3D Block Research Articles

Hyperspectral Image Classification Based on Novel Hybridization of Spatial-Spectral-Superpixelwise Principal Component Analysis and Dense 2D-3D Convolutional Neural Network Fusion Architecture

We propose a hybridized technique named Spatial-Spectral-Superpixelwise PCA-based Dense 2D-3D CNN Fusion Architecture (3SPCA-D-2D-3D-CNN), that deals with the detailed and complex study of dimensionality reduction and classification of Hyperspectal images (HSI). Our work is 2-fold: At first (1), 3SPCA is applied on raw HSI that adopts superpixels-based local reconstruction to filter the images, whereas PCA-based supplementary global features acquire the relevant and low-dimensional local features. Every HSI pixel is reconstituted by the pixels of its closest neighbors in the same superpixel block to reduce noise and improve spatial information. Next, PCA is conducted on every zone and the full HSI to get local and global features. The local-global and spatial-spectral properties are then concatenated. Secondly (2), the D-2D-3D-CNN fusion architecture is made up of three 3D convolution blocks, two 2D convolution blocks with varied kernel sizes and filters, and four fully connected (FC) dense layers, totaling nine distinguishing and information-enriched features. These features can generate precise class labels and apply them to the appropriate landcovers. The proposed method has been applied to three publicly available HSI landcover datasets, the Indian Pines, the Salinas Valley, and the Pavia University. It achieved respectively 98.33%, 99.99%, and 98.73% average accuracy scores. Due to its improved Feature Extraction capacity from a limited number of training samples and its classification performance with fewer epochs, this method outperforms other relevant state-of-the-art CNN-based methods.

A Dense Block Model Representing Western Continental United States Deformation for the 2023 Update to the National Seismic Hazard Model

Abstract Seismic hazard assessment, such as the U.S. Geological Survey (USGS) National Seismic Hazard Model (NSHM), relies on estimates of fault slip rate based on geology and/or geodetic observations such as the Global Navigation Satellite System (GNSS), including the Global Positioning System. Geodetic fault slip rates may be estimated within a 3D spherical block model, in which the crust is divided into microplates bounded by mapped faults; fault slip rates are determined by the relative rotations of adjacent microplates. Uncertainty in selecting appropriate block-bounding faults and in forming closed microplates has limited the interpretability of block models for seismic hazard modeling. By introducing an automated block closure algorithm and regularizing the resulting densely spaced block model with total variation regularization, I develop the densest and most complete block model of the western continental United States to date. The model includes 853 blocks bounded by 1017 geologically identified fault sections from the USGS NSHM Fault Sections database. Microplate rotations and fault slip rates are constrained by 4979 GNSS velocities and 1243 geologic slip rates. I identify a regularized solution that fits the GNSS velocity field with a root mean square misfit of 1.9 mm/yr and reproduces 57% of geologic slip rates within reported geologic uncertainty and model sensitivity, consistent with other geodetic-based models in this Focus Section. This block model includes slip on faults that are not included in the USGS NSHM Fault sections database (but are required to form closed blocks) for an estimate of “off-fault” deformation of 3.62×1019 N·m/yr, 56% of the total calculated moment accumulation rate in the model.

Transitive kriging for modeling tailings deposits: A case study in southwest Finland

