Search Results (625)

Low-Earth-Orbit (LEO) satellite networks face unique challenges in service function chain (SFC) orchestration due to their dynamic topology and resource constraints, making traditional terrestrial network solutions inadequate. This study addresses the challenge of maximizing service provider benefits through efficient SFC deployment and readjustment in LEO satellite networks. We propose PPOSFC, a novel dynamic SFC orchestration algorithm based on Proximal Policy Optimization (PPO), which incorporates future topology information into the decision-making process. The algorithm models the orchestration problem as a Markov decision process and employs a dual-objective optimization approach considering both deployment success rate and readjustment costs. Simulation results demonstrate that PPOSFC achieves a 13.07% increase in cumulative profit and improves deployment success rates by 6.78% compared to existing algorithms. The algorithm exhibits superior performance in both high and low user service request intensity, effectively balancing service quality and operational efficiency. Furthermore, our analysis reveals that incorporating predicted topology information significantly enhances orchestration performance. Full article

The proliferation of edge intelligence systems necessitates efficient and reliable connectivity for sensor networks deployed at the edge. This paper proposes a novel energy-efficient connectivity algorithm called Constrained Angle-aware Connectivity Optimization (CA-Opt), designed for directional sensor networks to address the challenges of limited resources and asymmetric network constraints in edge environments. CA-Opt constructs a hop-constrained, degree-bounded network topology while considering the directional coverage of sensor nodes. The algorithm incorporates an angle-aware child selection strategy to optimize the energy consumption by minimizing the number of active links and the total communication distance. Extensive simulations demonstrated that CA-Opt achieved comparable connectivity to the traditional Breadth-First Search (BFS) algorithms while significantly reducing the energy consumption. Furthermore, the impact of key parameters, such as the communication range, node density, maximum degree, and directional coverage angle, on CA-Opt’s performance was analyzed. The results underscore the potential of CA-Opt to balance asymmetry-driven connectivity control with energy-efficient operation, making it particularly suitable for resource-constrained edge applications, such as smart manufacturing, environmental monitoring, and intelligent transportation systems. Full article

A topological constraint, characterized by the Casimir invariant, imparts non-trivial structures in a complex system. We construct a kinetic theory in a constrained phase space (infinite-dimensional function space of macroscopic fields), and characterize a self-organized structure as a thermal equilibrium on a leaf of foliated phase space. By introducing a model of a grand canonical ensemble, the Casimir invariant is interpreted as the number of topological particles. Full article

Heat pumps are promising solutions for managing the increasing heating demand of residential houses, reducing the environmental impact when used with renewable energy. Accurate heat load predictions allow the heat pump to operate at the most efficient settings, maintaining comfortable temperatures while reducing excess energy use and lowering operating costs. Data-driven prediction solutions may have difficulty capturing the dynamics and nonlinearities of the thermodynamics involved. The physics-informed models combine the monitored observed data with theoretical knowledge of heat pumps and directly integrate physical constraints, allowing for better generalization and reducing the dependence on large volumes of data. However, they require detailed knowledge of the system topology and refrigerant parameters, which increases the model complexity. Therefore, in this paper, we propose a physics-informed neural network for predicting the heat load of heat pumps that integrates thermodynamics directly into the loss function of the neural network. We model the heat load as a function of the input variables, including the inlet temperature, outlet temperature, and water flow rate. We integrate the function during model training to reduce the model complexity. Our approach increases the accuracy of the predictions compared with data-driven models and generates prediction results that are consistent with the actual physical behavior of the heat pump. The results show superior prediction accuracy, with a 7.49% reduction in the RMSE and a 6.49% decrease in the MAPE, while the R² value shows an increase of 0.02%. Full article

