Interconnection Topology Research Articles

Synchronization of Generalized Lorenz Systems in a Loop

The conditions for the synchronization of three interconnected generalized Lorenz systems are given. The interconnection topology contains a loop. It is shown that, under certain conditions on the strength of the coupling of the systems, the full synchronization of all three systems is guaranteed. The results are illustrated by an example.

Review of Hybrid Energy Storage Systems for Hybrid Electric Vehicles

Energy storage systems play a crucial role in the overall performance of hybrid electric vehicles. Therefore, the state of the art in energy storage systems for hybrid electric vehicles is discussed in this paper along with appropriate background information for facilitating future research in this domain. Specifically, we compare key parameters such as cost, power density, energy density, cycle life, and response time for various energy storage systems. For energy storage systems employing ultra capacitors, we present characteristics such as cell voltage, cycle life, power density, and energy density. Furthermore, we discuss and evaluate the interconnection topologies for existing energy storage systems. We also discuss the hybrid battery–flywheel energy storage system as well as the mathematical modeling of the battery–ultracapacitor energy storage system. Toward the end, we discuss energy efficient powertrain for hybrid electric vehicles.

Coordinated configuration of hybrid energy storage for electricity-hydrogen integrated energy system

This paper proposes an optimal coordinated configuration method of hybrid electricity and hydrogen storage for the electricity‑hydrogen integrated energy system (EH-ES) to promote the renewable energy source (RES) utilization and reduce the deployment cost. To simulate the practical operation of EH-ES, an energy hub framework with a discrete state space system matrix is designed to characterize the internal interconnection topology and quantify the steady-state production, storage, conversion and utilization processes of electricity and hydrogen energy carriers within EH-ES. A chronological operation simulation based electricity and hydrogen storage configuration model over a year-round time horizon is formulated to collaboratively optimize the capacity of the electrolyzer, fuel cell, battery energy storage (BES) and hydrogen storage tank. In this model, the power and capacity complementarity of BES and generalized hydrogen storage is fully exploited to compensate for power and energy imbalances, and additional capacity configuration of BES is considered to mitigate the drastic electrolytic power fluctuation. Besides, a typical source-load scenario reduction method based on K-means and ordered clustering is developed to avoid a large number of operation variables due to year-round hourly simulation. Comparative studies demonstrate that the proposed methodology can achieve the RES consumption rate of 98.873 % while the equipment investment cost can be reduced by 10.034 %.

The diameter of rectangular twisted torus

The rectangular twisted torus is an attractive alternative to the classic torus as interconnection topology of high-performance computers. In this article, we establish the analytical expression of the diameter of a rectangular twisted torus with any aspect ratio and any twist slope. The result validates the optimality of a rectangular twisted torus with width-height-slope ratio 2:1:1.

Decoding range variability in electric vehicles: Unravelling the influence of cell-to-cell parameter variation and pack configuration

This study addresses the common occurrence of cell-to-cell variations arising from manufacturing tolerances and their implications during battery production. The focus is on assessing the impact of these inherent differences in cells and exploring diverse cell and module connection methods on battery pack performance and their subsequent influence on the driving range of electric vehicles (EVs). The analysis spans three battery pack sizes, encompassing various constant discharge rates and nine distinct drive cycles representative of driving behaviours across different regions of India. Two interconnection topologies, categorised as “string” and “cross”, are examined. The findings reveal that cross-connected packs exhibit reduced energy output compared to string-connected configurations, which is reflected in the driving range outcomes observed during drive cycle simulations. Additionally, the study investigates the effects of standard deviation in cell parameters, concluding that an increased standard deviation (SD) leads to decreased energy output from the packs. Notably, string-connected packs demonstrate superior performance in terms of extractable energy under such conditions.

Contract composition for dynamical control systems: Definition and verification using linear programming

Designing large-scale control systems to satisfy complex specifications is hard in practice, as most formal methods are limited to systems of modest size. Contract theory has been proposed as a modular alternative, in which specifications are defined by assumptions on the input to a component and guarantees on its output. However, current contract-based methods for control systems either prescribe guarantees on the state of the system, going against the spirit of contract theory, or are not supported by efficient computational tools. In this paper, we present a contract-based modular framework for discrete-time dynamical control systems. We extend the definition of contracts by allowing the assumption on the input at a time k to depend on outputs up to time k−1, which is essential when considering feedback systems. We also define contract composition for arbitrary interconnection topologies, and prove that this notion supports modular design, analysis and verification. This is done using graph theory methods, and specifically using the notions of topological ordering and backward-reachable nodes. Lastly, we present an algorithm for verifying vertical contracts, which are claims of the form “the conjunction of given component-level contracts implies given contract on the integrated system”. These algorithms are based on linear programming, and scale linearly with the number of components in the interconnected network. A numerical example is provided to demonstrate the scalability of the presented approach, as well as the modularity achieved by using it.

