Accurate Height Research Articles

A Calculating Method for the Height of Multi-Type Buildings Based on 3D Point Cloud

Building height is a critical variable in urban studies, and the automated acquisition of the precise building height is essential for intelligent construction, safety, and the sustainable development of cities. The building height is often approximated by the building’s highest point. However, the calculation method of the building height of the various roof types differs according to building codes, making it challenging to accurately calculate the height of buildings with complex roof structures or multiple upper appendages. Consequently, this paper utilizes point clouds to propose an automated method for calculating building heights conforming to design codes. The model considers roof types and allows for fast, automated, and highly accurate building height estimation. First, roofs are extracted from the point cloud by combining normal vector density clustering with a region-growing algorithm. Second, combined with variational Bayes, a Gaussian mixture model is employed to segment the roof surfaces. Finally, roofs are classified based on slope characteristics, achieving the automatic acquisition of building heights for various roof types over large areas. Experiments were conducted on Vaihingen and STPLS3D datasets. In the Vaihingen area, the maximum error, root-mean-square-error (RMSE), and mean absolute error (MAE) of the measured heights are 1.92 cm, 1.18 cm, and 1.13 cm, respectively. In the STPLS3D area, these values are 1.79 cm, 0.82 cm, and 0.68 cm, respectively. The results demonstrate that the proposed method is reliable and effective, which offers valuable data for the development, construction, and planning of three-dimensional (3D) cities.

CDP-MVS: Forest Multi-View Reconstruction with Enhanced Confidence-Guided Dynamic Domain Propagation

Using multi-view images of forest plots to reconstruct dense point clouds and extract individual tree parameters enables rapid, high-precision, and cost-effective forest plot surveys. However, images captured at close range face challenges in forest reconstruction, such as unclear canopy reconstruction, prolonged reconstruction times, insufficient accuracy, and issues with tree duplication. To address these challenges, this paper introduces a new image dataset creation process that enhances both the efficiency and quality of image acquisition. Additionally, a block-matching-based multi-view reconstruction algorithm, Forest Multi-View Reconstruction with Enhanced Confidence-Guided Dynamic Domain Propagation (CDP-MVS), is proposed. The CDP-MVS algorithm addresses the issue of canopy and sky mixing in reconstructed point clouds by segmenting the sky in the depth maps and setting its depth value to zero. Furthermore, the algorithm introduces a confidence calculation method that comprehensively evaluates multiple aspects. Moreover, CDP-MVS employs a decentralized dynamic domain propagation sampling strategy, guiding the propagation of the dynamic domain through newly defined confidence measures. Finally, this paper compares the reconstruction results and individual tree parameters of the CDP-MVS, ACMMP, and PatchMatchNet algorithms using self-collected data. Visualization results show that, compared to the other two algorithms, CDP-MVS produces the least sky noise in tree reconstructions, with the clearest and most detailed canopy branches and trunk sections. In terms of parameter metrics, CDP-MVS achieved 100% accuracy in reconstructing tree quantities across the four plots, effectively avoiding tree duplication. The accuracy of breast diameter extraction values of point clouds reconstructed by CDPMVS reached 96.27%, 90%, 90.64%, and 93.62%, respectively, in the four sample plots. The positional deviation of reconstructed trees, compared to ACMMP, was reduced by 0.37 m, 0.07 m, 0.18 m and 0.33 m, with the average distance deviation across the four plots converging within 0.25 m. In terms of reconstruction efficiency, CDP-MVS completed the reconstruction of the four plots in 1.8 to 3.1 h, reducing the average reconstruction time per plot by six minutes compared to ACMMP and by two to three times compared to PatchMatchNet. Finally, the differences in tree height accuracy among the point clouds reconstructed by the different algorithms were minimal. The experimental results demonstrate that CDP-MVS, as a multi-view reconstruction algorithm tailored for forest reconstruction, shows promising application potential and can provide valuable support for forestry surveys.