This paper addresses the issue of onboard inference for AI-based routing algorithms in dynamic LEO (Low Earth Orbit) satellite networks. In dynamic LEO networks, it is essential to maintain communication performance across varying topologies while considering link disconnections and overcoming computational constraints for real-time inference on embedded boards. This paper proposes a GPU-based inference acceleration method to reduce the computation time required for real-time onboard inference of a Dueling DQN (Deep Q-Network)-based routing algorithm in dynamic LEO satellite networks. The approach is composed of memory management, low-level operations, and efficient indexing methods, which collectively enhance computational efficiency. As a result, the proposed method achieves approximately 2.4 times faster inference compared to conventional CPU-based approaches. Additionally, the kernel performance analysis reveals that the proposed method reaches 10% of the peak computational performance and 20% of the peak memory performance. This demonstrates the compatibility of the proposed method for integration with additional applications in the multitasking systems of LEO satellites. Full article

The coupling of topology transition with flexible deformation and rigid motion presents significant challenges in the dynamic modeling of flexible variable topology mechanisms. Existing dynamics models are mostly special-purpose models for their particular cases and thus struggle to completely depict the general topology transition characteristics. To address this gap, this paper proposes an analytical framework for the global dynamic modeling of flexible variable topology mechanisms, focusing on general cases. Initially, the flexible variable topology mechanisms are rigorously defined by the ordered triples and the general topology transition approaches are presented. A novel concept, the “basic flexible kinematic chain set”, is suggested, which can easily transform into the topology of each submechanism by slightly extending. Based on this concept, basic and conditional constraints are established. The continuous dynamic modeling method for each topology is developed using Jourdain’s principle and the Lagrange multiplier method. Additionally, three classes of constraints related to topology transition are identified, and their equations are formulated, elucidating the topology transition nature. Compatibility equations are proposed to define the new coordinate system for describing the deformation of flexible components after the topology transition. An impact dynamic equation is established to describe abrupt velocity change. Integrating compatibility and impact equations, a discontinuous dynamic modeling method for topology transition is developed. Finally, a flexible variable topology mechanism example is studied, and simulations and experiments are conducted to validate the proposed framework. This analytical framework is general-purpose and efficient, guiding the global dynamic modeling of various flexible variable topology mechanisms and the development of sophisticated control techniques. Full article

In the field of wireless charging technology for electric vehicles, the charging process of lithium-ion batteries is typically divided into two stages: constant-current (CC) charging and constant-voltage (CV) charging. This two-stage charging method helps protect the battery and extend its service life. This paper proposes a family of circuit topology design schemes that achieve a smooth transition from CC to CV charging stages by using two relays. Additionally, the paper derives the corresponding system efficiency formulas and provides constraints on device parameters to ensure that the charging efficiency remains high during different charging stages. The proposed family of circuit topologies adopt unified device parameters and relay control logic, simplifying the design and operation process, and making these topologies more suitable for large-scale applications. To verify the practical performance of these topologies, the paper constructs experimental prototypes and conducts tests. The experimental results show that the proposed family of topologies can stably achieve CC and CV output, with smooth transitions between the two charging modes, and the efficiency can be maintained above 89% before and after mode switching over a wide load range. Furthermore, the mode switching points of the proposed family of topologies are multiples of two. Full article

In mobile networks, a content server can provide multiple services simultaneously to a mobile device, generating multiple data flows. As the device moves, the transmission path in the wired network may need to be switched to maintain service continuity. However, a single switching path may not be able to accommodate all the flows, potentially leading to congestion and a degraded user experience. To address this challenge, we propose a multi-topology routing-based mobile data scheduling method that dynamically switches flows across multiple paths to enhance flexibility and load balancing. The performance of this method is significantly influenced by the construction of logical topologies. Well-designed topologies provide high-bandwidth, low-latency paths to all possible destination nodes, while poorly designed topologies waste switch capacity and fail to achieve these goals. In this paper, we introduce an efficient multi-topology construction method for scheduling mobile data flows in software-defined networking (SDN). Our approach optimizes and balances transmission capacity for each destination node while adhering to the flow entry constraints of switches. Simulations demonstrate that our method consistently outperforms the single-path switching method and the other two multi-topology construction methods in terms of packet delay, packet loss rate, and network throughput, regardless of the device’s new location. Full article

