Dynamic Reconfiguration Research Articles

A Deep Learning-Driven Self-Conscious Distributed Cyber-Physical System for Renewable Energy Communities.

The Internet of Things (IoT) is transforming various domains, including smart energy management, by enabling the integration of complex digital and physical components in distributed cyber-physical systems (DCPSs). The design of DCPSs has so far been focused on performance-related, non-functional requirements. However, with the growing power consumption and computation expenses, sustainability is becoming an important aspect to consider. This has led to the concept of energy-aware DCPSs, which integrate conventional non-functional requirements with additional attributes for sustainability, such as energy consumption. This research activity aimed to investigate and develop energy-aware architectural models and edge/cloud computing technologies to design next-generation, AI-enabled (and, specifically, deep-learning-enhanced), self-conscious IoT-extended DCPSs. Our key contributions include energy-aware edge-to-cloud architectural models and technologies, the orchestration of a (possibly federated) edge-to-cloud infrastructure, abstractions and unified models for distributed heterogeneous virtualized resources, innovative machine learning algorithms for the dynamic reallocation and reconfiguration of energy resources, and the management of energy communities. The proposed solution was validated through case studies on optimizing renewable energy communities (RECs), or energy-aware DCPSs, which are particularly challenging due to their unique requirements and constraints; in more detail, in this work, we aim to define the optimal implementation of an energy-aware DCPS. Moreover, smart grids play a crucial role in developing energy-aware DCPSs, providing a flexible and efficient power system integrating renewable energy sources, microgrids, and other distributed energy resources. The proposed energy-aware DCPSs contribute to the development of smart grids by providing a sustainable, self-consistent, and efficient way to manage energy distribution and consumption. The performance demonstrates our approach's effectiveness for consumption and production (based on RMSE and MAE metrics). Our research supports the transition towards a more sustainable future, where communities adopting REC principles become key players in the energy landscape.

A digital-twin-based adaptive multi-objective Harris Hawks Optimizer for dynamic hybrid flow green scheduling problem with dynamic events

In many hybrid flow shop production environments, dynamic events (DEs) often occur and seriously impact production schedules. However, the traditional model rarely considers DEs, which makes the model seriously inconsistent with the actual situation. In addition, to reduce carbon emissions, there is an urgent need to carry out green optimization scheduling of production workshops, especially in emerging digital workshops. First, this paper proposes a dynamic digital-twin-based Multi-Objective Hybrid Flowshop Green Scheduling Model with DEs (MOHFGSM-DEs) to fill the above gaps. The MOHFGSM-DEs model’s bi-objectives are the makespan minimization and digital workshop total energy consumption minimization. In addition, the MOHFGSM-DEs model takes account of representative DEs in the actual dynamic production process, including controlled processing time, device dynamic reconfiguration events, and workpiece reworking events, which makes it more adaptive in practical production. Secondly, an Adaptive Multi-Objective Dynamic Harris Hawks Optimizer (AMODHHO) is presented with DEs parameters perceived by the digital twin of the workshop, adjusting the scheduling scheme adaptively in real time to address the MOHFGSM-DEs model efficiently. AMODHHO combines a nonlinear optimization strategy on balancing the exploration and exploitation of Harris Hawks Optimizer (HHO) and introduces the crossover operator of Genetic Algorithm (GA) to improve its optimal global ability. Moreover, a digital-twin-driven dynamic encoding methodology containing many optimization strategies is designed based on problem-specific characteristics. Finally, numerical experiments and application case comparisons are performed among AMODHHO, SPEA2, and NSGA-II. Results show that AMODHHO is superior to SPEA2 and NSGA-II regarding comprehensive performance. Therefore, the proposed model and AMODHHO are feasible to solve the real-world MOHFGSM-DEs adaptively. Furthermore, it performs dynamic adaptive adjustment for the actual scheduling schemes according to the real-time DEs the digital twin perceives.