Integration of very high-resolution stereo satellite images and airborne or satellite Lidar for Eucalyptus canopy height estimation

Eucalyptus plantations cover extensive areas in tropical regions and require accurate growth monitoring for efficient management. Traditional in-situ measurements, while necessary, are labor-intensive and impractical for large-scale assessments. Very high-resolution satellite stereo imagery is playing an increasingly important role in the estimation of fine Digital Surface Models (DSMs) across landscapes. However, its ability to estimate canopy height models (CHMs) has not been widely investigated. This study investigates the integration of high-resolution satellite stereo imagery from the Pleiades sensor with airborne or satellite Lidar data to estimate canopy height over eucalyptus plantations. Two study sites were selected in Brazil, representing flat and semi-mountainous topographies, Mato Grosso do Sul (MS) and Sao Paulo (SP), respectively. Digital Surface Models generated from Pleiades images (DSMP) were combined with Digital Terrain Models extracted from airborne Lidar data (DTMALS) to create Canopy Height Models (CHMALS). The evaluation of the CHMALS was based on two in situ canopy height measurements (Hmax and Hmean). For the SP site, the CHMALSmax, which is the average height of top 10% pixel values within each plot, correlated well with in situ Hmean, which is the average height of 10 central trees (r = 0.98), showing a bias of 1.4 m, RMSE of 3.1 m, and rRMSE of 18.5%. At the MS site, CHMALSmax demonstrated a bias of 1.9 m, RMSE of 2.3 m, rRMSE of 17.3%, and r correlation of 0.92. Despite a tendency to underestimate heights below 20 m in young tree plantations with open canopy, the results indicate reliable canopy height estimation. The study also investigates the potential of Global Ecosystem Dynamics Investigation (GEDI) elevation data as an alternative to DTMALS in absence of airborne Lidar data. The resulting CHMGedi is promising but slightly less accurate than Lidar-based CHMs. The best GEDI-based CHM (CHMGedimax) showed a bias and rRMSE of 1.3 m and 20.5% for the SP site, and 2.2 m and 24.9% for the MS site. These findings highlight the potential for integrating Pleiades and Lidar data for efficient and accurate canopy height monitoring in eucalyptus plantations.

STFM: Accurate Spatio-Temporal Fusion Model for Weather Forecasting

Meteorological prediction is crucial for various sectors, including agriculture, navigation, daily life, disaster prevention, and scientific research. However, traditional numerical weather prediction (NWP) models are constrained by their high computational resource requirements, while the accuracy of deep learning models remains suboptimal. In response to these challenges, we propose a novel deep learning-based model, the Spatiotemporal Fusion Model (STFM), designed to enhance the accuracy of meteorological predictions. Our model leverages Fifth-Generation ECMWF Reanalysis (ERA5) data and introduces two key components: a spatiotemporal encoder module and a spatiotemporal fusion module. The spatiotemporal encoder integrates the strengths of convolutional neural networks (CNNs) and recurrent neural networks (RNNs), effectively capturing both spatial and temporal dependencies. Meanwhile, the spatiotemporal fusion module employs a dual attention mechanism, decomposing spatial attention into global static attention and channel dynamic attention. This approach ensures comprehensive extraction of spatial features from meteorological data. The combination of these modules significantly improves prediction performance. Experimental results demonstrate that STFM excels in extracting spatiotemporal features from reanalysis data, yielding predictions that closely align with observed values. In comparative studies, STFM outperformed other models, achieving a 7% improvement in ground and high-altitude temperature predictions, a 5% enhancement in the prediction of the u/v components of 10 m wind speed, and an increase in the accuracy of potential height and relative humidity predictions by 3% and 1%, respectively. This enhanced performance highlights STFM’s potential to advance the accuracy and reliability of meteorological forecasting.

Developmental Validation of the Microreader 23HS Plex ID System: A Novel Supplementary Non-CODIS STR Multiplex Assay for Forensic Application.

