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The extraction of geometric features such as holes, arcs, and surfaces of mechanical parts is crucial for quality control. The existing methods for geometrical feature segmentations on 3D point clouds still have limitations, especially for simultaneously extracting multiple types of geometric features from comprehensive workpieces. To this end, this study investigates segmentation methods that take 3D point cloud datasets of mechanical parts as inputs, and employs an improved PointNet++ deep learning model to solve this extraction difficulty. Firstly, the Set Abstraction module in PointNet++ was modified by incorporating Self-Attention mechanisms to increase interactivity and global correlation among data points. Then, the local feature extraction Multilayer Perceptron (MLP) from PointNet-Transformer was integrated to enhance the feature extraction accuracy. Due to the inherent class imbalance issue, the Focal Tversky Loss is employed as the loss function to ensure that geometric features with relatively lower proportions can be fully trained. Finally, the Statistical filtering algorithm is utilized to mitigate noise and attenuate subtle irregularities, such that the smoothness of geometric features can be substantially enhanced. The experimental results demonstrate that the proposed model achieves an accuracy of 86.6% on geometric feature segmentations and a mean Intersection over Union (mIoU) of 0.84. The comparison with the original PointNet++ proves that the proposed method can improve accuracy and mIoU by 3.7% and 0.03 respectively. [ABSTRACT FROM AUTHOR]

In the current landscape dominated by Artificial Intelligence, the integration of Machine Learning and Deep Learning within the realm of Cultural Heritage, particularly within architectural contexts, is paramount for the efficient processing and interpretation of point clouds. These advanced methods facilitate automated segmentation and classification, significantly improving both the clarity and practical use of data acquired from laser scanning and photogrammetry. The present study investigates the Palacio de Sástago—a prominent Renaissance palace in Zaragoza, Spain—and introduces a cutting-edge modus operandi for the automated recognition of architectural elements within the palace's inner courtyard. Employing the well-established Random Forest algorithm, implemented in a Python environment, the framework begins with a comprehensive evaluation of the geometric features identified in the LiDAR point cloud. This process employs the Mean Decrease in Impurity metric to evaluate the relevance of each variable. To boost the accuracy and efficiency of the final classifications, the features are refined post-assessment, enhancing both the training phase and the algorithm's later evaluation. The research's findings demonstrate significant potential, supporting advancements in CAD systems and HBIM that will enable more precise, automated modelling of architectural elements, thereby enhancing the accuracy of digital reconstructions and improving conservation planning for heritage sites. [ABSTRACT FROM AUTHOR]

Many aerodynamic shape optimization methods often focus on utilizing the end-to-end relationship between design variables and aerodynamic performance to find the optimal design, while overlooking the exploration of geometric knowledge of the shape itself. To fully use geometric knowledge to improve optimization efficiency, this paper proposes an efficient method by exploring the potential correlation between geometric features and aerodynamic performance at a low cost. We use unsupervised isometric feature mapping in manifold learning to capture geometric features that can distinguish the aerodynamic performance of different airfoils without embedding any tags. Then a filter criterion is establish based on the geometric features. During the optimization process, airfoils that deliver poor aerodynamic performance can be filtered out with a high probability before being precisely evaluated through computational fluid dynamics simulations. This helps improve samples quality to enhance the optimization efficiency. We applied the proposed method to the unconstrained and constrained optimizations of the Royal Aircraft Establishment (RAE) 2822 airfoil to validate its performance. The results demonstrate that the proposed method can improve the efficiency of optimization by over 50% compared with the original evolutionary optimization algorithm. It performs well across various optimization problems, demonstrating high engineering practical value. [ABSTRACT FROM AUTHOR]

Mobile laser scanners are vital for intelligent transport infrastructure, capturing detailed 3D road representations, but their accuracy depends on factors like sensor positioning and environment. This study compares two van-mounted Mobile Laser Scanners (MLS): the dual head Lynx Mobile Mapper and the single head VUX-1 HA, along with the terrestrial laser scanner Faro Focus XX30. Using point cloud reference data from Faro Focus XX30 and GNSS data from Trimble R8, performance is assessed in road, urban, and semi-urban environments. Accuracy is measured by the difference between Trimble GNSS and MLS coordinates. Geometric features of each LiDAR are compared, and mapping tasks in road and urban areas are performed using a machine learning classifier. Results show the MLS-single head scanner achieves satisfactory accuracy in roads and semi-urban areas, while Faro performs better in urban settings for classification. MLS-single head excels in road environments, while Faro is superior in urban ones. This analysis aids researchers and professionals in selecting the appropriate mobile laser scanner for mapping transport infrastructure, providing valuable insights into MLS systems' comparative performance across different environments. [ABSTRACT FROM AUTHOR]

In the field of computer vision and pattern recognition, knowledge based on images of human activity has gained popularity as a research topic. Activity recognition is the process of determining human behavior based on an image. We implemented an Extended Kalman filter to create an activity recognition system here. The proposed method applies an HSI color transformation in its initial stages to improve the clarity of the frame of the image. To minimize noise, we use Gaussian filters. Extraction of silhouette using the statistical method. We use Binary Robust Invariant Scalable Keypoints (BRISK) and SIFT for feature extraction. The next step is to perform feature discrimination using Gray Wolf. After that, the features are input into the Extended Kalman filter and classified into relevant human activities according to their definitive characteristics. The experimental procedure uses the SUB-Interaction and HMDB51 datasets to a 0.88% and 0.86% recognition rate. [ABSTRACT FROM AUTHOR]