Realisation of the reconfigurable pulsed high magnetic field facility and its scientific application at Wuhan National Pulsed High Magnetic Field Centre

AbstractTo generate arbitrary shape magnetic field waveforms for advanced scientific research, a reconfigurable pulsed high magnetic field facility (PHMFF) was developed at Wuhan National Pulsed High Magnetic Field Centre (WHMFC). First, the basic elements of PHMFF are introduced, and a ternary physical model composed of energy storage unit, magnet, transmission circuits and other auxiliary units is summarised to describe various magnetic field systems. Second, in order to realise the reconfiguration of magnetic field systems based on detailed elements, the control system with the ability of dynamic construction and control sequence reconfiguration is designed. On this basis, the PHMFF has been realised and various magnetic field systems are constructed to produce arbitrary shape magnetic field waveforms, such as half‐sine, ultra‐high, flat‐top and repetitive field pulses, which not only enhances the capability and efficiency of PHMFF, but also makes more scientific research possible in pulsed magnetic fields. So far, a peak magnetic field of 94.8 T, a flat‐top magnetic field of 64 T/10 ms and a repetitive magnetic field waveform of 20 T/60 Hz have been achieved, and some experiments and studies on the physical properties of materials have been carried out at WHMFC.

Increased flexibility of brain dynamics in patients with multiple sclerosis.

Patients with multiple sclerosis consistently show widespread changes in functional connectivity. Yet, alterations are heterogeneous across studies, underscoring the complexity of functional reorganization in multiple sclerosis. Here, we aim to provide new insights by applying a time-resolved graph-analytical framework to identify a clinically relevant pattern of dynamic functional connectivity reconfigurations in multiple sclerosis. Resting-state data from 75 patients with multiple sclerosis (N = 75, female:male ratio of 3:2, median age: 42.0 ± 11.0 years, median disease duration: 6 ± 11.4 years) and 75 age- and sex-matched controls (N = 75, female:male ratio of 3:2, median age: 40.2 ± 11.8 years) were analysed using multilayer community detection. Local, resting-state functional system and global levels of dynamic functional connectivity reconfiguration were characterized using graph-theoretical measures including flexibility, promiscuity, cohesion, disjointedness and entropy. Moreover, we quantified hypo- and hyper-flexibility of brain regions and derived the flexibility reorganization index as a summary measure of whole-brain reorganization. Lastly, we explored the relationship between clinical disability and altered functional dynamics. Significant increases in global flexibility (t = 2.38, PFDR = 0.024), promiscuity (t = 1.94, PFDR = 0.038), entropy (t = 2.17, PFDR = 0.027) and cohesion (t = 2.45, PFDR = 0.024) were observed in patients and were driven by pericentral, limbic and subcortical regions. Importantly, these graph metrics were correlated with clinical disability such that greater reconfiguration dynamics tracked greater disability. Moreover, patients demonstrate a systematic shift in flexibility from sensorimotor areas to transmodal areas, with the most pronounced increases located in regions with generally low dynamics in controls. Together, these findings reveal a hyperflexible reorganization of brain activity in multiple sclerosis that clusters in pericentral, subcortical and limbic areas. This functional reorganization was linked to clinical disability, providing new evidence that alterations of multilayer temporal dynamics play a role in the manifestation of multiple sclerosis.

Resting State Dynamic Reconfiguration of Spatial Attention Cortical Networks and Visuospatial Functioning in Non-Verbal Learning Disability (NVLD): A HD-EEG Investigation.

Nonverbal learning disability (NVLD) is a neurodevelopmental disorder characterized by deficits in visuospatial processing but spared verbal competencies. Neurocognitive markers may provide confirmatory evidence for characterizing NVLD as a separate neurodevelopmental disorder. Visuospatial performance and high-density electroencephalography (EEG) were measured in 16 NLVD and in 16 typically developing (TD) children. Cortical source modeling was applied to assess resting-state functional connectivity (rs-FC) in spatial attention networks (dorsal (DAN) and ventral attention networks (VAN)) implicated in visuospatial abilities. A machine-learning approach was applied to investigate whether group membership could be predicted from rs-FC maps and if these connectivity patterns were predictive of visuospatial performance. Graph theoretical measures were applied to nodes inside each network. EEG rs-FC maps in the gamma and beta band differentiated children with and without NVLD, with increased but more diffuse and less efficient functional connections bilaterally in the NVLD group. While rs-FC of the left DAN in the gamma range predicted visuospatial scores for TD children, in the NVLD group rs-FC of the right DAN in the delta range predicted impaired visuospatial performance, confirming that NVLD is a disorder with a predominant dysfunction in right hemisphere connectivity patterns.