Global mining generates a large amount of mine tailings, which can produce negative effects on the environment. To counteract this, government guidelines and scientific interest have emerged to reuse tailings deposits in innovative ways, converting them from an environmental liability to an economic asset. Thus, the characterization of a tailings deposit is of great importance to analyze the content of critical raw materials, as well as for a possible revaluation of the deposit, facilitating the mining process to be carried out in a more sustainable way. To characterize their chemical composition, information from drilling campaigns can be used. However, the evaluation of resources in tailings deposits has several complexities such as the poor grade spatial continuity and its narrow geometry, reasons for which traditional geostatistics is not adapted to model this type of deposit. In contrast, transitive geostatistics can be an opportunity to tackle this taking advantage of the fully delimited domain in a tailings dam and a different structural analysis. By achieving a better characterization of tailings composition, it is possible to make better decisions for their reprocessing, thus supporting cleaner mining production.This study aims to provide a framework for the geostatistical modeling and prediction of remaining metal resources of the Haveri tailings in southwestern Finland. The modeled variables are the gold, cobalt, copper and iron grades, with cobalt being of special interest as a critical material. The grades have been measured at 165 drill holes, totaling 1201 samples composited at 1 meter.An exploratory data analysis is performed first to clean the database and to identify the statistical and spatial distributions of the data. Then a structural analysis is applied to model the grade spatial continuity. Leave-one-out cross-validation is subsequently used to validate the fitted model and to quantify the prediction errors. Finally, 3D block models of the gold, cobalt, copper and iron grade are constructed with ordinary kriging and transitive kriging and are compared.Cross-validation shows that both kriging methods yield a good precision of the predictions and perform equally well for copper and iron, but transitive kriging significantly outperforms ordinary kriging for gold and cobalt and also provides less smoothed block models than ordinary kriging. Transitive kriging thus appears as an effective alternative for assessing resources in tailings and other narrow deposits. Recommendations on the sampling design to optimize the coverage of the target area and to ease the covariogram inference are also given.

A modified 3D EfficientNet for the classification of Alzheimer's disease using structural magnetic resonance images

Structural magnetic resonance imaging (MRI) provides useful information for biomarker exploration and intelligent classification of Alzheimer's disease (AD). Machine learning and deep learning methods have been used in feature extraction and computer-aided diagnosis or prediction of AD. In this research, a new deep learning model is developed to detect or predict AD in an effective manner. A modified 3D EfficientNet with a sequentially connected 3D mobile inverted bottleneck convolution (MBConv) block is proposed to explore the multiscale characteristics of brain MR images for AD classification. The 3D MBConv block consists of depthwise convolution and a squeeze-and-excitation module to transform the input features into more compact features with fewer parameters than those in standard convolution. The proposed method was evaluated considering four classification tasks: (1) NC versus AD, (2) NC versus all MCI, (3) NC versus pMCI, and (4) sMCI versus pMCI. The proposed model achieved accuracies of 95.00%, 86.67%, and 83.33% for the classification of NC versus AD, NC versus pMCI, and sMCI versus pMCI, respectively, and exhibited a high performance in comparison with the classical networks and several existing methods. Efficient deep learning networks are powerful tools for the early diagnosis and prediction of AD.

Computational homogenization of additively manufactured lightweight structures with multiscale topology optimization

Abstract Topology optimization (TO) is an optimal design method to obtain an efficient structure with minimal usage of material by satisfying two conflicting objectives of weight reduction and structural safety. Owing to the recent advances in additive manufacturing technology, TO has been developed in connection with the use of microscale lattices, of which complicated geometries require considerable computational loads to verify their structural performance. This study aims to develop an efficient computational method to analyze a complex TO model. Computational homogenization was then developed for efficient computation of the TO model that contains a number of microscale lattices. The proposed homogenization scheme was then applied to perform three-dimensional (3D) finite element analysis (FEA) on various TO models with three scales (i.e., macroscale, microscale, and multiscale TOs). The homogenized FEAs were conducted to verify the static and dynamic deformation behaviors of three optimized meta-sandwich beams, and their results and computational efficiency were compared with those from full solid FEAs. Experimental verification revealed that the proposed homogenized FEA provided more reliable results and better computational efficiency for the microscale and multiscale TO models, whereas the conventional solid FEA was advantageous for the macroscale TO model. To apply the proposed simulation strategy to a more complex 3D geometry, three TO models were calculated for a 3D block under a compression load. The simulation strategy combining the full solid and homogenized FEAs was then applied to analyze the static and dynamic deformation behaviors of various TO models, which provided reliable predictions of the experimentally observed behaviors within an acceptable computational time.

