Pseudo Nodes Research Articles

Two-stage heuristic algorithm with pseudo node-based model for electric vehicle routing problem

Electric vehicle routing problem (EVRP) is a special vehicle routing problem, which owns several specific characteristics of EV technology including the limited charging stations and the limited cruising range of EVs. This paper proposes a two-stage heuristic algorithm for EVRP with a kind of pseudo node, termed EVRPPN-TSH, which not only contributes to the model innovation but also to the algorithm design. In model innovation, the pseudo node is introduced into the EVRP model, forming EVRPPN, to reduce the search space and time complexity for obtaining service orders of customers from O(n) to O(1). Moreover, different from other research works which only use total distance as the fitness function, an improved fitness function is proposed, which takes all the distance, the electricity and capacity constraints compliance into consideration. In algorithm design, we first adopt the two-stage idea, which divides EVRP into the capacitated vehicle routing problem (CVRP) and a fixed route vehicle charging problem (FRVCP). Then we design a two-stage heuristic algorithm, termed as TSH. Specifically, for CVRP, a route division heuristic algorithm and a depot move heuristic algorithm are designed to divide routes. Further, an adjustment strategy with three operators is designed for adjusting the customer order. For FRVCP, an existing heuristic algorithm is adopted and enhanced by a variable extent shaking algorithm to avoid local trapped. Experimental results show that EVRPPN-TSH generally outperforms other state-of-the-art algorithms. Moreover, EVRPPN-TSH can obtain the best-known solution in all the 7 test cases, and update the best-known solution in test case E101 from 834.84 to 834.22.

StructGNN: An efficient graph neural network framework for static structural analysis

In the field of structural analysis prediction via supervised learning, neural networks are widely employed. Recent advances in graph neural networks (GNNs) have expanded their capabilities, enabling the prediction of structures with diverse geometries by utilizing graph representations and GNNs' message-passing mechanism. However, conventional message-passing in GNNs doesn't align with structural properties, resulting in inefficient computation and limited generalization to extrapolated datasets. To address this, a novel structural graph representation, incorporating pseudo nodes as rigid diaphragms in each story, alongside an efficient GNN framework called StructGNN is proposed. StructGNN employs an adaptive message-passing mechanism tailored to the structure's story count, enabling seamless transmission of input loading features across the structural graph. Extensive experiments validate the effectiveness of this approach, achieving over 99% accuracy in predicting displacements, bending moments, and shear forces. StructGNN also exhibits strong generalization over non-GNN models, with an average accuracy of 96% on taller, unseen structures. These results highlight StructGNN's potential as a reliable, computationally efficient tool for static structural response prediction, offering promise for addressing challenges associated with dynamic seismic loads in structural analysis.

Pseudo node insertion method for synchronization in drone–truck combined operations

Drone–truck combined last-mile delivery systems present several advantages, which can be summarized in two folds: (1) enabling faster pick-up/delivery of items and (2) expanding operation areas beyond the range that could be achieved by using only one type of vehicle. Drone–truck combined operation (DTCO) solutions are based on vehicle routes and drone launch and retrieval locations, usually customer nodes. Since a perfect synchronization of both vehicles is very rare, most of the solutions obtained after designing combined delivery routes generally contain time losses, caused by the need for the first vehicle that reaches the meeting point to wait until the arrival of the other. In this paper, a pseudo node insertion method is proposed to make DTCO solutions more efficient by improving the truck–drone synchronization operations. To this end, starting from a given routing solution, pseudo nodes are created at the point where a drone and a truck can meet simultaneously. We present a detailed method to calculate the location of the pseudo nodes and provide a detailed analysis of the conditions that allow us to reduce the travel completion time for different route patterns. Furthermore, we explore a variety of scenarios to illustrate the efficiency of the pseudo node insertion method for different DTCO problem solutions. An algorithm for the pseudo node insertion method is provided, and numerical examples are presented to discuss the efficacy and efficiency of the proposed approach.

Collaborative Decision-Reinforced Self-Supervision for Attributed Graph Clustering.

