Reconstruction Model Research Articles

High-resolution investigations of fault architecture in space and time

Heterogeneous fault architecture affects crustal seismotectonics and fluid migration. When studying it, we commonly rely on static conceptual models that generally overlook the absolute time dimension of fault (re)activation. Heterogenous faults, however, represent the end-result of protracted, cumulative and intricate deformation histories. This may lead to inaccurate reconstructions of tectonic histories and flowed models of fault hydro-mechanical behavior. We adopt here a multitechnique approach building upon the examination of now juxtaposed but not coeval brittle structural facies (BSF), which offer multiscalar insights in the spatio-temporal-thermal fault evolution. Our approach is applied to the Carboneras Fault, unveiling a ~ 25 Myr-long polyphase structural and thermal evolution. This led to a complex fault architecture, where BSFs exhibit a stark heterogeneity in fault rock and permeability, ultimately generating very different space- and time-dependent fault hydro-mechanical behaviors. Therefore, fault architectures shall be seen as dynamic features from which to extrapolate time-integrated comprehensive fault models accounting for the entire deformed rock volume and fault life span. We demonstrate that high-resolution studies of fault architectures are required to elucidate modes of fault growth and evolution, decipher long-lived, polyphase tectonic and thermal histories, and understand the influence of heterogenous fault architecture on hydraulic compartmentalization and earthquake rupture dynamics.

Bionic Modeling Study on the Landing Mechanism of Flapping Wing Robot Based on the Thoracic Legs of Purple Stem Beetle, Sagra femorata

Flapping wing micro aerial vehicles (FWMAVs) are recognized for their significant potential in military and civilian applications, such as military reconnaissance, environmental monitoring, and disaster rescue. However, the lack of takeoff and landing capabilities, particularly in landing behavior, greatly limits their adaptability to the environment during tasks. In this paper, the purple stem beetle (Sagra femorata), a natural flying insect, was chosen as the bionic research object. The three-dimensional reconstruction models of the beetle’s three thoracic legs were established, and the adhesive mechanism of the thoracic leg was analyzed. Then, a series of bionic design elements were extracted. On this basis, a hook-pad cooperation bionic deployable landing mechanism was designed, and mechanism motion, mechanical performance, and vibration performance were studied. Finally, the bionic landing mechanism model can land stably on various contact surfaces. The results of this research guide the stable landing capability of FWMAVs in challenging environments.

Remote sensing image Super-resolution reconstruction by fusing multi-scale receptive fields and hybrid transformer

To enhance high-frequency perceptual information and texture details in remote sensing images and address the challenges of super-resolution reconstruction algorithms during training, particularly the issue of missing details, this paper proposes an improved remote sensing image super-resolution reconstruction model. The generator network of the model employs multi-scale convolutional kernels to extract image features and utilizes a multi-head self-attention mechanism to dynamically fuse these features, significantly improving the ability to capture both fine details and global information in remote sensing images. Additionally, the model introduces a multi-stage Hybrid Transformer structure, which processes features at different resolutions progressively, from low resolution to high resolution, substantially enhancing reconstruction quality and detail recovery. The discriminator combines multi-scale convolution, global Transformer, and hierarchical feature discriminators, providing a comprehensive and refined evaluation of image quality. Finally, the model incorporates a Charbonnier loss function and total variation (TV) loss function, which significantly improve training stability and accelerate convergence. Experimental results demonstrate that the proposed method, compared to the SRGAN algorithm, achieves average improvements of approximately 3.61 dB in Peak Signal-to-Noise Ratio (PSNR), 0.070 (8.2%) in Structural Similarity Index (SSIM), and 0.030 (3.1%) in Feature Similarity Index (FSIM) across multiple datasets, showing significant performance gains.

A robot in-place geometric model reconstruction method for additive manufacturing parts by fusing 3D scanner measuring data