Problem: Significant differences exist between the body dimensions of Chinese and Western individuals, and these differences affect the injury characteristics of passengers in traffic accidents. Most current parametric finite element human models are based on Western body data, which does not adequately assess the injury risks for Chinese individuals. Therefore, it is imperative to establish a digital model that reflects the anatomical features of Chinese humans. This requires comprehensive anthropometric data specific to the Chinese population. Aim: This study aims to collect and analyze skeletal geometric parameters from various representative regions in China across different age groups to fill the gap in Chinese anthropometric data. Additionally, it seeks to explore regional and age-related differences in these parameters, providing a scientific basis for developing finite element models that reflect the characteristics of the Chinese population. Methods: Clinical CT data of skeletal geometric parameters were collected from 224 individuals across seven representative regions of China, including parameters of the head, chest, and lower limbs. Descriptive statistics, K-W tests, and U-tests were used to analyze regional differences. Spearman correlation and linear regression analyses were employed to assess the impact of age. Furthermore, weighted averages and weighted variances of skeletal geometric parameters were calculated to reflect the characteristics of individuals from various regions across the country, which will be applied in the development of Chinese human models. Results: The results indicate significant regional differences in the skeletal geometric parameters of Chinese individuals, exhibiting regional patterns between the north–south and east–west areas. While skeletal geometric parameters showed significant differences with age, no clear statistical pattern was observed. Conclusion: This study systematically extracts and analyzes the skeletal geometric parameters of the Chinese population from regional and age perspectives for the first time. These data will be applied in the development of Chinese human models, providing crucial support for improving passenger safety in traffic accidents, enhancing vehicle safety design, and establishing anthropometric standards suitable for the Chinese context. [ABSTRACT FROM AUTHOR]

Due to different acquisition time, viewpoint, and sensor noise during the process of point cloud data acquisition, the captured point clouds typically exhibit partial overlapped and contain large amounts of noise and outliers. However, this circumstance tends to diminish the accuracy of point-to-point correspondence searches. Existing point-level methods rely on idealized point-to-point correspondences, which cannot be guaranteed in practical applications. To address above limitations, a noval network based on a geometry transformer and a Gaussian Mixture Model (GMM) is proposed, called GTGMM. Specifically, we formulate the registration problem as the problem of aligning the two Gaussian mixtures, leveraging the advantages of the statistic model and learned robust features to overcome the noise and outliers variants. We utilize a Local Feature Extractor (LFE) to extract structural features of point clouds, while the Transformer encoders establish global relations among the point clouds. Additionally, a geometry transformer network is introduced to capture geometric relations within the point cloud, and overlap scores are learned to reject non-overlapping regions. Utilizing overlap scores, point cloud features, and 3D point cloud coordinates, the matching parameters of GMM to calculate to guide the alignment of two point clouds. Experimental results on synthetic datasets and the real Terracotta Warriors data demonstrate that our method achieves high accuracy and robustness under various registration conditions. [ABSTRACT FROM AUTHOR]

High electrical conductivity, along with high piezoresistive sensitivity and stretchability, are crucial for designing and developing nanocomposite strain sensors for damage sensing and on-line structural health monitoring of smart carbon fiber-reinforced polymer (CFRP) composites. In this study, the influence of the geometric features and loadings of carbon-based nanomaterials, including reduced graphene oxide (rGO) or carbon nanofibers (CNFs), on the tunable strain-sensing capabilities of epoxy-based nanocomposites was investigated. This work revealed distinct strain-sensing behavior and sensitivities (gauge factor, GF) depending on both factors. The highest GF values were attained with 0.13 wt.% of rGO at various strains. The stability and reproducibility of the most promising self-sensing nanocomposites were also evaluated through ten stretching/relaxing cycles, and a distinct behavior was observed. While the deformation of the conductive network formed by rGO proved to be predominantly elastic and reversible, nanocomposite sensors containing 0.714 wt.% of CNFs showed that new conductive pathways were established between neighboring CNFs. Based on the best results, formulations were selected for the manufacturing of pre-impregnated materials and related smart CFRP composites. Digital image correlation was synchronized with electrical resistance variation to study the strain-sensing capabilities of modified CFRP composites (at 90° orientation). Promising results were achieved through the incorporation of CNFs since they are able to form new conductive pathways and penetrate between micrometer-sized fibers. [ABSTRACT FROM AUTHOR]

To study the influence of vegetation on shadow extraction, an anti-interference DCNN was developed to extract shadows and vegetation from QuickBird images. The vegetation features, color and geometric information were analyzed qualitatively and quantitatively, and the regression equation of shadow extraction accuracy was obtained. We got that the average brightness value of vegetation in R and G band and the standard deviation of brightness in G band are significantly negatively correlated with the recall rate of shadow extraction, P>0.05;and the standard deviation of the inner edge ratio of vegetation boundary complexity is significantly positively correlated with the recall rate. [ABSTRACT FROM AUTHOR]