A novel supplementary non-CODIS STR multiplex assay designated as the "Microreader 23HS Plex ID System" was developed. The Microreader 23HS Plex ID System enables simultaneous profiling of 23 STR loci and the amelogenin locus. The majority of these loci are non-CODIS STRs (D4S2408, D9S2157, D20S161, D3S2459, D18S1364, D13S305, D1S2142, D19S400, D6S1017, D7S1517, D2S1776, D2S1360, D3S1744, D16S3391, D3S1545, D11S4463, D20S85, D1S549, D10S2325, D21S2055), with the exception of three CODIS STRs (D2S441, D12S391, and D22S1045). Followed the recommendations of Scientific Working Group on DNA Analysis Methods (SWGDAM) and the Chinese validation standards, a comprehensive set of validation studies were conducted, encompassing PCR conditions, stutter ratio and peak height balance, sensitivity, precision and accuracy, reproducibility, species specificity, inhibition, as well as mixture testing. The results demonstrated that the Microreader 23HS Plex ID System is a reliable and robust assay, with well-balanced peak heights, high precision and accuracy, species specificity, and resistance to common inhibitors. The sensitivity of the assay was determined to be 0.125ng of template DNA. In mixture study, all minor alleles were detected in two-sample mixtures across various ratios (1:19, 1:9, 1:4, 3:7, 2:3, 1:1, 3:2, 4:1, 9:1, and 19:1). In population study, a total of 500 unrelated individuals of Han ethnicity from East China were genotyped. The allele frequencies and forensic population genetic parameters were calculated, with a cumulative random match probability of 7.757×10-27, and a total power of discrimination exceeding 0.999,999,999,999,999,999,999,999,99. In conclusion, the Microreader 23HS Plex ID System shows promise as a valuable supplementary tool for forensic applications, particularly in addressing complex kinship testing and challenges posed by STR mutation.

Effects of wind turbine dimensions on the collision risk of raptors: A simulation approach based on flight height distributions

Wind energy development is a key component of climate change mitigation. However, birds collide with wind turbines, and this additional mortality may negatively impact populations. Collision risk could be reduced by informed selection of turbine dimensions, but the effects of turbine dimensions are still unknown for many species.As analyses of mortality data have several limitations, we applied a simulation approach based on flight height distributions of six European raptor species. To obtain accurate flight height data, we used high-frequency GPS tracking (GPS tags deployed on 275 individuals). The effects of ground clearance and rotor diameter of wind turbines on collision risk were studied using the Band collision risk model.Five species had a unimodal flight height distribution, with a mode below 25 m above ground level, while Short-toed Eagle showed a more uniform distribution with a weak mode between 120 and 260 m. The proportion of positions within 32–200 m ranged from 11 % in Marsh Harrier to 54 % in Red Kite.With increasing ground clearance (from 20 to 100 m), collision risk decreased in the species with low mode (−56 to −66 %), but increased in Short-toed Eagle (+38 %). With increasing rotor diameter (from 50 to 160 m) at fixed ground clearance, the collision risk per turbine increased in all species (+151 to +558 %), while the collision risk per MW decreased in the species with low mode (−50 % to −57 %).These results underpin that wind turbine dimensions can have substantial effects on the collision risk of raptors. As the effects varied between species, wind energy planning should consider the composition of the local bird community to optimise wind turbine dimensions. For species with a low mode of flight height, the collision risk for a given total power capacity could be reduced by increasing ground clearance, and using fewer turbines with larger diameter.

View Factors Approach for Bifacial Photovoltaic Array Modeling: Bifacial Gain Sensitivity Analysis

Abstract Bifacial modules are highly valued in the global photovoltaic market since they are able to receive sunlight from both sides and can generate up to 10–30% additional energy compared to monofacial ones. They are integrated into various sectors, including building and notably Agrivoltaics. In this work, a coupled optical–thermal–electrical model was developed in matlab to simulate the energy performance and bifacial gain for a ground-raised bifacial system. The model allows optimization as a function of the photovoltaic (PV) system and bifacial module characteristics. A fixed, south-facing bifacial PV arrays composed of three rows assumed installed in Agadir, Morocco, is considered. The optical model is based on an analytical determination of view factors using the cross-string rule. These enable the determination of irradiances received by both sides of modules, which are subsequently used to evaluate energy yield of the system. Model validation was carried out based on various statistical metrics by comparing our results with simulation results generated by various softwares. The comparison shows a very good agreement, notably with 3dbifacialvf (R2 = 1, root-mean-square error (RMSE) = 0.03 W/m2 and R2 = 0.96, RMSE = 12.4 W/m2) for front and rear side irradiance, respectively. The DC energy generated by the system differs by less than 1% compared to pvsyst results. Sensitivity analysis revealed that all system parameters, particularly ground albedo, positively affect the bifacial gain. The bifacial gain increased from 10.6% to 20.1% as the ground albedo increased from 0.25 to 0.5.

