Spatial Topological Relations Research Articles

Exploring Brain Effective Connectivity Networks Through Spatiotemporal Graph Convolutional Models.

Learning brain effective connectivity networks (ECN) from functional magnetic resonance imaging (fMRI) data has gained much attention in recent years. With the successful applications of deep learning in numerous fields, several brain ECN learning methods based on deep learning have been reported in the literature. However, current methods ignore the deep temporal features of fMRI data and fail to fully employ the spatial topological relationship between brain regions. In this article, we propose a novel method for learning brain ECN based on spatiotemporal graph convolutional models (STGCM), named STGCMEC, in which we first adopt the temporal convolutional network to extract the deep temporal features of fMRI data and utilize the graph convolutional network to update the spatial features of each brain region by aggregating information from neighborhoods, which makes the features of brain regions more discriminative. Then, based on such features of brain regions, we design a joint loss function to guide STGCMEC to learn the brain ECN, which includes a task prediction loss and a graph regularization loss. The experimental results on a simulated dataset and a real Alzheimer's disease neuroimaging initiative (ADNI) dataset show that the proposed STGCMEC is able to better learn brain ECN compared with some state-of-the-art methods.

Topographic analysis supported by a knowledge graph: A case of ridge landscape recognition

Abstract. The intrinsic connections between geographical elements are important for uncovering hidden geo-scientific laws. However, current research on terrain and landform analysis mainly focuses on the landscapes themselves, with insufficient attention to the connections between them. Therefore, this study proposes a knowledge graph approach based on geographical units (TUKG). Specifically, fi-negrained geographical units are extracted based on three types of data: remote sensing images, DEM, and contour lines. These units serve as entity nodes in the TUKG and are described by their slope and aspect. Additionally, point-based and line-based connections between geographical units are proposed based on spatial topological relationships, serving as connections between entity nodes in the TUKG. Finally, inference rules for ridge landscape problems are extracted from typical cases of ridge land-scapes to support reasoning in the TUKG. Experimental results conducted in the Yarlung Zangbo Grand Canyon in southwest China demonstrate that the TUKG can accurately infer ridge landscapes and has the potential to identify more complex terrain landscapes.

Research on power generation prediction of hydropower in river basin based on multi-head attention graph convolutional neural network

For the power generation prediction of traditional hydropower stations, most of them only use time series prediction and neglect to study the spatial topological relationship of hydropower stations in the river basin, so that it is difficult to fully explore the characteristic relationship of space power stations. In this paper, a research method for power generation prediction of hydropower in river basin hydropower stations based on multi-head attention map convolutional neural network is proposed. This method establishes a first-level node neighborhood feature map based on the spatial geographic distribution information of hydropower stations in the basin, and uses the method of graph convolution to carry out node feature mining and feature learning, so as to transform the power generation capacity evaluation problem of the hydropower station in the basin into the node prediction problem in the graph, which is different from the global normalization rule. the multi-head attention mechanism introduced further improves the information aggregation quality of the graph node, and uses the historical temperature, power generation, electricity price, unit status and other data of each hydropower station in the basin for training. The reasoning results show that the proposed method achieves higher accuracy than other schemes, and the power prediction method is conducive to the formulation of power plans of hydropower stations in the basin, and can also play a positive role in guiding the site selection of hydropower plants.

Time series prediction for production quality in a machining system using spatial-temporal multi-task graph learning

The quality of multiple-machining-feature parts in batch computer numerical control machining systems is affected by complex spatial-temporal relationships, making accurate prediction difficult. Traditional quality prediction methods are difficult to cope with the challenges posed by the time impact effects generated by batch production sequences and the spatial impact effects generated by the adjacency of machining features of thin-walled parts. Hence, the time series prediction for production quality in a machining system using spatial-temporal multi-task graph learning is proposed to analyze the spatial-temporal impact relationship of machining quality and achieve accurate quality prediction. Specifically, a graph learning-based framework to capture the coupled spatial-temporal effects is designed to represent the spatial topological and temporal relationship of the machining features of batch parts. A novel global-local multi-task spatial-temporal graph learning framework is constructed to simultaneously predict the time series quality of each machining feature at different spatial positions. The global multi-task model aims to extract spatial-temporal coupling data features (i.e., input parameters for machining features at different positions of batch parts) for machining feature clusters based on gated networks and spatial-temporal graph convolution with multi-head attention. Then, the local multi-task model for each feature cluster (round holes, rectangular grooves, etc.) performs individual time series quality prediction regarding multiple quality indicators. Experimental results on a production dataset of a computer numerical control machining system for thin-walled parts show that the proposed method is superior to the conventional models such as a simple spatial-temporal graph convolution method (3.0%, 18.3% and 11.0% improvement in R2, MAE and RMSE) or the model without graph approach (6.5%, 29.8% and 19.8% improvement in R2, MAE and RMSE).

