Geometric Relationship Research Articles

Superimposed imaging of acoustic wave reflections for the detection of underground nonmetallic pipelines

Due to the non-conductive and non-magnetic properties, nonmetallic pipelines are difficult to be detected by traditional pipe location technologies. This paper presents a superimposed imaging method of acoustic wave reflections for pipe location, which operates on the propagation of elastic waves. The propagation and attenuation model of elastic waves in soil are constructed according to the geometric relationship between the acoustic source, geophone array and pipelines. The two-dimensional and three-dimensional acoustic field diagrams of underground pipeline are generated by superimposed imaging of the cross-correlation coefficients between the signals from the sound source and geophone array, with the attenuation caused by hysteretic damping and geometric dissipation considered in the imaging process. In order to suppress clutter interference on the imaging results, the strategy of ‘multi-point transmit, multi-point receive and cross-correlation coefficient superposition’ is adopted. In the simulation, comparison is made to demonstrate the influences of different excitation sources on the detection imaging results, including the single frequency signal, multi-frequency signal, Gaussian pulse signal and sweep signal. It is found that the excitation signals with rich frequency components are more conducive to improving the resolution of detection images. The effectiveness of the proposed imaging method is further verified in the experimental work, which may be beneficial for the visualization and determination of the location, depth and orientation of underground non-metallic pipelines.

Forced vibration response prediction of Miura-ori core sandwich panel with impact defects

Miura-ori core sandwich panels have great application potential in aviation aircraft. Subject to the impact and vibration loads, the small impact defect will increase the dynamic stress, resulting in fatigue failure. Moreover, the Miura creases complicate the impact deformation, which is extremely time-consuming to analysis the dynamic stress only through simulation. Therefore, a rapid dynamic stress prediction method for Miura-ori core sandwich panels is first proposed based on the predicted impact deformation by arbitrary defects in this paper. The dynamic stress of sandwich panels could be directly calculated with few numerical results by establishing the relationship with impact deformation in advance. The vibration mechanism of sandwich panels with defects is revealed. Although the deformation area is below 32.1 % of Miura cell projection area, the dynamic stress will increase by 1.984–2.635 times with different defects. The impact deformation is predicted by the geometric relationship of Miura creases and material properties, and the prediction errors of hemispherical and drilling defects with arbitrary defect positions are below 12.5 % and 10.6 %, respectively. Taking two types of defects (hemispherical and drilling defects), different defect depths and positions into consideration, the prediction errors of maximum dynamic stress are from -7.9 % to 16.8 % compared to simulation.

Extraction of intersecting palm‐vein and palmprint features for cancellable identity verification

AbstractA novel method based on the cross‐modality intersecting features of the palm‐vein and the palmprint is proposed for identity verification. Capitalising on the unique geometrical relationship between the two biometric modalities, the cross‐modality intersecting points provides a stable set of features for identity verification. To facilitate flexibility in template changes, a template transformation is proposed. While maintaining non‐invertibility, the template transformation allows transformation sizes beyond that offered by the conventional means. Extensive experiments using three public palm databases are conducted to verify the effectiveness the proposed system for identity recognition.

Mitigating bias in radiology: The promise of topological data analysis and simplicial complexes.

Topological Data Analysis (TDA) and simplicial complexes offer a novel approach to address biases in AI-assisted radiology. By capturing complex structures, n-way interactions, and geometric relationships in medical images, TDA enhances feature extraction, improves representation robustness, and increases interpretability. This mathematical framework has the potential to significantly improve the accuracy and fairness of radiological assessments, paving the way for more equitable patient care.

Optimization of a High Pressure Turbine Blade and Sector-Based Annular Rig Design for Supercritical CO2 Power Cycle Representative Testing

