3D Transformation Research Articles

Temperature-Adaptive reconfigurable chiral metamaterial for tailoring circular dichroism based on shape memory alloy

Self-adaptable materials with autonomous morphological transformation by sensing environmental changes have emerged as one of the most promising but challenging approaches for various chiral devices. Unlike previous work on reconfigurable chirality through human intervention, we proposed a temperature-adaptive chiral meta-device. It can autonomously sense the environment and manipulate the optical chirality through 3D-to-3D morphological transformation. As a proof of concept, a 3D morphable chiral meta-device was designed with shape memory alloy (SMA), resulting in reconfigurable circular dichroism (CD). The curving direction of SMA enables the meta-device to switch between left-handed, right-handed, and non-chirality by sensing environmental temperature. The analysis of surface currents and CD spectra reveals that the underlying physical mechanism behind the chirality originates from the surge of toroidal and electric quadrupole dipole moments. Simultaneously, the spin-to-orbital angular momentum conversion is realized based on the Pancharatnam-Berry phase, where the topological charge is modulated by the environmental temperature. Encouragingly, this work paves the way for the self-adaptive chiral meta-devices with 3D morphological transformation and can be applied to a series of novel photon-spin-selective devices for optical communication and biosensing.

A novel non-linear neuron model based on multiplicative aggregation in quaternionic domain

The learning algorithm for a three-layered neural structure with novel non-linear quaternionic-valued multiplicative (QVM) neurons is proposed in this paper. The computing capability of non-linear aggregation in the cell body of biological neurons inspired the development of a non-linear neuron model. However, unlike linear neuron models, most non-linear neuron models are built on higher order aggregation, which is more mathematically complex and difficult to train. As a result, building non-linear neuron models with a simple structure is a difficult and time-consuming endeavor in the neurocomputing field. The concept of a QVM neuron model was influenced by the non-linear neuron model, which has a simple structure and the great computational ability. The suggested neuron’s linearity is determined by the weight and bias associated with each quaternionic-valued input. Non-commutative multiplication of all linearly connected quaternionic input-weight terms accommodates the non-linearity. To train three-layered networks with QVM neurons, the standard quaternionic-gradient-based backpropagation (QBP) algorithm is utilized. The computational and generalization capabilities of the QVM neuron are assessed through training and testing in the quaternionic domain utilizing benchmark problems, such as 3D and 4D chaotic time-series predictions, 3D geometrical transformations, and 3D face recognition. The training and testing outcomes are compared to conventional and root-power mean (RPM) neurons in quaternionic domain using training–testing MSEs, network topology (parameters), variance, and AIC as statistical measures. According to these findings, networks with QVM neurons have greater computational and generalization capabilities than networks with conventional and RPM neurons in quaternionic domain.

Analytical dual quaternion algorithm of the weighted three-dimensional coordinate transformation

Considering that a unit dual quaternion can describe elegantly the rigid transformation including rotation and translation, the point-wise weighted 3D coordinate transformation using a unit dual quaternion is formulated. The constructed transformation model by a unit dual quaternion does not need differential process to eliminate the three translation parameters, while traditional models do. Based on the Lagrangian extremum law, the analytical dual quaternion algorithm (ADQA) of the point-wise weighted 3D coordinate transformation is proved existed and derived in detail. Four numerical cases, including geodetic datum transformation, the registration of LIDAR point clouds, and two simulated cases, are studied. This study shows that ADQA is valid as well as the modified procrustes algorithm (MPA) and the orthonormal matrix algorithm (OMA). ADQA is suitable for the 3D coordinate transformation with point-wise weight and no matter rotation angles are small or big. In addition, the results also indicate that if the distribution of common points degrades from 3D or 2D space to 1D space, the solvable correct transformation parameters decrease. In other words, all common points should not be located on a line. From the perspective of improving the transformation accuracy, high accurate control points (with small errors in the coordinates) should be chosen, and it is preferred to decrease the rotation angles as much as possible.Graphical

Massive Spatial Well Clustering Based on Conventional Well Log Feature Extraction for Fast Formation Heterogeneity Characterization