A machine learning approach to nearshore wave modeling in large lakes using land-based wind observations

This study presents a novel machine learning-based (ML) framework that utilizes the ConvLSTM-1D model to simulate wave heights at a nearshore location in Lake Michigan using a nonuniform land-based array of wind observations from a broad geographic region as an input. This approach was applied to Lake Michigan to perform a 70-year ice-free hindcast of waves near Chicago, IL (USA). Because of annual winter gaps in Great Lakes buoy observations, output from the Wave Information System model (WIS) was used for the training, validation, and testing of the ML model. Models with different numbers of wind stations were tested, showing that a single wind station as an input feature produced a reasonably accurate wave height. However, the wave height model accuracy increased as more wind input data was included from around the lake, largely plateauing beyond the inclusion of four stations that spanned Lake Michigan’s southern basin. The optimized model lookback period was found to be 10 h for all models, suggestive of a fetch-limited temporal coupling between the wind observations and nearshore waves. The extended time series showed a statistically significant increase in the interannual standard deviation of the storm wave height and the storm height over the mean wave height while no correlation was found with the annual water level time series. The ML framework offers a promising avenue for utilizing historical wind records to extend wave height time series for nearshore locations, particularly in enclosed and semi-enclosed basins where waves are strongly linked to local winds.

SA-SatMVS: Slope Feature-Aware and Across-Scale Information Integration for Large-Scale Earth Terrain Multi-View Stereo

Satellite multi-view stereo (MVS) is a fundamental task in large-scale Earth surface reconstruction. Recently, learning-based multi-view stereo methods have shown promising results in this field. However, these methods are mainly developed by transferring the general learning-based MVS framework to satellite imagery, which lacks consideration of the specific terrain features of the Earth’s surface and results in inadequate accuracy. In addition, mainstream learning-based methods mainly use equal height interval partition, which insufficiently utilizes the height hypothesis surface, resulting in inaccurate height estimation. To address these challenges, we propose an end-to-end terrain feature-aware height estimation network named SA-SatMVS for large-scale Earth surface multi-view stereo, which integrates information across different scales. Firstly, we transform the Sobel operator into slope feature-aware kernels to extract terrain features, and a dual encoder–decoder architecture with residual blocks is applied to incorporate slope information and geometric structural characteristics to guide the reconstruction process. Secondly, we introduce a pixel-wise unequal interval partition method using a Laplacian distribution based on the probability volume obtained from other scales, resulting in more accurate height hypotheses for height estimation. Thirdly, we apply an adaptive spatial feature extraction network to search for the optimal fusion method for feature maps at different scales. Extensive experiments on the WHU-TLC dataset also demonstrate that our proposed model achieves the best MAE metric of 1.875 and an RMSE metric of 3.785, which constitutes a state-of-the-art performance.

