Topological Map Research Articles

Corrections to “MR-TopoMap: Multi-Robot Exploration Based on Topological Map in Communication Restricted Environment”

Fig. 5b in [1] shows the geometric center of vertices in robots after map merging, but it shows the situation before map merging. The correct figure of Fig. 5 in [1] is shown in Fig. 1

Computationally Efficient Topological Mapping With Layered Spanning Trees

Based on layered spanning trees and associated inference algorithms, this article proposes a novel topological mapping approach handling multiple hypotheses, which significantly improves its computational efficiency with merely linear time cost. To facilitate this work, two spanning trees first establish a storage separation of map nodes and map edges. The correspondence between specific nodes and edges, as well as their inheritance, is then organized with intelligent pointers. On this basis, when a new observation is available, the robot will first evaluate loop closure by searching possible matches in the spanning tree of map nodes, which inherently relate to divergent understandings of historical observations. After the evaluation, related hypotheses are re-inferred based on a Bayes-based recursive inference method, and the spanning tree of map edges is updated accordingly. The correctness of hypotheses is sorted probabilistically, and the hypothetical map with the highest probability can be chosen to reconstruct combinatorially with corresponding nodes and edges in the layered spanning trees. To verify the proposed mapping approach, an automated simulation environment is developed modularly for large-scale scenarios, based on the robot operation system. Stress tests are finally carried out with both simulations and experiments, and the results demonstrate that the proposed approach is highly efficient, robust, and scalable. In particular, it effectively reduces the computation, as well as memory consumption, with linear complexity, regarding commonly reported exponential complexity in accumulated works.

Lifelong Topological Visual Navigation

Commonly, learning-based topological navigation approaches produce a local policy while preserving some loose connectivity of the space through a topological map. Nevertheless, spurious or missing edges in the topological graph often lead to navigation failure. In this work, we propose a sampling-based graph building method, which results in sparser graphs yet with higher navigation performance compared to baseline methods. We also propose graph maintenance strategies that eliminate spurious edges and expand the graph as needed, which improves lifelong navigation performance. Unlike controllers that learn from fixed training environments, we show that our model can be fine-tuned using only a small number of collected trajectory images from a real-world environment where the agent is deployed. We demonstrate successful navigation after fine-tuning on real-world environments, and notably show significant navigation improvements over time by applying our lifelong graph maintenance strategies.

Topology Inference With Multivariate Cumulants: The Möbius Inference Algorithm

Many tasks regarding the monitoring, management, and design of communication networks rely on knowledge of the routing topology. However, the standard approach to topology mapping—namely, active probing with traceroutes—relies on cooperation from increasingly non-cooperative routers, leading to missing information. Network tomography, which uses end-to-end measurements of additive link metrics (like delays or log packet loss rates) across monitor paths, is a possible remedy. Network tomography does not require that routers cooperate with traceroute probes, and it has already been used to infer the structure of multicast trees. This paper goes a step further. We provide a tomographic method to infer the underlying routing topology of an arbitrary set of monitor paths using the joint distribution of end-to-end measurements, without making any assumptions on routing behavior. Our approach, called the Möbius Inference Algorithm (MIA), uses cumulants of this distribution to quantify high-order interactions among monitor paths, and it applies Möbius inversion to “disentangle” these interactions. In addition to MIA, we provide a more practical variant called Sparse Möbius Inference, which uses various sparsity heuristics to reduce the number and order of cumulants required to be estimated. We show the viability of our approach using synthetic case studies based on real-world ISP topologies.

Object-Plane Co-Represented and Graph Propagation-Based Semantic Descriptor for Relocalization

Relocalization is a critical component of robotics applications, it poses challenges due to changes in lighting conditions, weather, and viewing point. Image feature-based approaches are appearance-sensitive, high-level semantic landmark-based methods are ambiguous, and topological map matching-based methods are not robust enough among available solutions. We propose a highly robust and highly expressive semantic descriptor for graph matching. Specifically, we begin by introducing an object-plane co-represented topological graph and a graph propagation algorithm to formulate descriptors for high-level landmarks; we then solve graph matching using the sKM algorithm. Finally, we develop a relocalization system that combines semantic objects and geometric planes for pose optimization and conducts experimental validation on various datasets. Experimental results demonstrate that the suggested method remains effective even when the viewpoint changes by more than 80 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> on the 2D-3D-S dataset, and the mean orientation error is less than 5 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> on the sceneNN dataset.

