Static Topology Research Articles

DFPC: Dynamic Fuzzy-based Primary User Aware clustering for Cognitive Radio Wireless Sensor Networks

Clustering-based routing solutions have proven to be efficient for wireless networks such as Wireless Sensor Networks (WSNs), Vehicular Ad Hoc Networks (VANETs), etc. Cognitive Radio WSN (CR-WSN) is a class of WSNs that consists of several resource-constrained Secondary Users (SUs), sink, and Primary Users (PUs). Compared to WSNs, there are several challenges in designing the clustering technique for CR-WSNs. As a result, one cannot directly apply the WSN clustering protocols to CR-WSNs. Developing a clustering protocol for CR-WSNs must address challenges such as ensuring PU protection, and SU connectivity, selecting the optimal Cluster Head (CH), and discovering the optimal cluster size. Present CR-WSN clustering solutions failed to resolve the trade-off among all essential clustering objectives. To address these challenges, this study presents a novel approach called Dynamic Fuzzy-based PU aware Clustering (DFPC) for CR-WSNs. DFPC uses a dynamic approach to discover the number of clusters, a fuzzy-based algorithm for optimal CH selection, and reliable multi-hop data transmission to ensure PU protection. To enhance the performance of CR-WSNs, an effective strategy was designed to define the optimal number of clusters using the network radius and live node. Fuzzy logic rules were formulated to assess the four CR-specific parameters for optimal CH selection. Finally, reliable intra- and intercluster data transmission routes are discovered to protect the PUs from malicious activities. The simulation results showed that the DFPC protocol achieved an improved average throughput of 48.04 and 46.49, a PDR of 93.36 and 84.37, and a reduced delay of 0.0271 and 0.0276 in static and dynamic topologies, respectively, which were better than those of ABCC, ATEEN, and LEACH protocols.

Dynamic Optimization Method of Knowledge Graph Entity Relations for Smart Maintenance of Cantilever Roadheaders

The fault maintenance scenario in coal-mine equipment intelligence is composed of videos, images, signals, and repair process records. Text data are not the primary data that reflect the fault phenomenon, but rather the secondary processing based on operation experience. Focusing on the difficulty of extracting fault knowledge from the limited textual maintenance process records, a forward static full-connected topology network modeling method based on domain knowledge from four dimensions of physical structure, internal association, condition monitoring, and fault maintenance, is proposed to increase the efficiency of constructing a fault-maintenance knowledge graph. Accurately identifying the intrinsic correlation between the equipment anomalies and the faults’ causes through only domain knowledge and loosely coupled data is difficult. Based on the static full-connected knowledge graph of the cantilever roadheader, the information entropy and density-based DBSCAN clustering algorithm is used to process and analyze many condition-monitoring historical datasets to optimize the entity relationships between the fault phenomena and causes. The improved DBSCAN algorithm consists of three stages: firstly, extracting entity data related to fault information from the static fully connected graph; secondly, calculating the information entropy based on the real dataset describing the fault information and the historical operating condition, respectively; and thirdly, comparing the entropy values of the entities and analyzing the intrinsic relationship between the fault phenomenon, the operating condition data, and the fault causes. Based on the static full-connected topology storage in the Neo4j database, the information entropy and density-based DBSCAN algorithm is computed by using Python to identify the relationship weights and dynamically display optimized knowledge graph topology. Finally, an example of EBZ200-type cantilever roadheader for smart maintenance is studied to analyze and evaluate the forward and four-mainlines knowledge graph modeling method and the dynamic entity relations optimization method for static full-connected knowledge graph.