Abstract Geometric model reconstruction of additive manufacturing (AM) parts based on in-place measurement (IPM) is meaningful in realizing the robotic post-processing of AM parts. However, the low motion accuracy of the robot and the low efficiency of the IPM system restrict the development of the in-place model reconstruction. This paper presents a reconstruction method that integrates the 3D scanner and the robotic IPM system to improve the accuracy and efficiency of the model reconstruction. First, considering the measurement errors in the robotic IPM system, an error correction model is developed by measuring the calibration ball (CB) at the boundary of the measurement space. For each CB, a modified correction matrix (MCM) is constructed based on measurement data and geometric parameters of the CB to compensate for measurement errors. Second, a CB in the measurement space is measured, and the MCMs of each CB at the boundary are combined using the neural network model. Based on the measurement data of the CB, a geometric error correction model for the entire measurement space is developed, which corrects the spatial distribution of geometric errors in the robotic IPM system and enhances its accuracy within the measurement space. Finally, a local motion algorithm based on IPM data is proposed to calibrate the large volume of point cloud data from a 3D scanner. A high-precision model of AM parts is quickly reconstructed by fusing the few high-accuracy robotic IPM data with the large volume of low-accuracy point cloud data obtained by the 3D scanner. In order to verify the effectiveness of the method proposed in this paper, the measurement experiments on the CB are conducted. The results show that the model reconstruction accuracy can reach 0.06 mm compared with point cloud data obtained by the 3D scanner. The AM concave and convex parts are measured and the manufacturing geometric accuracy is analyzed by the proposed method. Compared to the digital model, the maximum deviation of the AM concave part is 1.3864 mm, and the maximum deviation of the AM convex part is 1.6802 mm.

Three-dimensional Finite Element Analysis of Implant Prosthesis After Mandibular Reconstruction by Upper Distal Free Double-Barrel Fibula.

This study aimed to compare the biomechanics of implant prostheses and peri-implant bone among 6 different mandibular reconstruction models based on patient data involving the use of an upper free-end double-barrel fibula. This study was an observational study. Five models were reconstructed using fibular-supported and implant-supported partial dentures. Two of these models were double-barrel fibula models with different gaps between the upper and lower segments. The other 2 were single-layered fibular models (the fibula was placed at the inferior or superior mandibular borders). The fifth model was a double-barrel fibula with a free distal end on the upper segment. In addition, a typical mandibular model was created for comparison. Two fixed partial-denture models were used in this study. The von Mises stress and strain of the models were analyzed. The maximum stress decreased with increasing gap between the upper and lower fibula. The distal free-end double-barrel fibula showed a potential to reduce the maximum stress around the implant apex while increasing it around the implant neck compared with a fixed distal-end double-barrel fibula. Utilizing a free distal end on the upper fibula may be a viable option for mandibular reconstruction, especially when the fibula length is limited.

Computationally efficient collimator-detector response compensation in high energy SPECT using 1D convolutions and rotations

Objective. Modeling of the collimator-detector response (CDR) in single photon emission computed tomography (SPECT) reconstruction enables improved resolution and accuracy, and is thus important for quantitative imaging applications such as dosimetry. The implementation of CDR modeling, however, can become a computational bottleneck when there are substantial components of septal penetration and scatter in the acquired data, since a direct convolution-based approach requires large 2D kernels. This work proposes a 1D convolution and rotation-based CDR model that reduces reconstruction times but maintains consistency with models that employ 2D convolutions. To enable open-source development and use of these models in image reconstruction, we release a SPECTPSFToolbox repository for the PyTomography project on GitHub.Approach. A 1D/rotation-based CDR model was formulated and subsequently fit to Monte Carlo (MC) point source data representative of177Lu,131I, and225Ac imaging. Computation times of (i) the proposed 1D/rotation-based model and (ii) a traditional model that uses 2D convolutions were compared for typical SPECT matrix sizes. Both CDR models were then used in the reconstruction of MC, physical phantom, and patient data; the models were compared by quantifying total counts in hot regions of interest (ROIs) and activity contrast between hot ROIs and background regions.Results. For typical matrix sizes in SPECT reconstruction, application of the 1D/rotation-based model provides a two-fold computational speed-up over the 2D model when running on GPU. Only small differences between the 1D/rotation-based and 2D models (order of 1%) were obtained for count and contrast quantification in select ROIs.Significance. A technique for CDR modeling in SPECT was proposed that (i) significantly speeds up reconstruction times, and (ii) yields nearly identical reconstructions to traditional 2D convolution based CDR techniques. The released toolbox will permit open-source development of similar models for different isotopes and collimators.

