariate optical sensors and stacked by restricted Boltzmann machines is proposed. We introduced the DBN framework to classify spatial hyperspectral sensor data on the basis of DBN. Then, the improved method (combination of spectral and spatial information) was verified. After unsupervised pretraining and supervised fine-tuning, the DBN model could successfully learn features. Additionally, we added a logistic regression layer that could classify the hyperspectral images. Moreover, the proposed training method, which fuses spectral and spatial information, was tested over the Indian Pines and Pavia University datasets. The advantages of this method over traditional methods are as follows: (1) the network has deep structure and the ability of feature extraction is stronger than traditional classifiers; (2) experimental results indicate that our method outperforms traditional classification and other deep learning approaches. Full article
(This article belongs to the Special Issue Multi-Sensor Fusion and Data Analysis)
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14 pages, 6496 KiB  
Article
Fusion of High-Dynamic and Low-Drift Sensors Using Kalman Filters
by Bin Wu, Tiantian Huang, Yan Jin, Jie Pan and Kaichen Song
Sensors 2019, 19(1), 186; https://doi.org/10.3390/s19010186 - 07 Jan 2019
Cited by 10 | Viewed by 4465
Abstract
In practice, a high-dynamic vibration sensor is often plagued by the problem of drift, which is caused by thermal effects. Conversely, low-drift sensors typically have a limited sample rate range. This paper presents a system combining different types of sensors to address general [...] Read more.
In practice, a high-dynamic vibration sensor is often plagued by the problem of drift, which is caused by thermal effects. Conversely, low-drift sensors typically have a limited sample rate range. This paper presents a system combining different types of sensors to address general drift problems that occur in measurements for high-dynamic vibration signals. In this paper, the hardware structure and algorithms for fusing high-dynamic and low-drift sensors are described. The algorithms include a drift state estimation and a Kalman filter based on a linear prediction model. Key issues such as the dimension of the drift state vector, the order of the linear prediction model, are analyzed in the design of algorithm. The performance of the algorithm is illustrated by a simulation example and experiments. The simulation and experimental results show that the drift can be removed while the high-dynamic measuring ability is retained. A high-dynamic vibration measuring system with the frequency range starting from 0 Hz is achieved. Meanwhile, measurement noise was improved 9.3 dB through using the linear-prediction-based Kalman filter. Full article
(This article belongs to the Special Issue Multi-Sensor Fusion and Data Analysis)
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17 pages, 3544 KiB  
Article
A Multi-Model Combined Filter with Dual Uncertainties for Data Fusion of MEMS Gyro Array
by Qiang Shen, Jieyu Liu, Xiaogang Zhou and Lixin Wang
Sensors 2019, 19(1), 85; https://doi.org/10.3390/s19010085 - 27 Dec 2018
Cited by 6 | Viewed by 3255
Abstract
The gyro array is a useful technique in improving the accuracy of a micro-electro-mechanical system (MEMS) gyroscope, but the traditional estimate algorithm that plays an important role in this technique has two problems restricting its performance: The limitation of the stochastic assumption and [...] Read more.
