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14 pages, 3313 KiB  
Article
Assessment of Oak Roundwood Quality Using Photogrammetry and Acoustic Surveys
by Michela Nocetti, Giovanni Aminti, Margherita Vicario and Michele Brunetti
Forests 2025, 16(3), 421; https://doi.org/10.3390/f16030421 - 25 Feb 2025
Viewed by 549
Abstract
Hardwood has a variety of applications and can be used for low-value products, such as firewood, or for high-value applications, achieving significantly higher prices. Therefore, assessing the quality of raw material is essential for allocating the wood to the most suitable end use. [...] Read more.
Hardwood has a variety of applications and can be used for low-value products, such as firewood, or for high-value applications, achieving significantly higher prices. Therefore, assessing the quality of raw material is essential for allocating the wood to the most suitable end use. The aim of this study was to explore the use of the photogrammetry technique to determine dimensional characteristics and perform remote visual grading of round oak timber stored at a log yard. The results of the visual classification were then compared with non-destructive acoustic measurements to assess their level of agreement. Based on the point cloud obtained from photogrammetry, logs were classified into three quality groups according to the European standard for round timber grading. The diameter measurements of the logs obtained through the photogrammetry survey were comparable to those taken manually, with an average difference of 0.46 cm and a mean absolute error of 2.1 cm compared to field measurements. However, the log lengths measured from the 3D survey were, on average, 5 cm shorter than those obtained using a measuring tape. The visual classification performed on the 3D reconstruction was based on the evaluation of log size, knots, buckles, and sweep, resulting in 39%, 27%, and 24% of the pieces being grouped into the high-, medium-, and low-quality classes, respectively. Acoustic measurements, performed using both resonance and time-of-flight (ToF) methods, were highly correlated with each other and successfully distinguished the three quality classes only when sweep was excluded from the classification criteria. When curvature was also considered as a parameter for log grading, acoustic velocity only differentiated the lowest quality class from the other two. Full article
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19 pages, 2621 KiB  
Article
Multi-Scale Debris Flow Warning Technology Combining GNSS and InSAR Technology
by Xiang Zhao, Linju He, Hai Li, Ling He and Shuaihong Liu
Water 2025, 17(4), 577; https://doi.org/10.3390/w17040577 - 17 Feb 2025
Viewed by 697
Abstract
The dynamic loads of fluid impact and static loads, such as the gravity of a rock mass during the formation of debris flows, exhibit a coupled effect of mutual influence. Under this coupling effect, surface monitoring points in disaster areas experience displacement. However, [...] Read more.
The dynamic loads of fluid impact and static loads, such as the gravity of a rock mass during the formation of debris flows, exhibit a coupled effect of mutual influence. Under this coupling effect, surface monitoring points in disaster areas experience displacement. However, existing methods do not consider the dynamic–static coupling effects of debris flows on the surface. Instead, they rely on GNSS or InSAR technology for dynamic or static single-scale monitoring, leading to high Mean Absolute Percentage Error (MAPE) values and low warning accuracy. To address these limitations and improve debris flow warning accuracy, a multi-scale warning method was proposed based on Global Navigation Satellite System (GNSS) and Synthetic Aperture Radar Interferometry (InSAR) technology. GNSS technology was utilized to correct coordinate errors at monitoring points, thereby enhancing the accuracy of monitoring data. Surface deformation images were generated using InSAR and Small Baseline Subset (SBAS) technology, with time series calculations applied to obtain multi-scale deformation data of the surface in debris flow disaster areas. A debris flow disaster morphology classification model was developed using a support vector mechanism. The actual types of debris