Abstract As part of the ongoing research into the design of hardware for zero emission cycles, a first-stage high-pressure turbine (HPT) blade is optimized for a 300 MWe supercritical CO2 (sCO2) power cycle using the surrogate-assisted genetic algorithm optimizer in Numeca FINE/Design three-dimensional with objectives of increasing efficiency and decreasing heat load to the blade. Supercritical CO2 property tables are constructed from NIST REFPROP data for the condensable gas simulation in FINE/Turbo. A detailed mesh sensitivity study is performed for a baseline design to identify the proper-grid refinement and efficiently allocate resources for the optimization. Seventy design variables are selected for the initial population generation. Self-organizing maps are then used to focus the design variables on the most important ones affecting the objective functions. The optimization results in approximately 3000 three-dimensional Reynolds Averaged Navier Stokes simulations of different blade shapes with increases in efficiency of up to 0.85% and decreases in heat load of 14%. Families of blade shapes are identified for experimental testing in an annular rig at the Purdue Experimental Turbine Aerothermal Laboratory. A design to adapt the annular cascade for testing optimized geometries is introduced, which features eccentric radius sectors allowing for scaled-up geometries of sCO2 optimized blade profiles to be tested at design cycle representative conditions at high Reynolds numbers in dry air. Analysis into the effects of Reynolds number, working fluid, and geometric relations are presented to prove the efficacy of the test method.

Discourse, Power, and the Body A Study of Postcolonialism in the Works of J.M. Coetzee

J.M. Coetzee, one of the most esteemed authors in the English-speaking world today, exhibited clear postcolonial tendencies in his early works. In his later writings, he gradually shifted his focus away from the South African context to explore themes such as rewriting classics, marginalized communities, and immigration. This paper, rooted in Foucault's theory of discourse-power and theories of the body, examines Coetzee's reflections and critiques of the discourse-power relationship and social disciplinary practices in his works. Furthermore, drawing from postcolonial scholars like Homi Bhabha, the paper investigates the consistent central-peripheral colonial geometric relationships in Coetzee's works and summarizes the strategies of "appropriation" and "mimicry" as means to challenge and subvert these power dynamics.

Kiosk 3R-TB-10 - The Geometrical Relationship Between Left Atrium and Left Ventricle Might Impair Diastolic Filling in Patients with Repaired Tetralogy of Fallot

Kiosk 3R-TB-10 - The Geometrical Relationship Between Left Atrium and Left Ventricle Might Impair Diastolic Filling in Patients with Repaired Tetralogy of Fallot

Isolating switch state detection system based on depth information guidance

<abstract> <p>This study addressed the critical role of isolating switches in controlling circuit connections for the stable operation of the substation. Our research introduced an innovative state detection system that utilized depth information guidance, comprising a controllable pan-tilt mechanism, a depth camera, and an industrial computer. The software component employed a two-stage strategy for precise isolating switch detection. Initially, the red green blue with depth (RGB-D) saliency network identified the approximate area of the isolating switch target. Subsequently, a fully connected conditional random field was applied to extract accurate detection results. The real-time state of the isolating switch was determined based on the geometric relationship between its arms. This approach enhanced the accuracy of isolating switch detection, ensuring practical applicability in engineering scenarios. The significance of this research lies in its contribution to advancing isolating switch monitoring through depth information guidance, promoting a more robust and reliable power system. The key improvement is implementing a two-stage strategy, combining RGB-D saliency analysis and conditional random field processing, resulting in enhanced accuracy in isolating switch detection. As validated through extensive experiments, the proposed system's successful application in practical engineering underscores its effectiveness in meeting the accuracy requirements for isolating switch detection and state detection. This innovation holds promise for broader applications in power systems, showcasing its potential to elevate the reliability and efficiency of electrical networks. Code of the proposed system is available at: <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://github.com/miaomiao0909/Isolating-Switch-Detection/tree/master">https://github.com/miaomiao0909/Isolating-Switch-Detection/tree/master</ext-link>.</p> </abstract>

Effect of the Slenderness Relation on In-Plane Deformation in Stack Compression Tests

In forming technologies and their simulation, knowing the flow curve characteristic of the material is an essential parameter. Acquiring this knowledge is particularly challenging for sheet materials in high strain ranges. It is well-known that friction and geometric relationships have a distorting effect on the flow curves, thus compensation is necessary. However, the geometric ratio can not only influence the formation of the flow curve, if our material shows anisotropic behaviour. In our research, using compression tests, we examined the deformation relations of deformed specimens through digital imaging methods. The stack compression test is widely used to determine the flow curve in a broad range of large deformation. During the test, several disk specimens with the same geometric characteristics were stacked on top of each other to form a final test piece, and then compression tests were conducted on these assemblies. We found that at low values of the geometric ratio (0.1 in our study), the proportion of plastic, planar principal strains indicating anisotropic behaviour is greater than at higher geometric ratios (0.5 and 1.0 in our study).