Feature Line Extraction from Building Façade Point Clouds by Exploring Spatial Topological Relationship

After using a terrestrial laser scanner to acquire building facade point clouds, the extraction of feature lines can simplify the expression of building objects, thereby contributing to the accurate construction of building facade geometric models. To address the problems of missing extraction and low accuracy in existing methods, this study proposes a feature line extraction method for building facade point clouds by exploring the different spatial topological relationship between feature and non-feature points. The method comprises three steps: feature point extraction, feature line generation, and feature line merging and optimization. First, feature points are extracted using the convex hull distance value and relative angle, as defined in this study. Thereafter, feature points are clustered by combining the random sample consensus and region growing algorithms, and the feature lines are obtained by utilizing the iterative weighted least squares method based on the IGGIII weight function. Finally, the feature lines are merged and optimized using an endpoint search method to improve the discontinuity and missing common endpoints of the original feature lines. The experimental results obtained using simulated and measured point cloud data show that this method can accurately extract feature points, and the extracted feature lines obtained from building facade point clouds have better accuracy and completeness, which is more practical than the existing methods and can be used in many applications such as building facade measurement and urban three-dimensional modeling.

GIS Approach for Expressing Structural Landforms: Forms, Elements, and Relationships

A structural landform is defined by its surface morphology, controlled by tectonics, lithology (arrangement and resistance), and folded structures, and demonstrated by the characteristics and relationships between geological and geomorphic elements. It is very important to use geographic information system (GIS) technology to accurately describe and express elements of structural landforms and their relationships. In this study, a GIS approach for expressing structural landforms, based on “forms–elements–relationships”, was developed. The contributions of this paper are as follows: (1) Combined with the surface morphological characteristics, the structural landforms were abstracted into geological and geomorphic elements, and the characteristics and relationships of these elements were analyzed. (2) The elements of structural landforms and their relationships were abstracted into spatial objects and topological relationships. The spatial objects of the structural landform were designed based on the types and characteristics of structural landform elements. The topological relationships were developed based on the definition of the structural landform morphotype. (3) The structural landform markup language (SLML) method of “forms–elements–relationships” was created. (4) Two typical structural landforms, namely, Qixia Mountain and Gaoli Mountain, were used as examples to verify the feasibility and effectiveness of the GIS approach for expressing structural landforms. This paper describes and expresses the “forms–elements–relationships” of structural landforms from the perspective of GIS, which is expected to promote the joint development of structural geomorphology and GIS.

Topological relation expression and verification of symmetrical parallel mechanism based on the evolution of chemical molecule

AbstractThe research of parallel mechanism (PM) configuration involves many problems. From topology to configuration, dimensional constraint, etc., how to establish the relationship between topology and configuration with effective methods is a long-term challenge for the configuration design. In this paper, the chemical molecular spatial structure (CMSS) is linked with the configuration of symmetrical parallel mechanism (SPM). Starting from the methane molecule (CH4), a spatial structural topological relation is obtained. Based on graph theory and the spatial structural topological relation, a new expression method with topological graph and its kinematic pair adjacency matrix for spatial SPM is proposed. Then, the expression and analysis for the characteristics of spatial SPM are obtained. Finally, by taking the 3-RPS PM and the 3-RRC PM as examples, the effectiveness and corresponding consistency of the proposed expression method are successfully verified. The proposed new expression method paves the way for the subsequent digital and automated design and analysis of the SPM configuration.

A zero-watermarking scheme based on spatial topological relations for vector dataset

A zero-watermarking scheme based on spatial topological relations for vector dataset

Design of a class of reconfigurable hybrid mechanisms for large complex curved surface machining based on topological graph theory

A design method for hybrid mechanism configuration synthesis based on topological graph theory is proposed to address the challenge of machining large and complex curved surfaces in the aerospace domain. The method leads to the generation of a new class of reconfigurable large extension hybrid mechanisms. Firstly, the spatial mechanism topological graph (SMTG) is obtained by evolving the structure of chemical molecules, which is used to express the spatial mechanism topological relationships. Then, combining graph theory methods with the SMTG, the topological relationships between motion modules of the hybrid mechanism are expressed, and the mechanisms of each motion module are designed. Finally, mechanisms of different functional modules are topologically connected and combined using the proposed topological connection relationship for the motion modules of hybrid mechanisms, thus resulting in a class of reconfigurable hybrid mechanisms (RHMs) with large extensibility. The RHMs exhibit strong structural stability, a large working space, and a small volume of occupation. It can be installed on the ends of industrial robots or in large guideways in the future to achieve high-precision machining of large complex curved structures.