flow disasters were employed as training labels. Digital Elevation Model (DEM) files were utilized to extract datasets, including plane curvature, profile curvature, slope, and elevation of the monitoring area, which were then input into the training model for classification training. The model outputted the classification results of the hidden danger areas of debris flow disasters. Finally, the dynamic and static coupling variables of surface deformation were decomposed into valley-type internal factors (rock mass static load) and slope-type triggering factors (fluid impact dynamic load) using the moving average method. Time series prediction models for the variable of the dynamic–static coupling effects on surface deformation were constructed using polynomial regression and particle swarm optimization (PSO)–support vector regression (SVR) algorithms, achieving multi-scale early warning of debris flows. The experimental results showed that the error between the predicted surface deformation results using this method and the actual values is less than 5 mm. The predicted MAPE value reached 6.622%, the RMSE value reached 8.462 mm, the overall warning accuracy reached 85.9%, and the warning time was under 30 ms, indicating that the proposed method delivered high warning accuracy and real-time warning. Full article
(This article belongs to the Special Issue Flowing Mechanism of Debris Flow and Engineering Mitigation)
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13 pages, 5282 KiB  
Article
Parallel Farby–Perot Interferometers in an Etched Multicore Fiber for Vector Bending Measurements
by Kang Wang, Wei Ji, Cong Xiong, Caoyuan Wang, Yu Qin, Yichun Shen and Limin Xiao
Micromachines 2024, 15(12), 1406; https://doi.org/10.3390/mi15121406 - 21 Nov 2024
Cited by 1 | Viewed by 970
Abstract
Vector bending sensors can be utilized to detect the bending curvature and direction, which is essential for various applications such as structural health monitoring, mechanical deformation measurement, and shape sensing. In this work, we demonstrate a temperature-insensitive vector bending sensor via parallel Farby–Perot [...] Read more.
Vector bending sensors can be utilized to detect the bending curvature and direction, which is essential for various applications such as structural health monitoring, mechanical deformation measurement, and shape sensing. In this work, we demonstrate a temperature-insensitive vector bending sensor via parallel Farby–Perot interferometers (FPIs) fabricated by etching and splicing a multicore fiber (MCF). The parallel FPIs made in this simple and effective way exhibit significant interferometric visibility with a fringe contrast over 20 dB in the reflection spectra, which is 6 dB larger than the previous MCF-based FPIs. And such a device exhibits a curvature sensitivity of 0.207 nm/m−1 with strong bending-direction discrimination. The curvature magnitude and orientation angle can be reconstructed through the dip wavelength shifts in two off-diagonal outer-core FPIs. The reconstruction results of nine randomly selected pairs of bending magnitudes and directions show that the average relative error of magnitude is ~4.5%, and the average absolute error of orientation angle is less than 2.0°. Furthermore, the proposed bending sensor is temperature-insensitive, with temperature at a lower sensitivity than 10 pm/°C. The fabrication simplicity, high interferometric visibility, compactness, and temperature insensitivity of the device may accelerate MCF-based FPI applications. Full article
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13 pages, 1747 KiB  
Article
The Effect of Arterial Elongation on Isolated Common Iliac Artery Pathologies
by Ádám Szőnyi, Balázs Bence Nyárády, Márton Philippovich, Adrienn Dobai, Ekrem Anil Sari, András Szőnyi, Anikó Ilona Nagy and Edit Dósa
Life 2024, 14(11), 1440; https://doi.org/10.3390/life14111440 - 7 Nov 2024
Viewed by 1118
Abstract
Purpose: to investigate the effects of vessel geometry on steno-occlusive and dilatative common iliac artery (CIA) pathologies. Methods: this single-center, retrospective study included 100 participants, namely 60 participants with a unilateral, isolated CIA pathology who were divided into three pathology-based groups (a stenosis [...] Read more.