Brain network classification based on dynamic graph attention information bottleneck

Background and ObjectivesGraph neural networks (GNN) have demonstrated remarkable encoding capabilities in the context of brain network classification tasks. They excel at uncovering hidden static connections between brain states. However, brain network signals can be influenced by physiological traits and external variables during clinical detection, resulting in noisy brain graphs. Additionally, many existing algorithms for brain networks primarily focus on static topologies determined by threshold-based criteria, thereby overlooking the real-time variability in brain channel connectivity. These sources of noise and the persistence of static structures inevitably hinder the effective exchange of information during brain network computations. MethodsTo address these challenges, we propose a novel framework called the dynamic graph attention information bottleneck (DGAIB). This framework is designed to dynamically enhance the input raw brain graph structure from the perspective of information theory and graph theory. First, we employ the Spearman function to construct a raw graph. Then, we use a graph information bottleneck (GIB) to optimize the internal graph connections by selectively masking redundant feature embeddings. Finally, we enhance the feature aggregation of each brain state by utilizing a graph attention network (GAT), which promotes improved information exchange among distinct brain regions within the model. These processed representations serve as input for subsequent classification tasks. Experiment and ResultsWe systematically evaluated the robustness and generalizability of our proposed framework through a series of experiments. This evaluation included patient-specific experiments using the electroencephalography (EEG)-based CHB-MIT dataset and cross-patient experiments leveraging the functional magnetic resonance imaging (fMRI)-based ABIDE-I dataset from multiple perspectives.

Distributed Physical-layer Network Coding MAC protocol

Physical-layer Network Coding (PNC) was first proposed for a Two-Way Relay Channel (TWRC) to improve the spectrum efficiency since it allows nodes to transmit simultaneously via a relay node. This technique requires multiple nodes to transmit their packets with accurate synchronization. Therefore, in many works of literature, centralized scheduling with perfect synchronization has been assumed to be employed on top of PNC.Such assumptions are not applicable in general random access multi-hop wireless networks. Therefore, this paper proposes a distributed MAC protocol that supports PNC in static multi-hop wireless networks. The proposed MAC protocol is based on the Carrier Sense Multiple Access (CSMA) strategy, where RTS/CTS frames are used to detect PNC opportunities and to offer the appropriate scheduling of the involved transmissions that should occur simultaneously. This packet exchange process is coordinated by the relay node and was designed to guarantee compatibility with other conventional relaying schemes with specific concerns for the hidden node issues. Our solution was practically tested on a real testbed with different static wireless topologies and several physical settings.With numerical results, we investigate the effectiveness of PNC in distributed wireless multi-hop networks. Compared to the conventional CSMA/CA and the PNC opportunistic (PNCOPP) MAC protocols, the proposed protocol’s performances are advantageous in various scenarios, especially in large networks.

Fatigue analysis and optimization design of key components of synthesizing equipment for artificial diamond based on a method of information exchange system.

In this article, a method of information exchange system (MIES) has been proposed to optimize the structure of the hinge sleeve of cubic (HSC), a key component of synthetic diamond. The MIES method integrates static analysis, topology optimization, and fatigue failure analysis. By using this method, the lightweight design of the structure was ensured while meeting the fatigue life requirements. The weight of the optimized model was reduced from 5729.9 kg to 4593.4 kg, and the fatigue life was 1.127E+05, which meets the serviceability requirements. The steps of the method are as follows: First, the model of HSC was established. According to the loading conditions, the basic material data and boundary conditions were set, and the stresses and strains of the HSC were calculated. The optimized region was obtained by topological analysis of the HSC structure using the variable density method. The fatigue life of the model was then calculated by combining the stress life method and the average stress correction method. Simulations were performed using the above method to obtain the six nodes of maximum stress in the HSC. These nodes were used as control points for the structural optimization design. The HSC model was optimized by optimizing the structure in the region of the control variable points. Computational analysis of the optimized HSC model was carried out using the information exchange system. After repeated optimization of the structure of the HSC model, a model with a lightweight design was obtained. The ANSYS simulation results showed that the final mass of the HSC model was reduced by 19.83%. Stress and life were within the design requirements. The information exchange system has better computational performance, feasibility, and reliability compared to traditional theoretical methods.

