Adjacency Information Research Articles

BASYS-MTB: An integrative structural simulation platform based on adjacency matrices

The objective of this work is to develop and test a new data structure, which takes advantage of the latest programming technology to build interactive structural engineering modeling software that is robust, efficient, portable, modular, expandable, and easy to use. The data structure proposed is composed of a collection of matrices that describe both the geometrical properties and the topological adjacency information of the structural model, as well as the section, material, loading, and fixity attributes of the structural elements. This data structure is dubbed the Adjacency Matrix Data Structure (AMDS). There are eight matrices in the AMDS, each capturing the details of one type of structural entity or attribute: Vertex, Edge, Face, Group, Section, Material, Load, and Fixity. The design of the AMDS allows it to excel in terms of efficiency and speed when used in a matrix-based programming environment, such as MATLAB. The final outcome called “BASYS-MTB” is a structural modeling and visualization MATLAB Toolbox that is a demonstration implementation of the AMDS as the underlying data structure. The toolbox, which can serve as a preprocessor, solver, and postprocessor, employs the object-oriented programming capabilities in MATLAB to create and manipulate numerical and graphical objects. This system offers engineers and researchers with limited programming expertise a structural modeler that can be easily customized to their requirements using their existing knowledge in MATLAB.

MeshGraphNet: An Effective 3D Polygon Mesh Recognition With Topology Reconstruction

Three-dimensional polygon mesh recognition has a significant impact on current computer graphics. However, its application to some real-life fields, such as unmanned driving and medical image processing, has been restricted due to the lack of inner-interactivity, shift-invariance, and numerical uncertainty of mesh surfaces. In this paper, an interconnected topological dual graph that extracts adjacent information from each triangular face of a polygon mesh is constructed, in order to address the above issues. On the basis of the algebraic topological graph, we propose a mesh graph neural network, called MeshGraphNet, to effectively extract features from mesh data. In this concept, the graph node-unit and correlation between every two dual graph vertexes are defined, the concept of aggregating features extracted from geodesically adjacent nodes is introduced, and a graph neural network with available and effective blocks is proposed. With these methods, MeshGraphNet performs well in 3D shape representation by avoiding the lack of inner-interactivity, shift-invariance, and the numerical uncertainty problems of mesh data. We conduct extensive 3D shape classification experiments and provide visualizations of the features extracted from the fully connected layers. The results demonstrate that our method performs better than state-of-the-art methods and improves the recognition accuracy by 4-4.5%.

Superpixel-based segmentation algorithm for mature citrus

With the decrease of agricultural labors and the increase in production costs, harvesting robots have become a research hotspot in recent years. To guide harvesting robots to pick mature citrus more precisely under variable illumination conditions, an image segmentation algorithm based on superpixel was proposed. Efficient simple linear iterative clustering (SLIC) algorithm which takes similarity of adjacent pixels into account was adopted to segment the images captured under variable illumination conditions into superpixels. The color and texture features of these superpixels were extracted and fused into feature vectors as descriptors to train backpropagation neural networks (BPNN) classifier in the next step. The adjacency information of superpixels was considered by calculating the global-local binary pattern (LBP) in R component images when extracting texture features. To accelerate the classification process, the mean of Cr-Cb image was utilized to find superpixels of interest which were regarded as candidates of citrus superpixels. These candidates were then classified by a pre-trained BPNN model with superpixel-level accuracy of 98.77% and pixel-level accuracy of 94.96%, while the average time to segment one image was 0.4778 s. Therefore, the results indicated that a superpixel-based segmentation algorithm toward citrus images had decent light robustness as well as high accuracy that could guide harvesting robot to pick mature citrus efficiently. Keywords: superpixel, image segmentation, BPNN, variable illumination, mature citrus DOI: 10.25165/j.ijabe.20201304.5607 Citation: Yang Q H, Chen Y Q, Xun Y, Bao G J. Superpixel-based segmentation algorithm for mature citrus. Int J Agric & Biol Eng, 2020; 13(4): 166–171.