The gyro array is a useful technique in improving the accuracy of a micro-electro-mechanical system (MEMS) gyroscope, but the traditional estimate algorithm that plays an important role in this technique has two problems restricting its performance: The limitation of the stochastic assumption and the influence of the dynamic condition. To resolve these problems, a multi-model combined filter with dual uncertainties is proposed to integrate the outputs from numerous gyroscopes. First, to avoid the limitations of the stochastic and set-membership approaches and to better utilize the potentials of both concepts, a dual-noise acceleration model was proposed to describe the angular rate. On this basis, a dual uncertainties model of gyro array was established. Then the multiple model theory was used to improve dynamic performance, and a multi-model combined filter with dual uncertainties was designed. This algorithm could simultaneously deal with stochastic uncertainties and set-membership uncertainties by calculating the Minkowski sum of multiple ellipsoidal sets. The experimental results proved the effectiveness of the proposed filter in improving gyroscope accuracy and adaptability to different kinds of uncertainties and different dynamic characteristics. Most of all, the method gave the boundary surrounding the true value, which is of great significance in attitude control and guidance applications. Full article
(This article belongs to the Special Issue Multi-Sensor Fusion and Data Analysis)
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29 pages, 3618 KiB  
Article
An Efficient Incremental Mining Algorithm for Discovering Sequential Pattern in Wireless Sensor Network Environments
by Xin Lyu and Hongxu Ma
Sensors 2019, 19(1), 29; https://doi.org/10.3390/s19010029 - 21 Dec 2018
Cited by 9 | Viewed by 3787
Abstract
Wireless sensor networks (WSNs) are an important type of network for sensing the environment and collecting information. It can be deployed in almost every type of environment in the real world, providing a reliable and low-cost solution for management. Huge amounts of data [...] Read more.
Wireless sensor networks (WSNs) are an important type of network for sensing the environment and collecting information. It can be deployed in almost every type of environment in the real world, providing a reliable and low-cost solution for management. Huge amounts of data are produced from WSNs all the time, and it is significant to process and analyze data effectively to support intelligent decision and management. However, the new characteristics of sensor data, such as rapid growth and frequent updates, bring new challenges to the mining algorithms, especially given the time constraints for intelligent decision-making. In this work, an efficient incremental mining algorithm for discovering sequential pattern (novel incremental algorithm, NIA) is proposed, in order to enhance the efficiency of the whole mining process. First, a reasoned proof is given to demonstrate how to update the frequent sequences incrementally, and the mining space is greatly narrowed based on the proof. Second, an improvement is made on PrefixSpan, which is a classic sequential pattern mining algorithm with a high-complexity recursive process. The improved algorithm, named PrefixSpan+, utilizes a mapping structure to extend the prefixes to sequential patterns, making the mining step more efficient. Third, a fast support number-counting algorithm is presented to choose frequent sequences from the potential frequent sequences. A reticular tree is constructed to store all the potential frequent sequences according to subordinate relations between them, and then the support degree can be efficiently calculated without scanning the original database repeatedly. NIA is compared with various kinds of mining algorithms via intensive experiments on the real monitoring datasets, benchmarking datasets and synthetic datasets from aspects including time cost, sensitivity of factors, and space cost. The results show that NIA performs better than the existed methods. Full article
(This article belongs to the Special Issue Multi-Sensor Fusion and Data Analysis)
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20 pages, 3626 KiB  
Article
Hyperspectral Remote Sensing Image Classification Based on Maximum Overlap Pooling Convolutional Neural Network
by Chenming Li, Simon X. Yang, Yao Yang, Hongmin Gao, Jia Zhao, Xiaoyu Qu, Yongchang Wang, Dan Yao and Jianbing Gao
Sensors 2018, 18(10), 3587; https://doi.org/10.3390/s18103587 - 22 Oct 2018
Cited by 29 | Viewed by 4322
Abstract
In a traditional convolutional neural network structure, pooling layers generally use an average pooling method: a non-overlapping pooling. However, this condition results in similarities in the extracted image features, especially for the hyperspectral images of a continuous spectrum, which makes it more difficult [...] Read more.