Dynamic Metro Stations Importance Evaluation Based on Network Topology and Real-Time Passenger Flows

Identifying the importance of metro stations is crucial for enhancing the efficiency and safety of the urban rail transit system. This study proposes an integrated approach to evaluate the importance of metro stations based on jointly the static metro network topology and the dynamic real-time passenger flows. The static characteristics of metro stations are reflected through node degree and clustering coefficient of metro network topology based on the complex network theory. The dynamic characteristics of metro stations are assessed using the passenger flow time series, which were modeled using the autoregressive integrated moving average plus autoregressive conditional heteroscedasticity model (i.e., ARIMA+GARCH model). By combining the static and dynamic characteristics of metro stations, a comprehensive index is proposed to quantify the importance of metro stations. The integrated approach is tested using metro network and passenger flow data collected from Suzhou, China. The results indicate that compared with the topology analysis, the passenger flows could strengthen the importance of transfer stations and their neighboring stations. Especially for the loop lines that formed by the intersection of two or more lines within the network, the importance index of metro stations within these loop lines is inversely proportional to the number of stations in the loop. It indicates that as a metro network continues to expand with the addition of new lines and stations, this is a high likelihood of increased concentration of importance with the network. The concentration of importance in the metro network can be attributed to the emergence of highly connected loop lines that facilitate efficient movement of passengers.

Childhood trauma is linked to abnormal static-dynamic brain topology in adolescents with major depressive disorder

Childhood trauma is a leading risk factor for adolescents developing major depressive disorder (MDD); however, the underlying neuroimaging mechanisms remain unclear. This study aimed to investigate the association among childhood trauma, MDD and brain dysfunctions by combining static and dynamic brain network models. We recruited 46 first-episode drug-naïve adolescent MDD patients with childhood trauma (MDD-CT), 53 MDD patients without childhood trauma (MDD-nCT), and 90 healthy controls (HCs) for resting-state functional magnetic resonance imaging (fMRI) scans; all participants were aged 13–18 years. Compared to the HCs and MDD-nCT groups, the MDD-CT group exhibited significantly higher global and local efficiency in static brain networks and significantly higher temporal correlation coefficients in dynamic brain network models at the whole-brain level, and altered the local efficiency of default mode network (DMN) and temporal correlation coefficients of DMN, salience (SAN), and attention (ATN) networks at the local perspective. Correlation analysis indicated that altered brain network features and clinical symptoms, childhood trauma, and particularly emotional neglect were highly correlated in adolescents with MDD. This study may provide new evidence for the dysconnectivity hypothesis regarding the associations between childhood trauma and MDD in adolescents from the perspectives of both static and dynamic brain topology.

Nested loop approach for topology and shape optimization of crash-loaded deep-drawn components using contact forces for the inner loops

In this paper a new method for the optimization of deep-drawn shell components in crash by using substitute load cases in established sensitivity based static optimization is presented. The optimization process is split into an inner and an outer loop. In the outer loop the contact forces during crash are determined. These contact forces are used as substitute load cases in a static topology optimization, which builds the inner loop of the method. The presented method was inspired by the Equivalent Static Loads Method, but differs primarily in two aspects. These are the use of the contact forces as alternative to equivalent static loads in the linear static topology optimization and the extraction of a shell model that represents the voxel result obtained from topology optimization. A manufacturing constraint is applied in the topology optimization so that the results are shell components. This manufacturing constraint is enabling the mentioned extraction of a shell model. Two examples are presented where the method is applied to minimize the intrusion. The first example is a cantilever, the second example is a structure that is twisted. In both examples a rigid body impacts the structure. The method has shown to be numerical stable and suitable for the shown examples when the intrusion is the objective of the optimization. The intrusion was reduced significantly in both examples. The key novelty of the presented method is the ability to perform a static topology optimization of a voxel design space for a non-linear dynamic optimization model with the result being a shell model that can be analyzed for crash.