Cucumber Seedling Segmentation Network Based on a Multiview Geometric Graph Encoder from 3D Point Clouds.

Plant phenotyping plays a pivotal role in observing and comprehending the growth and development of plants. In phenotyping, plant organ segmentation based on 3D point clouds has garnered increasing attention in recent years. However, using only the geometric relationship features of Euclidean space still cannot accurately segment and measure plants. To this end, we mine more geometric features and propose a segmentation network based on a multiview geometric graph encoder, called SN-MGGE. First, we construct a point cloud acquisition platform to obtain the cucumber seedling point cloud dataset, and employ CloudCompare software to annotate the point cloud data. The GGE module is then designed to generate the point features, including the geometric relationships and geometric shape structure, via a graph encoder over the Euclidean and hyperbolic spaces. Finally, the semantic segmentation results are obtained via a downsampling operation and multilayer perceptron. Extensive experiments on a cucumber seedling dataset clearly show that our proposed SN-MGGE network outperforms several mainstream segmentation networks (e.g., PointNet++, AGConv, and PointMLP), achieving mIoU and OA values of 94.90% and 97.43%, respectively. On the basis of the segmentation results, 4 phenotypic parameters (i.e., plant height, leaf length, leaf width, and leaf area) are extracted through the K-means clustering method; these parameters are very close to the ground truth, and the R 2 values reach 0.98, 0.96, 0.97, and 0.97, respectively. Furthermore, an ablation study and a generalization experiment also show that the SN-MGGE network is robust and extensive.

Salt tectonics evolution in the Provençal Basin, Western Mediterranean Sea

The Messinian Salt Giant in the Provençal Basin represents a good example to study salt tectonics: salt deposition occurred throughout the basin well after basin opening, with a tectonic context stable since ∼16 Ma, in a closed system. Also, the youth of salt tectonics has led to less mature structures and an evolutionary history that is easier to decipher than in older salt-bearing margins. We conducted an analysis of the chronology of salt deformation, from its deposition to the present-day, thanks to the basin-wide correlation of the Late Miocene and Pliocene-Pleistocene stratigraphic markers. The large seismic dataset provided detailed analysis of the causes and timing of salt deformation at a regional level. The salt tectonics started relatively early, during the Messinian Upper Unit (UU) deposition (phase 1) in the deepest part of the basin. From the Pliocene to the present-day, salt movement is divided into two more main phases (phases 2 and 3), the first of small intensity, occurred during the Pliocene and the second, more intense, during the Pleistocene. The geometric relationship between salt tectonics and crustal nature domains has revealed, regardless of the timing deformation phases, a more rapid and intense salt deformation above the proto-oceanic crust domain than in the continental or transitional crust domain. This observation, remaining unexplained, emphasizes the role of the influence of crustal nature, associated thermal regime and fluid circulation system on salt tectonics.

Разработка метода определения угла наклона опор контактной сети железнодорожного транспорта

This paper discusses a new method for measuring and calculating the angle of inclination of a railway support or a contact network support, using an unmanned aerial vehicle flying along a straight path, parallel to the railway track. A review of existing measurement methods is carried out, their advantages and disadvantages are indicated. In the method under consideration, it is proposed to measure angles and distances with six laser scanning rangefinders installed in threes on horizontal and inclined planes on an unmanned aerial vehicle. This allows you to increase the speed and accuracy of determining the angle of inclination of vertical supports. The calculations use the minimum distances from the laser scanning range finder to the top and bottom of the support surface. The formulas use geometric relationships and the cosine theorem to calculate the roll of supports taking into account their taper. Measuring distances and angles three times allows for averaging over them, which significantly increases the accuracy of calculations. A model experiment is carried out on a model of a reinforced concrete contact network support in four orientations. A comparison is made between theoretically calculated and experimentally measured distances and inclination angles. The accuracy of parameter determination complies with regulatory requirements.

On Strictly Positive Real Improper Transfer Matrices

In this article we consider linear systems having real rational transfer matrices that may be improper. We investigate the properties of strictly positive real and weakly strictly positive real systems and their connections. Our first contribution is to establish a necessary and sufficient condition for a rational and possibly improper transfer matrix to be strictly positive real. Beside stability, this condition only involves the properties of the transfer matrix in the imaginary axis and a geometric relation between the feedthrough term and the residue at infinity of the transfer matrix. Our condition connects some of the properties of the system with those of its proper part. This leads to our second contribution: integrate in the improper setting all the several conditions derived in the literature for the proper case that discriminate weakly strictly positive real and strictly positive real systems.