Purpose: to investigate the effects of vessel geometry on steno-occlusive and dilatative common iliac artery (CIA) pathologies. Methods: this single-center, retrospective study included 100 participants, namely 60 participants with a unilateral, isolated CIA pathology who were divided into three pathology-based groups (a stenosis group, n = 20, an occlusion group, n = 20, and an aneurysm group, n = 20) and 40 participants without a CIA pathology (control group). All participants underwent abdominal and pelvic computed tomography angiography. The aortoiliac region of the participants was reconstructed into three-dimensional models. Elongation parameters (tortuosity index (TI) and absolute average curvature (AAC)) and bifurcation parameters (iliac take-off angle, iliac planarity angle, and bifurcation angle) were determined using an in-house-written piece of software. Demographic data, anthropometric data, cardiovascular risk factor data, and medical history data were obtained from participants’ electronic health records. The following statistical methods were used: one-way ANOVA, chi-square test, t-tests, Wilcoxon test, Kruskal–Wallis test, and multivariate linear regression. Results: in the occlusion group, both TI and AAC values were significantly higher on the contralateral side than on the ipsilateral side (both p < 0.001), whereas in the aneurysm group the AAC values were significantly higher on the ipsilateral side than on the contralateral side (p = 0.001). The ipsilateral and contralateral TI and AAC values of the iliac arteries were significantly higher in the aneurysm group than in the other three groups (all p < 0.001). Age significantly affected all of the elongation parameters except for the TI of the infrarenal aorta (all p < 0.010 except the TI of the infrarenal aorta). In addition, the AAC values for the iliac arteries were significantly associated with obesity (ipsilateral iliac artery, p = 0.045; contralateral iliac artery, p = 0.047). Aortic bifurcation parameters did not differ significantly either within each group (ipsilateral versus contralateral side) or between the individual groups. Conclusions: occlusions tend to develop in relatively straight iliac arteries, whereas unilateral, isolated CIA aneurysms are more likely to occur in elongated aortoiliac systems. Full article
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22 pages, 9512 KiB  
Article
Neural Network-Based Fusion of InSAR and Optical Digital Elevation Models with Consideration of Local Terrain Features
by Rong Gui, Yuanjun Qin, Zhi Hu, Jiazhen Dong, Qian Sun, Jun Hu, Yibo Yuan and Zhiwei Mo
Remote Sens. 2024, 16(19), 3567; https://doi.org/10.3390/rs16193567 - 25 Sep 2024
Cited by 2 | Viewed by 1216
Abstract
InSAR and optical techniques represent two principal approaches for the generation of large-scale Digital Elevation Models (DEMs). Due to the inherent limitations of each technology, a single data source is insufficient to produce high-quality DEM products. The increasing deployment of satellites has generated [...] Read more.
InSAR and optical techniques represent two principal approaches for the generation of large-scale Digital Elevation Models (DEMs). Due to the inherent limitations of each technology, a single data source is insufficient to produce high-quality DEM products. The increasing deployment of satellites has generated vast amounts of InSAR and optical DEM data, thereby providing opportunities to enhance the quality of final DEM products through the more effective utilization of the existing data. Previous research has established that complete DEMs generated by InSAR technology can be combined with optical DEMs to produce a fused DEM with enhanced accuracy and reduced noise. Traditional DEM fusion methods typically employ weighted averaging to compute the fusion results. Theoretically, if the weights are appropriately selected, the fusion outcome can be optimized. However, in practical scenarios, DEMs frequently lack prior information on weights, particularly precise weight data. To address this issue, this study adopts a fully connected artificial neural network for elevation fusion prediction. This approach represents an advancement over existing neural network models by integrating local elevation and terrain as input features and incorporating curvature as an additional terrain characteristic to enhance the representation of terrain features. We also investigate the impact of terrain factors and local terrain feature as training features on the fused elevation outputs. Finally, three representative study areas located in Oregon, USA, and Macao, China, were selected for empirical validation. The terrain data comprise InSAR DEM, AW3D30 DEM, and Lidar DEM. The results indicate that compared to traditional neural network methods, the proposed approach improves the Root-Mean-Squared Error (RMSE) ranges, from 5.0% to 12.3%, and the Normalized Median Absolute Deviation (NMAD) ranges, from 10.3% to 26.6%, in the test areas, thereby validating the effectiveness of the proposed method. Full article
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18 pages, 5388 KiB  
Article
Path Planning Algorithm of Orchard Fertilization Robot Based on Multi-Constrained Bessel Curve
by Fanxia Kong, Baixu Liu, Xin Han, Lili Yi, Haozheng Sun, Jie Liu, Lei Liu and Yubin Lan
Agriculture 2024, 14(7), 979; https://doi.org/10.3390/agriculture14070979 - 24 Jun 2024
Cited by 3 | Viewed by 1366
Abstract
Path planning is the core problem of orchard fertilization robots during their operation. The traditional full-coverage job path planning algorithm has problems, such as being not smooth enough and having a large curvature fluctuation, that lead to unsteady running and low working efficiency [...] Read more.