This study aims to create a non-contact system for recognizing the sitting postures of office workers, applicable to healthy sitting monitoring. Skeletal point data were obtained via a depth sensor-based Kinect device while subjects performed five different sitting postures. Five angles have been calculated that can differentiate these postures. A fuzzy rule-based automated approach using angle values is proposed to label the data. With this method, two different data sets were created using traditional time-based labeling methods. Angular and geometric features were used to classify the depth values, and 99.6% and 98.9% accuracy were obtained with KNN and Adaboost classifiers. The proposed labeling method outperformed the traditional time-based labeling method according to the classification results. This system offers a highperformance solution for promoting healthy sitting habits in office workers and has applications in health monitoring and robot vision. [ABSTRACT FROM AUTHOR]

Chip formation is considered one important indicator to evaluate machining processes. In particular, geometric features of chips may provide important insights for the assessment of machining stability and productivity. In fixed-abrasive machining processes, such as grinding and honing, chips are simultaneously produced by many of the geometrically undefined cutting edges. Despite being "undefined," geometric features of abrasive grains can still be statistically characterized or described. Accordingly, it can be assumed that, under stable machining conditions, the geometric features of abrasive chips may also conform to certain statistical patterns. However, statistical characterization of abrasive chips can be very challenging due to their large quantity, irregular shapes, minuscule size, and sometimes tangled condition. In this study, an analysis method combining metallographic preparation and micro-computed tomography (micro-CT) has been developed to characterize abrasive chips produced by a honing stone. The results regarding the geometric characteristics of the obtained massive abrasive chips, i.e., their sizes and shapes, were presented and statistically described. It was found most chips had a size around 50 µm and the shape being a slightly elongated and curved cone. Most of the geometric features could meet a positive skewness distribution. [ABSTRACT FROM AUTHOR]

The elliptic Fourier transform is a common method of describing the shape of objects by an unique sequence of coefficients that allow comparing the shapes by mathematical methods. However, the raw coefficients contain unnecessary data unrelated to the shape, which does not provide a correct comparison. For this reason, the coefficients are normalized. This removes some of the superfluous data, but leaves information about mirror symmetry and the order in which the contour vertices are declared, that are encoded in the signs of the coefficients. This also interferes with shape comparison. The paper describes an algorithm for harmonizing the coefficients, leveling the influence of the information mentioned. Based on the example of attachment organs of monogeneans, the advantages of using harmonized coefficients for comparing the shapes of biological structures are shown. [ABSTRACT FROM AUTHOR]

Automatic matching of multisource data is an important technique for achieving change detection, fusion and updating spatial data. However, most current learning methods for building footprint matching require a large number of samples, and labeling these samples is costly in terms of labor and time. Moreover, multisource building footprint data are complex and diverse leading to recognizing the different matching relationships is a hard task. Thus, this study proposes a learning-based method for recognizing multisource building footprints matching relationships by using a one-class support vector machine (OCSVM). The OCSVM was trained using only positive samples. First, a set of geometric indicators was designed to train a model and realize initial matching recognition. Then, a contextual metric was calculated based on the rough matching results, and geometric and contextual metrics were combined to train the model and realize relaxed matching recognition. Relaxed matching is an optimization process implemented after initial matching to recognize more relaxed matching relationships. In relaxed matching, a convex hull is used to recognize matching relationships besides 1:1, such as 1:n, m:1 and m:n. The experimental results showed that the proposed method outperformed indicator-weighted (weighted average) and learning-based matching methods, such as traditional SVMs and decision trees (DTs). The precision scores of the proposed model were 97.1%, 95% and 97.2% for the Wuhan (China), Beijing (China) and Richmond Hill (Canada) datasets, respectively. Furthermore, the proposed model identified the matching relationships of buildings with complex geometric features and high-density spatial distributions. [ABSTRACT FROM AUTHOR]

Mobile laser scanners are vital for intelligent transport infrastructure, capturing detailed 3D road representations, but their accuracy depends on factors like sensor positioning and environment. This study compares two van-mounted Mobile Laser Scanners (MLS): the dual head Lynx Mobile Mapper and the single head VUX-1 HA, along with the terrestrial laser scanner Faro Focus XX30. Using point cloud reference data from Faro Focus XX30 and GNSS data from Trimble R8, performance is assessed in road, urban, and semi-urban environments. Accuracy is measured by the difference between Trimble GNSS and MLS coordinates. Geometric features of each LiDAR are compared, and mapping tasks in road and urban areas are performed using a machine learning classifier. Results show the MLS-single head scanner achieves satisfactory accuracy in roads and semi-urban areas, while Faro performs better in urban settings for classification. MLS-single head excels in road environments, while Faro is superior in urban ones. This analysis aids researchers and professionals in selecting the appropriate mobile laser scanner for mapping transport infrastructure, providing valuable insights into MLS systems’ comparative performance across different environments.