A Robust Anomaly Detection System for Nuclear Power Plants Under Varying Environmental Conditions and Malfunction Levels

Nuclear power plants (NPPs) require rigorous monitoring systems for their safety and efficiency, thus extensive data are acquired continuously from instrumentation and control systems. Anomaly detection is one of the most widely used machine learning approaches for monitoring data, especially when available data for model training are limited or imbalanced, as are data from NPPs. This research presents an anomaly detection system for centralized online monitoring in NPPs that is composed of two modules: data reconstruction and anomaly determination. Considering the large feature dimensions of the data, and leveraging their sequential characteristics in the time domain, four different autoencoder models for data reconstruction, long short-term memory, convolutional neural network, fully connected neural network, and principal component analysis are employed and compared. Two anomaly determination methods are presented and analyzed from the perspective of the characteristics of residuals from the data reconstruction models. The developed system is validated with simulation data containing 239 process variables (sensors) from different subsystems in a NPP. This paper highlights the effectiveness of simulations not only in overcoming the limited amount of data acquired from real plants in malfunctioning status, but also for evaluating the performance of the given models in a more quantitative way by comparing the performance at different malfunction levels. Weighted areas under the receiver operating characteristic curves are suggested as metrics for the validation of the given models and methods, and performance metrics, which can reflect engineers’ preferences, are demonstrated as well.

Joint k-b space diffusion-weighted image reconstruction and apparent diffusion coefficient fitting for diffusion-weighted turbo-spin-echo imaging.

Diffusion-weighted (DW) turbo-spin-echo (TSE) imaging offers improved geometric fidelity compared to single-shot echo-planar-imaging (EPI). However, it suffers from low signal-to-noise ratio (SNR) and prolonged acquisition times, thereby restricting its applications in diagnosis and MRI-guided radiotherapy (MRgRT). To develop a joint k-b space reconstruction algorithm for concurrent reconstruction of DW-TSE images and the apparent diffusion coefficient (ADC) map with enhanced image quality and more accurate quantitative measurements. The joint k-b reconstruction model was formulated as an optimization problem subject to a self-consistency condition comprising the exponential decay relationship between DW images and the ADC map. The objective function included a data fidelity term confirming an agreement between the reconstructed images and measured k-space data, incorporating the mono-exponential decay relationship between DW images and ADC map as a constraint, along with spatial regularization terms on both amplitude and phase images. The optimization problem was solved using the alternating-direction method of multipliers (ADMM). We evaluated the performance of the joint reconstruction (JR) algorithm for DW-TSE in a phantom study and patient studies on five brain and head/neck cancer patients. Image distortion, accuracy, and repeatability of ADC measurements were assessed and compared with those from conventional Fourier-transformed (FFT)-based reconstruction and magnitude-based ADC fitting methods, as well as with the clinically used DW-EPI technique. The proposed joint k-b reconstruction method demonstrated improved SNR and enhanced accuracy of ADC measurements in DW-TSE compared to the conventional method. Additionally, JR-DW-TSE with fewer averages at high b-values provided better image quality than the conventional FFT-reconstructed DW-TSE with full averages. The proposed joint k-b reconstruction method for DW-TSE has the potential to deliver distortion-robust diffusion-weighted MRI (DWI) for clinical applications, which is particularly crucial for MRgRT.

Long-range trajectory reconstructions using the point mass model.

In shooting incident reconstructions, forensic examiners usually deal with scenes involving short-range trajectories, typically ≤30 m. Insituations such as this, a linear trajectory reconstruction model is appropriate. However, a forensic expert can also be asked to estimate a shooter's position by reconstructing a long-range trajectory where the bullet's path becomes arced as a result of gravity and the greater time in flight. In this study, the point mass model (PMM) was used, because it is accessible and considered sufficiently accurate. A computer program using PMM can perform long-range trajectory reconstructions starting from an impact point. The reconstruction results in an area where the shot is expected to be fired from, not a single location. This is caused by varying the input parameters of the PMM. The aim of this study is to assess the accuracy of the method and discuss the influence of the most relevant parameters. The model has been validated by comparing its performance with 20 handgun bullet trajectories that were determined using Doppler radar measurements over long ranges, i.e. from 500 m to 1800 m. Comparison between the area calculated using the model and the actual shooter position demonstrates the limits of these reconstructions, particularly at high incident angles. The differences between the reconstructed deflections and the deflections measured by the tracking radar are rather large. This phenomenon is caused by either measurement errors in the cross wind as a function of height or inaccuracy of the radar's deflection measurements.

An Autonomous Positioning Method for Drones in GNSS Denial Scenarios Driven by Real-Scene 3D Models.