Three-Dimensional Reconstruction of Forest Scenes with Tree–Shrub–Grass Structure Using Airborne LiDAR Point Cloud

Fine three-dimensional (3D) reconstruction of real forest scenes can provide a reference for forestry digitization and forestry resource management applications. Airborne LiDAR technology can provide valuable data for large-area forest scene reconstruction. This paper proposes a 3D reconstruction method for complex forest scenes with trees, shrubs, and grass, based on airborne LiDAR point clouds. First, forest vertical distribution characteristics are used to segment tree, shrub, and ground–grass points from an airborne LiDAR point cloud. For ground–grass points, a ground–grass grid model is constructed. For tree points, a method based on hierarchical canopy point fitting is proposed to construct a trunk model, and a crown model is constructed with the 3D α-shape algorithm. For shrub points, a shrub model is directly constructed based on the 3D α-shape algorithm. Finally, tree, shrub, and ground–grass models are spatially combined to achieve the reconstruction of real forest scenes. Taking six forest plots located in Hebei, Yunnan, and Guangxi provinces in China and Baden-Württemberg in Germany as study areas, experimental results show that the accuracy of individual tree segmentation reaches 87.32%, the accuracy of shrub segmentation reaches 60.00%, the height accuracy of the grass model is evaluated with an RMSE < 0.15 m, the volume accuracy of shrub and tree models is assessed with R2 > 0.848 and R2 > 0.904, respectively. Furthermore, we compared the model constructed in this study with simplified point cloud and voxel models. The results demonstrate that the proposed modeling approach can meet the demand for the high-accuracy and lightweight modeling of large-area forest scenes.

Simulating film boiling with sharp interface and direct calculation of mass transfer to investigate the hydrodynamic and thermal transport phenomena near the interface

The current study presents a computational fluid dynamics (CFD) model for film boiling using two fluids, including liquid nitrogen. Ansys Fluent was customized using user-defined functions (UDFs) to accurately model thermal and fluid dynamic interactions. The UDFs accommodate mass transfer at the liquid-vapor interface, maintaining a sharp interface by accounting for the saturation temperature. Direct heat and mass transfer calculations are implemented rather than relying on empirical correlations. Mesh analysis was performed with grid sizes of 615, 410, 307, and 123 μm, with the 307 μm grid selected for its agreement with average Nusselt number values and accurate bubble shape and height. Verification using the Stefan problem showed a maximum error of 0.45 % for interface displacement and 0.1 % for temperature distribution. Validation against the Berenson correlation demonstrated good agreement in Nusselt number values. Qualitative validation of the developed method is performed using the shapes of the growing bubble. The results obtained show that the sharp interface is preserved throughout the simulation, with temperature contours showing significant variations during bubble growth and departure. Local heat transfer coefficient analysis identified peak values of 2170 W/m²-K at locations of minimal film thickness. Velocity vector analysis revealed velocities of 2 m/s in the vapor phase, influencing bubble shape during growth and departure. The study highlights the impact of thermal film thickness on heat transfer, with the highest Nusselt number of 5.69 observed at a film thickness of 0.5 mm. The results demonstrate the application of VOF sharp interface tracking and direct calculation of interfacial conditions utilizing customized Ansys Fluent in the modeling of film boiling as a way to understand the fundamental aspects of the fluid and thermal behavior.

Fine spatial scale assessment of structure and configuration of vegetation cover for northern bobwhites in grazed pastures

BackgroundMonitoring forage in livestock operations is critical to sustainable rangeland management of soil and ecological processes that provide both livestock and wildlife habitat. Traditional ground-based sampling methods have been widely used and provide valuable information; however, they are time-consuming, labor-intensive, and limited in their ability to capture larger extents of the spatial and temporal dynamics of rangeland ecosystems. Drones provide a solution to collect data to larger extents than field-based methods and with higher-resolution than traditional remote sensing platforms. Our objectives were to (1) assess the accuracy of vegetation cover height in grasses using drones, (2) quantify the spatial distribution of vegetation cover height in grazed and non-grazed pastures during the dormant (fall–winter) and growing seasons (spring–summer), and (3) evaluate the spatial distribution of vegetation cover height as a proxy for northern bobwhite (Colinus virginianus) habitat in South Texas. We achieved this by very fine scale drone-derived imagery and using class level landscape metrics to assess vegetation cover height configuration.ResultsEstimated heights from drone imagery had a significant relationship with the field height measurements in September (r2 = 0.83; growing season) and February (r2 = 0.77; dormant season). Growing season pasture maintained residual landscape habitat configuration adequate for bobwhites throughout the fall and winter of 2022–2023 following grazing. Dormant season pasture had an increase in bare ground cover, and a shift from many large patches of tall herbaceous cover (40–120 cm) to few large patches of low herbaceous cover (5–30 cm) (p < 0.05).ConclusionsDrones provided high-resolution imagery that allowed us to assess the spatial and temporal changes of vertical herbaceous vegetation structure in a semi-arid rangeland subject to grazing. This study shows how drone imagery can be beneficial for wildlife conservation and management by providing insights into changes in fine-scale vegetation spatial and temporal heterogeneity from livestock grazing.