Many aerodynamic shape optimization methods often focus on utilizing the end-to-end relationship between design variables and aerodynamic performance to find the optimal design, while overlooking the exploration of geometric knowledge of the shape itself. To fully use geometric knowledge to improve optimization efficiency, this paper proposes an efficient method by exploring the potential correlation between geometric features and aerodynamic performance at a low cost. We use unsupervised isometric feature mapping in manifold learning to capture geometric features that can distinguish the aerodynamic performance of different airfoils without embedding any tags. Then a filter criterion is establish based on the geometric features. During the optimization process, airfoils that deliver poor aerodynamic performance can be filtered out with a high probability before being precisely evaluated through computational fluid dynamics simulations. This helps improve samples quality to enhance the optimization efficiency. We applied the proposed method to the unconstrained and constrained optimizations of the Royal Aircraft Establishment (RAE) 2822 airfoil to validate its performance. The results demonstrate that the proposed method can improve the efficiency of optimization by over 50% compared with the original evolutionary optimization algorithm. It performs well across various optimization problems, demonstrating high engineering practical value.

Three-dimensional (3D) branch structures provide vital information for understanding tree phenotypic characteristics and for ecological studies related to carbon sequestration. Light detection and ranging (LiDAR) has been widely applied to capture the 3D structural information of individual trees. Wood–leaf separation and tree skeleton extraction are essential prerequisites for accurately estimating tree attributes (e.g., stem volume, aboveground biomass, and crown characteristics) and representing the tree branch network. Owing to the complex internal branch morphology and intercanopy component occlusion, precise extraction of the tree skeleton from point clouds remains a challenging issue. In this study, we propose an improved approach for extracting tree skeletons on the basis of the geometric features of point clouds. The approach consists of two steps: separation of the wood and leaves, followed by extraction of the tree skeleton. In the first step, the point clouds of the trees are sliced horizontally. The density-based spatial clustering of applications with noise (DBSCAN) algorithm is then employed to cluster each layer of the point clouds and detect the main trunk. Subsequently, random sample consensus (RANSAC) circle feature detection and linear feature constraints are applied to achieve wood–leaf separation. In the second step, the wood point clouds are used to extract the initial tree skeleton via a minimum spanning tree (MST), and the initial tree skeleton is further optimized. Various comparative experiments are conducted on terrestrial-LiDAR-scanned data from nine trees across six species. The results show that the proposed method performs effectively, with overall wood–leaf separation accuracies ranging from 86% to 93%. Additionally, the extracted branch skeleton accurately reflects the natural geometric structure of the trees. The wood points and tree skeletons are further used to estimate tree attributes, demonstrating the potential of our method for the quantitative representation of trees and their ecological characteristics (e.g., carbon sequestration).

Size reduction of a point cloud or triangulated mesh is an intrinsic part of a three-dimensional (3D) documentation process, reducing the data volume and filtering out erroneous and redundant data obtained during acquisition. Additional reduction has an effect on the geometric accuracy of 3D data compared to the tangible object, and for 3D objects utilized in various cultural heritage applications, the small geometric properties of an object are equally as important as the large ones. In this paper, we investigate several simplification algorithms and various geometric features' relevance to geometric accuracy during the reduction of a 3D object's data size, and whether any of these features have a particular relation to the results of an algorithmic approach. Different simplification algorithms have been applied to several primitive geometric shapes at several reduction stages, and measured values for geometric features and accuracy have been tracked across every stage. We then compute and analyze the correlation between these values to see the effect each algorithm has on different geometries, and whether some of them are better suited for a simplification process based on the geometric features of a 3D object. [ABSTRACT FROM AUTHOR]

Three-dimensional point cloud evaluation is used in photogrammetry to validate and assess the accuracy of data acquisition in order to generate various three-dimensional products. This paper determines the optimal accuracy and correctness of a 3D point cloud produced by a low-cost spherical camera in comparison to the 3D point cloud produced by laser scanner. The fisheye images were captured from a chessboard using a spherical camera, which was calibrated using the commercial Agisoft Metashape software (version 2.1). For this purpose, the results of different calibration methods are compared. In order to achieve data acquisition, multiple images were captured from the inside area of our case study structure (an underpass in Wiesbaden, Germany) in different configurations with the aim of optimal network design for camera location and orientation. The relative orientation was generated from multiple images obtained by removing the point cloud noise. For assessment purposes, the same scene was captured with a laser scanner to generate a metric comparison between the correspondence point cloud and the spherical one. The geometric features of both point clouds were analyzed for a complete geometric quality assessment. In conclusion, this study highlights the promising capabilities of low-cost spherical cameras for capturing and generating high-quality 3D point clouds by conducting a thorough analysis of the geometric features and accuracy assessments of the absolute and relative orientations of the generated clouds. This research demonstrated the applicability of spherical camera-based photogrammetry to challenging structures, such as underpasses with limited space for data acquisition, and achieved a 0.34 RMS re-projection error in the relative orientation step and a ground control point accuracy of nearly 1 mm. Compared to the laser scanner point cloud, the spherical point cloud reached an average distance of 0.05 m and acceptable geometric consistency. [ABSTRACT FROM AUTHOR]