Attributed graph clustering aims to partition nodes of a graph structure into different groups. Recent works usually use variational graph autoencoder (VGAE) to make the node representations obey a specific distribution. Although they have shown promising results, how to introduce supervised information to guide the representation learning of graph nodes and improve clustering performance is still an open problem. In this article, we propose a Collaborative Decision-Reinforced Self-Supervision (CDRS) method to solve the problem, in which a pseudo node classification task collaborates with the clustering task to enhance the representation learning of graph nodes. First, a transformation module is used to enable end-to-end training of existing methods based on VGAE. Second, the pseudo node classification task is introduced into the network through multitask learning to make classification decisions for graph nodes. The graph nodes that have consistent decisions on clustering and pseudo node classification are added to a pseudo-label set, which can provide fruitful self-supervision for subsequent training. This pseudo-label set is gradually augmented during training, thus reinforcing the generalization capability of the network. Finally, we investigate different sorting strategies to further improve the quality of the pseudo-label set. Extensive experiments on multiple datasets show that the proposed method achieves outstanding performance compared with state-of-the-art methods. Our code is available at https://github.com/Jillian555/TNNLS_CDRS.

A multi-head pseudo nodes based spatial–temporal graph convolutional network for emotion perception from GAIT

In the recent years, the emotion recognition task has attracted a lot of attention in the field of human–computer interaction. Most existing research typically uses aural-visual analysis, which has been an effective approach to capturing emotional features. However, aural-visual signals are difficult to notice, in comparison to other human representations such as gait in remote situations. Recently, human gait can be effectively recognized in more complex backgrounds because of the advancement of Graph Convolutional Networks (GCNs). According to the anatomy of the human body, central torso joints play a key role in GCNs-based human gait recognition systems, instead of the body’s marginal limb joints. As a result, there is a major issue of receptive field imbalance. In this study, we propose a method for perceiving emotions based on the human gait skeleton. We present a multi-head pseudo nodes strategy to alleviate the receptive field imbalance problem and capture the non-local dependencies among different joints. The strategy employs a series of extra nodes that link to all physical human body joints and obtain global information from different feature spaces. The results of the experiments on a public emotion-gait dataset demonstrate that our proposed method outperforms existing skeleton-based methods. Further, to verify the effectiveness of our method, we use publicly available human action recognition datasets. Our results show that our method significantly improves performance in comparison to other baseline methods.

Bayesian learning of structures of ordered block graphical models with an application on multistage manufacturing processes

The Ordered Block Model (OBM) is a special form of directed graphical models and is widely used in various fields. In this article, we focus on learning of structures of OBM based on prior knowledge obtained from historical data. The proposed learning method is applied to a multistage car body assembly process to validate the learning efficiency. In this approach, Bayesian score is used to learn the graph structure and a novel informative structure prior distribution is constructed to help the learning process. Specifically, the graphical structure is represented by a categorical random variable and its distribution is treated as the informative prior. In this way, the informative prior distribution construction is equivalent to the parameter estimation of the graph random variable distribution using historical data. Since the historical OBMs may not contain the same nodes as those in the new OBM, the sample space of the graphical structure of the historical OBMs and the new OBM may be inconsistent. We deal with this issue by adding pseudo nodes with probability normalization, then removing extra nodes through marginalization to align the sample space between historical OBMs and the new OBM. The performance of the proposed method is illustrated and compared to conventional methods through numerical studies and a real car assembly process. The results show the proposed informative structure prior can effectively boost the performance of the graph structure learning procedure, especially when the data from the new OBM is small.

Controlling Physical Properties on Interfaces Using Parametrised Level Set Methods and Extended Finite Element Method

AbstractThe stress field information states a main point of interest, when regarding structural optimisation of bimaterial structures. The introduction of stress criteria along the volume is crucial in the development of new designs. In the case of bimaterials, the stress field along the interface deserves additional attention e.g. to prevent delamination. Tracing the interface through shape optimisation in CAD‐based methods is rather expensive due to the high cost in remeshing techniques. Therefore, level set methods with fixed background meshes are used as in [1].In this work an approach for controlling strains and stresses on the interface in a framework of shape optimisation is introduced. The geometry description is managed by parametrised level set functions and a sub‐meshing technique is coupled with the extended finite element method. The parametrisation with superellipses allows to reduce the number of design variables to a minimum of six variables per introduced ellipse while holding up a sufficient precision in the geometry description (see [2]). Moreover, it simplifies the shape derivatives as it provides an implicit description for the moving interfaces. The sub‐meshing technique makes it possible to keep existing strategies from homogeneous structures and to transform them on a discontinuous material using enriched shape functions provided by the standard extended finite element method (see [3]). Shape sensitivities are evaluated on the sub‐elements and extrapolated to the interface introducing pseudo nodes. The sensitivity information of the stress field among these pseudo nodes can be used in the framework of stress minimisation as well as for a side condition in a volume minimisation setup.