Multi-Physics Three-Dimensional Component Placement and Routing Optimization Using Geometric Projection

Abstract This article presents a novel three-dimensional topology optimization framework developed for 3D spatial packaging of interconnected systems using a geometric projection method (GPM). The proposed gradient-based topology optimization method simultaneously optimizes the locations and orientations of system components (or devices) and lengths, diameters, and trajectories of interconnects to reduce the overall system volume within the prescribed 3D design domain. The optimization is subject to geometric and physics-based constraints dictated by various system specifications, suited for a wide range of transportation (aerospace or automotive), heating, ventilation, air-conditioning, and refrigeration, and other complex system applications. The system components and interconnects are represented using 3D parametric shapes such as cubes, cuboids, and cylinders. These objects are then projected onto a three-dimensional finite element mesh using the geometric projection method. Sensitivities are calculated for the objective function (bounding box volume) with various geometric and physics-based (thermal and hydraulic) constraints. Several case studies were performed with different component counts, interconnection topologies, and system boundary conditions and are presented to exhibit the capabilities of the proposed 3D multi-physics spatial packaging optimization framework.

Q-Seg: Quantum Annealing-Based Unsupervised Image Segmentation.

We present Q-Seg, a novel unsupervised image segmentation method based on quantum annealing, tailored for existing quantum hardware. We formulate the pixel- wise segmentation problem, which assimilates spectral and spatial information of the image, as a graph-cut optimization task. Our method efficiently leverages the interconnected qubit topology of the D-Wave Advantage device, offering superior scalability over existing quantum approaches and outperforming several tested state-of-the-art classical methods. Empirical evaluations on synthetic datasets have shown that Q-Seg has better runtime performance than the state-of-the-art classical optimizer Gurobi. The method has also been tested on earth observation image segmentation, a critical area with noisy and unreliable annotations. In the era of noisy intermediate-scale quantum, Q-Seg emerges as a reliable contender for real-world applications in comparison to advanced techniques like Segment Anything. Consequently, Q-Seg offers a promising solution using available quantum hardware, especially in situations constrained by limited labeled data and the need for efficient computational runtime.

A New Neural Dynamic Learning Framework for Discrete-Time Strict-Feedback Systems: Internal Interaction-Based Weight Adaptive Laws.

This article investigates internal interaction-based dynamic learning control (LC) for uncertain discrete-time strict-feedback systems. On the basis of predict technology, the original system is converted into a common n -step-ahead input-output predict model. The predict model causes every estimated neural weight to converge to n different constants using the existing control framework. To solve such a problem, the predict model is further decomposed into n one-step-ahead subsystems, which can be viewed as n independent agents. Subsequently, the distributed cooperative weight adaptive laws are designed by introducing an undirected and connected interconnection topology among subsystems. By constructing the variable relationship between the subsystems and the n -step-ahead predict model, a new internal weight interaction-based neural dynamic LC framework is proposed for the whole closed-loop system, in which estimated weights at different times share their weight knowledge. The proposed framework ensures the ultimately uniform boundedness of the closed-loop system and achieves the excellent control performance. By combining the consensus theory and a cooperative persistent excitation condition, every estimated weight along the neural input orbit is verified to exponentially converge to a close vicinity of a unique ideal constant, rather than n different constants. Consequently, the developed LC framework facilitates constant weights storage, saves the knowledge storage space, and improves the robustness of knowledge utilization. These characteristics are verified by simulation results.

Dynamic reconfiguration of photovoltaic array for minimizing mismatch loss

Mismatch conditions owing to different internal and external factors, such as partial shading and module defects, can directly degrade the power generation of distributed photovoltaic arrays. In this study, a reconfiguration solution called multiple switching matrices is proposed to mitigate mismatch losses for any size of the total-cross-tied photovoltaic array. In the proposed solution, the photovoltaic array is divided into several sub-arrays using switching matrices. The current and voltage values of each module are collected by the electric measurement sensors and sent to the control unit, and the proposed reconfiguration solution can be implemented by controlling switching matrices. The performance of the proposed reconfiguration solution is evaluated extensively for a range of mismatched conditions, including partial shading patterns and partial shading with random failure patterns. The P–V and I–V characteristics are analyzed by comparison with the existing sudoku-based arrangement and the conventional total-cross-tied interconnection topology. Moreover, three main parameters and standard deviations of the maximum power point for each pattern, are considered. The numerical results confirm the effectiveness and flexibility of the proposed reconfiguration solution for optimizing photovoltaic array generation under various mismatch conditions.