Abstract Recent enhancements in computational capacity provide an opportunity for harnessing the enormous amount of reservoir data already acquired and extracting useful information for hydrocarbon exploration, development, and production. This article reports a three-step clustering technique to determine well groups based on subsurface geological heterogeneity using feature extraction, hierarchical ensemble clustering, and spatial mapping. The first step of the presented methodology is to group the wells into different clusters based on the formation rock composition and property features extracted from well logs using the expectation maximization algorithm. The one-dimensional (1D) stacking pattern of each well log curve is expressed through a two-dimensional (2D) transformation scheme. Thus, the clustering can capture the vertical stacking patterns of well logs, which is essential for reservoir heterogeneity characterization. This base clustering process generated a feature matrix which is further grouped through the hierarchical ensemble clustering in a latent space of well logs in the second step. Through the ensemble clustering, different clustering proposals obtained from the base clustering are integrated corroboratively to reflect a comprehensive feature of all studied logs. In the third step, the spatial clustering is performed based on the ensemble results, considering the spatial distances between well locations in the target area. The results of the 2D spatial map may provide insights into the sedimentary depositional environment in terms of the lateral geological heterogeneity features. Therefore, the proposed clustering technique can present a fast geological modeling method to integrate geological heterogeneity features presented in multiple well logs, which is not yet fully utilized in traditional geomodeling approaches. The results can also support further reservoir studies, such as petrophysical modeling, reservoir modeling, and fluid flow simulation studies.

3DUNetGSFormer: A deep learning pipeline for complex wetland mapping using generative adversarial networks and Swin transformer

Many ecosystems, particularly wetlands, are significantly degraded or lost as a result of climate change and anthropogenic activities. Simultaneously, developments in machine learning, particularly deep learning methods, have greatly improved wetland mapping, which is a critical step in ecosystem monitoring. Yet, present deep and very deep models necessitate a greater number of training data, which are costly, logistically challenging, and time-consuming to acquire. Thus, we explore and address the potential and possible limitations caused by the availability of limited ground-truth data for large-scale wetland mapping. To overcome this persistent problem for remote sensing data classification using deep learning models, we propose 3D UNet Generative Adversarial Network Swin Transformer (3DUNetGSFormer) to adaptively synthesize wetland training data based on each class's data availability. Both real and synthesized training data are then imported to a novel deep learning architecture consisting of cutting-edge Convolutional Neural Networks and vision transformers for wetland mapping. Results demonstrated that the developed wetland classifier obtained a high level of kappa coefficient, average accuracy, and overall accuracy of 96.99%, 97.13%, and 97.39%, respectively, for the data in three pilot sites in and around Grand Falls-Windsor, Avalon, and Gros Morne National Park located in Canada. The results show that the proposed methodology opens a new window for future high-quality wetland data generation and classification. The developed codes are available at https://github.com/aj1365/3DUNetGSFormer.

Development and characterisation of structurally reforming engineered flat‐rice xerogel for hot water cooking

SummaryShape transforming foods are an emerging area of interest that could be customised according to consumer needs with the desirable structure and reduced package space. The current work aims to investigate the shape transformation behaviour of a 2D xerogel developed from rice hydrogel at the concentration of 4% and 90 °C. This flat xerogel is coated with ethyl cellulose as constraint material in the form of linear equally spaced strips. Immersing the xerogels in hot water over a temperature of 70 °C, resulted in a 3D transformation of the discs in 12 s (Video S1). The transformation is achieved owing to xerogel's increased swelling power (0.27–0.78) due to the anisotropic nature. This is confirmed by the SEM images of top (smooth) and bottom (rough) xerogel. In order to characterise the process of shape transformation bending analysis was done wherein a significant difference in bending angle (35.86–63.10°), height (24.73–50.26 mm), curvature (0.57–2.38 μ mm−1) and end‐to‐end distance (118.23–15.11 mm) from 2 to 12 s. The formed 3D shape was similar to the commercial tubular (penne) pasta. This study opens up the utilisation of less concentrated rice xerogel in shape transformation for designing engineered food.

Unsupervised contrastive learning based transformer for lung nodule detection

Objective. Early detection of lung nodules with computed tomography (CT) is critical for the longer survival of lung cancer patients and better quality of life. Computer-aided detection/diagnosis (CAD) is proven valuable as a second or concurrent reader in this context. However, accurate detection of lung nodules remains a challenge for such CAD systems and even radiologists due to not only the variability in size, location, and appearance of lung nodules but also the complexity of lung structures. This leads to a high false-positive rate with CAD, compromising its clinical efficacy. Approach. Motivated by recent computer vision techniques, here we present a self-supervised region-based 3D transformer model to identify lung nodules among a set of candidate regions. Specifically, a 3D vision transformer is developed that divides a CT volume into a sequence of non-overlap cubes, extracts embedding features from each cube with an embedding layer, and analyzes all embedding features with a self-attention mechanism for the prediction. To effectively train the transformer model on a relatively small dataset, the region-based contrastive learning method is used to boost the performance by pre-training the 3D transformer with public CT images. Results. Our experiments show that the proposed method can significantly improve the performance of lung nodule screening in comparison with the commonly used 3D convolutional neural networks. Significance. This study demonstrates a promising direction to improve the performance of current CAD systems for lung nodule detection.