A full-morphology measurement method and system for tubing internal thread based on rotating-mirrored structured light vision

The major methods of tubing internal thread (TIT) measurement are low efficiency, poor accuracy and incomplete measurement. Therefore, a new full-morphology measurement method and system based on rotating-mirrored structured light vision (RMSLV) is proposed in this paper. Firstly, a measurement model of RMSLV is proposed, which establishes the mathematical relationship between two-dimensional (2D) pixel and three-dimensional (3D) topography data. Subsequently, a step-by-step calibration method for this model is put forward to accurately solve model parameters, so that the high-precision restoration from 2D images to intricate 3D full-morphology of TIT is realized. Finally, the measurement system is built, and the verification experiments are conducted. The results indicate that the system’s measurement accuracy of taper, pitch and tooth height is 6.61 times, 5.60 times and 4.48 times higher than traditional equipment, and the efficiency is exponentially improved. This study provides an effective means for highly precise and fast measurement of the TIT.

Improving GNSS-IR Sea Surface Height Accuracy Based on a New Ionospheric Stratified Elevation Angle Correction Model

Approximately 71% of the Earth’s surface is covered by vast oceans. With the exacerbation of global climate change, high-precision monitoring of sea surface height variations is of vital importance for constructing global ocean gravity fields and preventing natural disasters in the marine system. Global Navigation Satellite System Interferometry Reflectometry (GNSS-IR) sea surface altimetry is a method of inferring sea surface height based on the signal-to-noise ratio of satellite signals. It enables the retrieval of sea surface height variations with high precision. However, navigation satellite signals are influenced by the ionosphere during propagation, leading to deviations in the measured values of satellite elevation angles from their true values, which significantly affects the accuracy of GNSS-IR sea surface altimetry. Based on this, the contents of this paper are as follows: Firstly, a new ionospheric stratified elevation angle correction model (ISEACM) was developed by integrating the International Reference Ionosphere Model (IRI) and ray tracing methods. This model aims to improve the accuracy of GNSS-IR sea surface altimetry by correcting the ionospheric refraction effects on satellite elevation angles. Secondly, four GNSS stations (TAR0, PTLD, GOM1, and TPW2) were selected globally, and the corrected sea surface height values obtained using ISEACM were compared with observed values from tide gauge stations. The calculated average Root Mean Square Error (RMSE) and Pearson Correlation Coefficient (PCC) were 0.20 m and 0.83, respectively, indicating the effectiveness of ISEACM in sea surface height retrieval. Thirdly, a comparative analysis was conducted between sea surface height retrieval before and after correction using ISEACM. The optimal RMSE and PCC values with tide gauge station observations were 0.15 m and 0.90, respectively, representing a 20.00% improvement in RMSE and a 4.00% improvement in correlation coefficient compared to traditional GNSS-IR retrieval heights. These experimental results demonstrate that correction with ISEACM can effectively enhance the precision of GNSS-IR sea surface altimetry, which is crucial for accurate sea surface height measurements.

Precisely Tunable Instantaneous Carbon Rearrangement Enables Low-Working-Potential Hard Carbon Toward Sodium-Ion Batteries with Enhanced Energy Density.