Topo2Vec: A Novel Node Embedding Generation Based on Network Topology for Link Prediction

Link prediction of a scale-free network has become relevant for problems relating to social network analysis, recommendation system, and in the domain of bioinformatics. In recently proposed approaches, the sampling of nodes of a network is done by simulating random walk. The generated node samples are used to train a neural network to learn the contextual information through an embedding vector. This method has gained popularity as the embedding vector is produced from the primitive node adjacency information and has achieved an outstanding performance. In this article, a naive and scalable approach for generating the node samples based on the principle of goal-oriented greedy searching has been proposed. The generated node samples have low noisy structures, which can represent the relation of edges of the network in a better way, compared to the state-of-the-art methods. Consequently, better representation of feature embedding of nodes of the network is generated from the samples. The learned feature vectors are used for solving the link prediction problem using a pairwise kernel support vector machine (SVM) classifier, which is computationally and spatially expensive in nature. Therefore, as an alternative, we have chosen to deploy a random forest (RF) classifier with a new algebraic operation to obtain the symmetric pairwise feature representation of a node pair. We demonstrated the efficacy of the proposed Topo2vec by testing it against the state-of-the-art network context generation algorithms in several real-world networks. Altogether, the proposed Topo2vec algorithm is a new method for solving the link prediction problem with entirely different settings.

Handling Incomplete Sensor Measurements in Fault Detection and Diagnosis for Building HVAC Systems

Due to the development of sensor networks and information technology, data-driven fault detection and diagnosis (FDD) has been made possible with real-time multiple sensor measurements. However, due to inevitable sensor errors or communication failures, the raw data are usually incomplete with corrupted values, lost values, or undetected missing values. In practice, the incomplete data are usually dealt with by directly excluding incomplete measurements and abnormal spikes. In addition, some preprocessing methods, which naively impute data though averaging or smoothing, have also been widely applied. In this article, we address the building FDD problem with incomplete data by proposing a new approach, the adjacent information recovery (AIR) filter. The AIR filter is utilized to deal with the FDD for a typical air handling unit (AHU) system with incomplete data based on the ASHRAE Research Project 1312. Experimental results show that the proposed method improves FDD performance by recovering missing sensor measurements and outperforms the state-of-the-art methods. Note to Practitioners —Fault detection and diagnosis (FDD) for smart buildings by addressing the fact that FDD systems are of great importance for saving energy and improving occupancy comfort levels and building safety levels. Existing FDD methods are mainly based on the assumption that sensor data are complete and reliable, which are rarely true in real practice. To solve the building FDD problem with incomplete data, in this article, the adjacent information recovery (AIR) filter is proposed to recover the missing data before applying FDD methods. The AIR filter takes the time series adjacency information into consideration via hidden Markov model (HMM) and includes the channel adjacency information with the collaborating filtering technique.

Crossover and Mutation Strategies applied in Job Shop Scheduling Problems

This paper reviews modern genetic algorithm based approaches for solving job shop scheduling problems. This paper elaborates the types of crossover methods such as Partially Mapped crossover (PMX), Order One Crossover (OX), etc involved in Genetic Algorithm (GA) that are used for Job Shop Scheduling problems. Job Shop Scheduling represents one of the hardest combinatorial optimization problems where number of possible schedules drastically increases with the number of operations and machines. Normal crossover operators will often lead to inadmissible solution . Many specialized combining order or adjacency information from the two parents. The crossover method operators builds an offspring by choosing a subsequence of elements from one parent and preserving the order and position of as many elements as possible from the other parent. A subsequence of elements is selected by choosing two random cut points, which serve as boundaries for the swapping operations. There are few distinct mutation operators widely used for JSS problems such as swap, inversion, insertion (shift) and displacement mutation. Here some modern genetic algorithm-based approaches from the literature are also discussed as well.

A Comparison of Traditional Machine Learning and Deep Learning in Image Recognition

The growth of the mobile Internet, smartphones and social networks has brought in huge amounts of picture information, and traditional manual identification is not able to meet the demand well enough. Therefore, the automatical image recognition [1] has been proposed which can help us recognize the image efficiently and get the corresponding information. Although traditional machine learning methods [2] have already been widely used in the field of image recognition, most of these methods are designed to handle one-dimensional vector information. Thus, we should first stretch image matrix to one-dimensional vector or extract features from images to employ traditional image recognition methods, which would lose the adjacent information in images and miss some important features. With the development of computer technology, deep learning [3] is gradually applied to the field of image recognition. It can deal with two-dimensional image data naturally and extract features automatically. Compared with the traditional machine learning methods, deep learning is popular for its good learning ability and low generalization error. In this paper, we compare the differences between SVM [4] and deep learning on image recognition, with an application to handwritten digital images recognition. The results show that the deep learning method is more accurate and more stable in image recognition.