Coordinated planning for flexible interconnection and energy storage system in low-voltage distribution networks to improve the accommodation capacity of photovoltaic

The increasing proportion of distributed photovoltaics (DPVs) and electric vehicle charging stations in low-voltage distribution networks (LVDNs) has resulted in challenges such as distribution transformer overloads and voltage violations. To address these problems, we propose a coordinated planning method for flexible interconnections and energy storage systems (ESSs) to improve the accommodation capacity of DPVs. First, the power-transfer characteristics of flexible interconnection and ESSs are analyzed. The equipment costs of the voltage source converters (VSCs) and ESSs are also analyzed comprehensively, considering the differences in installation and maintenance costs for different installation locations. Second, a bilevel programming model is established to minimize the annual comprehensive cost and yearly total PV curtailment capacity. Within this framework, the upper-level model optimizes the installation locations and capacities of the VSCs and ESSs, whereas the lower-level model optimizes the operating power of the VSCs and ESSs. The proposed model is solved using a non-dominated sorting genetic algorithm with an elite strategy (NSGA-II). The effectiveness of the proposed planning method is validated through an actual LVDN scenario, which demonstrates its advantages in enhancing PV accommodation capacity. In addition, the economic benefits of various planning schemes with different flexible interconnection topologies and different PV grid-connected forms are quantitatively analyzed, demonstrating the adaptability of the proposed coordinated planning method.

ACO intelligent task scheduling algorithm based on Q-learning optimization in a multilayer cognitive radio platform

With the rapid development of cognitive radio technology, multilayer heterogeneous cognitive radio computing platforms with large computing, high-throughput, ultralarge bandwidth and ultralow latency have become a research hotspot. Aiming at the core scheduling problems of multilayer heterogeneous computing platforms, this paper abstracts the bidirectional interconnection topology, node computing capacity, and internode communication capability of the heterogeneous computing platform into an undirected graph model and abstracts the nodes with dependencies, nodes’ computing requirements, and internode communication requirements in streaming tasks into a directed acyclic graph (DAG) model so as to transform the task-scheduling problem into a deployment-scheduling problem from DAG to undirected graph. To efficiently solve this graph model, this paper calculates and forms a component scheduling sequence based on the dependencies of functional components in streaming domain tasks. Then, according to the scheduling sequence, ant colony optimization (ACO) algorithms, such as ant colonies and Q-learning select functional components, deploy components to different computing nodes, calculate the scheduling cost, guide the solution space search of agents, and complete the scenario migration adaptation of the scheduling algorithms to intelligent scheduling of domain tasks. So, this paper proposes the ACO field task intelligent scheduling algorithm based on Q-learning optimization (QACO). QACO uses the Q-table matrix of Q-learning as the initial pheromone of the ant colony algorithm, which not only solves the dimensional disaster of the Q-learning algorithm but also accelerates the convergence speed of the ant colony intelligent scheduling algorithm, reduces the task scheduling length, and further enhances the search ability of the existing scheduling algorithm to solve the spatial set. Based on the randomly generated DAG domain task map, three experimental test scenarios are designed to verify the algorithm performance. The experimental results show that compared with the Q-learning, ACO, and genetic algorithms (GA) algorithms, the proposed algorithm improves the convergence speed of the solution by 72.3%, 63.4%, and 64% on average, reduces the scheduling length by 2.8%, 2.2%, and 0.9% on average, and increases the parallel acceleration ratio by 2.8%, 2.1%, and 0.9% on average, respectively. The practical application value of the algorithm is analyzed through typical radar task simulation, but the load balancing of the algorithm needs to be further improved.

Advancements and challenges in hybrid energy storage systems: Components, control strategies, and future directions

Hybrid energy storage systems (HESSs) can considerably improve the dependability, efficiency, and sustainability of energy storage systems (ESSs). This study examines the components of HESS, including the different types of ESSs that are typically used in hybrid systems. The advantages of integrating various ESS types in hybrid systems are also covered, along with design considerations for HESS and the interconnection topologies, which include passive, active, and semi-active architectures. For HESS, several control techniques both traditional and intelligent-have been suggested, including filter-based control, droop control, sliding mode control, rule-based control, deadbeat control, model predictive control, fuzzy logic control, optimization-based control, supervisory control, feedforward control, unified control, and robust control. The current work also discusses HESS's difficulties and potential possibilities, including technological advancement, standardization, interdisciplinary cooperation, grid integration, environmental sustainability, security and dependability, and legislative and regulatory frameworks.