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The aim of the paper is to identify an efficient method for transforming the point cloud into parametric objects in the fields of architecture, engineering and construction by four main steps: 3D survey of the structure under investigation, generation of a new point cloud based on feature extraction and identification of suitable threshold values, geometry reconstruction by semi-automatic process performed in Rhinoceros/Grasshopper and BIM implementation. The developed method made it possible to quickly obtain geometries that were very realistic to the original ones as shown in the case study described in the paper. In particular, the application of ShrinkWrap algorithm on the simplify point cloud allowed us to obtain a polygonal mesh model without errors such as holes, non-manifold surfaces, compenetrating surfaces, etc. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to assess the relationship between the geometric features of tibial eminence and susceptibility to noncontact anterior cruciate ligament (ACL) injuries. Methods: Patients with unilateral noncontact knee injuries between 2015 and 2021 were consecutively enroled in this study. Based on knee magnetic resonance imaging (MRI) and arthroscopic visualisation, patients were categorised into the case group (ACL rupture) and control group (ACL intact). Using MRI, the geometric features of tibial eminence were characterised by measuring the sagittal slopes, depth of concavity and coronal slopes of the inclined surfaces of the tibial spines. Univariate and multivariate logistic regressions were conducted to explore independent associations between quantified geometric indices of tibial eminence and the risk of noncontact ACL injuries. Results: This study included 187 cases and 199 controls. A decreased sagittal slope of the medial tibial spine (MTSSS) (combined group: odds ratio [OR]: 0.87 [0.82, 0.92], p < 0.001; females: OR: 0.88 [0.80, 0.98], p = 0.020; males: OR: 0.87 [0.81, 0.93], p < 0.001) and an increased depth of concavity in the lateral tibial spine (LTSD) (combined group: OR: 1.51 [1.24, 1.85], p < 0.001; females: OR: 1.65 [1.12, 2.43], p = 0.012; males: OR: 1.44 [1.11, 1.89], p = 0.007) were independent risk factors for noncontact ACL injuries. Moreover, a steeper coronal slope of the inclined surface of the medial tibial spine was a significant predictor of noncontact ACL injuries for males (MTSCS: OR: 1.04 [1.01, 1.08], p = 0.015) but not for females. Conclusion: Geometric features of tibial eminence, particularly a decreased MTSSS and an increased LTSD, were identified as independent risk factors for noncontact ACL injuries. These findings will help clinicians identify individuals at high risk of ACL injury and facilitate the development of targeted prevention strategies. Level of Evidence: Level III. [ABSTRACT FROM AUTHOR]

The precise characterization of the rock microstructure is crucial for predicting the physical characteristics, flow behavior, and mechanical properties of rocks. This is particularly important for carbonate rocks, which depict a complex microstructure with multimodal pore radius distribution and natural fractures. Here, topological features that are typically ignored are taken into account to quantify the carbonate microstructure. Carbonate samples used are obtained from the Gaoshiti-Moxi block of the Sichuan Basin, which showed remarkable potential for oil and gas. Specifically, nuclear magnetic resonance (NMR), X-ray micro-computed tomography (micro-CT), and mercury injection capillary pressure (MICP) techniques are performed to describe the topological and geometric characteristics. The results indicate that NMR and MICP techniques can describe more rock pores than micro-CT. However, due to the presence of pore shielding in MICP tests, the pore radius obtained by MICP is smaller than that obtained by micro-CT and NMR. Furthermore, the effective method used for characterizing the pore structure is NMR technology. The hardest part is that the coefficient between the pore radius and T2 relaxation time is difficult to calculate. Therefore, a better calculation method must be found. In addition, micro-CT is an irreplaceable technique for obtaining a large number of topological and geometric features, and multi-phase or single-phase flow simulations can be conducted via digital rock models. However, for carbonates, micro-CT is not sufficient to describe the complete pore systems because macropores cannot be fully represented and sub-resolution micropores cannot be described. Those macropores and micropores have a very important effect on their seepage properties. Therefore, multi-scale digital rock modeling involving small and large pores is essential for complex rocks, which is of great significance for the analysis of pore systems and the simulation of rock physical properties. [ABSTRACT FROM AUTHOR]

Among different recent technologies proposed for human face classification and recognition, solutions based on analyzing the 3D geometric facial features emerged as a promising academic and practical direction. Researchers have examined both holistic and local approaches to analyzing the 3D face regions to study the impact of facial features in real-life applications such as medical and security implementations. However, a few works have investigated the relevant impact of the extracted geometric features from the descriptive local regions of the human face on identifying human ethnicity. This work proposes a classifier to categorize individuals into their distinctive ethnic groups and deeply analyzes the facial feature variations to highlight the most descriptive parts and features of the human face in race classification. The proposed ML-based classifier is preceded by extracting the 3D facial features from 3D meshes using the recent SIFT and Geodesic distance calculations. In addition, it implements and discusses the initial important preprocessing steps including, cropping the frontal parts, correcting the head pose, selecting the suitable initial key points, aligning the 3D meshes, and implementing the suitable template-based 3D registration. The proposed NN race classifiers are built and evaluated using Headspace, a well-known multi-ethnic dataset, and achieved high accuracy (90% globally, and 100% for the mouth area) especially while using the SIFT features. [ABSTRACT FROM AUTHOR]