A Semi-Supervised Matrixized Graph Embedding Machine for Roller Bearing Fault Diagnosis Under Few-Labeled Samples

Exploring historical measurement data-driven health monitoring schemes for roller bearings is a current research hotspot. In engineering practice, the type of fault data obtained is often unknown and requires expensive costs to be annotated. However, most current intelligent diagnostic methods are based on the assumption that the labeled fault data is sufficient, so as to effectively establish the nonlinear mapping relationship between monitoring signals and health status. For this issue, a newly intelligent diagnosis method based on semi-supervised matrixized graph embedding machine (SMGEM) is proposed. In SMGEM, the geometric similarity relationship of unlabeled and labeled samples is obtained, which is subsequently embedded by incorporating a manifold regularization into SMGEM model, so that SMGEM can use the structure information of unlabeled samples to assist modeling. Meanwhile, a weighted nuclear norm (WNN) is used to highlight the importance of large singular values, so that a more accurate weight matrix can be constructed. The proposed method is verified by several roller bearing fault datasets, and experimental results demonstrate that the proposed semi-supervised diagnosis method can use few labeled samples to obtain a better identification accuracy.

Skinned Motion Retargeting with Preservation of Body Part Relationships.

Motion retargeting is an active research area in computer graphics and animation, allowing for the transfer of motion from one character to another, thereby creating diverse animated character data. While this technology has numerous applications in animation, games, and movies, current methods often produce unnatural or semantically inconsistent motion when applied to characters with different shapes or joint counts. This is primarily due to a lack of consideration for the geometric and spatial relationships between the body parts of the source and target characters. To tackle this challenge, we introduce a novel spatially-preserving Skinned Motion Retargeting Network (SMRNet) capable of handling motion retargeting for characters with varying shapes and skeletal structures while maintaining semantic consistency. By learning a hybrid representation of the character's skeleton and shape in a rest pose, SMRNet transfers the rotation and root joint position of the source character's motion to the target character through embedded rest pose feature alignment. Additionally, it incorporates a differentiable loss function to further preserve the spatial consistency of body parts between the source and target. Comprehensive quantitative and qualitative evaluations demonstrate the superiority of our approach over existing alternatives, particularly in preserving spatial relationships more effectively.

Extracting multilane roads from OpenStreetMap through graph convolutional neural network and road mesh relationship analysis

Multilane roads in vector data are comprised of parallel line features that represent the same road. The extraction of these features is crucial for updating maps and cartographic generalization. This article addresses previous object-level extraction limitations by introducing a mesh-based method that employs a GCNN model to classify them based on geometric features and proximity relationship. A bidirectional region growing clustering method (BRGC) is designed to cluster the identified multilane road meshes belonging to the same road into groups considering their spatial relationships. The object-level multilane roads are extracted based on the clustering results. Validation using road data from OpenStreetMap in Chengdu and Zhengzhou achieved a classification f1-score >90% for multilane road mesh. Moreover, the object-level multilane detection precision and recall were 88.05% and 92.72% on the Chengdu dataset, and 86.99% and 91.37% on the Zhengzhou dataset, which demonstrates the proposed method effectively extracts multilane roads at the object level, aligning with real-world scenarios and human cognition.

A Graph Convolution Model for Intelligent Datum Features Selection

The datum is a crucial component in tolerance specification, which is the foundation for the selections of geometric tolerances and tolerance principles. Currently, intelligent datum reasoning is largely based on logical rules that are mainly extracted from human experience, resulting in the high uncertainty and low efficiency. To tickle these issues, this study proposes a data selection model based on the GCN (Graph Convolutional Networks), In the devised model, the different geometric features of a workpiece are represented in a graph structure. The geometric, spatial, and assembly relationships, as well as positioning features are computed to obtain vectorized representations of the different geometric features, which serve as inputs to the constructed GCN model. Then, based on the GCN, a datum discriminant classifier has been developed on the training samples. To enhance the classifier accuracy, multiple GCN layers are employed for training, with the output of each GCN module added to a list. Ultimately, the outputs of all GCN modules are concatenated and subjected to classification prediction through fully connected layers. Datum specifications are established based on the classification of geometric features. The effectiveness and feasibility of this method are validated through case studies, e.g. rear floor crossbeams, with comparative results indicating a similarity rate of 85.19% with manually designed outcomes.