A Depression Recognition Method Based on the Alteration of Video Temporal Angle Features

In recent years, significant progress has been made in the auxiliary diagnosis system for depression. However, most of the research has focused on combining features from multiple modes to enhance classification accuracy. This approach results in increased space-time overhead and feature synchronization problems. To address this issue, this paper presents a single-modal framework for detecting depression based on changes in facial expressions. Firstly, we propose a robust method for extracting angle features from facial landmarks. Theoretical evidence is provided to demonstrate the translation and rotation invariance of these features. Additionally, we introduce a flip correction method to mitigate angle deviations caused by head flips. The proposed method not only preserves the spatial topological relationship of facial landmarks, but also maintains the temporal correlation between frames preceding and following the facial landmarks. Finally, the GhostNet network is employed for depression detection, and the effectiveness of various modal data is compared. In the depression binary classification task using the DAIC-WOZ dataset, our proposed framework significantly improves the classification performance, achieving an F1 value of 0.80 for depression detection. Experimental results demonstrate that our method outperforms other existing depression detection models based on a single modality.

A similarity calculation model of road network and its application in map generalization quality evaluation

ABSTRACT As an important part of maps, road networks have always been the focus of research in map generalization. However, the fully automated quality evaluation of road network generalization has not been implemented. This paper proposes a framework of Sample-Map database to evaluate road network generalization quality using spatial similarity measures. The similarity calculation model considers spatial distribution, topological relations, and distance relations of road networks. A Sample-Maps database is created as an evaluation set for different road network types. The method is tested on multi-scale maps from Shenzhen, China, and validated by psychological cognition experiments and professional cartographers’ judgment.

A Spatial Relation Model of Three-Dimensional Electronic Navigation Charts Based on Point-Set Topology Theory

Spatial relation models are the basis for realising three-dimensional spatial analysis. More researchers are now focusing on models that combine topological relations with distance or directional relations; however, a model that unifies all three relations has not yet been developed. In particular, it is more effective to use different spatial relations between features with different spatial characteristics in three-dimensional electronic navigation charts (3D ENC). Therefore, this paper proposes a 3D ENC feature spatial relation model (3DSRM) based on point-set topology theory, which combines 3D topological relations, distance relations and directional relations, and uses a unified model framework to describe 64 topological relations of 3D ENC features from both horizontal and vertical directions. Through the comparison and derivation of feature topological relations, it is demonstrated that the model can distinguish 3D spatial topological relations more comprehensively, realise the mutual derivation between spatial relations and spatial features, and improve the robustness of spatial relations judgement. The model can be used to judge the topological relations between features, realise 3D topological relation checking and topological creation of complex features, and improve the accuracy and interactivity of 3D ENC.

Enhancing Online UAV Multi-Object Tracking with Temporal Context and Spatial Topological Relationships

Multi-object tracking in unmanned aerial vehicle (UAV) videos is a critical visual perception task with numerous applications. However, existing multi-object tracking methods, when directly applied to UAV scenarios, face significant challenges in maintaining robust tracking due to factors such as motion blur and small object sizes. Additionally, existing UAV methods tend to underutilize crucial information from the temporal and spatial dimensions. To address these issues, on the one hand, we propose a temporal feature aggregation module (TFAM), which effectively combines temporal contexts to obtain rich feature response maps in dynamic motion scenes to enhance the detection capability of the proposed tracker. On the other hand, we introduce a topology-integrated embedding module (TIEM) that captures the topological relationships between objects and their surrounding environment globally and sparsely, thereby integrating spatial layout information. The proposed TIEM significantly enhances the discriminative power of object embedding features, resulting in more precise data association. By integrating these two carefully designed modules into a one-stage online MOT system, we construct a robust UAV tracker. Compared to the baseline approach, the proposed model demonstrates significant improvements in MOTA on two UAV multi-object tracking benchmarks, namely VisDrone2019 and UAVDT. Specifically, the proposed model achieves a 2.2% improvement in MOTA on the VisDrone2019 benchmark and a 2.5% improvement on the UAVDT benchmark.