The classification of urban functional areas is important for understanding the characteristics of urban areas and optimizing the utilization of urban land resources. Existing related methods have improved accuracy. However, they neglect cognitive differences amongst humans in the different scales of regional functions. Moreover, how to build the correlations of cross-scale characteristics is still unresolved when realizing the classification of multiscale urban functional zones. To resolve these problems, a transportation analysis zone involving urban buildings as research units is created and these units are described by geometric and functional characteristics using multiple data sources. Then, a hierarchical clustering model is built for the recognition of urban functional areas at varying scales with landmark semantic constraints. In the experiments, Shanghai served as the study area, and multiscale zones were created using different levels of road networks considering the constraint correlation of the significance between cross-scale maps. The experiential results show the proposed method has excellent performance and optimizes the functional zone classification at different scales. This study not only enriches the multiscale urban functional area-recognition methods but also can be used in other aspects, like cartographic generalization or spatial analysis. [ABSTRACT FROM AUTHOR]

Automatic detection and assessment of surface cracks are beneficial for understanding the mechanical performance of ultra-high performance concrete (UHPC). This study detects crack evolution using a novel dynamic mode decomposition (DMD) method. In this method, the sparse matrix 'determined' from images is used to reconstruct the foreground that contains cracks, and the global threshold method is adopted to extract the crack patterns. The application of the DMD method to the three-point bending test demonstrates the efficiency in inspecting cracks with high accuracy. Accordingly, the geometric features, including the area and its projection in two major directions, are evaluated over time. The relationship between the geometric properties of cracks and load-displacement curves of UHPC is discussed. Due to the irregular shape of cracks in the spatial domain, the cracks are then transformed into the Fourier domain to assess their development. Results indicate that crack patterns in the Fourier domain exhibit a distinct concentration around a central position. Moreover, the power spectral density of cracks exhibits an increasing trend over time. The investigation into crack evolution in both the spatial and Fourier domains contributes significantly to elucidating the mechanical behavior of UHPC. [ABSTRACT FROM AUTHOR]

The microstructure characteristics of rocks are the internal basic properties of fine-grained rocks, which affect their physicochemical behavior. This paper takes an example as fine-grained grains in the Yanchang Formation 7 Member of Ordos Basin. First, the geometric morphology characteristics of fine-grained grains are analyzed quantitatively by field scanning electron microscope and image recognition technology. Then, we characterized the influence of the sedimentary environment on the geometric shape and orientation arrangement of fine-grained grains. Results show that the grains in the deep-semi-deep lacustrine facies take the characteristics of fine grain size (2-3 μm), flat and angular shape. The grains are directionally distributed along with a continuous interval. The intensity of the distribution shows a flat quasi-cone shape and well-directional orientation. The fine grains in shallow lake facies are medium-fine grains (2-8 μm), flat and angular type. Besides, the grains show a sharp distribution a-long with a certain interval with no obvious regularity, which has a moderate orientation arrangement. The fine grains in delta facies are fine-grained (8-12 μm) and quasi-circular type. This type of fine-grained rock has an irregular distribution. The intensity distribution of fine grains is circular or elliptical. The circumferential angularity index and flatness of grains can promote the formation of oriented arrangement structure of grains. By contrast, the anisotropy rate and grain size are unfavorable for the formation of an oriented structure. The results are helpful to understand the microstructural characteristics of fine-grained rocks and establish the relationship between microstructures and macrostructures of rocks. [ABSTRACT FROM AUTHOR]

Recognizing human emotions simultaneously from multiple data modalities (e.g., face, and speech) has drawn significant research interest, and numerous research contributions have been investigated in the affective computing community. However, most methods concentrate less on facial alignment and keyframe selection for audio-visual input. Hence, this paper proposed a new audio-visual descriptor, mainly concentrating on describing the emotion through only a few frames. For this purpose, we proposed a new self-similarity distance matrix (SSDM), which computes the spatial, and temporal distances through landmark points on the facial image. The audio signal is described through an asset of composite features, including statistical features, spectral features, formant frequencies, and energies. A support vector machine (SVM) algorithm is employed to classify both models, and the final results are fused to predict the emotion. Surrey audiovisual expressed emotion (SAVEE) and Ryerson multimedia research lab (RML) datasets are utilized for experimental validation, and the proposed method has shown significant improvement from the state of art methods. [ABSTRACT FROM AUTHOR]

A malignant tumor in brain is detected using images from Magnetic Resonance scanners. Malignancy detection in brain and separation of its tissues from normal brain cells allows to correctly localizing abnormal tissues in brain's Magnetic Resonance Imaging (MRI). In this article, a new method is proposed for the segmentation and classification of brain tumor based on improved saliency segmentation and best features selection approach. The presented method works in four pipe line procedures such as tumor preprocessing, tumor segmentation, feature extraction and classification. In the first step, preprocessing is performed to extract the region of interest (ROI) using manual skull stripping and noise effects are removed by Gaussian filter. Then tumor is segmented in the second step by improved thresholding method which is implemented by binomial mean, variance and standard deviation. In the third step, geometric and four texture features are extracted. The extracted features are fused by a serial based method and best features are selected using Genetic Algorithm (GA). Finally, support vector machine (SVM) of linear kernel function is utilized for the classification of selected features. The proposed method is tested on two data sets including Harvard and Private. The Private data set is collected from Nishtar Hospital Multan, Pakistan. The proposed method achieved average classification accuracy of above 90% for both data sets which shows its authenticity. [ABSTRACT FROM AUTHOR]