3D mesh transformer: A hierarchical neural network with local shape tokens

Self-attention networks have revolutionized Natural Language Processing (NLP) and are making impressive strides in image analysis tasks such as image classification and object detection. Inspired by this success, we specifically design a novel self-attention mechanism between local shapes and build a shape Transformer. We split the 3D mesh model into shape patches, which we call shape tokens, and provide polynomial fitting representations of these patches as input to the shape Transformer. The shape token encodes local geometric information and resembles the token (word) status in NLP. The simplification of the mesh model provides a hierarchical multiresolution structure, which allows us to realize the feature learning of a multilayer Transformer. We set high-level features formed by the shape Transformer as visual tokens and propose a vector-type self-attention mechanism to construct a 3D visual Transformer. Finally, we realized a hierarchical network structure based on local shape tokens and high-level visual tokens. Experiments show that our fusion network of 3D shape Transformer with explicit local shape context augmentation and 3D visual Transformer with multi-level structural feature learning achieves excellent performance on shape classification and part segmentation tasks.

The characteristic and transformation of 3D urban morphology in three Chinese mega-cities

Urban morphology exerts an important influence on human settlements and serves to promote sustainable development. However, few studies have focused on the three dimensions (3D) of urban morphology. Therefore, to explore 3D urban morphological characteristics and transformations, we adopted a standard urban morphology classification scheme, the local climate zone (LCZ), and selected three Chinese megacities as case studies. Remote sensing images and machine-learning technology were used to create spatiotemporal LCZ maps. Our results reveal that cities have diverse forms, with the dominant urban morphology varying across different cities and periods. However, cities still share several common patterns. The dynamic spatial distribution of LCZ density can be divided into three patterns: spreading, shrinking and relocation. In recent years, Chinese megacities have mainly expanded with open high-rise (OHR) and large low-rise (LLR) classes. Combined with the increase in building height, urban landscapes have been homogeneous. Additionally, 3D urban morphology is driven by several factors, such as economic activity and the dissociation of population growth from urban expansion. Detailed spatiotemporal LCZ maps can help us better understand urban morphology transformations and explore the corresponding driving forces, providing a reference for urban planners and policymakers seeking to make cities healthier and more livable.

2D Transformations of Energy Signals for Energy Disaggregation.

The aim of Non-Intrusive Load Monitoring is to estimate the energy consumption of individual electrical appliances by disaggregating the overall power consumption that has been sampled from a smart meter at a house or commercial/industrial building. Last decade’s developments in deep learning and the utilization of Convolutional Neural Networks have improved disaggregation accuracy significantly, especially when utilizing two-dimensional signal representations. However, converting time series’ to two-dimensional representations is still an open challenge, and it is not clear how it influences the performance of the energy disaggregation. Therefore, in this article, six different two-dimensional representation techniques are compared in terms of performance, runtime, influence on sampling frequency, and robustness towards Gaussian white noise. The evaluation results show an advantage of two-dimensional imaging techniques over univariate and multivariate features. In detail, the evaluation results show that: first, the active and reactive power-based signatures double Fourier based signatures, as well as outperforming most of the other approaches for low levels of noise. Second, while current and voltage signatures are outperformed at low levels of noise, they perform best under high noise conditions and show the smallest decrease in performance with increasing noise levels. Third, the effect of the sampling frequency on the energy disaggregation performance for time series imaging is most prominent up to 1.2 kHz, while, above 1.2 kHz, no significant improvements in terms of performance could be observed.

Misleading attention and classification: An adversarial attack to fool object detection models in the real world

Object detection is a hot topic in computer vision (CV), and it has many applications in various security fields. However, many works have demonstrated that neural network-based object detection is vulnerable to adversarial attacks. In this paper, we study adversarial attacks on object detectors in the real world and propose a new adversarial attack called Misleading Attention and Classification Attack (MACA), which can generate adversarial patches to mislead the object detectors. Specifically, we propose a new scheme to generate adversarial patches to fool the object detector. Our scheme restricts the noise of the adversarial patches and aims to ensure that the generated adversarial patches are visually similar to natural images. The attack simulates the complex external physical environment and the 3D transformations of non-rigid objects to increase the robustness of adversarial patches. We attack the up-to-date object detectors (e.g., Yolo-V5), and we prove that our technique has strong transferability among different detectors. Extensive experiments show that it is feasible to transfer the digital adversarial patches to the real world while maintaining the transferability of adversarial patches among different models and the success rate of adversarial attacks.