Intentional controlled islanding (ICI) is a crucial strategy to avert power system collapse and blackouts caused by severe disturbances. This paper introduces an innovative IoT-based ICI strategy that identifies the optimal location for system segmentation during emergencies. Initially, the algorithm transmits essential data from phasor measurement units (PMUs) to the IoT cloud. Subsequently, it calculates the coherency index among all pairs of generators. Leveraging IoT technology increases system accessibility, enabling the real-time detection of changes in network topology post-disturbance and allowing the coherency index to adapt accordingly. A novel algorithm is then employed to group coherent generators based on relative coherency index values, eliminating the need to transfer data points elsewhere. The “where to island” subproblem is formulated as a mixed integer linear programming (MILP) model that aims to boost system transient stability by minimizing power flow interruptions in disconnected lines. The model incorporates constraints on generators’ coherency, island connectivity, and node exclusivity. The subsequent layer determines the optimal generation/load actions for each island to prevent system collapse post-separation. Signals from the IoT cloud are relayed to the circuit breakers at the terminals of the optimal cut-set to establish stable isolated islands. Additionally, controllable loads and generation controllers receive signals from the cloud to execute load and/or generation adjustments. The proposed system’s performance is assessed on the IEEE 39-bus system through time-domain simulations on DIgSILENT PowerFactory connected to the ThingSpeak cloud platform. The simulation results demonstrate the effectiveness of the proposed ICI strategy in boosting power system stability. Full article

This work explores the geometry of extremal Kerr-Newman black holes by analyzing their mass/energy relationships and the conditions ensuring black hole existence. Using differential geometry in $E^3$, we examine the topology of the event horizon surface and identify two distinct families of extremal black holes, each defined by unique proportionalities between their core parameters: mass (m), charge (Q), angular momentum (L), and the irreducible mass ($m_{ir}$). In the first family, these parameters are proportionally related to the irreducible mass by irrational numbers, with a characteristic flat Gaussian curvature at the poles. In the second family, we uncover a more intriguing structure where m, Q, and L are connected to $m_{ir}$ through coefficients involving the golden ratio $-{\varphi }_{-}$. Within this family lies a unique black hole whose physical parameters converge on the golden ratio, including the irreducible mass and polar Gauss curvature. This black hole represents the highest symmetry achievable within the constraints of the Kerr-Newman metric. This remarkable symmetry invites further speculation about its implications, such as the potential determination of the dark energy density parameter ${\Omega }_{\Lambda }$ for Kerr-Newman-de Sitter black holes. Additionally, we compute the maximum energy that can be extracted through reversible transformations. We have determined that the second, golden-ratio-linked family allows for a greater energy yield than the first. Full article

Correctly identifying influential nodes in a complex network and implementing targeted protection measures can significantly enhance the overall security of the network. Currently, indicators such as degree centrality, closeness centrality, betweenness centrality, H-index, and K-shell are commonly used to measure node influence. Although these indicators can identify critical nodes to some extent, they often consider node attributes from a narrow perspective and have certain limitations. Therefore, evaluating the importance of nodes using most existing indicators remains incomplete. In this paper, we propose the multi-attribute CRITIC-TOPSIS network decision indicator, or MCTNDI, which integrates closeness centrality, betweenness centrality, H-index, and network constraint coefficients to identify critical nodes in a network. This indicator combines information from multiple perspectives, including local neighborhood importance, network topological location, path centrality, and node mutual information, thereby solving the issue of the one-sided perspective of single indicators and providing a more comprehensive measure of node importance. Additionally, MCTNDI is validated through the analysis of several real-world networks, including the Contiguous USA network, Dolphins network, USAir97 network, and Tech-routers-rf network. The validation is conducted from four aspects: the results of simulated network attacks, the distribution of node importance, the monotonicity of rankings, and the similarity of indicators, illustrating MCTNDI’s effectiveness in real networks. Full article