Drones are extensively utilized in both military and social development processes. Eliminating the reliance of drone positioning systems on GNSS and enhancing the accuracy of the positioning systems is of significant research value. This paper presents a novel approach that employs a real-scene 3D model and image point cloud reconstruction technology for the autonomous positioning of drones and attains high positioning accuracy. Firstly, the real-scene 3D model constructed in this paper is segmented in accordance with the predetermined format to obtain the image dataset and the 3D point cloud dataset. Subsequently, real-time image capture is performed using the monocular camera mounted on the drone, followed by a preliminary position estimation conducted through image matching algorithms and subsequent 3D point cloud reconstruction utilizing the acquired images. Next, the corresponding real-scene 3D point cloud data within the point cloud dataset is extracted in accordance with the image-matching results. Finally, the point cloud data obtained through image reconstruction is matched with the 3D point cloud of the real scene, and the positioning coordinates of the drone are acquired by applying the pose estimation algorithm. The experimental results demonstrate that the proposed approach in this paper enables precise autonomous positioning of drones in complex urban environments, achieving a remarkable positioning accuracy of up to 0.4 m.

C3DGS: Compressing 3D Gaussian Model for Surface Reconstruction of Large-Scale Scenes Based on Multi-View UAV Images

C3DGS: Compressing 3D Gaussian Model for Surface Reconstruction of Large-Scale Scenes Based on Multi-View UAV Images

A Physics-Based Computational Forward Model for Efficient Image Reconstruction in Magnetic Particle Imaging

A Physics-Based Computational Forward Model for Efficient Image Reconstruction in Magnetic Particle Imaging

Introductory learning of quantum probability and quantum spin with physical models and observations

Quantum probability and quantum spin are foundational concepts in quantum physics, not normally taught in pre-university education. In line with recent efforts to modernize quantum science education in schools, this paper presents learning sequences designed to provide an introductory conceptual understanding of these core physical concepts. This is achieved by choosing physical models that provide tangible classical representations that can be contrasted with quantum observations relevant to modern science and technology. This approach allows activity-based learning without the use of algebra. The concept of quantum probability is taught through activities with phasor wheels in which quantum probability is obtained graphically and interpreted using single-photon interference data. Quantum spin and spin vectors are taught by contrasting quantum spin with classical spin, explored through activities with gyroscopes and spinning tops. Quantum concepts are evoked by emphasizing how spin is different and how it manifests in real-world technologies such as magnets and MRI imaging. We describe learning sequences developed following the Model of Educational Reconstruction in which historical discoveries are combined with activities based on classical models and relevant quantum applications. We summarize test results from programs conducted with students aged 11–15. Most students connect the models and activities to quantum concepts and retain knowledge of quantum spin six months after the program. The successful learning outcomes indicate that teachers can effectively introduce quantum probability and quantum spin concepts to middle school using learning sequences such as those presented here.

Identification of post-disaster housing reconstruction using a lean reconstruction approach (case study: Pasaman earthquake in 2022)

Housing reconstruction for affected communities is a priority need following a disaster. Mechanisms and strategic steps for implementation are needed to ensure that the handling is quicker and more targeted. This aligns with the lean principle of “Let the customer pull—do not make anything until it is needed, then make it quickly,” and pursue perfection through continuous improvement. This research aims to identify the stages and mechanisms of housing reconstruction implementation in West Pasaman following the earthquake disaster in 2022 through a lean reconstruction approach. This research is part of a study on developing a lean reconstruction model for post-disaster housing reconstruction in Indonesia. The method used in this research is qualitative with a case study approach. The study results indicate that implementing post-disaster housing reconstruction in Pasaman has not met the lean principle, with several stages being repeated so that it can be classified as a wasteful process. The main problem that causes the repetition of the process is the problem of data collection. In addition, the distribution pattern of post-disaster housing reconstruction assistance has several weaknesses at various stages, so the final value of recipient satisfaction cannot be fully achieved.

Investigation of Ti nanostructures via laboratory scanning-free GEXRF.

