Dynamic Reconfiguration Research Articles

Industrial internet platform-driven decentralised multi-level synchronised reconfiguration of assembly supply chains

Disruptive environments, characterised by frequent contingencies, significantly challenge supply chain operations, particularly assembly supply chains with complex structures and decentralised decision-making. Failure to promptly reconfigure chains during disruptions may ripple adverse effects through the network, causing substantial losses. Consequently, decentralised dynamic reconfiguration of assembly supply chains under contingencies warrants attention. However, existing research on this topic is limited, necessitating further exploration utilising emerging technologies. Isolated information systems, such as MES and ERP, are widely deployed across firms and increasingly integrated into industry-level Industrial Internet Platforms (IIPs) to enhance information sharing and utilisation. Nevertheless, limited research explores the integration of real-time IIP data with reconfiguration mechanisms for resilient, flexible, and decentralised chain reconfiguration. Thus, this paper proposes an IIP-driven synchronised reconfiguration of supply chains (SyncRSC) solution inspired by the concepts of synchronisation and reconfigurable supply chains. SyncRSC employs the IIP architecture for real-time information support, a three-state mechanism as the qualitative method, and improved graph neural networks alongside augmented lagrangian coordination as quantitative methods. A case study of an air-conditioning supply chain verifies the superiority of the proposed methods and analyzes SyncRSC's performance under different disruption levels.

Physics-informed Graph Neural Networks for Collaborative Dynamic Reconfiguration and Voltage Regulation in Unbalanced Distribution Systems

Physics-informed Graph Neural Networks for Collaborative Dynamic Reconfiguration and Voltage Regulation in Unbalanced Distribution Systems

Reconfiguration of brain network dynamics in bipolar disorder: a hidden Markov model approach

Bipolar disorder (BD) is a neuropsychiatric disorder characterized by severe disturbance and fluctuation in mood. Dynamic functional connectivity (dFC) has the potential to more accurately capture the evolving processes of emotion and cognition in BD. Nevertheless, prior investigations of dFC typically centered on larger time scales, limiting the sensitivity to transient changes. This study employed hidden Markov model (HMM) analysis to delve deeper into the moment-to-moment temporal patterns of brain activity in BD. We utilized resting-state functional magnetic resonance imaging (rs-fMRI) data from 43 BD patients and 51 controls to evaluate the altered dynamic spatiotemporal architecture of the whole-brain network and identify unique activation patterns in BD. Additionally, we investigated the relationship between altered brain dynamics and structural disruption through the ridge regression (RR) algorithm. The results demonstrated that BD spent less time in a hyperconnected state with higher network efficiency and lower segregation. Conversely, BD spent more time in anticorrelated states featuring overall negative correlations, particularly among pairs of default mode network (DMN) and sensorimotor network (SMN), DMN and insular-opercular ventral attention networks (ION), subcortical network (SCN) and SMN, as well as SCN and ION. Interestingly, the hypoactivation of the cognitive control network in BD may be associated with the structural disruption primarily situated in the frontal and parietal lobes. This study investigated the dynamic mechanisms of brain network dysfunction in BD and offered fresh perspectives for exploring the physiological foundation of altered brain dynamics.

Dynamic reconfiguration of multiobjective distribution networks considering the variation of load and DG using a novel LDEDBO algorithm

To address the challenges related to active power dissipation and node voltage fluctuation in the practical transformation of power grids in the field of new energy such as wind and photovoltaic power generation, an improved Dung Beetle Optimization Algorithm Based on a Hybrid Strategy of Levy Flight and Differential Evolution (LDEDBO) is proposed. This paper systematically addresses this issue from three aspects: firstly, optimizing the DBO algorithm using Chebyshev chaotic mapping, Levy flight strategy, and differential evolution algorithm; secondly, validating the algorithm’s feasibility through real-time network reconfiguration at random time points within a 24-h period; and finally, applying the LDEDBO to address the dynamic reconfiguration problems of the IEEE-33 and IEEE-69 node bus. The simulation indicates that the power dissipation of the IEEE-33 node bus is decreased by 28.94% and the minimum node voltage is elevated from 0.9273 p.u to 0.9447 p.u after the reconstruction with LDEDBO. The power dissipation of the IEEE-69 node bus is reduced by 36.45%, and the minimum node voltage is increased from 0.9224 p.u to 0.9481 p.u. The LDEDBO enhances both the pace of convergence and the precision of the optimization model, leading to a superior solution for the switching combination.