Path planning is the core problem of orchard fertilization robots during their operation. The traditional full-coverage job path planning algorithm has problems, such as being not smooth enough and having a large curvature fluctuation, that lead to unsteady running and low working efficiency of robot trajectory tracking. To solve the above problems, an improved A* path planning algorithm based on a multi-constraint Bessel curve is proposed. First, by improving the traditional A* algorithm, the orchard operation path can be fully covered by adding guide points. Second, according to the differential vehicle kinematics model of the orchard fertilization robot, the robot kinematics constraint is combined with a Bessel curve to smooth the turning path of the A* algorithm, and the global path meeting the driving requirements of the orchard fertilization robot is generated by comprehensively considering multiple constraints such as the minimum turning radius and continuous curvature. Finally, the pure tracking algorithm is used to carry out tracking experiments to verify the robot’s driving accuracy. The simulation and experimental results show that the maximum curvature of the planned trajectory is 0.67, which meets the autonomous operation requirements of the orchard fertilization robot. When tracking the linear path in the fertilization area, the average transverse deviation is 0.0157 m, and the maximum transverse deviation is 0.0457 m. When tracking the U-turn path, the average absolute transverse deviation is 0.1081 m, and the maximum transverse deviation is 0.1768 m, which meets the autonomous operation requirements of orchard fertilization robots. Full article
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28 pages, 11502 KiB  
Article
Bathymetric Modelling of High Mountain Tropical Lakes of Southern Ecuador
by Raúl F. Vázquez, Pablo V. Mosquera and Henrietta Hampel
Water 2024, 16(8), 1142; https://doi.org/10.3390/w16081142 - 18 Apr 2024
Cited by 4 | Viewed by 2179
Abstract
Very little is known on high mountain tropical lakes of South America. Thus, the main motivation of this research was obtaining base bathymetric data of 119 tropical lakes of the Cajas National Park (CNP), Ecuador, that could be used in future geomorphological studies. [...] Read more.
Very little is known on high mountain tropical lakes of South America. Thus, the main motivation of this research was obtaining base bathymetric data of 119 tropical lakes of the Cajas National Park (CNP), Ecuador, that could be used in future geomorphological studies. Eleven interpolation methods were applied with the intention of selecting the best one for processing the scattered observations that were collected with a low-cost fishing echo-sounder. A split-sample (SS) test was used and repeated several times considering different proportions of available observations, selected randomly, for training of the interpolation methods and accuracy evaluation of the respective products. This accuracy was assessed through the use of empirical exceedance probability distributions of the mean absolute error (MAE). A single best interpolation method could not be identified. Instead, the study suggested six better-performing methods, including the complex methods Kriging (ordinary), minimum curvature (spline), multiquadric, and TIN with linear interpolation but also the much simpler methods natural neighbour and nearest neighbour. A sensitivity analysis (SA), considering several data error magnitudes, confirmed this. This advocated that sophisticated interpolation methods do not always produce the best products as geomorphological characteristics of the study site(s) together with observation data characteristics are likely to play important roles in their performance. As such, this type of assessment should be carried out in any terrestrial mapping of bathymetry that is based on the interpolation of scattered observations. Upon the analysis of the relative hypsometric curves of the 119 study lakes, they were classified into three average form categories: convex, concave, and mixed. The separated accuracy analysis of these three groups of lakes did not help in identifying a single best method. Finally, the interpolated bathymetries of 114 of the study lakes were incorporated into the best DEM of the study site by equalising their elevation reference systems. It is believed that the resulting enhanced DEM could be a very useful tool for a more appropriate management of these very beautiful but fragile high mountain tropical lakes. Full article
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13 pages, 2999 KiB  
Article
Effect of Sex, Age, and Cardiovascular Risk Factors on Aortoiliac Segment Geometry
by Ádám Szőnyi, György Balázs, Balázs Bence Nyárády, Márton Philippovich, Tamás Horváth and Edit Dósa
J. Clin. Med. 2024, 13(6), 1705; https://doi.org/10.3390/jcm13061705 - 15 Mar 2024
Cited by 1 | Viewed by 1402
Abstract
Background: To investigate the geometry of the aortoiliac (AI) segment and its correlation with sex, age, and cardiovascular (CV) risk factors. Methods: Abdominal and pelvic CTA/MRA scans of 204 subjects (120 males; median age: 53 [IQR, 27–75] years) without AI steno-occlusive disease or [...] Read more.