An efficient maximum bound principle preserving p-adaptive operator-splitting method for three-dimensional phase field shape transformation model

In this paper, a novel numerical algorithm for efficient modeling of three-dimensional shape transformation governed by the modified Allen-Cahn (A-C) equation is developed, which has important significance for computer science and graphics technology. The new idea of the proposed method is as follows. Firstly, the operator splitting method is used to decompose the three-dimensional problem into a series of one-dimensional subproblems that can be solved in parallel in the same direction. Secondly, a temporal p-adaptive strategy, which is based on the extrapolation technique, is proposed to improve the convergence order in time and preserve the computational efficiency simultaneously. Finally, a parallel least distance modification technique is developed to force the discrete maximum bound principle. The proposed method achieves high precision and high efficiency at the same time. Numerical examples include the effectiveness of the p-adaptive method and the bound preserving least distance modification, and a series of complex three-dimensional shape transformation modelings. • The operator splitting method is used to solve 3D shape transformation PDE. • A temporal p-adaptive strategy is developed to improve computational efficiency. • A least-distance modification is developed to force the discrete maximum bound.

An Effective Camera-to-Lidar Spatiotemporal Calibration Based on a Simple Calibration Target.

In this contribution, we present a simple and intuitive approach for estimating the exterior (geometrical) calibration of a Lidar instrument with respect to a camera as well as their synchronization shifting (temporal calibration) during data acquisition. For the geometrical calibration, the 3D rigid transformation of the camera system was estimated with respect to the Lidar frame on the basis of the establishment of 2D to 3D point correspondences. The 2D points were automatically extracted on images by exploiting an AprilTag fiducial marker, while the detection of the corresponding Lidar points was carried out by estimating the center of a custom-made retroreflective target. Both AprilTag and Lidar reflective targets were attached to a planar board (calibration object) following an easy-to-implement set-up, which yielded high accuracy in the determination of the center of the calibration target. After the geometrical calibration procedure, the temporal calibration was carried out by matching the position of the AprilTag to the corresponding Lidar target (after being projected onto the image frame), during the recording of a steadily moving calibration target. Our calibration framework was given as an open-source software implemented in the ROS platform. We have applied our method to the calibration of a four-camera mobile mapping system (MMS) with respect to an integrated Velodyne Lidar sensor and evaluated it against a state-of-the-art chessboard-based method. Although our method was a single-camera-to-Lidar calibration approach, the consecutive calibration of all four cameras with respect to the Lidar sensor yielded highly accurate results, which were exploited in a multi-camera texturing scheme of city point clouds.

An Adaptive and Reliable Guided Digital Volume Correlation Algorithm for Sandstone Based on 3D Scale-Invariant Feature Transform

An Adaptive and Reliable Guided Digital Volume Correlation Algorithm for Sandstone Based on 3D Scale-Invariant Feature Transform

Solid isotropic material with thickness penalization – A 2.5D method for structural topology optimization

SIMP is the most common topology optimization scheme to minimize the material utilization of a structural component using element densities as a design variable. This paper presents a new methodology, SIMTP, Solid Isotropic Material with Thickness Penalization, introducing a nodal thickness variable to the plane stress element. SIMTP uses a 2.5D element, and it is developed considering the varying thickness as a 3D problem and planar transformation as a 2D problem. The developed 2.5D element is used for projecting the 2D strain energy onto a 3D space. Classical optimization problems like Cantilever, MBB, and L-, beams are solved using 2.5D SIMTP, and results are compared with SIMP. The implementation is simple, directly representing the thickness, and nodal design variables enhance the resolution. Checkerboarding or other topology-related issues are not noticed during the process. Also, 2.5D SIMTP can yield desired results with fewer elements, thereby reducing the computational efforts. Besides, a 211-line MATLAB implementation of 2.5D SIMTP with examples is provided at the end of this paper.

An aspect of transformer inrush current

Paper analyzes a sparking phenomena that were observed on power transformers during their first few energizing. Sparks occurred on the flange between tank cover and tank side, in spite of the fact that a jumper (copper link) was used. The phenomenon was observed on transformers from different manufacturers, with various transformer ratings, three or five limb core. Paper proposes a simple electromagnetic model for analyzing the phenomena. Three-phase power transformer is modeled in finite element method (FEM) magnetic software. Simplified 3D transformer model is used in order to simulate well known core saturation during the inrush current event. Voltages between tank cover and tank side are calculated for simulated conditions. The same model is used for calculation of possible currents flowing between tank cover and tank side, showing that a value of several kA can be reached. In spite of the fact that the observed phenomenon is harmless mitigation measures are proposed. However, by doing nothing, sparking would disappear very soon.