An Integrated Method for Selecting Architecture Alternatives and Reconfiguration Options Towards System-of-Systems Resilience

Delivering persistent values in a dynamic environment is a challenging but imperative capability for a system-of-systems (SoS). Practitioners in the SoS and defense domains are exploring the benefits of the operational-level reconfiguration strategies via new operational concepts such as mosaic warfare. However, an architecture design that allows reconfiguration is also a crucial task, but has not yet received adequate attention, not to mention accounting for the mutual impact between architecture design alternatives and reconfiguration options. Therefore, this paper proposes an integrated method that can select the architecture with a specific inherent structure in the design phase that supports dynamic reconfiguration during the operational phase. This method firstly builds a structural framework that connects architecture design and reconfiguration, and identifies the enablers for SoS architecture reconfiguration. After developing an SoS effectiveness evaluator, the method constructs an integrated multi-objective formulation for the initial architecture selection and reconfiguration process, and provides a solution algorithm based on a fast non-dominated sorting genetic algorithm. An application to an air and missile defense SoS illustrates the effectiveness of the proposed method. The generated Pareto optimal set of solutions that have non-dominated recoverability and survivability provide useful decision support for SoS composition and initial architecture configuration, based upon which an SoS can also respond effectively to disruptions by computing the reconfiguration decisions.

A Study on the Application of FPGAs in Digital Signal Processing

Field-Programmable Gate Arrays (FPGAs) have emerged as a transformative technology in digital signal processing (DSP), offering unmatched flexibility, high performance, and real-time capabilities. This study explores the architecture of FPGAs, their integration with other digital systems, and the optimization methodologies that enhance their efficiency in diverse applications. From telecommunications and multimedia to biomedical and aerospace systems, FPGAs address the growing demand for adaptable, low-latency DSP solutions. The research highlights case studies, market trends, and future outlooks, demonstrating the pivotal role of FPGAs in shaping next-generation DSP technologies. By leveraging FPGA-specific features such as parallelism, pipelining, and dynamic reconfiguration, developers can meet the performance, efficiency, and scalability requirements of modern industries. The findings underscore the importance of continued innovation in FPGA-based DSP systems to drive advancements across emerging fields like AI, IoT, and autonomous systems. Keywords: FPGA, Digital Signal Processing (DSP), Real-Time Systems, Optimization Techniques, High- Performance Computing

Opinions on dynamic topology reconfiguration of distribution networks for PV hosting capacity enhancement

This paper investigates on dynamic topology reconfiguration of distribution networks to enhance PV hosting capacity. Firstly, a dynamic topology reconfiguration model of distribution networks considering N-1 security constraints is proposed to accommodate intermittent PV generation, and thus high penetration of renewable resources can be ensured to satisfy operational requirements under both normal and fault conditions with load transfer of feeders. Then, a hosting capacity enhancement strategy is presented to decrease risks of voltage and line overloading with reactive power flexibility of energy storage inverters, and is implemented to enhance the maximum hosting capacity of distributed renewable energy sources. Finally, simulation results have validated the effectiveness of the proposed method for PV hosting capacity enhancement of distribution networks.

Cortisol awakening response prompts dynamic reconfiguration of brain networks in emotional and executive functioning

Emotion and cognition involve an intricate crosstalk of neural and endocrine systems that support dynamic reallocation of neural resources and optimal adaptation for upcoming challenges, an active process analogous to allostasis. As a hallmark of human endocrine activity, the cortisol awakening response (CAR) is recognized to play a critical role in proactively modulating emotional and executive functions. Yet, the underlying mechanisms of such proactive effects remain elusive. By leveraging pharmacological neuroimaging and hidden Markov modeling of brain state dynamics, we show that the CAR proactively modulates rapid spatiotemporal reconfigurations (state) of large-scale brain networks involved in emotional and executive functions. Behaviorally, suppression of CAR proactively impaired performance of emotional discrimination but not working memory (WM), while individuals with higher CAR exhibited better performance for both emotional and WM tasks. Neuronally, suppression of CAR led to a decrease in fractional occupancy and mean lifetime of task-related brain states dominant to emotional and WM processing. Further information-theoretic analyses on sequence complexity of state transitions revealed that a suppressed or lower CAR led to higher transition complexity among states primarily anchored in visual-sensory and salience networks during emotional task. Conversely, an opposite pattern of transition complexity was observed among states anchored in executive control and visuospatial networks during WM, indicating that CAR distinctly modulates neural resources allocated to emotional and WM processing. Our findings establish a causal link of CAR with brain network dynamics across emotional and executive functions, suggesting a neuroendocrine account for CAR proactive effects on human emotion and cognition.