The ability to characterize periodic nanostructures in the laboratory gains more attention as nanotechnology is widely utilized in a variety of application fields. Scanning-free grazing-emission X-ray fluorescence spectroscopy (GEXRF) is a promising candidate to allow non-destructive, element-sensitive characterization of sample structures down to the nanometer range for process engineering. Adopting a complementary metal-oxide semiconductor (CMOS) detector to work energy-dispersively via single-photon detection, the whole range of emission angles of interest can be recorded at once. In this work, a setup based on a Cr X-ray tube and a CMOS detector is used to investigate two TiO2 nanogratings and a TiO2 layer sample in the tender X-ray range. The measurement results are compared to simulations of sample models based on known sample parameters. The fluorescence emission is simulated using the finite-element method together with a Maxwell-solver. In addition, a reconstruction of the sample model based on the measurement data is conducted to illustrate the feasibility of laboratory scanning-free GEXRF as a technique to non-destructively characterize periodic nanostructures in the tender X-ray range.

Optimization Method for DSM Reconstruction of Equivalent Pinhole Model Based on Iterative Resampling and Chunking Process

Optimization Method for DSM Reconstruction of Equivalent Pinhole Model Based on Iterative Resampling and Chunking Process

High-resolution magnetic resonance image reconstruction model for early epilepsy detection using hybrid optimisation approach

High-resolution magnetic resonance image reconstruction model for early epilepsy detection using hybrid optimisation approach

Adhesive damage of class V restorations under shrinkage stress and occlusal forces using cohesive zone modeling.

This study aims to investigate adhesive damage caused by the synergistic effects of polymerization shrinkage and occlusal forces via finite element analysis (FEA), based on damage mechanics with the cohesive zone model (CZM). The objective is to obtain the adhesive damage distribution and investigate how the material properties of resin composite impact adhesive damage. A 3D reconstruction model of an mandibular first molar was constructed through CBCT imaging, and a Class V cavity was prepared using computer-aided engineering (CAE) software. Common clinical resin composite and an universal adhesive were selected for restorative filling. A 3D FEA was performed, incorporating the pre-stress induced by polymerization shrinkage of the resin composite, followed by occlusal forces. The cohesive zone model (CZM) was employed to represent the adhesive damage. To emphasize the impact of synergistic loading on adhesive damage, three types of loads were separately applied to the model: polymerization shrinkage, occlusal forces, and combined loading. Subsequently, three clinical resin composites with varying polymerization shrinkage and elastic modulus were used as restorative materials. Sensitivity analysis was conducted on dozens of hypothetical materials to provide definitive results. Polymerization shrinkage was undergone by the cured resin composite, resulting in extensive adhesive damage. Occlusal forces induced microdamage in regions already damaged by shrinkage stress, especially in the gingival wall. Predictably, the regions with severe adhesive damage were prone to marginal microleakage. The properties of the resin composite can affect adhesive damage. The adhesive damage with bulk-fill resin composite was milder than that with flowable and conventional resin composite. The extent of adhesive damage correlated markedly positively with the polymerization shrinkage of the resin composite and mildly positively with its elastic modulus. Adhesive damage has been directly implicated in marginal microleakage. The cohesive zone model (CZM) can effectively elucidate the distribution of adhesive damage and provide a clear representation of the impact of varying material properties of resin composite on adhesive damage.

Research on the Technology of 3D Model Reconstruction of Irregular Buildings Based on Point Clouds

Research on the Technology of 3D Model Reconstruction of Irregular Buildings Based on Point Clouds

A fully linearized ADMM algorithm for optimization based image reconstruction.

Optimization based image reconstruction algorithm is an advanced algorithm in medical imaging. However, the corresponding solving algorithm is challenging because the model is usually large-scale and non-smooth. This work aims to devise a simple and convergent solver for optimization model. The alternating direction method of multipliers (ADMM) algorithm is a simple and effective solver of the optimization model. However, there always exists a sub-problem that has not close-form solution. One may use gradient descent algorithm to solve this sub-problem, but the step-size selection via line search is time-consuming. Or, one may use fast Fourier transform (FFT) to get a close-form solution if the sparse transform matrix is of special structure. In this work, we propose a fully linearized ADMM (FL-ADMM) algorithm that avoids line search to determine step-size and applies to sparse transform of any structure. We derive the FL-ADMM algorithm instances for three total variation (TV) models in 2D computed tomography (CT). Further, we validate and evaluate one FL-ADMM algorithm and explore how two important factors impact convergence rate. These studies show that the FL-ADMM algorithm may accurately solve the optimization model. The FL-ADMM algorithm is a simple, effective, convergent and universal solver of optimization model in image reconstruction. Compared to the standard ADMM algorithm, the new algorithm does not need time-consuming step-size line-search or special demand to sparse transform. It is a rapid prototyping tool for optimization based image reconstruction.