Review: tunable nanophotonic metastructures.

Tunable nanophotonic metastructures offer new capabilities in computing, networking, and imaging by providing reconfigurability in computer interconnect topologies, new optical information processing capabilities, optical network switching, and image processing. Depending on the materials and the nanostructures employed in the nanophotonic metastructure devices, various tuning mechanisms can be employed. They include thermo-optical, electro-optical (e.g. Pockels and Kerr effects), magneto-optical, ionic-optical, piezo-optical, mechano-optical (deformation in MEMS or NEMS), and phase-change mechanisms. Such mechanisms can alter the real and/or imaginary parts of the optical susceptibility tensors, leading to tuning of the optical characteristics. In particular, tunable nanophotonic metastructures with relatively large tuning strengths (e.g. large changes in the refractive index) can lead to particularly useful device applications. This paper reviews various tunable nanophotonic metastructures' tuning mechanisms, tuning characteristics, tuning speeds, and non-volatility. Among the reviewed tunable nanophotonic metastructures, some of the phase-change-mechanisms offer relatively large index change magnitude while offering non-volatility. In particular, Ge-Sb-Se-Te (GSST) and vanadium dioxide (VO2) materials are popular for this reason. Mechanically tunable nanophotonic metastructures offer relatively small changes in the optical losses while offering large index changes. Electro-optically tunable nanophotonic metastructures offer relatively fast tuning speeds while achieving relatively small index changes. Thermo-optically tunable nanophotonic metastructures offer nearly zero changes in optical losses while realizing modest changes in optical index at the expense of relatively large power consumption. Magneto-optically tunable nanophotonic metastructures offer non-reciprocal optical index changes that can be induced by changing the magnetic field strengths or directions. Tunable nanophotonic metastructures can find a very wide range of applications including imaging, computing, communications, and sensing. Practical commercial deployments of these technologies will require scalable, repeatable, and high-yield manufacturing. Most of these technology demonstrations required specialized nanofabrication tools such as e-beam lithography on relatively small fractional areas of semiconductor wafers, however, with advanced CMOS fabrication and heterogeneous integration techniques deployed for photonics, scalable and practical wafer-scale fabrication of tunable nanophotonic metastructures should be on the horizon, driven by strong interests from multiple application areas.

Compositional synthesis of control barrier certificates for networks of stochastic systems against [formula omitted]-regular specifications

This paper is concerned with a compositional scheme for the construction of control barrier certificates for interconnected discrete-time stochastic systems. The main objective is to synthesize switching controllers against ω-regular properties that can be described by accepting languages of deterministic Streett automata (DSA) along with providing probabilistic guarantees for the satisfaction of such specifications. The proposed framework leverages the interconnection topology and a notion of so-called control sub-barrier certificates of subsystems, which are used to compositionally construct control barrier certificates of interconnected systems by imposing some dissipativity-type compositionality conditions. We propose a systematic approach to decompose high-level ω-regular specifications into simpler tasks by utilizing the automata corresponding to the specifications. In addition, we formulate an alternating direction method of multipliers (ADMM) optimization problem in order to obtain suitable control sub-barrier certificates of subsystems while satisfying compositionality conditions. For systems with polynomial dynamics, we provide a sum-of-squares (SOS) optimization problem for the computation of control sub-barrier certificates and local controllers of subsystems. Finally, we demonstrate the effectiveness of our proposed approaches by applying them to a physical case study.

Graph based routing algorithm for torus topology and its evaluation for the Angara interconnect

Several approaches and techniques exist to resolve load balancing problem in general and torus topology networks. Graph methods are natural ways to perform balancing of routing paths. A routing balancing algorithm must operate within the constraints of the underlying network architecture that limits several parameters, such as the number of logical paths in the network. In this paper, we consider a torus topology that is one of the common topologies that are used in high performance computing systems. We introduce a routing graph that corresponds to a interconnect topology, abstracts interconnect routing rules, providing one-to-one correspondence of network routes and graph paths. We propose a deadlock-free routing algorithm based on a fast single-source shortest path algorithm in a routing graph for the deterministic routing of a torus topology interconnect with a single virtual channel. The proposed algorithm is a tradeoff between two existing routing algorithms, also the algorithm is optimized for multidimensional torus topology and can be applied to any torus topology HPC system. We present a complete description of a routing graph that abstracts the Angara interconnect with 4D torus topology. We evaluated the proposed routing algorithm and benchmarked the maximum performance improvement of 71% for the Alltoall pattern for torus topology systems up to 432 nodes on the Angara interconnect simulator. The performance improvement of more than 5% was obtained for the NPB FT and IS application kernels on a 32-node supercomputer.