A fast quadtree‐based terrain crack locating method that accounts for adjacency relationships

AbstractThe repair of terrain cracks is an important process in multi‐resolution three‐dimensional terrain visualization, and rapid and accurate pinpointing of cracks between terrain blocks is of the utmost importance for this process. The existing quadtree‐based crack locating methods are generally slow and inefficient, as they require the traversal of terrain quadtrees to locate terrain cracks. In this research, we propose a fast quadtree‐based terrain crack locating method that accounts for adjacency information. First, terrain quadtrees are constructed that contain information regarding adjacency relationships. Second, dynamic updating is performed for terrain quadtrees and their adjacency information. Finally, terrain cracks are located by using the adjacencies of the quadtree nodes. This proposed method was experimentally validated via the terrain data of a mountainous region in Sichuan Province. The experimental results indicate that the proposed method can be used to efficiently and accurately locate terrain cracks with a strong performance in practical applications.

Collective Affinity Learning for Partial Cross-Modal Hashing.

In the past decade, various unsupervised hashing methods have been developed for cross-modal retrieval. However, in real-world applications, it is often the incomplete case that every modality of data may suffer from some missing samples. Most existing works assume that every object appears in both modalities, hence they may not work well for partial multi-modal data. To address this problem, we propose a novel Collective Affinity Learning Method (CALM), which collectively and adaptively learns an anchor graph for generating binary codes on partial multi-modal data. In CALM, we first construct modality-specific bipartite graphs collectively, and derive a probabilistic model to figure out complete data-to-anchor affinities for each modality. Theoretical analysis reveals its ability to recover missing adjacency information. Moreover, a robust model is proposed to fuse these modality-specific affinities by adaptively learning a unified anchor graph. Then, the neighborhood information from the learned anchor graph acts as feedback, which guides the previous affinity reconstruction procedure. To solve the formulated optimization problem, we further develop an effective algorithm with linear time complexity and fast convergence. Last, Anchor Graph Hashing (AGH) is conducted on the fused affinities for cross-modal retrieval. Experimental results on benchmark datasets show that our proposed CALM consistently outperforms the existing methods.

Study on Unknown Term Translation Mining from Google Snippets

Bilingual web pages are widely used to mine translations of unknown terms. This study focused on an effective solution for obtaining relevant web pages, extracting translations with correct lexical boundaries, and ranking the translation candidates. This research adopted co-occurrence information to obtain the subject terms and then expanded the source query with the translation of the subject terms to collect effective bilingual search engine snippets. Afterwards, valid candidates were extracted from small-sized, noisy bilingual corpora using an improved frequency change measurement that combines adjacent information. This research developed a method that considers surface patterns, frequency–distance, and phonetic features to elect an appropriate translation. The experimental results revealed that the proposed method performed remarkably well for mining translations of unknown terms.

A framework for self‐evolving computational material models inspired by deep learning

SummaryThere exists a deep chasm between machine learning (ML) and high‐fidelity computational material models in science and engineering. Due to the complex interaction of internal physics, ML methods hardly conquer or innovate them. To fill the chasm, this paper finds an answer from the central notions of deep learning (DL) and proposes information index and link functions, which are essential to infuse principles of physics into ML. Like the convolution process of DL, the proposed information index integrates adjacent information and quantifies the physical similarity between laboratory and reality, enabling ML to see through a complex target system with the perspective of scientists. Like the hidden layers' weights of DL, the proposed link functions unravel the hidden relations between information index and physics rules. Like the error backpropagation of DL, the proposed framework adopts fitness‐based spawning scheme of evolutionary algorithm. The proposed framework demonstrates that a fusion of information index, link functions, evolutionary algorithm, and Bayesian update scheme can engender self‐evolving computational material models and that the fusion will help rename ML as a partner of researchers in the broad science and engineering.

Regularized topic-aware latent influence propagation in dynamic relational networks

On social networks, investigating how the influence is propagated is crucial in understanding the network evolution and the social impact of different topics. In previous study, the influence propagation is either modeled based on the static network structure, or the infection between two connected users is recovered from some given event cascades. Unfortunately, existing solutions are incapable of identifying the user susceptibility delivered by user generated content. In this paper, we propose RegInfoIbp, a general regularized learning framework for modeling topic-aware influence propagation in dynamic network structures. Specifically, the observed time-sequential user topic preference and user adjacency information are factorized by the prior information reflected by a user-influential bipartite relation graph. The influence propagation is approximated with a nonparametric regularized Bayesian matrix factorization model with tractable polynomial complexity. and the influential users are identified by several sampling algorithms with slightly different approximation qualities. To further model dynamic temporal evolution, we construct Markov conditional probabilistic model on the compact latent feature representation. By integrating both topic and structure information into the regularized non-parametric probabilistic learning process, RegInfoIbp is more efficient and accurate in discovering the key factors in the content and influential users in dynamic network structure. Extensive experiments demonstrate that RegInfoIbp better adapts to real data, and achieves better approximation in influence propagation over existing approaches.