Background: To investigate the geometry of the aortoiliac (AI) segment and its correlation with sex, age, and cardiovascular (CV) risk factors. Methods: Abdominal and pelvic CTA/MRA scans of 204 subjects (120 males; median age: 53 [IQR, 27–75] years) without AI steno-occlusive disease or scoliosis were retrospectively analyzed. The participants were enrolled consecutively, ensuring the representation of each age decade. An in-house written software was developed to assess AI elongation using the tortuosity index (TI) and absolute average curvature (AAC). Aortic bifurcation angle, common iliac artery (CIA) take-off and planarity angles, bifurcation asymmetry, and deviation from optimal bifurcation were calculated and evaluated. Demographic data, CV risk factors, and medical history were collected from electronic health records. Results: The elongation of the iliac arteries was more pronounced in males (TI: left CIA, p = 0.011; left EIA, p < 0.001; right CIA, p = 0.023; right EIA, p < 0.001; AAC: left EIA, p < 0.001; right EIA, p = 0.001). Age significantly influenced TI and AAC in all AI segments (all p < 0.001), but was also positively associated with the aortic bifurcation angle (p < 0.001), both CIA planarities (left, p < 0.001; right, p = 0.002), aortic bifurcation asymmetry (p = 0.001), and radius discrepancy (p < 0.001). Significant positive correlations were found between infrarenal aortic TI/AAC and chronic kidney disease (CKD) (p = 0.027 and p = 0.016), AAC of both CIAs and hypertension (left, p = 0.027; right, p = 0.012), right CIA take-off angle and CKD (p = 0.031), and left CIA planarity and hyperlipidemia (p = 0.006). Conclusion: Sex, age, and CV risk factors have a significant effect on the geometry of the AI segment. Full article
(This article belongs to the Section Vascular Medicine)
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15 pages, 2417 KiB  
Article
Computational Flow Diverter Implantation—A Comparative Study on Pre-Interventional Simulation and Post-Interventional Device Positioning for a Novel Blood Flow Modulator
by Maximilian Thormann, Janneck Stahl, Laurel Marsh, Sylvia Saalfeld, Nele Sillis, Andreas Ding, Anastasios Mpotsaris, Philipp Berg and Daniel Behme
Fluids 2024, 9(3), 55; https://doi.org/10.3390/fluids9030055 - 23 Feb 2024
Cited by 1 | Viewed by 2951
Abstract
Due to their effect on aneurysm hemodynamics, flow diverters (FD) have become a routine endovascular therapy for intracranial aneurysms. Since over- and undersizing affect the device’s hemodynamic abilities, selecting the correct device diameter and accurately simulating FD placement can improve patient-specific outcomes. The [...] Read more.