As the preferred anode material for sodium-ion batteries, hard carbon (HC) confronts significant obstacles in providing a long and dominant low-voltage plateau to boost the output energy density of full batteries. The critical challenge lies in precisely enhancing the local graphitization degree to minimize Na+ ad-/chemisorption, while effectively controlling the growth of internal closed nanopores to maximize Na+ filling. Unfortunately, traditional high-temperature preparation methods struggle to achieve both objectives simultaneously. Herein, a transient sintering-involved kinetically-controlled synthesis strategy is proposed that enables the creation of metastable HCs with precisely tunable carbon phases and low discharge/charge voltage plateaus. By optimizing the temperature and width of thermal pulses, the high-throughput screened HCs are characterized by short-range ordered graphitic micro-domains that possess accurate crystallite width and height, as well as appropriately-sized closed nanopores. This advancement realizes HC anodes with significantly prolonged low-voltage plateaus below 0.1V, with the best sample exhibiting a high plateau capacity of up to 325mAhg-1. The energy density of the HC||Na3V2(PO4)3 full battery can therefore be increased by 20.7%. Machine learning study explicitly unveils the "carbon phase evolution-electrochemistry" relationship. This work promises disruptive changes to the synthesis, optimization, and commercialization of HC anodes for high-energy-density sodium-ion batteries.

Aerial Hybrid Adjustment of LiDAR Point Clouds, Frame Images, and Linear Pushbroom Images

In airborne surveying, light detection and ranging (LiDAR) strip adjustment and image bundle adjustment are customarily performed as separate processes. The bundle adjustment is usually conducted from frame images, while using linear pushbroom (LP) images in the bundle adjustment has been historically challenging due to the limited number of observations available to estimate the exterior image orientations. However, data from these three sensors conceptually provide information to estimate the same trajectory corrections, which is favorable for solving the problems of image depth estimation or the planimetric correction of LiDAR point clouds. Thus, our purpose with the presented study is to jointly estimate corrections to the trajectory and interior sensor states in a scalable hybrid adjustment between 3D LiDAR point clouds, 2D frame images, and 1D LP images. Trajectory preprocessing is performed before the low-frequency corrections are estimated for certain time steps in the following adjustment using cubic spline interpolation. Furthermore, the voxelization of the LiDAR data is used to robustly and efficiently form LiDAR observations and hybrid observations between the image tie-points and the LiDAR point cloud to be used in the adjustment. The method is successfully demonstrated with an experiment, showing the joint adjustment of data from the three different sensors using the same trajectory correction model with spline interpolation of the trajectory corrections. The results show that the choice of the trajectory segmentation time step is not critical. Furthermore, photogrammetric sub-pixel planimetric accuracy is achieved, and height accuracy on the order of mm is achieved for the LiDAR point cloud. This is the first time these three types of sensors with fundamentally different acquisition techniques have been integrated. The suggested methodology presents a joint adjustment of all sensor observations and lays the foundation for including additional sensors for kinematic mapping in the future.

Research on numerical modeling of two-dimensional freak waves and prediction of freak wave heights based on LSTM deep learning networks

Freak waves have posed a serious threat to the safety of marine structures. Thus, the accurate simulation and prediction of freak waves are crucial for maritime safety. This paper proposes a nonlinear numerical method based on phase modulation. This method achieves precise time and location simulation of two-dimensional freak waves, while preserving the key statistical characteristics of the wave sequence, thereby obtaining more accurate two-dimensional wave height information. Additionally, this paper constructs a Long Short-Term Memory (LSTM) deep neural network integrated with a Sequence-to-Sequence (seq2seq) framework for predicting sequences of freak wave heights. In this study, we conduct an in-depth predictive analysis of the experimental data for sequences of freak wave heights. And this paper provides a comprehensive assessment of the performance of Recurrent Neural Networks (RNNs), Gated Recurrent Units (GRUs), and Long Short-Term Memory (LSTM) networks in the task of freak wave prediction. The experimental results indicate that the seq2seq-LSTM model demonstrates significant performance advantages in predicting the wave heights of freak waves, particularly for long-term predictions. In summary, the numerical simulation method based on phase modulation and the improved seq2seq-LSTM deep learning model are of significant practical value for enhancing the safety of offshore platforms and ships.

3D-printed zirconia orthodontic brackets: Effect of printing method on dimensional accuracy.