To explore the influence of fracture structure on mechanical properties of rock mass, compression simulation experiment were carried out on rock samples with prefabricated fractures. Based on SEM, NMR and Vic-3D technology, multi-scale study was carried out on rock mass in combination with acoustic and macro-meso-structural characteristics. The rock sample strength decreases with the increase of fracture length, penetration and number, and decreases first and then increases with the increase of fracture dip angle. The rock sample strength of 30 mm fracture is the lowest, and the rock sample strength of 90° fracture is closest to the intact rock sample. Tensile failure occurred in intact rock samples, 0° and 90° fracture samples and tensile and shear composite failure occurred in rock samples with different fracture penetration degrees. Shear failure occurred in rock samples with different fracture lengths, numbers, 30°, 45° and 60° fractures. After loading, the proportion of micro pores decreases while that of medium and large pores increases, indicating that the initial damage and loading caused by prefabricated fractures can change the internal pore structure of rock samples, thus affecting its macroscopic mechanical properties.

The aim of the paper is to identify an efficient method for transforming the point cloud into parametric objects in the fields of architecture, engineering and construction by four main steps: 3D survey of the structure under investigation, generation of a new point cloud based on feature extraction and identification of suitable threshold values, geometry reconstruction by semi-automatic process performed in Rhinoceros/Grasshopper and BIM implementation. The developed method made it possible to quickly obtain geometries that were very realistic to the original ones as shown in the case study described in the paper. In particular, the application of ShrinkWrap algorithm on the simplify point cloud allowed us to obtain a polygonal mesh model without errors such as holes, non-manifold surfaces, compenetrating surfaces, etc.

Three-dimensional point cloud evaluation is used in photogrammetry to validate and assess the accuracy of data acquisition in order to generate various three-dimensional products. This paper determines the optimal accuracy and correctness of a 3D point cloud produced by a low-cost spherical camera in comparison to the 3D point cloud produced by laser scanner. The fisheye images were captured from a chessboard using a spherical camera, which was calibrated using the commercial Agisoft Metashape software (version 2.1). For this purpose, the results of different calibration methods are compared. In order to achieve data acquisition, multiple images were captured from the inside area of our case study structure (an underpass in Wiesbaden, Germany) in different configurations with the aim of optimal network design for camera location and orientation. The relative orientation was generated from multiple images obtained by removing the point cloud noise. For assessment purposes, the same scene was captured with a laser scanner to generate a metric comparison between the correspondence point cloud and the spherical one. The geometric features of both point clouds were analyzed for a complete geometric quality assessment. In conclusion, this study highlights the promising capabilities of low-cost spherical cameras for capturing and generating high-quality 3D point clouds by conducting a thorough analysis of the geometric features and accuracy assessments of the absolute and relative orientations of the generated clouds. This research demonstrated the applicability of spherical camera-based photogrammetry to challenging structures, such as underpasses with limited space for data acquisition, and achieved a 0.34 RMS re-projection error in the relative orientation step and a ground control point accuracy of nearly 1 mm. Compared to the laser scanner point cloud, the spherical point cloud reached an average distance of 0.05 m and acceptable geometric consistency.

The health services research network is showing a lot of interest in the Internet of Medical Things (IoMT). In IoMT, the Internet is used to help compile important health-related data. A brain tumor is caused by a mass of random cells inside the brain, which is dangerous and harmful to the brain. Today, it is difficult to accurately recognise brain images. In order to find and correctly categorize malignant cells in recognizing brain pictures, this research offers a support value-based deep neural network (SDNN) in e-Health care administration utilizing the IoMT innovation. As a starting point, a database of investigation is created using picture data based on IoT innovation and clinical images. The input brain picture is subjected to skull stripping during the preprocessing stage in order to isolate the desired brain area. The preprocessed output pictures are then used to extract the useful characteristics, such as entropy, geometric, and texture features. Finally, based on the collected characteristics, the proposed support value based adaptive deep neural network (SDNN) identification classifies the brain pictures as normal or abnormal. The results of the experiments are examined to show how the suggested recognition approach outperforms the ones already in use.

The use of a Machine Learning (ML) classification algorithm to classify airborne urban Light Detection And Ranging (LiDAR) point clouds into main classes such as buildings, terrain, and vegetation has been widely accepted. This paper assesses two strategies to enhance the effectiveness of the Deep Learning (DL) classification algorithm. Two ML classification approaches are developed and compared in this context. These approaches utilize the DL Pipeline Network (DLPN), which is tailored to minimize classification errors and maximize accuracy. The geometric features calculated from a point and its neighborhood are analyzed to select the features that will be used in the input layer of the classification algorithm. To evaluate the contribution of the proposed approach, five point-clouds datasets with different urban typologies and ground topography are employed. These point clouds exhibit variations in point density, accuracy, and the type of aircraft used (drone and plane). This diversity in the tested point clouds enables the assessment of the algorithm's efficiency. The obtained high classification accuracy between 89% and 98% confirms the efficacy of the developed algorithm. Finally, the results of the adopted algorithm are compared with both rule-based and ML algorithms, providing insights into the positioning of DL classification algorithms among other strategies suggested in the literature. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As a typical application of intelligent transportation systems (ITS), the electronic toll collection (ETC) system has been widely used on highways due to its excellent toll efficiency. In the actual traffic environment, there usually exists more than one vehicle in the radiation area of the road-side units (RSU) at the same time, and the front vehicle will affect the ray paths between the RSU and the on-board units (OBU) of the back vehicle. Here we studied the front vehicle’s impact on the ETC system’s path propagation loss by ray tracing technology and Uniform Theory of Diffraction (UTD). Firstly, we simplified the vehicle body structure into two equivalent geometric models. Four situations were considered according to the vehicles’ front and rear positions. We analyzed each scenario’s propagation loss models under line-of-sight (LOS) and non-line-of-sight (NLOS) conditions based on the distance change among RSU, OBU, and the front vehicle. Finally, we developed an ETC comprehensive test equipment to measure the propagation loss of the four scenarios at a toll station. Both simulation and experiment results indicate that the propagation loss models proposed in this paper are valid.