Due to their effect on aneurysm hemodynamics, flow diverters (FD) have become a routine endovascular therapy for intracranial aneurysms. Since over- and undersizing affect the device’s hemodynamic abilities, selecting the correct device diameter and accurately simulating FD placement can improve patient-specific outcomes. The purpose of this study was to validate the accuracy of virtual flow diverter deployments in the novel Derivo® 2 device. We retrospectively analyzed blood flows in ten FD placements for which 3D DSA datasets were available pre- and post-intervention. All patients were treated with a second-generation FD Derivo® 2 (Acandis GmbH, Pforzheim, Germany) and post-interventional datasets were compared to virtual FD deployment at the implanted position for implanted stent length, stent diameters, and curvature analysis using ANKYRAS (Galgo Medical, Barcelona, Spain). Image-based blood flow simulations of pre- and post-interventional configurations were conducted. The mean length of implanted FD was 32.61 (±11.18 mm). Overall, ANKYRAS prediction was good with an average deviation of 8.4% (±5.8%) with a mean absolute difference in stent length of 3.13 mm. There was a difference of 0.24 mm in stent diameter amplitude toward ANKYRAS simulation. In vessels exhibiting a high degree of curvature, however, relevant differences between simulated and real-patient data were observed. The intrasaccular blood flow activity represented by the wall shear stress was qualitatively reduced in all cases. Inflow velocity decreased and the pulsatility over the cardiac cycle was weakened. Virtual stenting is an accurate tool for FD positioning, which may help facilitate flow FDs’ individualization and assess their hemodynamic impact. Challenges posed by complex vessel anatomy and high curvatures must be addressed. Full article
(This article belongs to the Special Issue Advances in Hemodynamics and Related Biological Flows)
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12 pages, 2223 KiB  
Article
A Neural Network Approach for Inertial Measurement Unit-Based Estimation of Three-Dimensional Spinal Curvature
by T. H. Alex Mak, Ruixin Liang, T. W. Chim and Joanne Yip
Sensors 2023, 23(13), 6122; https://doi.org/10.3390/s23136122 - 3 Jul 2023
Cited by 3 | Viewed by 2182
Abstract
The spine is an important part of the human body. Thus, its curvature and shape are closely monitored, and treatment is required if abnormalities are detected. However, the current method of spinal examination mostly relies on two-dimensional static imaging, which does not provide [...] Read more.
The spine is an important part of the human body. Thus, its curvature and shape are closely monitored, and treatment is required if abnormalities are detected. However, the current method of spinal examination mostly relies on two-dimensional static imaging, which does not provide real-time information on dynamic spinal behaviour. Therefore, this study explored an easier and more efficient method based on machine learning and sensors to determine the curvature of the spine. Fifteen participants were recruited and performed tests to generate data for training a neural network. This estimated the spinal curvature from the readings of three inertial measurement units and had an average absolute error of 0.261161 cm. Full article
(This article belongs to the Section Biomedical Sensors)
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18 pages, 5494 KiB  
Article
Estimating Body Weight in Captive Rabbits Based on Improved Mask RCNN
by Enze Duan, Hongyun Hao, Shida Zhao, Hongying Wang and Zongchun Bai
Agriculture 2023, 13(4), 791; https://doi.org/10.3390/agriculture13040791 - 30 Mar 2023
Cited by 6 | Viewed by 2391
Abstract
Automated body weight (BW) estimation is an important indicator to reflect the automation level of breeding, which can effectively reduce the damage to animals in the breeding process. In order to manage meat rabbits accurately, reduce the frequency of manual intervention, and improve [...] Read more.
Automated body weight (BW) estimation is an important indicator to reflect the automation level of breeding, which can effectively reduce the damage to animals in the breeding process. In order to manage meat rabbits accurately, reduce the frequency of manual intervention, and improve the intelligent of meat rabbit breeding, this study constructed a meat rabbit weight estimation system to replace manual weighing. The system consists of a meat rabbit image acquisition robot and a weight estimation model. The robot stops at each cage in turn and takes a top view of the rabbit through an RGB camera. The images from the robot are automatically processed in the weight estimation model, which consists of the meat rabbit segmentation network based on improved Mask RCNN and the BW fitting network. Attention mechanism, PointRend algorithm, and improved activation function are proposed to improve the performance of Mask RCNN. Six morphological parameters (relative projected area, contour perimeter, body length, body width, skeleton length, and curvature) are extracted from the obtained mask, and are sent into the BW fitting network based on SVR-SSA-BPNN. The experiment shows that the system achieves a 4.3% relative error and 172.7 g average absolute error in BW estimation for 441 rabbits, while the meat rabbit segmentation network achieves a 99.1% mean average precision (mAP) and a 98.7% mean pixel accuracy (MPA). The system provides technical support for automatic BW estimation of meat rabbits in commercial breeding, which is helpful to promote precision breeding. Full article
(This article belongs to the Special Issue Artificial Intelligence in Livestock Farming)
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14 pages, 4237 KiB  
Article
Research on Posture Sensing and Error Elimination for Soft Manipulator Using FBG Sensors
by Wenyu Li, Yanlin He, Peng Geng and Yi Yang
Electronics 2023, 12(6), 1476; https://doi.org/10.3390/electronics12061476 - 21 Mar 2023
Cited by 3 | Viewed by 1970
Abstract
Fiber-optic sensors are highly promising within soft robot sensing applications, but sensing methods based on geometry-based reconstruction limit the sensing capability and range. In this study, a fiber-optic sensor with a different deployment strategy for indirect sensing to monitor the outside posture of [...] Read more.