Generalizacija linija i AutoCAD Map

The paper offers the results of original research made on the application of AutoCADMap in line generalisation. The differences and similarities have been found out between theDouglas-Peucker method and the method of line simplification that is incorporated inAutoCAD Map. There have been also the inaccuracies found out in AutoCAD Map manualrelating to the issues of buffer width and tolerance, and the line width before and aftersimplification. The paper gives recommendations about pseudo nodes dissolving. It has beennoticed that AutoCAD Map simplification method is not independent of the order of points.The application of the method is illustrated by an example of coastal line of Istria.

Modifying the United States National Hydrography Dataset to improve data quality for ecological models

The US National Hydrography Dataset (NHD) is the primary spatial hydrography data source for research and conservation of stream ecosystems in the United States due to its national coverage, high resolution, and delivery of high-volume attribute data. However, despite its wide use, numerous topological errors limit the utility of the NHD for the critical needs of ecological and environmental modeling when network connectivity is of interest. In this paper we demonstrate an automated process that integrates a set of GIS tools to identify, track, and correct four major types of topological errors in the entire NHD network (at the scale of 1:100,000) of the conterminous United States, thereby quantifying and summarizing the type, magnitude, and spatial distribution of topological errors in the NHD. Topological errors occurred on approximately 20% of the flowline features in the NHD dataset, with pseudo nodes being the dominant type of error. In a case study of New River Basin of North Carolina, Virginia, and West Virginia USA, after correcting the topology of the NHD network the number of streams in the database designated as first order decreased by 14%, and the average length of stream between confluences (i.e., inter-confluence stream segments) increased by slightly over half of a kilometer (0.53km). The topology-corrected NHD dataset should particularly facilitate large-scale (e.g., national or regional) ecological and environmental applications requiring topologically sound stream networks.

Improvements to the Transmutation Trajectory Analysis of depletion evaluation

To simulate the nuclide evolution process in a nuclear reactor core, the Transmutation Trajectory Analysis (TTA) method solves the depletion equations by decomposing the depletion system into a number of linear chains and then solving each one analytically. In this paper, two improvements are proposed for TTA to obtain better efficiency. Firstly, the pseudo node evaluation for linear chain cutoff check has been removed. Instead, a time-averaged nuclide number density is employed as the chain termination criteria, which in theory can improve the computational efficiency by a factor of two. Secondly, a new recursive formula has been derived to replace the legacy direct solution formula for solving the linear chains. Numerical tests have been carried out based on a typical PWR fuel cycle to demonstrate that these improvements enable the TTA method to solve decay problems efficiently and accurately.

IMMUNOLOGICAL AND MORPHOLOGICAL PREDICTORS OF CLINICAL HETEROGENEITY OF PATIENTS WITH GRAVES' DISEASE (BASED ON RESEARCH OF THE THYROID GLAND OPERATIONAL MATERIAL)