In a traditional convolutional neural network structure, pooling layers generally use an average pooling method: a non-overlapping pooling. However, this condition results in similarities in the extracted image features, especially for the hyperspectral images of a continuous spectrum, which makes it more difficult to extract image features with differences, and image detail features are easily lost. This result seriously affects the accuracy of image classification. Thus, a new overlapping pooling method is proposed, where maximum pooling is used in an improved convolutional neural network to avoid the fuzziness of average pooling. The step size used is smaller than the size of the pooling kernel to achieve overlapping and coverage between the outputs of the pooling layer. The dataset selected for this experiment was the Indian Pines dataset, collected by the airborne visible/infrared imaging spectrometer (AVIRIS) sensor. Experimental results show that using the improved convolutional neural network for remote sensing image classification can effectively improve the details of the image and obtain a high classification accuracy. Full article
(This article belongs to the Special Issue Multi-Sensor Fusion and Data Analysis)
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21 pages, 8715 KiB  
Article
Underwater Target Detection and 3D Reconstruction System Based on Binocular Vision
by Guanying Huo, Ziyin Wu, Jiabiao Li and Shoujun Li
Sensors 2018, 18(10), 3570; https://doi.org/10.3390/s18103570 - 21 Oct 2018
Cited by 33 | Viewed by 6207
Abstract
To better solve the problem of target detection in marine environment and to deal with the difficulty of 3D reconstruction of underwater target, a binocular vision-based underwater target detection and 3D reconstruction system is proposed in this paper. Two optical sensors are used [...] Read more.
To better solve the problem of target detection in marine environment and to deal with the difficulty of 3D reconstruction of underwater target, a binocular vision-based underwater target detection and 3D reconstruction system is proposed in this paper. Two optical sensors are used as the vision of the system. Firstly, denoising and color restoration are performed on the image sequence acquired by the vision of the system and the underwater target is segmented and extracted according to the image saliency using the super-pixel segmentation method. Secondly, aiming to reduce mismatch, we improve the semi-global stereo matching method by strictly constraining the matching in the valid target area and then optimizing the basic disparity map within each super-pixel area using the least squares fitting interpolation method. Finally, based on the optimized disparity map, triangulation principle is used to calculate the three-dimensional data of the target and the 3D structure and color information of the target can be given by MeshLab. The experimental results show that for a specific size underwater target, the system can achieve higher measurement accuracy and better 3D reconstruction effect within a suitable distance. Full article
(This article belongs to the Special Issue Multi-Sensor Fusion and Data Analysis)
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27 pages, 7021 KiB  
Article
Partial Discharge Recognition with a Multi-Resolution Convolutional Neural Network
by Gaoyang Li, Xiaohua Wang, Xi Li, Aijun Yang and Mingzhe Rong
Sensors 2018, 18(10), 3512; https://doi.org/10.3390/s18103512 - 18 Oct 2018
Cited by 73 | Viewed by 4484
Abstract
Partial discharge (PD) is not only an important symptom for monitoring the imperfections in the insulation system of a gas-insulated switchgear (GIS), but also the factor that accelerates the degradation. At present, monitoring ultra-high-frequency (UHF) signals induced by PDs is regarded as one [...] Read more.
Partial discharge (PD) is not only an important symptom for monitoring the imperfections in the insulation system of a gas-insulated switchgear (GIS), but also the factor that accelerates the degradation. At present, monitoring ultra-high-frequency (UHF) signals induced by PDs is regarded as one of the most effective approaches for assessing the insulation severity and classifying the PDs. Therefore, in this paper, a deep learning-based PD classification algorithm is proposed and realized with a multi-column convolutional neural network (CNN) that incorporates UHF spectra of multiple resolutions. First, three subnetworks, as characterized by their specified designed temporal filters, frequency filters, and texture filters, are organized and then intergraded by a fully-connected neural network. Then, a long short-term memory (LSTM) network is utilized for fusing the embedded multi-sensor information. Furthermore, to alleviate the risk of overfitting, a transfer learning approach inspired by manifold learning is also present for model training. To demonstrate, 13 modes of defects considering both the defect types and their relative positions were well designed for a simulated GIS tank. A detailed analysis of the performance reveals the clear superiority of the proposed method, compared to18 typical baselines. Several advanced visualization techniques are also implemented to explore the possible qualitative interpretations of the learned features. Finally, a unified framework based on matrix projection is discussed to provide a possible explanation for the effectiveness of the architecture. Full article
(This article belongs to the Special Issue Multi-Sensor Fusion and Data Analysis)
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15 pages, 4571 KiB  
Article
A Bimodal Model to Estimate Dynamic Metropolitan Population by Mobile Phone Data
by Jie Feng, Yong Li, Fengli Xu and Depeng Jin
Sensors 2018, 18(10), 3431; https://doi.org/10.3390/s18103431 - 12 Oct 2018
Cited by 6 | Viewed by 3368
Abstract
Accurate, real-time and fine-spatial population distribution is crucial for urban planning, government management, and advertisement promotion. Limited by technics and tools, we rely on the census to obtain this information in the past, which is coarse and costly. The popularity of mobile phones [...] Read more.