Fiber-optic sensors are highly promising within soft robot sensing applications, but sensing methods based on geometry-based reconstruction limit the sensing capability and range. In this study, a fiber-optic sensor with a different deployment strategy for indirect sensing to monitor the outside posture of a soft manipulator is presented. The internal support structure’s curvature was measured using the FBG sensor, and its mapping to the external pose was then modelled using a modified LSTM network. The error was assumed to follow the Gaussian distribution in the LSTM neural network and was rectified by maximum likelihood estimation to address the issue of noise generated during the deformation transfer and curvature sensing of the soft structure. For the soft manipulator, the network model’s sensing performance was demonstrated. The proposed method’s average absolute error for posture sensing was 63.3% lower than the error before optimization, and the root mean square error was 56.9% lower than the error before optimization. The comparison results between the experiment and the simulation demonstrate the viability of the indirect measurement of the soft structure posture using FBG sensors based on the data-driven method, as well as the significant impact of the error optimization method based on the Gaussian distribution assumption. Full article
(This article belongs to the Special Issue Advanced Wearable/Flexible Devices and Systems in Bioelectronics)
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25 pages, 9451 KiB  
Article
Multilayer Perceptron and Their Comparison with Two Nature-Inspired Hybrid Techniques of Biogeography-Based Optimization (BBO) and Backtracking Search Algorithm (BSA) for Assessment of Landslide Susceptibility
by Hossein Moayedi, Peren Jerfi Canatalay, Atefeh Ahmadi Dehrashid, Mehmet Akif Cifci, Marjan Salari and Binh Nguyen Le
Land 2023, 12(1), 242; https://doi.org/10.3390/land12010242 - 12 Jan 2023
Cited by 25 | Viewed by 3344
Abstract
Regarding evaluating disaster risks in Iran’s West Kurdistan area, the multi-layer perceptron (MLP) neural network was upgraded with two novel techniques: backtracking search algorithm (BSA) and biogeography-based optimization (BBO). Utilizing 16 landslide conditioning elements such as elevation (aspect), plan (curve), profile (curvature), geology, [...] Read more.
Regarding evaluating disaster risks in Iran’s West Kurdistan area, the multi-layer perceptron (MLP) neural network was upgraded with two novel techniques: backtracking search algorithm (BSA) and biogeography-based optimization (BBO). Utilizing 16 landslide conditioning elements such as elevation (aspect), plan (curve), profile (curvature), geology, NDVI (land use), slope (degree), stream power index (SPI), topographic wetness index (TWI), rainfall, and sediment transport index (STI), and 504 landslides as target variables, a large geographic database is constructed. Applying the techniques mentioned above to the synthesis of the MLP results in the suggested BBO-MLP and BSA-MLP ensembles. As accuracy standards, we benefit from mean absolute error, mean square error, and area under the receiving operating characteristic curve to assess the utilized models, we have also designed a scoring system. The MLP’s accuracy increases thanks to the application of the BBO and BSA algorithms. Comparing the BBO with the BSA, we find that the former achieves higher average MLP optimization ranks (20, 15, and 14). A further finding showed that the BBO is superior to the BSA at maximizing the MLP. Full article
(This article belongs to the Special Issue Remote Sensing Application in Landslide Detection and Assessment)
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13 pages, 1871 KiB  
Article
Investigation of the Gas-Phase Reaction of Nopinone with OH Radicals: Experimental and Theoretical Study
by Gisèle El Dib, Angappan Mano Priya and Senthilkumar Lakshmipathi
Atmosphere 2022, 13(8), 1247; https://doi.org/10.3390/atmos13081247 - 5 Aug 2022
Cited by 6 | Viewed by 2323
Abstract
Monoterpenes are the most essential reactive biogenic volatile organic compounds. Their removal from the atmosphere leads to the formation of oxygenated compounds, such as nopinone (C9H14O), one of the most important first-generation β-pinene oxidation products that play a pivotal [...] Read more.