Request delay and survivability optimization for software defined‐wide area networking (SD‐WAN) using multi‐agent deep reinforcement learning

AbstractData exchange between headquarters and local branches represents a major challenge issue for business success. For this issue, traditional solutions applied to wide area networks (WAN) remain unrealistic and require a good knowledge of the systems. Recently, software‐defined wide area networking (SD‐WAN) plays a pivotal role and constitutes, in general, a reliable solution for wide area networking. Compared to the classical WAN, SD‐WAN decouples the control plane from gateway devices. Moreover, SD‐WAN are based on centralized WAN management with dynamic reconfiguration, allowing it to control the entire service requirements. Nevertheless, traffic management is the most usual metric in SD‐WAN. It gives a clear indication on propagation latency, request delay and survivability; that is, network connectivity. In this article, to guarantee an efficient quality of service and deal with the increased delay problem, we investigate the problem of joint optimization of average request delay and survivability in the proposed SD‐WAN model. The optimization approaches are formulated so as to minimize the average request delay and maximize the network connectivity. Subsequently, we adopt a multi‐agent deep Q‐Network algorithm to solve it, where the reward function is reformulated based on the optimization objective. Simulation results show that our strategy improves the system's performance significantly in terms of request delay and survivability, compared to traditional baseline algorithms.

Automated design and usage of the Fault-Tolerant dynamic partial reconfiguration controller for FPGAs

This article presents a new design automation method for Fault-Tolerant (FT) systems implemented on dynamically reconfigurable Field Programmable Gate Arrays (FPGAs). The method aims at minimizing the human interactions needed to incorporate FT mechanisms into an existing system. It starts with a source code of an original unhardened circuit. It continues by automated manipulation of the source code, algorithmic strategic selection of suitable FT techniques, design space exploration of candidate FT implementations, and selection of the resulting implementation. The method also includes efficient evaluation of achieved FT parameters performed on the target HW. As a novel approach working on the level of HW description languages is employed, the code modification is separated, which differentiates our method from others. The case study utilizing this method targets the design of an experimental FT dynamic partial reconfiguration controller for an FPGA. This controller is helpful for the restoration of faulty components due to a single-event upset on an FPGA. We used the method to generate a set of Pareto-optimal controllers concerning the design’s Mean Time to Failure (MTTF) parameter, power consumption, and size. Then, the FT controller is connected to several benchmark circuits, and the reliability parameters are evaluated at the entire system level. Our results show that by replacing the standard reconfigurable controller with our automatically-designed FT controller for one specific benchmark, the design size increased by 20.1%, and MTTF increased by 11.7%. However, the efficiency is highly dependent on the target system size, MTTF, and circuit functionality. We also estimate that a complex system defined by half a million configuration bits would improve MTTF by more than 50%.

Automatic Information Model Generation for Industrial Edge Applications Based on IEC 61499 and OPC UA

The Industry 4.0 and Industrial Internet provides vertical and horizontal integration between edge devices and industrial cloud platforms. A flexible and interoperable information model is crucial for enabling device-level intelligence as well as cloud-edge collaboration. Deep integration between the control and information model is necessary for supporting closed-loop optimization between the cloud and edge devices. This article proposes an automatic information model generation method based on the IEC 61499 and OPC unified architecture standards. Model transformation rules between two models from design time to runtime are presented. A discrete manufacturing case study is used to prove that the proposed method can significantly reduce the development time for distributed information models. The integrated control and information models at runtime can provide strong support for dynamic reconfiguration to address changing requirements.

Encoding Coacervate Droplets with Paramagnetism for Dynamical Reconfigurability and Spatial Addressability.