Very little research is devoted to the study of communication systemic immunological changes and local immunological changes in the tissue of the thyroid in autoimmune thyrophaties. The goal of the research was to establish immunologic and morphologic predictors of clinical course and functional outcomes of Graves’s disease (GD).Material and methods. We examined 45 patients with GD (14 men and 31 women) aged 18–55 years (47.0 (35.0–53.0)) years, matching the following criteria: a verified diagnosis GD, accommodation in Tomsk oblast and the Tomsk not less than 10 years, the patient's consent for the study. The control group included 30 people, with an average age of (45.3 ± 5.6) years and was comparable by age and sex with GD patients. The study included: definition of the standard for GD hormonal and serological profile (free T4, free T3, and TSH, antibodies to TPO, antibodies to the TSH receptor), the cultivation of mononuclear leukocytes in complete culture medium within 24 hours with subsequent determination of interleukins concentrations (IL-2, IL-4, TNF-alpha) in culture medium using enzyme-linked immunosorbent assay, determination of the number of blood cells carrying membrane-bound receptors to IL-2, IL-4, TNF-R1 by flow laser cytometry on cytometer BD FACS Canto tmΙΙ (USA) using labeled monoclonal antibody, the standard postoperative histological examination of thyroid gland tissue samples and immunohistochemical detection in samples of thyroid tissue receptors to IL-2, IL-4, TNF-alpha.Results. On the basis of the received results it is possible to allocate 2 clinical-immunologic and morphologic cluster of autoimmune hyperthyroidism syndrome in patients with clinical diagnosis of GD. The first includes the formation of 1 and 2 histological options GD (minimally expressed monocytes infiltration, lack of oncocytic transformation of thyroid epithelium), with some clinical characteristics (persistent and pronounced hyperthyroidism, the large size of goiter, higher titer of receptor TSH-antibody and smaller TPO-antibody, diffuse nature of the lesion), and the second – is represented by 3d histological option with oncocytic restructuring of follicular epithelium, expressed the monocyte/macrophage infiltration with the TNF-RI expression and clinical patterns, including: the older age group of patients, the smaller size of goiter, the emergence of “pseudo nodes” ultrasound, reflecting the presence of lymphoid follicles-infiltrates, more lenient for hyperthyroidism. Probably, that the 2nd cluster is a combination of “classical GD” with autoimmune thyroiditis, however, a set of clinical and laboratory-instrumental signs led to the fact that these patients were in the group of GD patients.Conclusion. This approach (combining study of indicators of systemic and local tissue-specific autoimmune inflammation) is a promising from the point of view of separate parts coverage in the autoimmune thyroids diseases pathogenesis, approximating researchers to develop immune based therapeutic technologies.

Supervoxels based Graph Cut for Medical Organ Segmentation

Organ segmentation is one of the most fundamental and challenging tasks in computer aid diagnosis system. Researches successfully working on interactive segmentation for medical image include graph cuts and random walk. However, graph cut-based organ segmentation for 3D medical volume data requires an optimization procedure of cutting the object/background regions on a very large-scale graph, which not only consumes large amount of memory and but also requires expensive computational cost. This paper conquers the drawbacks of graph cut-based organ segmentation via instead of voxel with supervoxel as nodes for constructing graph in interactive 3D organ segmentation that can greatly reduces node number and connected edges; voxels of medical data with similar intensity magnitude and near spatial relation are grouped into the supervoxels and such supervoxels are used as pseudo nodes of graph for cutting object (organ) and background regions, named as supervoxel-based graph cut. To validate the effectiveness and efficiency of the proposed method, we conduct experiments on 10 medical data, which possibly have tumors inside organ, or have abnormal deformed organ shape. The experimental results show that the proposed method is much superior than conventional graph cut-based method in term of accuracy, computational time and memory usage.

A Secure Transmission Protocol for Wireless Body Sensor Networks

A wireless body sensor network (WBSN) is a typically wearable wireless network deployed on a user' body, which consists of biosensors and a local personal wireless hub, which we commonly call wireless body sensor network controller (WBSNC). The sensitive micro data (SMD) of WBSN users is collected by biosensors and forwarded to the WBSNC before it is delivered to the data process center (healthcare terminal or remote server) for further processing. Since SMD involves user's personal privacy, it is important to protect secure transmission of SMD. Therefore it is crucial to admit only legitimate biosensors and WBSNC into the network in a WBSN. Ensuring the safety of data transmission from each biosensor to WBSNC or between WBSNC and the data process center is essential because of the open feature in wireless channel. In this paper, we present a novel kind of cryptography and authentication method to assure a secure network admission and transmission in a WBSN based on ID. The procedures in this system to establish keys for each biosensor and WBSNC must offer high communication validity and as low energy consumption as possible. In addition, we also propose a new way to protect BSN secure access for biosensor and WBSNC in order to block interference from Pseudo Node.