Full Point Encoding for Local Feature Aggregation in 3-D Point Clouds.

Point cloud processing methods exploit local point features and global context through aggregation which does not explicitly model the internal correlations between local and global features. To address this problem, we propose full point encoding which is applicable to convolution and transformer architectures. Specifically, we propose full point convolution (FuPConv) and full point transformer (FPTransformer) architectures. The key idea is to adaptively learn the weights from local and global geometric connections, where the connections are established through local and global correlation functions, respectively. FuPConv and FPTransformer simultaneously model the local and global geometric relationships as well as their internal correlations, demonstrating strong generalization ability and high performance. FuPConv is incorporated in classical hierarchical network architectures to achieve local and global shape-aware learning. In FPTransformer, we introduce full point position encoding in self-attention, that hierarchically encodes each point position in the global and local receptive field. We also propose a shape-aware downsampling block that takes into account the local shape and the global context. Experimental comparison to existing methods on benchmark datasets shows the efficacy of FuPConv and FPTransformer for semantic segmentation, object detection, classification, and normal estimation tasks. In particular, we achieve state-of-the-art semantic segmentation results of 76.8% mIoU on S3DIS sixfold and 73.1% on S3DIS Area 5. Our code is available at https://github.com/hnuhyuwa/FullPointTransformer.

High-Quality Fusion and Visualization for MR-PET Brain Tumor Images via Multi-Dimensional Features.

The fusion of magnetic resonance imaging and positron emission tomography can combine biological anatomical information and physiological metabolic information, which is of great significance for the clinical diagnosis and localization of lesions. In this paper, we propose a novel adaptive linear fusion method for multi-dimensional features of brain magnetic resonance and positron emission tomography images based on a convolutional neural network, termed as MdAFuse. First, in the feature extraction stage, three-dimensional feature extraction modules are constructed to extract coarse, fine, and multi-scale information features from the source image. Second, at the fusion stage, the affine mapping function of multi-dimensional features is established to maintain a constant geometric relationship between the features, which can effectively utilize structural information from a feature map to achieve a better reconstruction effect. Furthermore, our MdAFuse comprises a key feature visualization enhancement algorithm designed to observe the dynamic growth of brain lesions, which can facilitate the early diagnosis and treatment of brain tumors. Extensive experimental results demonstrate that our method is superior to existing fusion methods in terms of visual perception and nine kinds of objective image fusion metrics. Specifically, in the results of MR-PET fusion, the SSIM (Structural Similarity) and VIF (Visual Information Fidelity) metrics show improvements of 5.61% and 13.76%, respectively, compared to the current state-of-the-art algorithm. Our code is publicly available at: https://github.com/22385wjy/MdAFuse.

Research on Informatization Construction and Teaching Reform Path of Physical Education Teaching in Colleges and Universities

Abstract Firstly, the improved OpenPose is used to obtain the skeletal point coordinate data and normalized, and then the human gesture features are extracted according to the spatial geometric relationship of human morphology to improve the differentiation degree of the gesture features on similar actions in the basic sports actions, which is used for the gesture matching of the basic sports actions. On this basis, the DTW posture matching algorithm based on human differentiated posture features is proposed, which realizes the posture matching and carries out the evaluation and result feedback of students’ sports movements. Finally, we construct a sports movement evaluation system based on the aforementioned method, which compares the movement video with the standard movement from the template library and provides feedback on the movement’s evaluation results. The system allows teachers to access the learning situations of numerous students’ movements, while the system provides feedback for movement learners to adjust their movements, thereby facilitating a convenient and informative interaction in sports movement teaching. After seven weeks of experimentation, there is a significant difference between the assessment scores of the experimental group and the control group, and 90% of the students are satisfied with the application of the sports movement assessment teaching system to assist teaching. It proves that the intelligent teaching structure and diversified teaching methods promote students’ learning efficiency and performance, significantly improve students’ motivation to learn physical education courses, and further improve the demand for an information-based teaching environment.