Registration Strategy of Handheld Scanner (HS) and Terrestrial Laser Scanner Integration for Building Utility Mapping

Currently, the need for a 3D model that represents the existing condition of a building is needed, especially for building management. While performed the 3D mapping of building utilities such as pipelines in buildings, the Terrestrial Laser Scanner (TLS) technology couldn’t cover the object in the ceiling, so Handheld Scanner (HS) was used to complete the scanned objects. The purpose of this article is conducting the process of mapping between HS and TLS for building pipelines. The integration between two different sensor resolutions and sensor scan coverage requires a separate strategy to generate 3D models for pipeline objects in building. The use of specific target and 3D transformation method could do the registration between two set of point clouds which was obtained by two different technologies. The target object, such as spherical object which is usually used as a tie point between scan results in the registration process, cannot be recorded by HS. The specific targets were pasted on the pipelines in order to be captured by the HS. The specific targets become tie point in the registration process. Meanwhile, the registration process using the Iterative Closest Point (ICP) algorithm cannot be carried out because the two scan results do not meet the overlap percentage standard, so that the 3D transformation would be used in registration process. The result is that the registration accuracy is 0.062 m.

Opto‐regulation for the 2D to 3D transformation of a liquid crystal network membrane

AbstractLight loading mode plays essential roles in the deformation morphology of the photosensitive liquid crystal network (LCN) membrane. However, owing to the strong non‐linearity of membrane deformation and the constraint effect of membrane boundary, the precise surface pattern control of a LCN membrane through pre‐designing the light loading pattern have not been fully carried out. In this article, a second light‐engraving strategy is proposed to decouple the local and integral deformation of the LCN membrane, and the accurate opto‐regulation of the 2D to 3D pattern transformation could be realized. Firstly, the opto‐deformation governing equations for the LCN membrane under nonuniform illumination are established, and solved by the finite difference method. The deformation characteristics of the LCN membrane under nonuniform illumination has been discussed, and the relation between the light loading mode and the spontaneous deformation in the LCN membrane is clarified. Then a second‐engraving technology based on the deformed membrane is proposed, and the precise opto‐tuning of the 2D to 3D deformation for a LCN membrane is realized. “Robert face” and three‐dimensional word printing is presented as example. This work provides theoretical and numerical guidance on the LCN membrane based three dimensional modeling technology.

A new approach for geometric correction of UAV-based pushbroom images through the processing of simultaneously acquired frame images

Video-assisted navigation is a strategy that hyperspectral camera manufacturers usually use to deal with the geometric distortions imposed on raw pushbroom images, due to perturbations. In this strategy, a simultaneous video is captured during a pushbroom raw image acquisition. This article proposes an automatic geometric correction method applicable to the sensors utilizing this strategy that does not need ground control data. This method starts with an inter-calibration procedure between the two sensors, which allows using frame-based images in the correction procedure. Then, the method uses sequential transformations among consecutive video frames to produce geometrically corrected products. The experimental results demonstrate that the proposed method's sequential nature makes it so flexible that it causes more reduction in the scenes' random geometric distortions than the common methods. Hence, the accuracy of 2D and 3D transformations relating the image and the ground spaces is increased by 66.9% compared to the raw pushbroom images.

Harmony: A Generic Unsupervised Approach for Disentangling Semantic Content from Parameterized Transformations.

In many real-life image analysis applications, particularly in biomedical research domains, the objects of interest undergo multiple transformations that alters their visual properties while keeping the semantic content unchanged. Disentangling images into semantic content factors and transformations can provide significant benefits into many domain-specific image analysis tasks. To this end, we propose a generic unsupervised framework, Harmony, that simultaneously and explicitly disentangles semantic content from multiple parameterized transformations. Harmony leverages a simple cross-contrastive learning framework with multiple explicitly parameterized latent representations to disentangle content from transformations. To demonstrate the efficacy of Harmony, we apply it to disentangle image semantic content from several parameterized transformations (rotation, translation, scaling, and contrast). Harmony achieves significantly improved disentanglement over the baseline models on several image datasets of diverse domains. With such disentanglement, Harmony is demonstrated to incentivize bioimage analysis research by modeling structural heterogeneity of macromolecules from cryo-ET images and learning transformation-invariant representations of protein particles from single-particle cryo-EM images. Harmony also performs very well in disentangling content from 3D transformations and can perform coarse and fast alignment of 3D cryo-ET subtomograms. Therefore, Harmony is generalizable to many other imaging domains and can potentially be extended to domains beyond imaging as well.