Energy Level Diagram of 3d2 Configuration in Tetrahedral Crystal Field and Its Applications to Cr4 + /Mn5 + ‐Doped Luminescent Materials

AbstractTanabe–Sugano diagram is a successful guideline for the energy levels of 3dn electrons for half a century. However, it is developed only under the regular octahedral crystal field. With the surging research interests in the near‐infra‐red luminescence of 3d block transition‐metal elements, it becomes an urgent demand to know the energy level diagram of Cr4 + and Mn5 + with 3d2 configuration, which are usually embedded in the tetrahedral site due to the smaller ionic size. Although the Tanabe–Sugano diagram of 3d8 configuration in octahedron is usually used as a substitution for that of 3d2 configuration in tetrahedron, the result mismatches with the crystal field strength and emission profile. In this work, irreducible tensor operator method is applied to derive the full analytical expressions of 3d2 energy levels in the tetrahedral crystal field, based on which the Tanabe–Sugano diagram of 3d2 configuration in tetrahedron is built for the first time. Also provided is a set of formulas for fast estimation of values, which shows consistency between the crystal field strength and the emission profile among Cr4+, Mn5+‐doped luminescent materials. As it is hoped, this work could provide more deep insights into the luminescence of 3d2 elements.

Recognition of Emotion with Intensity from Speech Signal Using 3D Transformed Feature and Deep Learning

Speech Emotion Recognition (SER), the extraction of emotional features with the appropriate classification from speech signals, has recently received attention for its emerging social applications. Emotional intensity (e.g., Normal, Strong) for a particular emotional expression (e.g., Sad, Angry) has a crucial influence on social activities. A person with intense sadness or anger may fall into severe disruptive action, eventually triggering a suicidal or devastating act. However, existing Deep Learning (DL)-based SER models only consider the categorization of emotion, ignoring the respective emotional intensity, despite its utmost importance. In this study, a novel scheme for Recognition of Emotion with Intensity from Speech (REIS) is developed using the DL model by integrating three speech signal transformation methods, namely Mel-frequency Cepstral Coefficient (MFCC), Short-time Fourier Transform (STFT), and Chroma STFT. The integrated 3D form of transformed features from three individual methods is fed into the DL model. Moreover, under the proposed REIS, both the single and cascaded frameworks with DL models are investigated. A DL model consists of a 3D Convolutional Neural Network (CNN), Time Distribution Flatten (TDF) layer, and Bidirectional Long Short-term Memory (Bi-LSTM) network. The 3D CNN block extracts convolved features from 3D transformed speech features. The convolved features were flattened through the TDF layer and fed into Bi-LSTM to classify emotion with intensity in a single DL framework. The 3D transformed feature is first classified into emotion categories in the cascaded DL framework using a DL model. Then, using a different DL model, the intensity level of the identified categories is determined. The proposed REIS has been evaluated on the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) benchmark dataset, and the cascaded DL framework is found to be better than the single DL framework. The proposed REIS method has shown remarkable recognition accuracy, outperforming related existing methods.

Depth Estimation for Integral Imaging Microscopy Using a 3D–2D CNN with a Weighted Median Filter

This study proposes a robust depth map framework based on a convolutional neural network (CNN) to calculate disparities using multi-direction epipolar plane images (EPIs). A combination of three-dimensional (3D) and two-dimensional (2D) CNN-based deep learning networks is used to extract the features from each input stream separately. The 3D convolutional blocks are adapted according to the disparity of different directions of epipolar images, and 2D-CNNs are employed to minimize data loss. Finally, the multi-stream networks are merged to restore the depth information. A fully convolutional approach is scalable, which can handle any size of input and is less prone to overfitting. However, there is some noise in the direction of the edge. A weighted median filtering (WMF) is used to acquire the boundary information and improve the accuracy of the results to overcome this issue. Experimental results indicate that the suggested deep learning network architecture outperforms other architectures in terms of depth estimation accuracy.

Volumetric wall detection in unorganized indoor point clouds using continuous segments in 2D grids

The quality of 3D models of existing buildings reconstructed from point clouds is strongly related to the segmentation process used to detect structural elements. A new wall detection method in the indoor point clouds of buildings is presented in this study. The point clouds are segmented into horizontal layers, and a concept of continuous segments in a 2D grid representation is used to extract the footprints of the wall structures, and 2D blocks are projected into 3D space to obtain the wall segments in the initial 3D point cloud. The results obtained from the execution of the proposed method demonstrate that wall blocks in indoor point clouds are detected independently of their shape. Executing the proposed method on a set of 9 in-door point clouds revealed better performance in terms of result quality and execution time compared to RANSAC. The robustness of the method can be improved by adding a classification step to eliminate non-consistent blocks.