VizTract: Visualization of Complex Social Networks for Easy User Perception

Social networking platforms connect people from all around the world. Because of their user-friendliness and easy accessibility, their traffic is increasing drastically. Such active participation has caught the attention of many research groups that are focusing on understanding human behavior to study the dynamics of these social networks. Oftentimes, perceiving these networks is hard, mainly due to either the large size of the data involved or the ineffective use of visualization strategies. This work introduces VizTract to ease the visual perception of complex social networks. VizTract is a two-level graph abstraction visualization tool that is designed to visualize both hierarchical and adjacency information in a tree structure. We use the Facebook dataset from the Social Network Analysis Project from Stanford University. On this data, social groups are referred as circles, social network users as nodes, and interactions as edges between the nodes. Our approach is to present a visual overview that represents the interactions between circles, then let the user navigate this overview and select the nodes in the circles to obtain more information on demand. VizTract aim to reduce visual clutter without any loss of information during visualization. VizTract enhances the visual perception of complex social networks to help better understand the dynamics of the network structure. VizTract within a single frame not only reduces the complexity but also avoids redundancy of the nodes and the rendering time. The visualization techniques used in VizTract are the force-directed layout, circle packing, cluster dendrogram, and hierarchical edge bundling. Furthermore, to enhance the visual information perception, VizTract provides interaction techniques such as selection, path highlight, mouse-hover, and bundling strength. This method helps social network researchers to display large networks in a visually effective way that is conducive to ease interpretation and analysis. We conduct a study to evaluate the user experience of the system and then collect information about their perception via a survey. The goal of the study is to know how humans can interpret the network when visualized using different visualization methods. Our results indicate that users heavily prefer those visualization techniques that aggregate the information and the connectivity within a given space, such as hierarchical edge bundling.

A spatial heterogeneity-based rough set extension for spatial data

ABSTRACTWhen classical rough set (CRS) theory is used to analyze spatial data, there is an underlying assumption that objects in the universe are completely randomly distributed over space. However, this assumption conflicts with the actual situation of spatial data. Generally, spatial heterogeneity and spatial autocorrelation are two important characteristics of spatial data. These two characteristics are important information sources for improving the modeling accuracy of spatial data. This paper extends CRS theory by introducing spatial heterogeneity and spatial autocorrelation. This new extension adds spatial adjacency information into the information table. Many fundamental concepts in CRS theory, such as the indiscernibility relation, equivalent classes, and lower and upper approximations, are improved by adding spatial adjacency information into these concepts. Based on these fundamental concepts, a new reduct and an improved rule matching method are proposed. The new reduct incorporates spatial heterogeneity in selecting the feature subset which can preserve the local discriminant power of all features, and the new rule matching method uses spatial autocorrelation to improve the classification ability of rough set-based classifiers. Experimental results show that the proposed extension significantly increased classification or segmentation accuracy, and the spatial reduct required much less time than classical reduct.

A linear-time algorithm to compute the conjugate of convex piecewise linear-quadratic bivariate functions

We propose the first algorithm to compute the conjugate of a bivariate Piecewise Linear-Quadratic (PLQ) function in optimal linear worst-case time complexity. The key step is to use a planar graph, called the entity graph, not only to represent the entities (vertex, edge, or face) of the domain of a PLQ function but most importantly to record adjacent entities. We traverse the graph using breadth-first search to compute the conjugate of each entity using graph-matrix calculus, and use the adjacency information to create the output data structure in linear time.

Efficient searching in meshfree methods

Meshfree methods such as the Reproducing Kernel Particle Method and the Element Free Galerkin method have proven to be excellent choices for problems involving complex geometry, evolving topology, and large deformation, owing to their ability to model the problem domain without the constraints imposed on the Finite Element Method (FEM) meshes. However, meshfree methods have an added computational cost over FEM that come from at least two sources: increased cost of shape function evaluation and the determination of adjacency or connectivity. The focus of this paper is to formally address the types of adjacency information that arises in various uses of meshfree methods; a discussion of available techniques for computing the various adjacency graphs; propose a new search algorithm and data structure; and finally compare the memory and run time performance of the methods.