A Novel Graph Reinforcement Learning-Based Approach for Dynamic Reconfiguration of Active Distribution Networks with Integrated Renewable Energy

The dynamic reconfiguration of active distribution networks (ADNDR) essentially belongs to a complex high-dimensional mixed-integer nonlinear stochastic optimization problem. Traditional mathematical optimization algorithms tend to encounter issues like slow computational speed and difficulties in solving large-scale models, while heuristic algorithms are prone to fall into local optima. Furthermore, few scholars in the existing research on distribution network (DN) reconfiguration have considered the graph structure information, resulting in the loss of critical topological information and limiting the effect of optimization. Therefore, this paper proposes an ADNDR approach based on the graph convolutional network deep deterministic policy gradient (GCNDDPG). Firstly, a nonlinear stochastic optimization mathematical model for the ADNDR is constructed, taking into account the uncertainty of sources and loads. Secondly, a loop-based encoding method is employed to reduce the action space and complexity of the ADNDR. Then, based on graph theory, the DN structure is transformed into a dynamic network graph model, and a GCNDDPG-based ADNDR approach is proposed for the solution. In this method, graph convolutional networks are used to extract features from the graph structure information, and the state of the DN, and the deep deterministic policy gradient is utilized to optimize the ADNDR decision-making process to achieve the safe, stable, and economic operation of the DN. Finally, the effectiveness of the proposed approach is verified on an improved IEEE 33-bus power system. The simulation results demonstrate that the method can effectively enhance the economy and stability of the DN, thus validating the effectiveness of the proposed approach.

Granular metamaterials with dynamic bond reconfiguration.

Biological materials dynamically reconfigure their underlying structures in response to stimuli, achieving adaptability and multifunctionality. Conversely, mechanical metamaterials have fixed interunit connections that restrict adaptability and reconfiguration. This study introduces granular metamaterials composed of discrete bimaterial structured particles that transition between assembled and unassembled states through mechanical compression and thermal stimuli. These materials enable dynamic bond reconfiguration, allowing reversible bond breaking and formation, similar to natural systems. Leveraging their discrete nature, these materials can adaptively reconfigure their shape and respond dynamically to varying conditions. Our investigations reveal that these granular metamaterials can substantially alter their mechanical properties, like compression, shearing, and bending, offering tunable mechanical characteristics across different states. Furthermore, they exhibit collective behaviors like directional movement, object capture, transportation, and gap crossing, showcasing their potential for reprogrammable functionalities. This work highlights the dynamic reconfigurability and robust adaptability of granular metamaterials, expanding their potential in responsive architecture and autonomous robotics.

Non-Reciprocity, Metastability, and Dynamic Reconfiguration in Co-Assembly of Active and Passive Particles.

Living organisms often exhibit non-reciprocal interactions where the forces acting on the objects are not equal in magnitude or opposite in direction. The combination of reciprocal and non-reciprocal interactions between synthetic building blocks remains largely unexplored. Here, out-of-equilibrium assemblies of non-motile isotropic passive and metal-patched motile active particles are formed by overlapping bulk interactions with directed self-propulsion. An external alternating current (AC) electric field generates concurrent dipolar and induced-charge electrophoretic forces between the particles which are evaluated using microscopy. The interaction force measurements allow to determine the degree of reciprocity in interactions, which is tunable by designing the active particle and its trajectory. While linearly-propelled active particles evade assembly with passive particles, helically propelled active particles form active-passive clusters with dynamic reconfiguration and long-lived metastability. Large clusters display programmable fluctuations and reconfigurability by controlling the fraction of active particles. The study establishes principles of integrating reciprocal and non-reciprocal interactions in guided colloidal assembly of reconfigurable metastable structures.