Block Model of Laterite Nickel Reserve in the Sorowako Village Research Area, South Sulawesi Province

The manufacture of laterite nickel deposit model blocks is one of the stages after exploration activities are carried out in an area that shows potential resources that are feasible to be exploited. The purpose of this study was to determine the tonnage value of an estimated resource of saprolite and limonite ore deposits in the drilling area. The research method is the implementation of fieldwork, which includes a survey of the area to be drilled, tools and materials used in drilling production, and determining the stages in making model blocks. The data used in this study are data from analysis of nickel content, data on the total depth of drill points, lithology data of laterite nickel profiles, and data on coordinates and elevation of drill points. In research conducted in the concession area of ​​the research area with a number of drill points of as many as 275 points and a drill spacing of 25 meters and 50 meters, the number of resources that have been calculated in the form of a 3D block model and a cross-sectional vertical correlation where in the limonite layer the volume value is obtained. of 757,031 m3 with an average Ni content: 0.84%, while the saprolite layer obtained a volume value of 210,781 m3 with an average Ni content: 1.80%. The conclusion obtained is that the tonnage value for the limonite layer is 1,211,250 tons, and for the saprolite layer the tonnage value is 400,484 tons.

New insight into bonding energy and stress distribution of graphene oxide/hexagonal boron nitride: Functional group and grain boundary effect

The stress accumulation and nonlinear buckling induced by grain boundaries (GBs) in two-dimensional (2D) interface and functional groups (FGs) on the surface of 2D are different from those of three-dimensional (3D) blocks. Topological and geometrical effects of defects and FGs in 2D materials will significantly affect their mechanical properties and transport behavior. A new graphene oxide/h-BN interface with GBs model (GO-BN-GBs) was constructed to study the built-in distortion stress and out-of-plane deformation caused by GBs and FGs under different strains. Considering GBs type, FGs type, FGs density and also the effect of distance between FGs and GBs on the bonding energy and stress distribution of GO-BN-GBs interface were studied by molecular dynamics (MD). The results show that the GBs and FGs exert a remarkable reduction in failure stress and strain as well as Von-mises stress, thereby decreasing the stability capacity and interfacial mechanical properties of GS under axial tension. However, the presence of GBs and FGs can slight improve the bending rigidities of 2D materials due to their interactions induced out-plane deformation. In addition, the geometrical effects and built-in distorted stress very sensitive to FGs types, FGs density and distance between GBs and FGs. The distortion stress and geometrical deformation caused by GBs and FGs will enrich the growth morphology of 2D materials, and will also lead to more interesting scientific problems, which is expected to become a new growth point of 2D materials research in the future.

Learning-based low-rank denoising

The denoising of 2D images through low-rank methods is a relevant topic in digital image processing. This paper proposes a novel method that trains a learning network to predict the optimal thresholds of the singular value decomposition involved in the low-rank denoising of 2D images. To improve the denoising results, we apply the block-matching algorithm and classify each 3D block according to four parameters, which increase the specificity of the network for the prediction of the thresholds. Our method outperforms state-of-the-art methods for image denoising; furthermore, it is general with respect to the type of noise and provides an upper bound to the accuracy of the denoising of 2D images through the Singular Value Decomposition.

BM3D-based denoising method for color polarization filter array.

Color split-focal plane polarization imaging systems are composed of image sensors with a color polarization filter array (CPFA). The noise generated during image acquisition leads to incorrect estimation of the color polarization information. Therefore, it is necessary to denoise CPFA image data. In this study, we propose a CPFA block-matching and 3D filtering (CPFA-BM3D) algorithm for CPFA image data. The algorithm makes full use of the correlation between different polarization channels and different color channels, restricts the grouping of similar 2D image blocks to form 3D blocks, and attenuates Gaussian noise in the transform domain. We evaluate the denoising performance of the proposed algorithm using simulated and real CPFA images. Experimental results show that the proposed method significantly suppresses noise while preserving the image details and polarization information. Its peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) indicators are superior to those of the other existing methods. The mean values of the PSNR and SSIM of the degree of linear polarization (DoLP) color images calculated through CPFA image interpolation can be increased to 200% and 400%, respectively, by denoising with the proposed method.