Enabling Quasi-Static Reconfigurable Networks With Robust Topology Engineering

Many optical circuit switched data center networks (DCN) have been proposed in the last decade to attain higher capacity and topology reconfigurability, though commercial adoption of these architectures have been minimal. One major challenge these architectures face is the difficulty of handling uncertain traffic demands using commercial optical circuit switches (OCS) with high switching latency. Prior works have generally focused on developing fast-switching OCS prototypes to quickly react to traffic variations through frequent reconfigurations. This approach, however, adds tremendous complexity overhead to the control plane, and raises the barrier for commercial adoption of optical circuit switched data center networks. We propose, a robust topology and routing optimization framework for reconfigurable optical circuit switched data centers. co-optimizes topology and routing based on a convex set of traffic matrices, and offers strict throughput guarantees for any future traffic matrices bounded by the convex set. For the bursty traffic demands that are unbounded by the convex set, we employ a desensitization technique to reduce performance hit. This enables to generate topology and routing solutions capable of handling unexpected traffic changes without relying on frequent topology reconfigurations. Our extensive evaluations based on Facebook’s production DCN traces show that, even with daily reconfigurations which could be realized by current commercial MEMS-based OCSs from Calient Technologies, achieves about 20% lower max link utilization, and about 32% lower average hop count compared to cost-equivalent static topologies. Our work shows that adoption of reconfigurable topologies in commercial DCNs is feasible even without fast OCSs.

Traffic Flow Prediction Based on Dynamic Graph Spatial-Temporal Neural Network

More accurate traffic prediction can further improve the efficiency of intelligent transportation systems. However, the complex spatiotemporal correlation issues in transportation networks pose great challenges. In the past, people have carried out a great deal of research to solve this problem. Most studies are based on graph neural networks to model traffic graphs and attempt to use fixed graph structures to obtain relationships between nodes. However, due to the time-varying spatial correlation of the transportation network, there is no stable node relationship. To address the above issues, we propose a new traffic prediction framework called the Dynamic Graph Spatial-Temporal Neural Network (DGSTN). Unlike other models that use predefined graphs, this model represents stable node relationships and time-varying node relationships by constructing static topology maps and dynamic information maps during the training and testing stages, to capture hidden node relationships and time-varying spatial correlations. In terms of network architecture, we designed multi-scale causal convolution and adaptive spatial self-attention mechanisms to capture temporal and spatial features, respectively, and assisted learning through static and dynamic graphs. The proposed framework has been tested on two real-world traffic datasets and can achieve state-of-the-art performance.

Quantum Genetic Algorithm for the Design of Miniaturized and Reconfigurable IoT Antennas

This paper leverages an automated quantum genetic algorithm (QGA) to design miniaturized antennas for Internet of Things (IoT) devices. The QGA implemented in this work automates the design process and enables the antenna designer to generate static or reconfigurable miniaturized microstrip folded antenna topologies. The QGA is also implemented to generate a pixelation pattern within the antenna radiating surface to optimally tune the antenna operation for IoT communication needs as well as for Wi-Fi operation. In this paper, we also present a QGA-optimized folded miniaturized antenna in both static and reconfigurable topologies. The reconfiguration is achieved through a single RF MEMS whose location is optimized through the automated process. The automated design process achieves 87% antenna size reduction at 868 MHz compared to a typical patch antenna operating at the same frequency. As a result, the static and reconfigurable antenna prototypes achieve good radiation efficiencies in comparison to their miniature size with acceptable gains thus satisfying the IoT physical and operational constraints. Both antennas are fabricated and tested, where excellent agreement is noticed between simulated and measured results.

Static and dynamic topology optimization: an innovative unifying approach

Static and dynamic topology optimization: an innovative unifying approach

Design and Evaluation of Dynamic Topology for Mega Constellation Networks

Due to numerous Low Earth Orbit (LEO) satellites, urgent analysis of many temporary inter-satellite links (ISLs) is necessary for mega constellation networks. Therefore, introducing a dynamic link in topology design is crucial for increasing constellation redundancy and improving routing options. This study presents one class of static topology of satellites (STLS) and two types of dynamic topology of satellites (DTLS). Firstly, a call model based on global population density distribution is determined using world population density by provincial administrative divisions. Then, using a common simulation platform, the Dijkstra algorithm obtains random paths between 10,000 pairs of urban ground stations, adopting a time slice division strategy. Finally, 3 indexes are obtained within 66-time slices: average call distance, number of hops, and total time delay. Results show that DTLS1 reduces these indexes by 3.58%, 3.72%, and 3.57%, respectively, compared with DTLS2 under the same conditions, indicating that DTLS1 has the best network performance, transmitting traffic quickly in any direction through the reverse track, thereby verifying the related hypothesis.