Topological Mappings Based on SPG*-Closed Set

In this paper, we introduce the concept of SPG*-closed mapping and continuous mapping among which SPG-closed mappings, SPG*-closed mappings and SPG**-closed mappings and the relationship between them, and also SPG-continuous mappings, SPG*-continuous mappings and SPG**-continuous mappings and the relationship between them. Among the result we obtain are the concepts of SPG**-closed mappings and closed mappings are independent also PG**-continuous mappings and continuous mapping are independent.

Hybrid cascaded MLI development for PV‐grid connection applications

AbstractFor grid‐connected solar photovoltaic (PV) applications, a hybrid cascaded MLI (H‐CMLI) topology based on cascaded H‐bridge MLI is proposed in this study. The proposed configuration is a hybrid of two common MLIs: Cascaded H‐bridge and three‐phase cascaded voltage source inverter (VSI). For the same voltage level, the proposed topology enhances the number of the generated voltage levels while employing fewer components, relative to other traditional MLI topologies. Furthermore, the voltage stresses on switches in the proposed topology are decreased, compared to when each part operates independently. The proposed topology could be utilized for many applications, such as PV‐grid connection, active power filters, and STATCOM. To verify the effectiveness of the proposed topology, the model is developed in MATLAB/Simulink environment, in which a closed‐loop control scheme based on the traditional one is used to achieve the PV‐grid connection. Alongside the simulation results, the proposed topology and its control scheme are experimentally implemented in the laboratory. Two cases were experimentally applied to validate the successful performance of the proposed H‐CMLI: Open‐loop control and closed‐loop control for PV‐grid integration. The proposed control scheme effectively maintains the DC link voltage at its reference voltages. Also, the closed‐loop control scheme is capable of providing the power factor at unity and maintaining the total harmonic distortion (THD) of grid current within the acceptable limit. To demonstrate the proposed topology's functionality and viability, experimental results are provided.

Analysis of the general tool life function of cutting tools by application of the catastrophe theory

Production technology planning requires information on tool life T and its relation to cutting speed v. These technological parameters fundamentally affect the economics of the operation of the entire production system. The aim of the present study is to investigate the v-T relationship under technological conditions where this relationship cannot be described by the methods used so far – typically the Taylor formula. As the Taylor formula often cannot be linearized on an log-log scale, a general tool life function has been developed for describing a v-T function with a convex-concave part. The function was validated by the boring of hardened ball bearing rings with the technological data D = 45, 75 and 100 mm, ap = 0.14 mm, fz = 0.005 mm/rev. The general v-T tool life function has been transformed into a quadratic polynomial known in catastrophe theory as cusp catastrophe and allows the description of abrupt changes in the system under study. Using catastrophe theory, an analogy is established between the general tool life function and the cusp catastrophe, allowing topological mapping of the general v-T function. Results were verified by machinability tests in the turning of C35 and C60 conventional and specially deoxidized C-steels during steelmaking. Cutting tests were performed on specimens with a diameter of Dexp = 76 mm and a length of L = 700 mm at a speed of v = 100, 150, 200 m/min and a carbide tool of category P20. It was found that in the convex-concave section of this function, 2–3 cutting speeds can be selected for a given tool life, which is advantageous for harmonizing tool changes in multi-operation technology.