It is an ongoing endeavor in chemistry and materials science to regulate coacervate droplets on a physiologically relevant spatiotemporal scale to ultimately match or even surpass living cells' precision, complexity, and functionality. Herein, we develop a magnetic strategy orthogonal to the thermal, pH, light, or chemical counterparts that are commonly employed by biotic or artificial systems; its successful implementation thus adds a missing piece to the current arsenal of manipulative methodologies. Specifically, we paramagnetize the otherwise diamagnetic coacervate droplets by cooperatively combining paramagnetic ingredients (including organic radicals, metal ions, and Fe3O4 nanoparticles) and coacervate ingredients to obtain "MagCoa" droplets. A simple model is derived theoretically to account for migration and division of MagCoa droplets in an uneven magnetic field. Experimentally, we produce an array of compartmentalized and monodispersed droplets using microfluidics and magnetically steer them with uniformity and synchronicity. We design and fabricate spatial magnetic modulators to engineer the landscape of a magnetic field that, in turn, directs the MagCoa droplets into predesigned patterns in a reconfigurable fashion. These programmable liquid patterns can be potentially extended to dynamic assembly and information encryption. We envision that the toolbox established here is of generality and multitudes to serve as a practical guide to control droplets magnetically.

A dynamic and proactive multi-microgrid network reconfiguration model for load factor improvement

Independent operation of a single microgrid (MG) is facing significant operational challenges such as high operational expenses, limited self-consumption of local renewable energy, and fluctuating peak and valley load because of the increased uncertainty and variability of net load generation caused by high penetrations of renewable energy. In this paper, connected multiple microgrids reconfiguration strategy is proposed while considering renewable generation uncertainty. Reconfiguration strategy is proposed to aggregate different load patterns belonging to various microgrids and match them with a certain microgrid. Then, the new aggregation of loads is aimed to improve the load factor of each MG within the network. The contribution of this study is to solve the peak load problem within the power network without affecting the costumers’ life routine by using a mixed integer linear programming model for the dynamic proactive network reconfiguration of connected microgrids. The formulated model aims to maximize the load factor of each microgrid in the network and flatten the peak load curve without disturbance for the households’ consumption patterns. The proposed model determines the near optimal topology for the network, The proposed mixed integer linear programming model is used to solve the reconfiguration problem with respect to distribution losses, microgrids output and households’ consumption. Moreover, a linearization approach of the load factor equation is proposed to reduce the model complexity and computational time. An artificial dataset contains 3 microgrids and 15 households is used to validate the model performance. The obtained results show that the network reconfiguration could affect the average demand of each individual MG by reducing the fluctuations within the curve. Therefore, the load factor for the three microgrids increases with a percentage ranges from 8% to 53% in comparison with the unconnected system.

Reconfiguration of brain network dynamics underlying spatial deficits in subjective cognitive decline

Brain dynamics and the associations with spatial navigation in individuals with subjective cognitive decline (SCD) remain unknown. In this study, a hidden Markov model (HMM) was inferred from resting-state functional magnetic resonance imaging data in a cohort of 80 SCD and 77 normal control (NC) participants. By HMM, 12 states with distinct brain activity were identified. The SCD group showed increased fractional occupancy in the states with less activated ventral default mode, posterior salience, and visuospatial networks, while decreased fractional occupancy in the state with general network activation. The SCD group also showed decreased probabilities of transition into and out of the state with general network activation, suggesting an inability to dynamically upregulate and downregulate brain network activity. Significant correlations between brain dynamics and spatial navigation were observed. The combined features of spatial navigation and brain dynamics showed an area under the curve of 0.854 in distinguishing between SCD and NC. The findings may provide exploratory evidence of the reconfiguration of brain network dynamics underlying spatial deficits in SCD.

Unsupervised Data-Driven Reconstruction of Molecular Motifs in Simple to Complex Dynamic Micelles.