Study on the Regeneration of City Centre Spatial Structure Pedestrianisation Based on Space Syntax: Case Study on 21 City Centres in the UK

Pedestrian priority is an important requirement for city centre regeneration. The quantitative analysis of the separation degree of pedestrians and vehicles is a key technique to measure the walkability of city centre regeneration. This paper proposes a method for measuring the walkability of the spatial structure in city centres, based on spatial topological relationships. Using space syntax as a platform, the walkability of the spatial structure of city centres is quantitatively analysed from the perspective of separation of pedestrians and vehicles, and the regeneration of pedestrianisation. Based on 21 cases of major city centres in the United Kingdom (UK), the trend of pedestrianisation regeneration from the early 20th century to the present is analysed. The analysis of the separation degree of pedestrians and vehicles and the analysis of regeneration models and the comparative analysis found that: (1) from the early 20th century to the present, the spatial structure of major city centres in the UK clearly trended toward pedestrianisation. (2) The regeneration process can be categorised into three models: the Gradual Growth Model, One Step Model and Long-Term Planning Model. (3) The three models contribute differently to the separation of pedestrians and vehicles in city centres, and their advantages and disadvantages differ. This study has implications for the theory and practice of pedestrianisation regeneration in city centres.

Formal topological description of three-dimensional topological entities based on k-dimensional pseudo-manifold

This paper clarifies the necessity of studying three-dimensional spatial data models, analyzes the research progress and existing problems of topological spatial relationship description methods, and proposes a complete and formal description framework of three-dimensional topological spatial relations based on point set topological theory and dimension expansion method. On this basis, the topological spatial relations existing in three-dimensional spatial objects are classified, and five fundamental topological spatial relations are defined. The mutual exclusivity and completeness of the minimal set of 3D topological space relations are proved. Based on point set topology and k-dimensional pseudo-manifold, this paper proposes the formal description of three-dimensional topological properties and spatial relations between spatial entities. The simplicial homology is applied to manifolds, the topological properties of three-dimensional space entities are revealed, and the formal description method of three-dimensional space entities is given through the geometric structural relationship between simplex and simplicial k-complex.

Formal Verification of a Topological Spatial Relations Model for Geographic Information Systems in Coq

Geographic information systems have undergone rapid growth for decades. Topology has provided valuable modeling tools in the development of this field. Formal verification ofthe model of topological spatial relations can provide a reliable guarantee for the correctness of geographic information systems. We present a proof of the topological spatial relations model that has been formally verified in the Coq proof assistant. After an introduction to the formalization of the axiomatic set theory of Morse–Kelley, the formal description of the elementary concepts and properties of general topology is developed. The topological spatial relations between two sets are described by using the concept of the intersection value. Finally, we formally proved the topological spatial relations between two sets which are restricted to the regularly closed and the planar spatial regions. All the proof details are strictly completed in Coq, which shows that the correctness of the theoretical model for geographic information systems can be checked by a computer. This paper provides a novel method to verify the correctness of the topological spatial relations model. This work can also contribute to the creation and validation of various geological models and software.

Soil Erosion Type and Risk Identification from the Perspective of Directed Weighted Complex Network

Identifying the geographic distribution and erosion risks of various soil erosion regions are critical inputs to the implementation of extensive and effective land protection planning. To obtain more accurate and sufficient erosion information on a large scope, this paper introduced the complex network theory to quantitatively simulate the topographic spatial structure and topological relationship of the erosion area. The watershed was selected as the basic study unit and the directed weighted complex network (DWCN) of each watershed was constructed from DEM data. The directed weighted complex network factor (DWCNF) of each watershed was calculated by the DWCN. After combining DWCNFs with existing SEEF, the soil erosion types and risks of sample areas in the Chinese Loess Plateau were identified by the random forest model. The results show that in both typical and atypical sample areas, the identification performance of soil erosion by combining DWCNFs with existing SEEFs was performed better than that by employing only the DWCNFs or SEEFs dataset. It is suggested that the quantitative description of the spatial structure and topological relationship of the watershed from the perspective of a complex network contributes to obtaining more accurate soil erosion information. The DWCNF of structural entropy, betweenness centrality, and degree centrality were of high importance, which can reliably and effectively identify the types and risks of soil erosion, thus providing a broader factor reference for relevant research. The method proposed in this paper of vectoring terrain into complex network structures is also a novel sight for geological research under complex terrain conditions.