Parameter Setting of Load Forecasting Model for Adjacent Base Stations of Mobile Communication Based on Particle Swarm Optimization

In order to study the parameter setting of load forecasting model of mobile communication adjacent base station, the particle swarm optimization algorithm is used to make intelligent optimization of the parameter values in the support vector regression model, so as to obtain a prediction model with better parameters. Through the verification of base station load prediction scheme assisted by the nearest neighbor base station, we establish the support vector regression model only using the base station itself historical information that introducing adjacent base station historical information, and compare the performance of the two parts. It is proved that the base station assisted load prediction scheme with assisted adjacent base stations can achieve better performance.

Phylogeny analysis from gene-order data with massive duplications

BackgroundGene order changes, under rearrangements, insertions, deletions and duplications, have been used as a new type of data source for phylogenetic reconstruction. Because these changes are rare compared to sequence mutations, they allow the inference of phylogeny further back in evolutionary time. There exist many computational methods for the reconstruction of gene-order phylogenies, including widely used maximum parsimonious methods and maximum likelihood methods. However, both methods face challenges in handling large genomes with many duplicated genes, especially in the presence of whole genome duplication.MethodsIn this paper, we present three simple yet powerful methods based on maximum-likelihood (ML) approaches that encode multiplicities of both gene adjacency and gene content information for phylogenetic reconstruction.ResultsExtensive experiments on simulated data sets show that our new method achieves the most accurate phylogenies compared to existing approaches. We also evaluate our method on real whole-genome data from eleven mammals. The package is publicly accessible at http://www.geneorder.org.ConclusionsOur new encoding schemes successfully incorporate the multiplicity information of gene adjacencies and gene content into an ML framework, and show promising results in reconstruct phylogenies for whole-genome data in the presence of massive duplications.

POINT CLOUDS TO INDOOR/OUTDOOR ACCESSIBILITY DIAGNOSIS

Abstract. This work presents an approach to automatically detect structural floor elements such as steps or ramps in the immediate environment of buildings, elements that may affect the accessibility to buildings. The methodology is based on Mobile Laser Scanner (MLS) point cloud and trajectory information. First, the street is segmented in stretches along the trajectory of the MLS to work in regular spaces. Next, the lower region of each stretch (the ground zone) is selected as the ROI and normal, curvature and tilt are calculated for each point. With this information, points in the ROI are classified in horizontal, inclined or vertical. Points are refined and grouped in structural elements using raster process and connected components in different phases for each type of previously classified points. At last, the trajectory data is used to distinguish between road and sidewalks. Adjacency information is used to classify structural elements in steps, ramps, curbs and curb-ramps. The methodology is tested in a real case study, consisting of 100 m of an urban street. Ground elements are correctly classified in an acceptable computation time. Steps and ramps also are exported to GIS software to enrich building models from Open Street Map with information about accessible/inaccessible entrances and their locations.

Unsupervised image segmentation using the Deffuant–Weisbuch model from social dynamics

Unsupervised image segmentation algorithms aim at identifying disjoint homogeneous regions in an image and have been subject to considerable attention in the machine vision community. In this paper, a popular theoretical model with its origins in statistical physics and social dynamics, known as the Deffuant–Weisbuch model, is applied to the image segmentation problem. The Deffuant–Weisbuch model has been found to be useful in modelling the evolution of a closed system of interacting agents characterised by their opinions or beliefs, leading to the formation of clusters of agents who share a similar opinion or belief at steady state. In the context of image segmentation, this paper considers a pixel as an agent and its colour property as its opinion, with opinion updates as per the Deffuant–Weisbuch model. Apart from applying the basic model to image segmentation, this paper incorporates adjacency and neighbourhood information in the model, which factors in the local similarity and smoothness properties of images. Convergence is reached when the number of unique pixel opinions, i.e., the number of colour centres, matches the pre-specified number of clusters. Experiments are performed on a set of images from the Berkeley image segmentation dataset, and the results are analysed both qualitatively and quantitatively, which indicate that this simple and intuitive method is promising for image segmentation. To the best of the knowledge of the author, this is the first work where a theoretical model from statistical physics and social dynamics has been successfully applied to image processing.