Tapir: A tool for topologies, amplitudes, partial fraction decomposition and input for reductions

The demand for precision predictions in the field of high energy physics has dramatically increased over recent years. Experiments conducted at the LHC, as well as precision measurements at the intensity frontier such as Belle II require equally precise theoretical predictions to make full use of the acquired data. To match the experimental precision, second-, third- and, for certain quantities, even higher-order calculations in perturbative quantum field theory are required.To facilitate such calculations, computer software automating as many steps as possible is required. Yet, each calculation poses different challenges and thus, a high level of configurability is required. In this context we present tapir: a tool for identification, manipulation and minimization of Feynman integral families. It is designed to integrate in toolchains based on the computer algebra system FORM, the use of which is common practice in the field. tapir can be used to reduce the complexity of multi-loop problems with cut-filters, topology mapping, partial fraction decomposition and alike. Program summaryProgram Title:tapirCPC Library link to program files:https://doi.org/10.17632/ptc9t46xyn.1Developer's repository link:https://gitlab.com/tapir-devs/tapirLicensing provisions: GPLv3Programming language:python 3, C++Nature of problem: Multi-loop computations require the automatization of a large number of different tasks related to Feynman integral topologies. Among them are the identification and minimization of integral topologies, partial fraction decomposition of topologies in the case of linearly dependent propagators as well as mapping scalar products of loop momenta to scalar functions.Solution method: The minimization of topologies is performed by comparison of their respective Nickel indices [1], even further minimization utilizes Pak's algorithm [2]. To efficiently map scalar products of loop momenta to scalar functions FORM [3] code is generated.Additional comments including restrictions and unusual features: Minimization based on Pak's algorithm slows down for many lines and scales. A coarser minimization using the Nickel indices, however, is still possible.

Performances Recovery of Flax Fiber Reinforced Composites after Salt-Fog Aging Test

In the present paper, the performance recovery under conditions of discontinuous exposure to a marine environment of a natural fiber-reinforced composite (NFRC) reinforced by flax fibers was assessed. In particular, this laminate was initially exposed to salt-fog for 15 and 30 days, and then stored in a controlled air condition for up to 21 days. The flax fiber-reinforced composite showed coupled reversible and irreversible aging phenomena during the wet stage, as well as evidencing a significant mechanical recovery during the dry stage. Unlike the stiffness, the laminate showed a noticeable recovery of its flexural strength. This behavior affected the composite material toughness. A simplified approach was applied to define a topological map of the material toughness at varying drying times. The results highlight that the composite shows maximum toughness at intermediate drying times thanks to the strength recovery, in addition to its residual plasticity. This approach allows us to better determine that the strength is more closely related to reversible degradation phenomena, whereas the stiffness is mainly correlated to irreversible ones, implying relevant effects on the toughness of the composite exposed to a wet/dry cycle.

A pagerank self-attention network for traffic flow prediction

Traffic information is collected from sensors in the urban road network, and traffic information can be said to be a mapping of people’s activities, which are difficult to model as a linear function, so this makes traffic information difficult to be predicted. In other words, traffic information is difficult to build effective models to predict traffic information because of its non-linear characteristics that are difficult to capture. As researchers go deeper, researchers have been able to extract good spatio-temporal features for modern urban road networks. However, it is worth mentioning that most researchers have neglected the importance of models for global potential features under the topology map of urban road networks, yet this global potential feature is very important for traffic prediction. In this paper, we propose a new spatio-temporal graph convolutional network model A Pagerank Self-attention Network (hereafter we abbreviate as PSN) in order to solve this problem based on a full consideration of the urban road network topology features, in which we employ a global spatio-temporal self-attention module to capture the global spatio-temporal features well. and the graph wandering module is used to propagate the spatio-temporal feature information effectively and widely. It is worth mentioning that experiments on two well-known datasets show that our proposed method achieves better prediction results compared to existing baseline methods.