Torus-Connected Toroids: An Efficient Topology for Interconnection Networks

Recent supercomputers embody hundreds of thousands of compute nodes, and sometimes millions; as such, they are massively parallel systems. Node interconnection is thus critical to maximise the computing performance, and the torus topology has come out as a popular solution to this crucial issue. This is the case, for example, for the interconnection network of the Fujitsu Fugaku, which was ranked world no. 1 until May 2022 and is the world no. 2 at the time of the writing of this article. Here, the number of dimensions used by the network topology of such torus-based interconnects stays rather low: it is equal to three for the Fujitsu Fugaku’s interconnect. As a result, it is necessary to greatly increase the arity of the underlying torus topology to be able to connect the numerous compute nodes involved, and this is eventually at the cost of a higher network diameter. Aiming at avoiding such a dramatic diameter rise, topologies can also combine several layers: such interconnects are called hierarchical interconnection networks (HIN). We propose, in this paper, which extends an earlier study, a novel interconnect topology for massively parallel systems, torus-connected toroids (TCT), whose advantage compared to existing topologies is that while it retains the torus topology for its desirable properties, the TCT network topology combines it with an additional layer, toroids, in order to significantly lower the network diameter. We both theoretically and empirically evaluate our proposal and quantitatively compare it to conventional approaches, which the TCT topology is shown to supersede.

Simultaneous AC/DC distribution for the sustainable operation of smart building clusters in hybrid urban microgrids

Hybrid AC/DC urban microgrids (HUMG) have emerged as a candidate solution to reliably, efficiently, and economically meet the increasing consumer electric demand and to ensure the quality of delivered power. Smart buildings (SB) are prevalent prosumers in HUMGs. To effectively utilise renewable resources in SBs, it is imperative to have assistance from battery energy storage systems (BESSs). Also, uncertainty in generation and demand necessitates real-time ancillary power support for SBs. Smart building interconnection forming SB clusters is an emerging solution for providing ancillary power support. Existing SB interconnection topologies require multiple power conversion stages, additional infrastructure, and underutilise the existing AC grid infrastructure. Most of these SB interconnections are not sustainable due to higher losses from more power conversion stages, increased cost, and excessive dependence on BESSs. A composite transmission system-based ancillary power dispatching architecture (CTS-APDA) for the sustainable operation of SB clusters is proposed in this paper. A multi-agent system based hierarchical control and decision logic in the CTS-APDA ensures the optimal ustilisation of battery resources. The operation of the proposed CTS-APDA and its efficacy as a fast-acting ancillary support is validated through extensive case studies. The proposed CTS-APDA will foster participation of electric vehicles in the transactive energy market of emerging SBs.

Nonsmooth Control Barrier Function design of continuous constraints for network connectivity maintenance

This paper considers the problem of maintaining global connectivity of a multi-robot system while executing a desired coordination task. Our approach builds on optimization-based feedback design formulations, where the nominal cost function and constraints encode desirable control objectives for the resulting input. Our solution uses the algebraic connectivity of the multi-robot interconnection topology as a control barrier function and critically embraces its nonsmooth nature. We take advantage of the understanding of how Laplacian eigenvalues behave as their multiplicities change, in combination with the flexibility provided by the concept of control barrier function, to carefully design additional constraints that guarantee the resulting optimization-based controller is continuous and maintains network connectivity. The technical treatment combines elements from set-valued theory, nonsmooth analysis, and algebraic graph theory to imbue the proposed constraints with regularity properties so that they can be smoothly combined with other control constraints. We provide simulations and experimental results illustrating the effectiveness and continuity of the proposed approach in a resource gathering problem.

On the Cube Polynomials of Padovan and Lucas–Padovan Cubes

The hypercube is one of the best models for the network topology of a distributed system. Recently, Padovan cubes and Lucas–Padovan cubes have been introduced as new interconnection topologies. Despite their asymmetric and relatively sparse interconnections, the Padovan and Lucas–Padovan cubes are shown to possess attractive recurrent structures. In this paper, we determine the cube polynomial of Padovan cubes and Lucas–Padovan cubes, as well as the generating functions for the sequences of these cubes. Several explicit formulas for the coefficients of these polynomials are obtained, in particular, they can be expressed with convolved Padovan numbers and Lucas–Padovan numbers. In particular, the coefficients of the cube polynomials represent the number of hypercubes, a symmetry inherent in Padovan and Lucas–Padovan cubes. Therefore, cube polynomials are very important for characterizing these cubes.