Incorporating circuit theory, complex networks, and carbon offsets into the multi-objective optimization of ecological networks: A case study on karst regions in China

Ecological networks (ENs) are important for maintaining regional ecological security and improving ecosystem service capacity. Therefore, it is important to consider spatial topological relationships and the need for carbon neutrality in the optimization of ENs. The optimization of EN structures and functions should be a systematic multi-objective process. This study combined landscape ecology, the complex network model, and spatial analysis technology, proposing an ecological barrier–topological feature–carbon offset-based (BTC-based) model research framework to deeply analyze and optimize ENs. First, the EN was preliminarily identified using the circuit theory model and the ecological barrier areas were simulated. Then, based on the complex network model, an undirected and unweighted complex network was established and its topological structural characteristics were analyzed. By constructing the estimation model for the carbon offset rate, its spatial distribution characteristics were calculated and interpreted. Finally, the BTC-based model was used to optimize the EN. Based on this model, Guizhou Province (a typical karst region in China) was taken as the research area for this study. The results showed that ecological improvement and degradation coexisted, and measures must be taken to optimize the EN. Based on the 2018 EN, the areas that required optimization were identified. Additionally, a restoration strategy was proposed for edge addition optimization for ecological corridors to promote the ecological protection and quality improvement of ecological sources and corridors. This approach would ensure the accurate implementation of ecological restoration projects and the effective management of ecosystems. The model proposed in this study also provides methodological support for EN optimization in other types of ecological regions.

A Method for the Automatic Extraction of Support Devices in an Overhead Catenary System Based on MLS Point Clouds

A mobile laser scanning (MLS) system can acquire railway scene information quickly and provide a data foundation for regular railway inspections. The location of the catenary support device in an electrified railway system has a direct impact on the regular operation of the power supply system. However, multi-type support device data accounts for a tiny proportion of the whole railway scene, resulting in its poor characteristic expression in the scene. Therefore, using traditional point cloud filtering or point cloud segmentation methods alone makes it difficult to achieve an effective segmentation and extraction of the support device. As a result, this paper proposes an automatic extraction algorithm for complex railway support devices based on MLS point clouds. First, the algorithm obtains hierarchies of the pillar point clouds and the support device point clouds in the railway scene through high stratification and then realizes the noise that was point-cloud-filtered in the scene. Then, the center point of the pillar device is retrieved from the pillar corridor by a neighborhood search, and then the locating and initial extracting of the support device are realized based on the relatively stable spatial topological relationship between the pillar and the support device. Finally, a post-processing optimization method integrating the pillar filter and the voxelized projection filter is designed to achieve the accurate and efficient extraction of the support device based on the feature differences between the support device and other devices in the initial extraction results. Furthermore, in the experimental part, we evaluate the treatment effect of the algorithm in six types of support devices, three types of support device distribution scenes, and two types of railway units. The experimental results show that the average extraction IoU of the multi-type support device, support device distribution scenes, and railway unit were 97.20%, 94.29%, and 96.11%, respectively. In general, the proposed algorithm can achieve the accurate and efficient extraction of various support devices in different scenes, and the influence of the algorithm parameters on the extraction accuracy and efficiency is elaborated in the discussion section.

Enhanced Multiview Fuzzy Clustering Using Double Visible-Hidden View Cooperation and Network LASSO Constraint

Multiview clustering is an important topic in multiview learning, where the cooperation of different views is used to improve clustering performance. Although multiview clustering has made considerable progress, most existing methods only utilize the information of the original visible views, or only consider some hidden space information shared by different views. Two of the challenges are: 1) insufficient exploitation of cooperative learning between visible and hidden information despite some preliminary attempts, and 2) inadequate consideration of topological information for improving multiview clustering. To meet the challenges, we propose the cooperation enhanced multiview fuzzy clustering method (CE-MVFC) in this article. First, we characterize multiview data with two hidden views, which are obtained by adaptive multiview non-negative matrix factorization (NMF) and fuzzy partition information of each sample in different clusters. Then, we integrated the hidden views and the original visible views to realize visible-hidden cooperation learning. Furthermore, we establish a similarity matrix for each visible view and the hidden view obtained through NMF to describe the data topology in these views. Based on the spatial topological relationship of the samples and the representation of hidden view obtained by fuzzy partition, the network least absolute shrinkage and selection operator is constructed to constrain multiview learning. Finally, we develop the multiview clustering method by exploiting the visible-hidden information cooperation and the spatial topological information constraints. Experiments on benchmark multiview datasets are conducted to demonstrate the highly competitive performance of the proposed CE-MVFC against the state-of-the-art methods.