Fault locating and severity assessment for power distribution systems based on elasticity network topology mapping

Although fault locating in power distribution systems has been widely studied, very few research works consider fault severity assessment, i.e., fault influence on other lines. The main reason behind this is the lack of a unified standard to quantify and assess the severity of different type of faults. In this paper, a novel power distribution system fault locating and severity assessment method is proposed based on elasticity network mapping. The principle of the proposed method is based on elastic coefficient, which reflects the state of the elastic spring by its numerical value. The partial derivative of distribution line power flow to its phase difference, denoted as distribution rate (DR), is analyzed to map the line fault occurrence to the numerical change in DR. Besides, the method normalizes the DR values of different distribution lines into standardized values between 0 and 1, so that normalized comparison can be achieved. Because DR value is inversely proportional to the severity the fault, by comparing the DR of each line, the fault location and severity can be accurately evaluated. Simulation tests are performed in 10 different test feeders with around 35% of nodes installed with measurement units, reporting a 99% accuracy.

Spatial-temporal flood inundation nowcasts by fusing machine learning methods and principal component analysis

• Build a novel forecast model (PCA-SOM-NARX) by statistical & machine learning tools. • 2-dimensional SOBEK simulates the full-scale flooding processes of rainfall patterns. • PCA extracts principal components to present spatial distributions of inundation. • SOM clusters inundation data to form a spatial–temporal topological feature map. • PCA-SOM-NARX makes reliable and accurate multi-step-ahead regional flood nowcasts. The frequency and severity of floods have noticeably increased worldwide in the last decades due to climate change and urbanization. This study aims to build an urban flood warning system for reducing the impact of flood disasters. A great number of storm-induced rainfall data were collected in Taipei, Taiwan, and the corresponding 2-D inundation maps were simulated for illustrating urban rainfall-flood inundation processes. We proposed a novel urban flood forecast methodology framed by machine learning and statistical techniques to mine the spatial–temporal features between rainfall patterns and inundation maps for making multi-step-ahead regional flood inundation forecasts. The proposed methodology (PCA-SOM-NARX) integrated the advantages of Principal Component Analysis (PCA), Self-Organizing Map (SOM), and Nonlinear Autoregressive with Exogenous Inputs (NARX). PCA was used to extract principal components representing the different spatial distributions of urban inundation. SOM was used to cluster high dimensional inundation datasets to form a two-dimensional topological feature map. NARX was used to establish multi-step-ahead flood forecast models for the next hour at a 10-minute scale. The results show that the PCA-SOM-NARX approach not only produced more stable and accurate multi-step-ahead forecasts on flood inundation depth but was also more indicative of the spatial distribution of inundation caused by torrential rain events, compared to the SOM-NARX approach (the benchmark). The results demonstrate the proposed methodology can adequately grasp the inundation status associated with different rainfall distributions to reliably and accurately forecast regional flood inundation depths, which can help decision makers respond to flooding earlier and mitigate flood disasters.

Explicit presentations of topological categories of gestures

Thanks to Mazzola's notion of gestures on topological categories we can appreciate how the notion of gesture transcends its manifestation as the movement of the body's limbs and takes a more abstract form that blends diagrammatic (discrete gesturality) and bodily aspects (continuous gesturality). These two aspects are strongly related to two main branches of mathematical music theory, namely, a discrete branch and a continuous branch. The discrete branch corresponds to the diagrams of transformational theory, that is, to networks in musical analysis. The continuous branch corresponds to the movement of the musical performer's body. Informally, a gesture on a topological category is a diagram of continuous paths of morphisms in the category. This definition amounts to that of topological category of gestures, whose structure we study in this article. Specifically, we study the presentation of topological categories of gestures as suitable categories of topological functors and as suitable categories of sequences, and the explicit presentation of morphisms of a typical topological category of gestures. In particular, we present an exhaustive study, not included in previous publications, of the topological category of continuous paths of an arbitrary digraph. This article can be regarded as a continuation of a previous publication, in a previous issue of this journal, on the presentation of spaces of gestures as function spaces. We include an application of the theory to the variations in Mozart's Piano Sonata K. 331. We provide an Online Supplement, in which we include some technical passages.