Touchless palmprint recognition based on 3D Gabor template and block feature refinement

With the growing demand for hand hygiene, touchless palmprint recognition has made great strides recently, providing an effective solution for personal identification. Despite many efforts devoted to this area, the discriminative ability of touchless palmprint is still uncertain, especially for large-scale datasets. To address this problem, in this study, a large-scale touchless palmprint dataset was built containing 2334 palms from 1167 individuals. To the best of our knowledge, this is currently the largest touchless palmprint image benchmark regarding the number of individuals and palms. In addition, a novel deep learning framework was proposed for touchless palmprint recognition, referred to as 3D convolution palmprint recognition network (3DCPN), which leverages 3D convolutions to dynamically integrate multiple Gabor features. In 3DCPN, a novel variant of the Gabor filter is embedded into the first layer to enhance curve feature extraction. Using a well-designed ensemble scheme, low-level 3D features are convolved to extract high-level features. Finally, a region-based loss function is implemented to strengthen the discriminative ability of both global and local descriptors. Extensive experiments were conducted on the newly built dataset and other popular databases to demonstrate the superiority of our method. The results show that the proposed 3DCPN achieves state-of-the-art or comparable performance.

Material Removal Optimization Strategy of 3D Block Cutting Based on Geometric Computation Method

During the material removal stage in stone rough processing, milling type has been widely explored, which, however, may cause time and material consumption, as well as substantial stress for the environment. To improve the material removal rate and waste reuse rate in the rough processing stage for three-dimensional stone products with a special shape, in this paper, circular saw disc cutting is explored to cut a convex polyhedron out of a blank box, which approaches a target product. Unlike milling optimization, this problem cannot be well solved by mathematical methods, which have to be solved by geometrical methods instead. An automatic block cutting strategy is proposed intuitively by considering a series of geometrical optimization approaches for the first time. To obtain a big removal block, constructing cutting planes based on convex vertices is uniquely proposed. Specifically, the removal vertices (the maximum thickness of material removal) are searched based on the octree algorithm, and the cutting plane is constructed based on this thickness to guarantee a relatively big removal block. Moreover, to minimize the cutting time, the geometrical characteristics of the intersecting convex polygon of the cutting plane with the convex polyhedron are analyzed, accompanied by the constraints of the guillotine cutting mode. The optimization algorithm determining the cutting path is presented with a feed direction accompanied by the shortest cutting stroke, which confirms the shortest cutting time. From the big removal block and shortest cutting time, the suboptimal solution of the average material removal rate (the ratio of material removal volume to cutting time) is generated. Finally, the simulation is carried out on a blank box to approach a bounding sphere both on MATLAB and the Vericut platform. In this case study, for the removal of 85% of material with 19 cuts, the proposed cutting strategy achieves five times higher the average material removal rate than that of one higher milling capacity case.

Three-dimensional discontinuous deformation analysis derived from the virtual work principle with a simplex integral on the boundary

Three-dimensional discontinuous deformation analysis (3D-DDA) is a powerful deformable discrete element method that is derived from the principle of minimum potential energy. However, the principle cannot be easily applied for all constitutive models, and certain forces do not have corresponding potential energy, which can impede DDA from being applied to more complex problems. In addition, since simplex integration in 3D can only be applied to tetrahedrons, no accurate integral is presented in 3D-DDA for polygonal boundaries of 3D blocks. Thus, face constraints have rarely been employed in 3D DDA. This study derives the DDA governing equation from the virtual work principle. The submatrices of stress, inertia, point force, face loading, body force, fixed boundary, roller boundary, bolts, implicit contact forces, and explicit contact forces are reformulated. An accurate integral on polygonal boundaries of 3D blocks is specified with the face integral of scalar fields and 2D simplex integration. Several numerical examples corroborate the correctness of the reformulation and merits of the face constraints.