Investigation on CFRP 3D printing build parameters and their effect on topologically optimised complex models

This research investigates the effects of building parameters for 3D printing Carbon Fibre Reinforced Polymers (CFRP) and their effect on topologically optimised complex models. The work is conducted by initially developing a DOE varying two parameters in 3D printing namely (i) infill ratio and (ii) infill pattern. Then based on standards ASTM D638 and ISO178, for tensile and flexural tests, specimens are 3D printed and tested for the material Nylon with CFRP (Onyx). From the results it can be observed that (i) specimen with an infill ratio of 85% (constant triangular infill pattern) was found to have the best performance recording a length extension of approximately 5.6 mm under a tensile load of 700 N (ii) in case of infill pattern, triangular shape (constant infill ratio of 37%) recorded the highest the length extension of 7.3 mm under tensile load of 650 N. (iii) 85% infill ratio (constant triangular infill pattern) recorded a bending deflection of approximately 6 mm under a compressive load of 250 N and (iv) the gyroid infill pattern (constant infill ratio of 37%) provided the highest flexural strength with an approximate extension of 5.6 mm under a compressive load of 350 N. After the experimental study and analysing the best parameters, a static analysis and topology optimisation for the 3D printed material (Nylon with CFRP(Onyx)) has been performed on an industrial part for its design validation. Based on the analysis, the original part is redesigned, and again a static analysis simulation is performed to determine the effects of the optimisation process for the same material comparing with 316L-Stainless Steel (SS). Finally, the redesigned model is manufactured with the best 3D printing parameters and validated against the original operating conditions. This study will help industries to use these 3D printing parameters where a metal-based components needs to be replaced with CFRP.

Static and dynamic topology optimization: an innovative unifying approach

This paper presents a topology optimization approach that is innovative with respect to two distinct matters. First of all the proposed formulation is capable to handle static and dynamic topology optimization with virtually no modifications. Secondly, the approach is inherently a multi-input multi-output one, i.e., multiple objectives can be pursued in the presence of multiple loads. The input-to-output transfer matrix, say {{{varvec{G}}}}, is the key ingredient that governs the algebraic mapping between applied loads and structural response. In statics {{{varvec{G}}}} depends on the design variables only, whereas it depends on the frequency variable as well in the dynamic case. The Singular Value Decomposition (SVD) of {{{varvec{G}}}} represents then the core of the proposed approach. Singular values are shown to be the gains of the input/output mapping and are used to compute proper norms of {{varvec{G}}} that represent the goal functions to be minimized. Singular vectors provide at no extra cost the plant directions, i.e., the load combination factors that stress the structure the most. Numerical examples are discussed in much detail and open issues object of ongoing investigations are highlighted. A full Matlab code handling the static topology optimization problem is provided as an online Appendix to the manuscript. Its extension to the dynamic case may be gathered following the formulation proposed in Sect. 5.

Large-Signal Stability Analysis for Inverter-Based Dynamic Microgrids Reconfiguration

Compared to conventional microgrids (MGs) with pre-defined and static topology, dynamic MGs feature varying electric boundaries and enhanced operational resiliency. This paper evaluates the operation stability of inverter-based dynamic MGs in terms of fulfilling the requested MGs topology change and associated network reconfiguration, which falls into the category of large-signal stability analysis. A complete nonlinear state-space model of inverter-based dynamic MGs is derived, where coordinated controls among inverters along with the effect of communication delays have been considered. In response to reconfiguration demands, an evaluation scheme is developed to determine whether the system could perform seamless topology variation without losing synchronism. Specifically, the system’s stability region is quantified by the domain of attraction (DoA) around the desired operating point using Takagi-Sugeno fuzzy modeling (T-S multimodeling). The large-signal stability of a multi-bus inverter-based dynamic MG with both interface inverters and sectionalizing switches have been analyzed. The stability regions estimated under different scenarios of topology variation have been validated using time-domain simulation in MATLAB/Simulink. The developed evaluation scheme is used to quantify the effects of control gain designs and communication delays on system large-signal stability.