Research on influencing factors of artificial intelligence multi-cloud scheduling applied talent training based on DEMATEL-TAISM

With the rapid development of Internet of Things (IoT) technology and the rising popularity of IoT devices, an increasing number of computing intensive IoT applications have been developed. However, due to the limited resources of IoT devices, cloud computing systems are required to compute intensive IoT applications. Furthermore, to be subject to a single cloud computing service provider, multi-cloud computing has become an IoT service cloud computing solution. As a result of the complexity of multi-cloud scheduling, the application of artificial intelligence is an important technology to solve IoT multi-cloud scheduling. The corresponding talent training plays an important role in the development and implementation of the IoT artificial intelligence multi-cloud scheduling. First, this paper studies the key influencing factors of IoT artificial intelligence multi-cloud scheduling applied talent training. Combined with the characteristics of the development of China’s artificial intelligence industry, this paper summarizes the influencing factors from the four-dimensional training path of government departments, universities, enterprises and scientific research institutes. The purpose of artificial intelligence multi-cloud scheduling applied talent training is to build an artificial intelligence multi-cloud scheduling applied talent training influencing factor index system. Then, the DEMATEL method is used to establish multiple correlation matrices according to the direct influence correlation between the factors and calculate the degree of influence, the degree of being influenced, the center degree and the cause degree of the factors. Using the improved AISM method, based on the idea of game confrontation, from the two opposite extraction rules of result priority and cause priority, a group of confrontation level topological maps with comprehensive influence values reflecting the interacting factors are obtained, and relevant suggestions are presented to provide a reference for the training of artificial intelligence multi-cloud scheduling applied talent.

Primate brain pattern-based automated Alzheimer's disease detection model using EEG signals.

Electroencephalography (EEG) may detect early changes in Alzheimer's disease (AD), a debilitating progressive neurodegenerative disease. We have developed an automated AD detection model using a novel directed graph for local texture feature extraction with EEG signals. The proposed graph was created from a topological map of the macroscopic connectome, i.e., neuronal pathways linking anatomo-functional brain segments involved in visual object recognition and motor response in the primate brain. This primate brain pattern (PBP)-based model was tested on a public AD EEG signal dataset. The dataset comprised 16-channel EEG signal recordings of 12 AD patients and 11 healthy controls. While PBP could generate 448 low-level features per one-dimensional EEG signal, combining it with tunable q-factor wavelet transform created a multilevel feature extractor (which mimicked deep models) to generate 8,512 (= 448 × 19) features per signal input. Iterative neighborhood component analysis was used to choose the most discriminative features (the number of optimal features varied among the individual EEG channels) to feed to a weighted k-nearest neighbor (KNN) classifier for binary classification into AD vs. healthy using both leave-one subject-out (LOSO) and tenfold cross-validations. Iterative majority voting was used to compute subject-level general performance results from the individual channel classification outputs. Channel-wise, as well as subject-level general results demonstrated exemplary performance. In addition, the model attained 100% and 92.01% accuracy for AD vs. healthy classification using the KNN classifier with tenfold and LOSO cross-validations, respectively. Our developed multilevel PBP-based model extracted discriminative features from EEG signals and paved the way for further development of models inspired by the brain connectome.

Semi-supervised classification of fundus images combined with CNN and GCN.

Diabetic retinopathy (DR) is one of the most serious complications of diabetes, which is a kind of fundus lesion with specific changes. Early diagnosis of DR can effectively reduce the visual damage caused by DR. Due to the variety and different morphology of DR lesions, automatic classification of fundus images in mass screening can greatly save clinicians' diagnosis time. To alleviate these problems, in this paper, we propose a novel framework-graph attentional convolutional neural network (GACNN). The network consists of convolutional neural network (CNN) and graph convolutional network (GCN). The global and spatial features of fundus images are extracted by using CNN and GCN, and attention mechanism is introduced to enhance the adaptability of GCN to topology map. We adopt semi-supervised method for classification, which greatly improves the generalization ability of the network. In order to verify the effectiveness of the network, we conducted comparative experiments and ablation experiments. We use confusion matrix, precision, recall, kappa score, and accuracy as evaluation indexes. With the increase of the labeling rates, the classification accuracy is higher. Particularly, when the labeling rate is set to 100%, the classification accuracy of GACNN reaches 93.35%. Compared with DenseNet121, the accuracy rate is improved by 6.24%. Semi-supervised classification based on attention mechanism can effectively improve the classification performance of the model, and attain preferable results in classification indexes such as accuracy and recall. GACNN provides a feasible classification scheme for fundus images, which effectively reduces the screening human resources.