Unmanned Aerial Vehicles (UAVs) have become essential tools across various sectors due to their versatility and advanced capabilities in autonomy, perception, and networking. Despite over a decade of experimental efforts in multi-UAV systems, substantial theoretical challenges concerning coordination mechanisms still need to be solved, particularly in maintaining network connectivity and optimizing routing. Current research has revealed the absence of an efficient algorithm tailored for the routing problem of multiple UAVs connected to a central station, especially under the constraints of maintaining constant network connectivity and minimizing the average goal revisit time. This paper proposes a heuristic routing algorithm for multiple UAV systems to address the return visit challenge in flying ad hoc networks (FANETs) linked to a central station. Our approach introduces a composite valuation function for target prioritization and a mathematical model for task assignment with relay allocation, allowing any UAV to visit various objectives and gain an advantage or incur a cost for each. We exclusively utilized a simulation environment to mimic UAV operations, assessing communication range, connectivity, and routing performance. Extensive simulations demonstrate that our routing algorithm remains efficient in the face of frequent topological alterations in the network, showing robustness against dynamic environments and superior performance compared to existing methods. This paper presents different approaches to efficiently directing UAVs and explains how heuristic algorithms can enhance our understanding and improve current methods for task assignments. Full article

The present study introduces an integrated software approach that provides an automated product design toolkit for customized products like knives, incorporating topology optimization (TO) and numerical simulations in order to streamline engineering workflows during the product development procedure. The modeling framework combines state-of-the-art technologies into a single platform, enabling the design and the optimization of mechanical structures with minimal human intervention. In particular, the proposed solution leverages artificial intelligence (AI), shape optimization methods, and computational tools in order to iteratively optimize material utilization as well as the design of products based on certain criteria. By embedding simulation within the design optimization loop, the developed software module ensures that performance constraints are respected throughout the design process. The case studies are concentrated in designing knives, demonstrating the platform’s ability to reduce design time, enhance product performance and provide rapid iterations of structurally optimized geometries. Finally, it should be noted that this research showcases the potential of integrated modeling technologies towards the transformation of traditional design paradigms, in this way contributing to faster, more reliable and efficient product development in various engineering industries through the training and deployment of AI models in these scientific fields. Full article

Topology optimization is a powerful structural design method that determines the optimal configuration by distributing materials efficiently within a given design domain while satisfying specified load, performance, and volume constraints. Unlike size and shape optimization, topology optimization is independent of the initial design, offering a broader design space. This paper provides a systematic review of topology optimization methods, covering two theoretical frameworks: linear elasticity and nonlinear theory. Specifically, the review focuses on sensitivity analysis, optimization criteria, and topology solution smoothing within the context of linear elasticity. In the context of nonlinear theory, the review primarily addresses nonlinear phenomena arising from stress constraints, geometric, material, and contact nonlinearities. The paper concludes by summarizing the current state of the field, identifying limitations in existing methods, and suggesting directions for future research. Full article

We demonstrate that at the rim of the photon sphere of a black hole, the quantum statistics transition takes place in any multi-particle system of indistinguishable particles, which passes through this rim to the inside. The related local departure from Pauli exclusion principle restriction causes a decay of the internal structure of collective fermionic systems, including the collapse of Fermi spheres in compressed matter. The Fermi sphere decay is associated with the emission of electromagnetic radiation, taking away the energy and entropy of the falling matter without unitarity violation. The spectrum and timing of the related e-m radiation agree with some observed short giant gamma-ray bursts and X-ray components of the luminosity of quasars and of short transients powered by black holes. The release of energy and entropy when passing the photon sphere rim of a black hole significantly modifies the premises of the information paradox at the falling of matter into a black hole. Full article