Accurate, real-time and fine-spatial population distribution is crucial for urban planning, government management, and advertisement promotion. Limited by technics and tools, we rely on the census to obtain this information in the past, which is coarse and costly. The popularity of mobile phones gives us a new opportunity to investigate population estimation. However, real-time and accurate population estimation is still a challenging problem because of the coarse localization and complicated user behaviors. With the help of the passively collected human mobility and locations from the mobile networks including call detail records and mobility management signals, we develop a bimodal model beyond the prior work to better estimate real-time population distribution at metropolitan scales. We discuss how the estimation interval, space granularity, and data type will influence the estimation accuracy, and find the data collected from the mobility management signals with the 30 min estimation interval performs better which reduces the population estimation error by 30% in terms of Root Mean Square Error (RMSE). These results show us the great potential of using bimodal model and mobile phone data to estimate real-time population distribution. Full article
(This article belongs to the Special Issue Multi-Sensor Fusion and Data Analysis)
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14 pages, 1487 KiB  
Article
Domain Adaptation and Adaptive Information Fusion for Object Detection on Foggy Days
by Zhe Chen, Xiaofang Li, Hao Zheng, Hongmin Gao and Huibin Wang
Sensors 2018, 18(10), 3286; https://doi.org/10.3390/s18103286 - 30 Sep 2018
Cited by 6 | Viewed by 2875
Abstract
Foggy days pose many difficulties for outdoor camera surveillance systems. On foggy days, the optical attenuation and scattering effects of the medium significantly distort and degenerate the scene radiation, making it noisy and indistinguishable. Aiming to solve this problem, in this paper we [...] Read more.
Foggy days pose many difficulties for outdoor camera surveillance systems. On foggy days, the optical attenuation and scattering effects of the medium significantly distort and degenerate the scene radiation, making it noisy and indistinguishable. Aiming to solve this problem, in this paper we propose a novel object detection method that has the ability to exploit the information in the color and depth domains. To prevent the error propagation problem, we clean the depth information before the training process and remove false samples from the database. A domain adaptation strategy is employed to adaptively fuse the decisions obtained in the color and depth domains. In the experiments, we evaluate the contribution of the depth information for object detection on foggy days. Moreover, the advantages of the multiple-domain adaptation strategy are experimentally demonstrated via comparison with other methods. Full article
(This article belongs to the Special Issue Multi-Sensor Fusion and Data Analysis)
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24 pages, 9676 KiB  
Article
Reconstructed Order Analysis-Based Vibration Monitoring under Variable Rotation Speed by Using Multiple Blade Tip-Timing Sensors
by Zhongsheng Chen, Jianhua Liu, Chi Zhan, Jing He and Weimin Wang
Sensors 2018, 18(10), 3235; https://doi.org/10.3390/s18103235 - 26 Sep 2018
Cited by 35 | Viewed by 3619
Abstract
On-line vibration monitoring is significant for high-speed rotating blades, and blade tip-timing (BTT) is generally regarded as a promising solution. BTT methods must assume that rotating speeds are constant. This assumption is impractical, and blade damages are always formed and accumulated during variable [...] Read more.