Monoterpenes are the most essential reactive biogenic volatile organic compounds. Their removal from the atmosphere leads to the formation of oxygenated compounds, such as nopinone (C9H14O), one of the most important first-generation β-pinene oxidation products that play a pivotal role in environmental and biological applications. In this study, experimental and theoretical rate coefficients were determined for the gas-phase reaction of nopinone with hydroxyl radicals (OH). The absolute rate coefficient was measured for the first time using a cryogenically cooled cell along with the pulsed laser photolysis–laser-induced fluorescence technique at 298 K and 7 Torr. The hydrogen abstraction pathways were found by using electronic structure calculations to determine the most favourable H-abstraction position. Pathway 5 (bridgehead position) was more favourable, with a small barrier height of −1.23 kcal/mol. The rate coefficients were calculated based on the canonical variational transition state theory with the small-curvature tunnelling method (CVT/SCT) as a function of temperature. The average experimental rate coefficient (1.74 × 10−11 cm3 molecule−1 s−1) was in good agreement with the theoretical value (2.2 × 10−11 cm3 molecule−1 s−1). Conclusively, the results of this study pave the way to understand the atmospheric chemistry of nopinone with OH radicals. Full article
(This article belongs to the Special Issue Measurements and Chemistry of Atmospheric Radical)
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26 pages, 6021 KiB  
Article
LiDAR-Inertial-GNSS Fusion Positioning System in Urban Environment: Local Accurate Registration and Global Drift-Free
by Xuan He, Shuguo Pan, Wang Gao and Xinyu Lu
Remote Sens. 2022, 14(9), 2104; https://doi.org/10.3390/rs14092104 - 27 Apr 2022
Cited by 18 | Viewed by 4039
Abstract
Aiming at the insufficient accuracy and accumulated error of the point cloud registration of LiDAR-inertial odometry (LIO) in an urban environment, we propose a LiDAR-inertial-GNSS fusion positioning algorithm based on voxelized accurate registration. Firstly, a voxelized point cloud downsampling method based on curvature [...] Read more.
Aiming at the insufficient accuracy and accumulated error of the point cloud registration of LiDAR-inertial odometry (LIO) in an urban environment, we propose a LiDAR-inertial-GNSS fusion positioning algorithm based on voxelized accurate registration. Firstly, a voxelized point cloud downsampling method based on curvature segmentation is proposed. Rough classification is carried out by the curvature threshold, and the voxelized point cloud downsampling is performed using HashMap instead of a random sample consensus algorithm. Secondly, a point cloud registration model based on the nearest neighbors of the point and neighborhood point sets is constructed. Furthermore, an iterative termination threshold is set to reduce the probability of the local optimal solution. The registration time of a single frame point cloud is increased by an order of magnitude. Finally, we propose a LIO-GNSS fusion positioning model based on graph optimization that uses GNSS observations weighted by confidence to globally correct local drift. The experimental results show that the average root mean square error of the absolute trajectory error of our algorithm is 1.58m on average in a large-scale outdoor environment, which is approximately 83.5% higher than that of similar algorithms. It is fully proved that our algorithm can realize a more continuous and accurate position and attitude estimation and map reconstruction in urban environments. Full article
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