Single-shot compressed optical field topography

Femtosecond lasers are powerful in studying matter’s ultrafast dynamics within femtosecond to attosecond time scales. Drawing a three-dimensional (3D) topological map of the optical field of a femtosecond laser pulse including its spatiotemporal amplitude and phase distributions, allows one to predict and understand the underlying physics of light interaction with matter, whose spatially resolved transient dielectric function experiences ultrafast evolution. However, such a task is technically challenging for two reasons: first, one has to capture in single-shot and squeeze the 3D information of an optical field profile into a two-dimensional (2D) detector; second, typical detectors are only sensitive to intensity or amplitude information rather than phase. Here we have demonstrated compressed optical field topography (COFT) drawing a 3D map for an ultrafast optical field in single-shot, by combining the coded aperture snapshot spectral imaging (CASSI) technique with a global 3D phase retrieval procedure. COFT can, in single-shot, fully characterize the spatiotemporal coupling of a femtosecond laser pulse, and live stream the light-speed propagation of an air plasma ionization front, unveiling its potential applications in ultrafast sciences.

Dual Encoder-Based Dynamic-Channel Graph Convolutional Network With Edge Enhancement for Retinal Vessel Segmentation.

Retinal vessel segmentation with deep learning technology is a crucial auxiliary method for clinicians to diagnose fundus diseases. However, the deep learning approaches inevitably lose the edge information, which contains spatial features of vessels while performing down-sampling, leading to the limited segmentation performance of fine blood vessels. Furthermore, the existing methods ignore the dynamic topological correlations among feature maps in the deep learning framework, resulting in the inefficient capture of the channel characterization. To address these limitations, we propose a novel dual encoder-based dynamic-channel graph convolutional network with edge enhancement (DE-DCGCN-EE) for retinal vessel segmentation. Specifically, we first design an edge detection-based dual encoder to preserve the edge of vessels in down-sampling. Secondly, we investigate a dynamic-channel graph convolutional network to map the image channels to the topological space and synthesize the features of each channel on the topological map, which solves the limitation of insufficient channel information utilization. Finally, we study an edge enhancement block, aiming to fuse the edge and spatial features in the dual encoder, which is beneficial to improve the accuracy of fine blood vessel segmentation. Competitive experimental results on five retinal image datasets validate the efficacy of the proposed DE-DCGCN-EE, which achieves more remarkable segmentation results against the other state-of-the-art methods, indicating its potential clinical application.

Effects of wall temperature on separation structures in supersonic flow over a semi-circular cavity

The influence of wall temperature on the vortex structure and flow characteristics of flow in semi-circular cavities is numerically investigated in this paper. The results show that the separation and reattachment points move downstream, and the secondary vortex increases with increases in wall temperature. In the secondary vortex section, normalized wall shear stress in the polar map has good consistency at different wall temperatures, and the flow properties of the three extreme points on the map are similar to Couette flow. In addition, the secondary vortex region can be regarded as an isobaric high-pressure region, while the pressure gradients slowly vary as the wall temperature increases. We confirm the independence of separation pressure for the wall temperature using pressure distribution and find that the peak and inflection points are at the reattachment points and separation points, respectively. Moreover, using a series of numerical calculations of the positions of the vortex center at different wall temperatures, explicit empirical formulas for estimating the positions of the primary vortex center are put forward. Using a topological mapping method, cavity flow is converted into quasi-one-dimensional steady-state compressible viscous flow through a variable cross-section pipe, and the flow parameter distribution, including the Mach number and pressure, conforms to the rule of quasi-one-dimensional flow.