On-line vibration monitoring is significant for high-speed rotating blades, and blade tip-timing (BTT) is generally regarded as a promising solution. BTT methods must assume that rotating speeds are constant. This assumption is impractical, and blade damages are always formed and accumulated during variable operational conditions. Thus, how to carry out BTT vibration monitoring under variable rotation speed (VRS) is a big challenge. Angular sampling-based order analyses have been widely used for vibration signals in rotating machinery with variable speeds. However, BTT vibration signals are well under-sampled, and Shannon’s sampling theorem is not satisfied so that existing order analysis methods will not work well. To overcome this problem, a reconstructed order analysis-based BTT vibration monitoring method is proposed in this paper. First, the effects of VRS on BTT vibration monitoring are analyzed, and the basic structure of angular sampling-based BTT vibration monitoring under VRS is presented. Then a band-pass sampling-based engine order (EO) reconstruction algorithm is proposed for uniform BTT sensor configuration so that few BTT sensors can be used to extract high EOs. In addition, a periodically non-uniform sampling-based EO reconstruction algorithm is proposed for non-uniform BTT sensor configuration. Next, numerical simulations are done to validate the two reconstruction algorithms. In the end, an experimental set-up is built. Both uniform and non-uniform BTT vibration signals are collected, and reconstructed order analysis are carried out. Simulation and experimental results testify that the proposed algorithms can accurately capture characteristic high EOs of synchronous and asynchronous vibrations under VRS by using few BTT sensors. The significance of this paper is to overcome the limitation of conventional BTT methods of dealing with variable blade rotating speeds. Full article
(This article belongs to the Special Issue Multi-Sensor Fusion and Data Analysis)
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18 pages, 3325 KiB  
Article
Polar Transversal Initial Alignment Algorithm for UUV with a Large Misalignment Angle
by Zheping Yan, Lu Wang, Tongda Wang, Honghan Zhang and Zewen Yang
Sensors 2018, 18(10), 3231; https://doi.org/10.3390/s18103231 - 25 Sep 2018
Cited by 5 | Viewed by 3397
Abstract
The conventional initial alignment algorithms are invalid in the polar region. This is caused by the rapid convergence of the Earth meridians in the high-latitude areas. However, the initial alignment algorithms are important for the accurate navigation of Unmanned Underwater Vehicles. The polar [...] Read more.
The conventional initial alignment algorithms are invalid in the polar region. This is caused by the rapid convergence of the Earth meridians in the high-latitude areas. However, the initial alignment algorithms are important for the accurate navigation of Unmanned Underwater Vehicles. The polar transversal initial alignment algorithm is proposed to overcome this problem. In the polar transversal initial alignment algorithm, the transversal geographic frame is chosen as the navigation frame. The polar region in the conventional frames is equivalent to the equatorial region in the transversal frames. Therefore, the polar transversal initial can be effectively applied in the polar region. According to the complex environment in the polar region, a large misalignment angle is considered in this paper. Based on the large misalignment angle condition, the non-linear dynamics models are established. In addition, the simplified unscented Kalman filter (UKF) is chosen to realize the data fusion. Two comparison simulations and an experiment are performed to verify the performance of the proposed algorithm. The simulation and experiment results indicate the validity of the proposed algorithm, especially when large misalignment angles occur. Full article
(This article belongs to the Special Issue Multi-Sensor Fusion and Data Analysis)
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13 pages, 1318 KiB  
Article
Support Vector Machine Optimized by Genetic Algorithm for Data Analysis of Near-Infrared Spectroscopy Sensors
by Di Wang, Lin Xie, Simon X. Yang and Fengchun Tian
Sensors 2018, 18(10), 3222; https://doi.org/10.3390/s18103222 - 25 Sep 2018
Cited by 15 | Viewed by 4086
Abstract
Near-infrared (NIR) spectral sensors deliver the spectral response of the light absorbed by materials for quantification, qualification or identification. Spectral analysis technology based on the NIR sensor has been a useful tool for complex information processing and high precision identification in the tobacco [...] Read more.