Understanding the binding behavior and conformational dynamics of intrinsically disordered proteins (IDPs) is crucial for unraveling their regulatory roles in biological processes. However, their lack of stable 3D structures poses challenges for analysis. To address this, we propose an algorithm that explores IDP binding behavior with protein complexes by extracting topological and geometric features from the protein surface model. Our algorithm identifies a geometrically favorable binding pose for the IDP and plans a feasible trajectory to evaluate its transition to the docking position. We focus on IDPs from Homo sapiens and Mus-musculus, investigating their interaction with the Plasmodium falciparum (PF) pathogen associated with malaria-related deaths. We compare our algorithm with HawkDock and HDOCK docking tools for quantitative (computation time) and qualitative (binding affinity) measures. Our results indicated that our method outperformed the compared methods in computation performance and binding affinity in experimental conformations. [ABSTRACT FROM AUTHOR]

An algorithm providing information on the key geometric features of an aorta extracted from multi-slice computed tomography images is proposed. Using the numerical method, the aorta's geometric characteristics, such as vessel cross-sectional areas and diameters, as well as distances between arteries, can be determined. This step is crucial for training the meta-model necessary to construct an expert system with a significantly reduced volume of data and for identifying key relationships between diagnoses and geometric and hydrodynamic features. This methodology is expected to be part of an innovative decision-making software product for clinical implementation. Based on clinical and anamnestic data as well as calculations, the software will provide the shunt parameters (in particular, its diameter) and installation position to ensure regular blood flow. [ABSTRACT FROM AUTHOR]

Copyright of Journal of East China University of Science & Technology is the property of Journal of East China University of Science & Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The design of machine parts of different sizes and shapes has become relevant in the manufacturing industry which requires five-axis machine tools of high dynamic performance; different machining test pieces have been used to test and reflect the machine tool's performance. The S-shaped is still under development and consideration of which a new test piece better than the S-shaped part has been recommended to be put forward making the NAS979 the only standardized test piece; however, it has some limitations. Hence, this study proposes a new test piece to objectively satisfy the demand for machine tools with higher dynamic performance, which shows much improvement over the standard NAS979 and is the best alternative to the S-shaped test piece, and it combines the geometric and kinematic features of both test pieces. Geometrically, it has non-uniform surface continuity, variable twist angle, and variable curvature; and the cutting tool moves in close and opened angles along the tool path; there is sudden rise and fall of axes' velocity, acceleration, and jerk with much impact during machining which makes the S-cone test piece be machined by only five-axis machine tools with high dynamic performance, and has a better dynamic performance identification effect than the S-shaped test piece based on the trajectory test. Detailed work on the validation of the machine tool's dynamic performance using the S-cone part will be captured next part of this study. [ABSTRACT FROM AUTHOR]

Geometric map features, such as line segments and planes, are receiving increasing attention due to their advantages in simultaneous localization and mapping applications. However, large structures in different environments are very likely to appear repeatedly in several consecutive time steps, resulting in redundant features in the final map. These redundant features should be properly fused, in order to avoid ambiguity and reduce the computation load. In this paper, three criteria are proposed to evaluate the closeness between any two features extracted at two different times, in terms of their included angle, feature circle overlapping and relative distance. These criteria determine whether any two features should be fused in the mapping process. Using the three criteria, all features in the global map are categorized into different clusters with distinct labels, and a fused feature is then generated for each cluster by means of least squares fitting. Two competing methods are employed for comparative verification. The comparison results indicate that using the commonly used KITTI dataset and the commercial software PreScan, the proposed feature fusion method outperforms the competing methods in terms of conciseness and accuracy. [ABSTRACT FROM AUTHOR]

In applications based on a three-dimensional point cloud, massive point cloud data often brings processing difficulties. To deal with the problem, many point cloud sampling methods were proposed. But there are still some issues in these methods: (i) lack the consideration of geometric features and (ii) how to train the distribution of projected points by the observed coefficient effectively. This paper introduces a fine-tuned pointnet module, which extracts the geometric features of points and applies the offset-attention mechanism to enhance the feature expression ability. Furthermore, it corrects the positions of simplified points by a mutual supervision loss. The experimental results show our method can improve the effectiveness and robustness of the point cloud sampling. [ABSTRACT FROM AUTHOR]