The reshuffling mobility of molecular building blocks in self-assembled micelles is a key determinant of many their interesting properties, from emerging morphologies and surface compartmentalization, to dynamic reconfigurability and stimuli-responsiveness. However, the microscopic details of such complex structural dynamics are typically nontrivial to elucidate, especially in multicomponent assemblies. Here we show a machine-learning approach that allows us to reconstruct the structural and dynamic complexity of mono- and bicomponent surfactant micelles from high-dimensional data extracted from equilibrium molecular dynamics simulations. Unsupervised clustering of smooth overlap of atomic position (SOAP) data enables us to identify, in a set of multicomponent surfactant micelles, the dominant local molecular environments that emerge within them and to retrace their dynamics, in terms of exchange probabilities and transition pathways of the constituent building blocks. Tested on a variety of micelles differing in size and in the chemical nature of the constitutive self-assembling units, this approach effectively recognizes the molecular motifs populating them in an exquisitely agnostic and unsupervised way, and allows correlating them to their composition in terms of constitutive surfactant species.

A Design Concept of an Intelligent Onboard Computer Network

The article suggests design principles of an advanced onboard computer network with an intelligent control system. It describes the main advantages of designing an onboard computer network based on fibre optics, which allows the implementation of an integrated intellectual system performing intelligent inference in emergency situations. The suggested principles significantly increase the reliability and fault tolerance of avionics suits, which, in turn, enhances flight safety. The suggested concept aims to solve a number of important problems including the design of a switchless computing environment, the development of the methods for dynamic reconfiguration of avionics suits with such an environment, and the implementation of a specialised multilevel intelligent avionics system within this environment.

The role of security assistance in reconfiguring Tunisia’s transition

ABSTRACT In many contexts in the Mediterranean, Security Assistance (SA) is perceived to have contributed to state fracture or prolonged conflict. In Tunisia, in contrast, SA has increased the performance and capacities of the security forces in matters of counterterrorism and migration control. However, in this article, by analysing SA’s reordering impacts, I argue that it has interacted with political developments to reconfigure Tunisia’s volatile political landscape in two ways. Firstly, SA programmes have acted to reinforce the coercive capacities of Tunisia’s security forces vis-à-vis the general population. Secondly, vertical SA practices contributed to the reconfiguration of power dynamics between different actors in Tunisia’s security sector, helping consolidate power in the increasingly authoritarian hands of the executive as well as the increasingly politicized security forces. Furthermore, in the final section of this article, I reflect upon the entanglements between security assistance, Tunisian political economy and the economic interests of many provider states. In doing so, I make the case for a ‘recoupling’ of security studies and political economy in analysis of external interventions in the Mediterranean to better understand their reordering impacts.

Utilizing XMG-Based Synthesis to Preserve Self-Duality for RFET-Based Circuits

Individual transistors based on emerging reconfigurable nanotechnologies exhibit electrical conduction for both types of charge carriers. These transistors [referred to as reconfigurable field-effect transistors (RFETs)] enable dynamic reconfiguration to demonstrate either a p- or an n-type functionality. This duality of functionality at the transistor level is efficiently abstracted as a self-dual Boolean logic, that can be physically realized with fewer RFET transistors compared to the contemporary CMOS technology. Consequently, to achieve better area reduction for RFET-based circuits, the self-duality of a given circuit should be preserved during logic optimization and technology mapping. In this article, we specifically aim to preserve self-duality by using Xor-majority graphs (XMGs) as the logic representation during logic synthesis and technology mapping. We propose a synthesis flow that uses new restructuring techniques, called rewriting and resubstitution for XMGs to preserve self-duality during technology-independent logic synthesis. For technology mapping, we use a novel open-source and a logic-representation agnostic mapping tool. Using the above-proposed XMG-based flow, we demonstrate its benefits by comparing post-mapping areas for synthetic and cryptographic benchmarks with three different synthesis flows: 1) AIG-based optimization and AIG-based mapping; 2) XMG-based optimization with AIG-based mapping; and 3) AIG-based optimization with logic-representation agnostic mapping. Our experiments show that the proposed XMG-based flow efficiently preserves self-duality and achieves the best area results for RFET-based circuits (up to 12.36% area reduction) with respect to the baseline.