Near-infrared (NIR) spectral sensors deliver the spectral response of the light absorbed by materials for quantification, qualification or identification. Spectral analysis technology based on the NIR sensor has been a useful tool for complex information processing and high precision identification in the tobacco industry. In this paper, a novel method based on the support vector machine (SVM) is proposed to discriminate the tobacco cultivation region using the near-infrared (NIR) sensors, where the genetic algorithm (GA) is employed for input subset selection to identify the effective principal components (PCs) for the SVM model. With the same number of PCs as the inputs to the SVM model, a number of comparative experiments were conducted between the effective PCs selected by GA and the PCs orderly starting from the first one. The model performance was evaluated in terms of prediction accuracy and four parameters of assessment criteria (true positive rate, true negative rate, positive predictive value and F1 score). From the results, it is interesting to find that some PCs with less information may contribute more to the cultivation regions and are considered as more effective PCs, and the SVM model with the effective PCs selected by GA has a superior discrimination capacity. The proposed GA-SVM model can effectively learn the relationship between tobacco cultivation regions and tobacco NIR sensor data. Full article
(This article belongs to the Special Issue Multi-Sensor Fusion and Data Analysis)
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12 pages, 1302 KiB  
Article
Domain Correction Based on Kernel Transformation for Drift Compensation in the E-Nose System
by Yang Tao, Juan Xu, Zhifang Liang, Lian Xiong and Haocheng Yang
Sensors 2018, 18(10), 3209; https://doi.org/10.3390/s18103209 - 23 Sep 2018
Cited by 13 | Viewed by 3138
Abstract
This paper proposes a way for drift compensation in electronic noses (e-nose) that often suffers from uncertain and unpredictable sensor drift. Traditional machine learning methods for odor recognition require consistent data distribution, which makes the model trained with previous data less generalized. In [...] Read more.
This paper proposes a way for drift compensation in electronic noses (e-nose) that often suffers from uncertain and unpredictable sensor drift. Traditional machine learning methods for odor recognition require consistent data distribution, which makes the model trained with previous data less generalized. In the actual application scenario, the data collected previously and the data collected later may have different data distributions due to the sensor drift. If the dataset without sensor drift is treated as a source domain and the dataset with sensor drift as a target domain, a domain correction based on kernel transformation (DCKT) method is proposed to compensate the sensor drift. The proposed method makes the distribution consistency of two domains greatly improved through mapping to a high-dimensional reproducing kernel space and reducing the domain distance. A public benchmark sensor drift dataset is used to verify the effectiveness and efficiency of the proposed DCKT method. The experimental result shows that the proposed method yields the highest average accuracies compared to other considered methods. Full article
(This article belongs to the Special Issue Multi-Sensor Fusion and Data Analysis)
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12 pages, 3873 KiB  
Article
Grey Model Optimized by Particle Swarm Optimization for Data Analysis and Application of Multi-Sensors
by Chenming Li, Hongmin Gao, Junlin Qiu, Yao Yang, Xiaoyu Qu, Yongchang Wang and Zhuqing Bi
Sensors 2018, 18(8), 2503; https://doi.org/10.3390/s18082503 - 01 Aug 2018
Cited by 8 | Viewed by 2825
Abstract
Data on the effective operation of new pumping station is scarce, and the unit structure is complex, as the temperature changes of different parts of the unit are coupled with multiple factors. The multivariable grey system prediction model can effectively predict the multiple [...] Read more.
Data on the effective operation of new pumping station is scarce, and the unit structure is complex, as the temperature changes of different parts of the unit are coupled with multiple factors. The multivariable grey system prediction model can effectively predict the multiple parameter change of a nonlinear system model by using a small amount of data, but the value of its q parameters greatly influences the prediction accuracy of the model. Therefore, the particle swarm optimization algorithm is used to optimize the q parameters and the multi-sensor temperature data of a pumping station unit is processed. Then, the change trends of the temperature data are analyzed and predicted. Comparing the results with the unoptimized multi-variable grey model and the BP neural network prediction method trained under insufficient data conditions, it is proved that the relative error of the multi-variable grey model after optimizing the q parameters is smaller. Full article
(This article belongs to the Special Issue Multi-Sensor Fusion and Data Analysis)
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