Water-Triggered Reconfigurable Glycerogels for Sustainable All-Gel Supercapacitors.

The polymeric structures of synthetic gels are typically static, which makes them damage-prone and nonrecyclable. Inspired by the dynamic reconfigurability of biological tissues, which eliminate old/damaged cells and regenerate new ones via biological triggers/signals, a reconfigurable biopolymer gel is presented based on a glycerol-mediated supramolecular gelation strategy. In response to an eco-friendly triggering agent water, this gel undergoes on-demand molecular-level reconfiguration. The versatility of the approach enables the development of reconfigurable gels with modulated functionality. As a proof-of-concept, a reconfigurable glycerogel electrode and electrolyte are developed and used to prototype an all-gel supercapacitor that exhibits exceptional self-healing, degradation, and rebuilding abilities. Furthermore, it can tolerate extreme mechanical deformations (e.g., stretching, bending, and twisting) and temperatures (-20 to 80°C). The device exhibits excellent energy storage performance, with a maximum areal capacitance of 450 mF cm-2 (at 0.035mA cm-2) and remarkable capacitance retention of 89% following 20000 charge/discharge cycles (at 0.35mA cm-2). Moreover, following self-healing and rebuilding, the capacitance remains at 91% and 110% (at 0.35mA cm-2) of the original value, respectively. This generalized strategy for preparing multifunctional reconfigurable gels will facilitate the development of high-performance flexible and wearable devices with improved durability and recyclability.

Dynamic Mobile X-Haul Reconfiguration by Fast Network Switching for Low-Latency MEC Service

Dynamic Mobile X-Haul Reconfiguration by Fast Network Switching for Low-Latency MEC Service

Evolving alterations of structural organization and functional connectivity in feedforward neural networks after induced P301L tau mutation.

Reciprocal structure-function relationships underlie both healthy and pathological behaviours in complex neural networks. Thus, understanding neuropathology and network dysfunction requires a thorough investigation of the complex interactions between structural and functional network reconfigurations in response to perturbation. Such adaptations are often difficult to study in vivo. For example, subtle, evolving changes in synaptic connectivity, transmission and the electrophysiological shift from healthy to pathological states, for example alterations that may be associated with evolving neurodegenerative disease, such as Alzheimer's, are difficult to study in the brain. Engineered in vitro neural networks are powerful models that enable selective targeting, manipulation and monitoring of dynamic neural network behaviour at the micro- and mesoscale in physiological and pathological conditions. In this study, we engineered feedforward cortical neural networks using two-nodal microfluidic devices with controllable connectivity interfaced with microelectrode arrays (mMEAs). We induced P301L mutated tau protein to the presynaptic node of these networks and monitored network dynamics over three weeks. Induced perturbation resulted in altered structural organization and extensive axonal retraction starting in the perturbed node. Perturbed networks also exhibited functional changes in intranodal activity, which manifested as an overall decline in both firing rate and bursting activity, with a progressive increase in synchrony over time and a decrease in internodal signal propagation between pre- and post-synaptic nodes. These results provide insights into dynamic structural and functional reconfigurations at the micro- and mesoscale as a result of evolving pathology and illustrate the utility of engineered networks as models of network function and dysfunction.

Coordinated Optimization Method for Distributed Energy Storage and Dynamic Reconfiguration to Enhance the Economy and Reliability of Distribution Network

To fully leverage the application potential of distributed energy storage systems (DESS) and network reconfiguration, a coordinated optimization method is proposed to enhance the economic efficiency of distribution networks under normal conditions and the reliability of a power supply during fault conditions. First, a scenario-generation method is developed based on Latin hypercube sampling and Kantorovich distance synchronous back-substitution reduction is used to obtain the typical scenario of wind and solar output. Next, a planning operation coordinated optimization framework and model are established, considering both normal and fault states of the distribution network. In the planning layer, the objective is to minimize the annual comprehensive capital expenditures for the distribution network to improve the economic efficiency of the distribution network. The operation layer includes both normal operation and fault operation states, with the optimization goal of minimizing the sum of normal operation costs and the fault costs associated with load shedding. Subsequently, a hybrid optimization algorithm combining an improved Aquila Optimizer-Second-Order Cone Programming (IAO-SOCP) is proposed to solve the coordinated optimization model. Finally, the proposed coordinated optimization method is validated using an enhanced IEEE 33-bus distribution network case study. The results demonstrate that the method effectively reduces network losses and minimizes load shedding costs during fault conditions, thereby ensuring a balance between the economic efficiency and reliability of the distribution network.