Static analysis and optimization design of a special-shaped rod of the crab-like robot based on ANSYS Workbench

This paper takes a self-designed crab-like robot special-shaped rod as the research object. To solve the problem that the actual gait does not conform to the theory encountered in the debugging stage, the following operations are carried out: after static analysis, the problem is that the special-shaped rod is complex and stressed. It is complicated, and the resulting deformation is too large, resulting in a decrease in the movement stability of the overall mechanism. To improve the stability of the mechanism, SolidWorks was used to complete the construction of the crab-like robot model, and ANSYS Workbench was used to perform static analysis and topology optimization on the special-shaped rod, so as to obtain the preliminary optimization results. On this basis, to better improve the stability of the mechanism, the secondary optimization of the special-shaped rod is carried out. The static analysis is carried out again and the obtained results are compared with the original design. The deformation of the special-shaped rod during the movement process is reduced by 13%. Under the premise of structural rigidity, the weight is reduced by 44. 73% and the stress distribution is more uniform. The above analysis results show that after the analysis and processing of the above methods, the special-shaped rod structure is further optimized, the stability of the overall structure is improved, and the effect of green design is achieved at the same time.

Quantum quenches in a pseudo-Hermitian Chern insulator

We propose to uncover the topology of a pseudo-Hermitian Chern insulator by quantum quench dynamics. The Bloch Hamiltonian of the pseudo-Hermitian Chern insulator is defined in the basis of the $q$-deformed Pauli matrices, which are related to the representation of the deformed algebras. We show the bulk-surface duality of the pseudo-Hermitian phases, then further build a concrete relation between the static band topology and quench dynamics, in terms of the time-averaged spin textures. The results are also generalized into a fully nonequilibrium case where the postquench evolution is governed by a Floquet pseudo-Hermitian Hamiltonian. Furthermore, we propose a possible scheme to realize the seemingly challenging model in a bilayer lattice and detect the dynamics with a double-quench protocol.

Disentangled Dynamic Deviation Transformer Networks for Multivariate Time Series Anomaly Detection.

Graph neural networks have been widely used by multivariate time series-based anomaly detection algorithms to model the dependencies of system sensors. Previous studies have focused on learning the fixed dependency patterns between sensors. However, they ignore that the inter-sensor and temporal dependencies of time series are highly nonlinear and dynamic, leading to inevitable false alarms. In this paper, we propose a novel disentangled dynamic deviation transformer network (D3TN) for anomaly detection of multivariate time series, which jointly exploits multiscale dynamic inter-sensor dependencies and long-term temporal dependencies to improve the accuracy of multivariate time series prediction. Specifically, to disentangle the multiscale graph convolution, we design a novel disentangled multiscale aggregation scheme to better represent the hidden dependencies between sensors to learn fixed inter-sensor dependencies based on static topology. To capture dynamic inter-sensor dependencies determined by real-time monitoring situations and unexpected anomalies, we introduce a self-attention mechanism to model dynamic directed interactions in various potential subspaces influenced by various factors. In addition, complex temporal correlations across multiple time steps are simulated by processing the time series in parallel. Experiments on three real datasets show that the proposed D3TN significantly outperforms the state-of-the-art methods.

Dynamic and Static Multiobjective Topology Optimization for Gears of Directional Drill Transmission System

Aiming at the multiobjective topology optimization design of the structure, this paper proposes a method to establish the comprehensive objective function based on the normalized subobjective of the compromise programming method and to determine the weight coefficient of the subobjective of the comprehensive objective function by the analytic hierarchy process (AHP). By using statistical analysis and modal analysis, the static and dynamic characteristics of the gear can be obtained. The single objective topology optimization is used to reduce the compliance of the gear and elevate the low-order natural frequency, and the frequency weighting method is used to suppress the oscillation phenomenon in the frequency-single-objective optimization. AHP was used to determine the weight coefficient of each subtarget. The compromise programming method is used for multiobjective topology optimization, and the gear design is improved according to the optimization results. By analyzing the improved gear structure, the weight of the optimized gear is reduced by 25.3%. The overall stiffness performance and strength performance are enhanced, and the natural frequencies of each order are improved to different degrees.