Simulation of Polyvinylidene Fluoride, Zinc Sulfide, and Cadmium Sulfide as Lead-Free Piezoelectric Material

This paper presents a simulation of three different types of lead-free piezoelectric materials for energy harvesting. Polyvinylidene Fluoride (PVDF), Zinc Sulfide (ZnS), and Cadmium Sulfide (CdS) are simulated using COMSOL Multiphysics to evaluate the frequency response and electrical potential for each materials. The simulation consisted of two parts which is 3D block cantilever for simulating frequency response and total displacement. The second part is 2D block bimorph to simulate power generated by varying frequency responses. The simulated result for the first shows that frequency response for each materials is differents for ZnS, PVDF and CdS which 30.897 kHz, 8.517 kHz, and 22.118 kHz. For total displacement is 303 µm which same for each materials. Each material is simulated for various cantilever beam thicknesses ranging from 1-4 µm and result ZnS having the greatest frequency response. For 2D block bimorph model, the highest electric potential is 0.75 V at 60 Hz frequency for ZnS. Meanwhile for CdS and PVDF has less electric potential which 0.6 V and 0.4V at 60 Hz frequency response. For power disspation, ZnS generate 10% more power compare to CdS and PVDF. In the end of the paper, ZnS is excellent lead free material compared to CdS and PVDF in term of aforementioned parameter studied.

RDAU-Net: Based on a Residual Convolutional Neural Network With DFP and CBAM for Brain Tumor Segmentation.

Due to the high heterogeneity of brain tumors, automatic segmentation of brain tumors remains a challenging task. In this paper, we propose RDAU-Net by adding dilated feature pyramid blocks with 3D CBAM blocks and inserting 3D CBAM blocks after skip-connection layers. Moreover, a CBAM with channel attention and spatial attention facilitates the combination of more expressive feature information, thereby leading to more efficient extraction of contextual information from images of various scales. The performance was evaluated on the Multimodal Brain Tumor Segmentation (BraTS) challenge data. Experimental results show that RDAU-Net achieves state-of-the-art performance. The Dice coefficient for WT on the BraTS 2019 dataset exceeded the baseline value by 9.2%.

Structural DNA Nanotechnology: Immobile Holliday Junctions to Artifi.

DNA nanotechnology marvels the scientific world with its capabilities to design, engineer, and demonstrate nanoscale shapes. This review is a condensed version walking the reader through the structural developments in the field over the past 40 years starting from the basic design rules of the double-stranded building block to the most recent advancements in self-assembled hierarchically achieved structures to date. It builds from the fundamental motivation of building 3-dimensional (3D) lattice structures of tunable cavities going all the way up to artificial nanorobots fighting cancer. The review starts by covering the most important developments from the fundamental bottom-up approach of building structures, which is the 'tile' based approach covering 1D, 2D, and 3D building blocks, after which the top-down approach using DNA origami and DNA bricks is also covered. Thereafter, DNA nanostructures assembled using not so commonly used (yet promising) techniques like i-motifs, quadruplexes, and kissing loops are covered. Highlights from the field of dynamic DNA nanostructures have been covered as well, walking the reader through various approaches used within the field to achieve movement. The article finally concludes by giving the authors a view of what the future of the field might look like while suggesting in parallel new directions that fellow/future DNA nanotechnologists could think about.

Virtual strike and dip – advancing inclusive and accessible field geology

Abstract. Accessibility and inclusivity in field geology have become increasingly important issues to address in geoscience education and have long been set aside due to the tradition of field geology and the laborious task of making it inclusive to all. Although a popular saying among geologists is “the best geologists see the most rocks”, field trips cost money, time, and are only accessible to those who are physically able to stay outside for extended periods. With the availability of 3D block diagrams, an onslaught of virtual learning environments is becoming increasingly viable. Strike and dip is at the core of any field geologist's education and career; learning and practicing these skills is fundamental to making geologic maps and understanding the regional geology of an area. In this paper, we present the Strike and Dip virtual tool (SaD) with the objective of teaching the principles of strike and dip for geologic mapping to introductory geology students. We embedded the SaD tool into an introductory geology course and recruited 147 students to participate in the study. Participants completed two maps using the SaD tool and reported on their experiences through a questionnaire. Students generally perceived the SaD tool positively. Furthermore, some individual differences among students proved to be important contributing factors to their experiences and subjective assessments of learning. When controlling for participants' past experience with similar software, our results indicate that students highly familiar with navigating geographical software perceived the virtual environment of the tool to be significantly more realistic and easier to use compared with those with lower levels of familiarity. Our results are corroborated by a qualitative assessment of participants' feedback to two open-ended questions, highlighting both the overall effectiveness of the SaD tool and the effect of geographical software familiarity on measures of experience and learning.