This study investigated the effect of additive manufacturing (AM) methods on the slot height dimensions and accuracy of 3D-printed orthodontic brackets. A 3D model of a standard Mclaughlin Bennett Trevisi bracket was used as a reference to print the ceramic bracket in a 90° orientation using two representative AM methods: digital light processing (DLP) and material jetting (MJ). The dimensional accuracy and slot heights were determined using a scanning electron microscope and an optical scanner. Also, all specimens were analysed using the Geomagic Control X 3D inspection software. The root mean square (RMS) values were used for trueness and precision assessment. Statistical analyses were performed using an independent sample t-test. Slot height dimensions, trueness RMS, and precision RMS were statistically affected by different AM methods (p < .01). There was a significant difference between the different printing methods, with DLP meeting the tolerance requirements (mean slot height = 0.557 ± 0.018 mm) and MJ being slightly below them (mean slot height = 0.544 ± 0.021 mm). However, MJ significantly outperformed DLP in terms of accuracy. Among the two printing methods, MJ was associated with higher trueness (RMS = 0.025 ± 0.004 mm) and precision (RMS = 0.038 ± 0.005 mm). Both tested AM methods yielded clinically acceptable outcomes, with the RMS range set to ±100 μm and the slot height tolerance established at 0.549-0.569 mm. The MJ technology achieved the highest accuracy.

Hybrid framework of deep extreme learning machine (DELM) based on sparrow search algorithm for non-stationary wave prediction

Accurate and trustworthy wave height forecast is essential for the efficient exploitation of wave energy. However, the non-linearity and non-stationarity of waves present a challenge task when using the conventional statistical models in the ocean wave forecasting. Aiming at these problems, this paper proposes a short-term wave height prediction method based on the Variational Mode Decomposition (VMD), Deep Extreme Learning Machine (DELM) and Sparrow Search Algorithm (SSA). Specifically, the original wave height data is initially decomposed into numerous sub-series from high to low frequency by VMD technique. Subsequently, the SSA method is employed to optimize the DELM parameters to enhance the functionality of the basic DELM model. Three wave height datasets located in the North Pacific Ocean are employed as cases in this paper. In comparison with other benchmark models, the RMSE parameters of VMD-SSA-DELM model are reduced by 35.59%–73.33% even at 10-h lead time by analyzing different forecast durations, which demonstrate the proposed model's superiority in short-term wave height prediction.

Retrieval performance of mangrove tree heights using multiple machine learning regression models and UAV-LiDAR point clouds

ABSTRACT Mangroves are vital coastal ecosystems that provide crucial links between land and sea. Tree height is a key indicator for assessing mangroves’ health status. Currently, there are still numerous challenges in estimating mangrove tree height. In this study, multiple deep learning and shallow machine learning regression models were developed to accurately estimate mangrove tree height using multi-dimensional Light Detection and Ranging (LiDAR) point clouds and their derivatives. We constructed a novel CNN_RepMLP model for mangrove tree height mapping. We also further verified the applicability of different types of regression models in estimating mangrove tree heights, and explored the influence of different LiDAR-derived features on the inversion accuracy for tree heights. The results indicated the following: (1) The CNN_RepMLP displayed satisfactory performance in the inversion of mangrove tree heights, and exhibited better robustness and generalization ability than the convolutional neural network (CNN) model. (2) Among the different LiDAR-derived feature combinations, combining height variables with intensity variables can not only mitigate the negative impact of height variables on inversion models, but also enhance the mangrove tree height inversion accuracy. (3) The ensemble learning framework with ExtraTrees as the meta-model can make better use of the differences and complementarities between different single base models, and exhibited better accuracy in estimating the height of mangrove trees compared with other ensemble learning models. (4) Multiple machine learning models based on multi-dimensional UAV-LiDAR point-cloud-derived features are suitable for the inversion of mangrove tree height. The CNN_RepMLP model outperformed the CNN and stacking ensemble learning models and had more detailed differentiation in terms of mangrove tree height. Its prediction results can more realistically reflect the spatial differentiation characteristics of mangrove tree height.