Power Distribution Network Reconfiguration Techniques: A Thorough Review

Distribution network reconfiguration (DNR) plays a vital role in enhancing network sustainability by optimizing its topology. This process achieves key objectives such as reducing power losses, improving voltage profiles, balancing loads, and increasing network reliability, aligning with sustainability metrics. Depending on the goals and equipment available, reconfiguration may be applied for short-term or long-term durations. Long-term or static reconfiguration suits both conventional switches and traditional as well as modern networks. In modern networks equipped with remote-control switches, however, reconfiguration can be implemented rapidly to meet specific operational objectives. This study provides a comprehensive review of recent advancements in network reconfiguration, categorizing methods into four groups: heuristic, metaheuristic, conventional, and modern approaches. Each category is broadly defined and compared, with applications discussed for both static and dynamic reconfiguration. Dynamic reconfiguration is highlighted as a key area for future exploration in smart and modern distribution networks. This article serves as a resource for engineers and researchers, helping them select the most suitable method based on network equipment and performance goals.

Coacervate-pore complexes for selective molecular transport and dynamic reconfiguration

Despite surging interests on liquid-state coacervates and condensates, confinement within solid-state pores for selective permeation remains an unexplored area. Drawing inspiration from nuclear pore complexes (NPCs), we design and construct coacervate-pore complexes (CPCs) with regulatable permeability. We demonstrate universal CPC formation across 19 coacervate systems and 5 pore types, where capillarity drives the spontaneous imbibition of coacervate droplets into dispersed or interconnected pores. CPCs regulate through-pore transport by forming a fluidic network that modulates guest molecule permeability based on guest-coacervate affinity, mimicking NPC selectivity. While solid constructs of NPC mimicries are limited by spatial fixation of polymer chains, CPCs of a liquid nature feature dynamic healing and rapid phase transitioning for permeability recovery and regulation, respectively. Looking forward, we expect the current work to establish a basis for developing liquid-based NPC analogs using a large pool of synthetic coacervates and biomolecular condensates.

Dynamic Reconfiguration of Distribution Networks Considering the Real-time Topology Variation

Dynamic Reconfiguration of Distribution Networks Considering the Real-time Topology Variation

Research on Voltage Self-Calibration Sensing Technology Based on Measurement Circuit Topology Changes

In capacitance-coupled voltage-sensing technology, the degree of coupling capacitance is affected by the sensing area, relative position deviation, and other factors, and thus the measurement coefficient is often difficult to determine accurately and presents greater implementation difficulties in actual deployment. This paper proposes a dynamic reconfiguration based on the measurement circuit topology of the voltage sensor adaptive calibration method in order to measure voltage sensor gain in the process of automatic measuring. Firstly, the basic principle of voltage measurement is introduced, and the self-calibration method is proposed, considering the influence of the sensing area and the relative position error on the change in the coupling capacitance. On this basis, the influence of calibration accuracy on sensor structure parameters is analyzed using network sensitivity analysis, and the parameter selection principle is given, according to which the selection criterion of parameter optimization is formulated to complete the sensor design. By analyzing the coupling effect of the three-phase measurement, the installation method of the sensing structure is proposed. An experimental platform is built to test the accuracy of the voltage measurement of the sensor under laboratory conditions. The experimental results show that the maximum relative error of the voltage measurement amplitude is 2.24%. In order to verify the feasibility of the sensor technology designed, the measurement models that integrate communication, acquisition, and processing are installed on both ends of the circuit breaker wire, and the voltage waveform generated during the circuit breaker closing process is recorded in real time. The experimental results show that the sensor technology can accurately record the voltage waveform of the signal to be measured, and the feasibility of its application in switchgear equipment signal measurement is preliminarily verified by the results.

Reduction of losses in electric power distribution system–dynamic reconfiguration case study

Reduction of losses in electric power distribution system–dynamic reconfiguration case study