Assessing the effectiveness of k-shortest path sets in problems of network interdiction

This paper focuses on the network interdiction problem and integration of a solution methodology based on k-shortest path sets. We design and conduct an experiment using the road network of Northridge, California to assess performance of the k-shortest path approach under varying assumptions which include homogeneous and heterogeneous arc metrics, the use or non-use of pseudo nodes and alternate origin-destination scenarios. We compare the obtained solutions from the k-shortest path set approach to optimal solutions obtained by implementing Benders Decomposition and observe the differences in computation time and the objective gap for all cases of the experimental design. We also introduce a similarity measure based on spatial analysis techniques to compare coverage as a secondary measure of performance. The observed findings support the use of a k-shortest path set approach in situations where computation time prohibits the need to derive an optimal solution. Additionally, we show that the k-shortest path set approach performs better on heterogeneous networks and that only a small number need be used for k to achieve strong interdiction solutions.

Development of New Hybrid Shell Element Takinging the Normal Stress into Account

The calculation of the stress distribution in the forming stage of sheet metal forming is critical to the accuracy of springback simulation. In order to improve the accuracy of stress prediction, a new mixed shell element is presented. Two pseudo nodes are added in the thickness direction so that the normal strain can be calculated using the displacements of the nodes. Mixed formulation is used instead of that of pure displacement. As a result, the accuracy of stress prediction is improved. Examples are given to show the improvement.

The design and implementation of a low-cost and programmable home automation module

This paper presents a home automation module with programmable control instructions (PCIs). Although it is implemented by using a low-cost 8-bit microcontroller with only 8 Kbytes of ROM spaces, a time-sharing task scheduler and 10 PCIs are included in the firmware. Timer and counter control, logic functions, and temperature control are examples of PCIs. Thanks to the design of pseudo nodes, these PCIs can also be combined to implement more complex functions that home automation needs. These modules, up to 65536 theoretically, share the same IP address, and can be found over the network via different port numbers. No dedicated home server is necessary because the network communication (TCP/IP) is handled by the module. A remote control and monitoring software is also developed to let users control the home automation modules on the Internet. The programmable home automation module has a lower cost and better cost/performance ratio than current home automation modules

Development of a new quadratic shell element considering the normal stress in the thickness direction for simulating sheet metal forming

The existing degenerated shell element based on Mindlin–Reissner's theory of plates assumes that the thickness of the element remains constant and that the normal stress in the thickness direction is zero. These assumptions lead to problems in simulating sheet metal forming, especially in predicting the amount of springback. In this paper, we present a new non-linear shell element by considering the normal stress in the thickness direction for plain strain analysis, which allows for large deformation. Two pseudo nodes are introduced on the top surface and bottom surface, respectively. As a result, the incremental normal strain in the thickness direction can be calculated directly and the incremental normal stress in the thickness direction can be calculated using the constitutive relation directly. Thus, it is possible to deal with the problem that pressure acts on the top surface and the bottom surface is simultaneously constrained. Numerical examples show that the stress prediction is improved. The normal stress in the thickness direction exhibits a correspondence with boundary loads and the bending moment decreases as the normal stress in the thickness direction increases. This indicates the element can simulate the effect of bottoming, i.e. sheet metal is stamped further with a considerable punch force after the punch reaches the dead point in the process of V-type bending for the purpose of reducing springback.

Introducing regulated bias into co‐citation ranking schemes on the Web

AbstractLink structure analysis is widely recognized as an important tool in WWW information retrieval and is extensively used in the retrieval and ranking algorithms of major WWW search engines. Common extensions of link analyses involve changing the Web subgraphs that are analyzed. In this work, we present two techniques which bias co‐citation based link analyses towards favorable pages and away from undesired pages. Both techniques are integrated seamlessly into existing link‐analyzing algorithms.Our first technique extends the notion of hubs by introducing pseudo hubs, which represent any trait of Web pages that is desirable to users. Pseudo hubs are added as pseudo nodes to the Web‐graph and are linked to those pages which exhibit the desirable traits. The ensuing co‐citation analysis then treats these pseudo nodes as if they were regular pages. This results in the rankings of pages with favorable traits being boosted. The second technique incorporates negative relevance judgments into the analysis, causing the results to shift away from the surroundings of undesired pages.