Stochastic Dynamic Reconfiguration in Smart Distribution System Considering Demand-Side Management, Energy Storage System, Renewable and Fossil Resources and Electric Vehicle

Stochastic Dynamic Reconfiguration in Smart Distribution System Considering Demand-Side Management, Energy Storage System, Renewable and Fossil Resources and Electric Vehicle

Dynamically reconfigurable variable-precision sparse-dense matrix acceleration in Tensorflow Lite

In this paper, we present a dynamically reconfigurable hardware accelerator called FADES (Fused Architecture for DEnse and Sparse matrices). The FADES design offers multiple configuration options that trade off parallelism and complexity using a dataflow model to create four stages that read, compute, scale and write results. FADES is mapped to the programmable logic (PL) and integrated with the TensorFlow Lite inference engine running on the processing system (PS) of a heterogeneous SoC device. The accelerator is used to compute the tensor operations, while the dynamically reconfigurable approach can be used to switch precision between int8 and float modes. This dynamic reconfiguration enables better performance by allowing more cores to be mapped to the resource-constrained device and lower power consumption compared with supporting both arithmetic precisions simultaneously. We compare the proposed hardware with a high-performance systolic architecture for dense matrices obtaining 25% better performance in dense mode with half the DSP blocks in the same technology. In sparse mode, we show that the core can outperform dense mode even at low sparsity levels, and a single-core achieves up to 20x acceleration over the software-optimized NEON RUY library.

Hosting capacity enhancement of hybrid AC/DC distribution network based on static and dynamic reconfiguration

AbstractThe hybrid AC/DC distribution networks will have a crucial role in the future of smart grids due to their adaptiveness to a variety of DC loads and energy resources. Thus, it is necessary to assess and enhance the Hosting Capacity (HC) of these hybrid networks for the integration of Renewable Energy Sources (RESs). One way to increase the HC without further expansion in the grid is using the potential of Network Reconfiguration (NR). This paper proposes an HC enhancement model that includes AC/DC distribution NR. Two types of NR are considered, namely static, that is, embedding the NR capabilities at the planning stage, and dynamic, that is, using remotely controlled switches as an Active Network Management (ANM) scheme. Besides, two unique potential sources of hybrid AC/DC distribution networks that could be used for the HC enhancement are included, that is, reactive power control of Voltage Source Converters (VSCs) and the mesh operation capability. The problem is formulated as a mixed‐integer linear optimal power flow that aims to maximize the RESs HC under thermal and voltage constraints. Also, stochastic programming is utilized for managing the RESs uncertainties. The simulation results show the accuracy and efficiency of the proposed formulation from various perspectives.

Dynamic Reconfigurable DNA Nanostructures, Networks and Materials

AbstractDNA nanotechnology relies on the structural and functional information encoded in nucleic acids. Specifically, the sequence‐guided reconfiguration of nucleic acids by auxiliary triggers provides a means to develop DNA switches, machines and stimuli‐responsive materials. The present Review addresses recent advances in the construction and applications of dynamic reconfigurable DNA nanostructures, networks and materials. Dynamic transformations proceeding within engineered origami frames or between origami tiles, and the triggered dynamic reconfiguration of scaled supramolecular origami structures are addressed. The use of origami frameworks to assemble dynamic chiroplasmonic optical devices and to operate switchable chemical processes are discussed. Also, the dynamic operation of DNA networks is addressed, and the design of “smart” stimuli‐responsive all‐DNA materials and their applications are introduced. Future perspectives and applications of dynamic reconfigurable DNA nanostructures are presented.

Dynamic Reconfigurable DNA Nanostructures, Networks and Materials.

DNA nanotechnology relies on the structural and functional information encoded in nucleic acids. Specifically, the sequence-guided reconfiguration of nucleic acids by auxiliary triggers provides a means to develop DNA switches, machines and stimuli-responsive materials. The present Review addresses recent advances in the construction and applications of dynamic reconfigurable DNA nanostructures, networks and materials. Dynamic transformations proceeding within engineered origami frames or between origami tiles, and the triggered dynamic reconfiguration of scaled supramolecular origami structures are addressed. The use of origami frameworks to assemble dynamic chiroplasmonic optical devices and to operate switchable chemical processes are discussed. Also, the dynamic operation of DNA networks is addressed, and the design of "smart" stimuli-responsive all-DNA materials and their applications are introduced. Future perspectives and applications of dynamic reconfigurable DNA nanostructures are presented.