Synchronous Model Research Articles

A noise‐tolerant fuzzy‐type zeroing neural network for robust synchronization of chaotic systems

SummaryAs a significant research issue in control and science field, chaos synchronization has attracted wide attention in recent years. However, it is difficult for traditional control methods to realize synchronization in predefined time and resist external interference effectively. Inspired by the excellent performance of zeroing neural network (ZNN) and the wide application of fuzzy logic system (FLS), a noise‐tolerant fuzzy‐type zeroing neural network (NTFTZNN) with fuzzy time‐varying convergent parameter is proposed for the synchronization of chaotic systems in this paper. Notably the fuzzy parameter generated from FLS combined with traditional convergent parameter embedded into this NTFTZNN can adjust the convergence rate according to the synchronization errors. For the sake of emphasizing the advantages of NTFTZNN model, other three sets of contrast models (FTZNN, VPZNN, and PTZNN) are constructed for the purpose of comparison. Besides, the predefined‐time convergence and noise‐tolerant ability of NTFTZNN model are distinctly demonstrated by detailed theoretical analysis. Furthermore, synchronization simulation experiments including two chaotic systems with different dimensions are provided to verify the related mathematical theories. Finally, the schematic of NTFTZNN model for chaos synchronization is accomplished completely through Simulink, further accentuating its effectiveness and potentials in practical applications.

A new transient phenomenon caused by active current dynamics of grid-following converters during severe grid faults

Existing transient stability analysis of grid-following (GFL) converters mainly focuses on the dynamics of the phase-locked loop (PLL), while current loop dynamics are usually neglected due to their faster response than PLL. However, this article reveals that active current may not be able to track its reference quickly during severe grid faults even with high current loop bandwidth, which leads to a non-negligible impact on the transient stability of GFL converters. Furthermore, this article discusses the intrinsic mechanism of why active current cannot track its reference quickly during severe grid faults and establishes a refined third-order transient synchronization model that offers a more accurate assessment of transient stability during severe grid faults than the conventional second-order model.

The dynamic analysis of the rumor spreading and behavior diffusion model with higher-order interactions

Rumor spreading occurs not only between two individuals but also among multiple individuals or influenced by groups. However, pairwise interactions in complex networks are insufficient to describe this process. In this study, we propose a rumor spreading model with higher-order interactions, in which the rumor propagation process is represented by simplicial complexes. By selecting the propagation coefficient and time delay as the threshold, the model shows richer dynamic behaviors, such as the bi-stability, discontinuous transition, forward bifurcation, backward bifurcation, Hopf bifurcation, and periodic oscillation. Besides, for exploring the collective behavior induced by rumors, the rumor synchronization model is first established by applying the adaptive feedback mechanism, which erects a theoretical bridge between rumor spreading and behavior diffusion. Moreover, an improved optimal control strategy considering system gains is performed to curb rumor diffusion. Numerical simulations suggest that rumor spreading models with higher-order interactions are more consistent with actual data than network-based ones. Our results may shed some light on comprehending higher-order interactions in rumor spreading and the coupling between rumor and behavior, and provide a promising approach to control rumors and irrational behaviors.

Edge-based strategies enhance cooperation in intertwined dynamics of cooperation and synchronization

In various ecosystems and human societies, living organisms and systems often exhibit cooperative and synchronous behaviors during survival. Recently, co-evolutionary models of cooperation and synchronization have shed light on the underlying mechanisms driving such behaviors, offering an intriguing avenue for studying these phenomena. However, current research predominantly focuses on a point-strategy decision mode, where each individual is limited to taking the same actions towards its neighbors. Such an assumption implies that an individual is unable to differentiate its neighbors and adjust its strategy accordingly, making it difficult to capture the complexity and diversity of interactive behaviors observed in reality. To this end, we introduce the interaction mode based on the edge-strategy and investigate how synchronization and cooperation co-evolve under interaction diversity. Specifically, we explore a scenario in which individuals can adopt different strategic decisions toward their neighbors based on their differences, thereby extending the traditional point strategy. Our research clarifies the positive role of interaction diversity in alleviating the evolutionary Kuramoto’s dilemma. Moreover, it provides new insights and ideas for comprehending the influence of edge-strategy on the co-evolution of cooperative and synchronous behaviors.

Multi-band image synchronous fusion model based on task-interdependency

Synchronous multi-band image fusion is a challenging, yet urgent task in the development of high-precision detection systems. This study proposes a novel method for synchronous fusion modeling of multi-band images based on task-interdependency. In the proposed method, the task of image fusion is divided into two mutually exclusive sub-tasks that produce bright thermal targets and obtain precise textural details. First, two generators with different network structures and several discriminators produce a preliminary fused image. Second, an image fusion strategy is defined using a model- and data-driven theory to obtain fused images. Then, each discriminator classifies the fused image and source images of each band to force the generators to produce the desired results. A novel loss function is constructed to enhance the fused effect by selecting the most significant gradient loss and loss of brightness. Finally, the network is trained based on a multi-generative adversarial framework.The trained generators can be used individually or jointly as a model for fusing multiple images. We verified our method with several datasets and determined that it outperforms other current methods.

A method for calculating the time‐varying equivalent circuit parameters of large‐capacity synchronous condenser considering field mutual leakage reactance

AbstractAccurate calculation of equivalent circuit parameters is a prerequisite for accurately calculating the large‐capacity synchronous condenser parameter model. Due to its special transient operating conditions, the high transient magnetic saturation effect during operation causes the non‐linearity and time‐varying of the equivalent circuit parameters. The field mutual leakage reactance is the critical factor affecting the field current, and the time‐varying of the equivalent circuit parameters is closely related to the field current. However, the existing equivalent circuit parameter calculation methods considering field leakage reactance cannot achieve time‐varying parameters. A calculation method of time‐varying equivalent circuit parameters based on a back propagation neural network algorithm is proposed, which solves the calculation problem of time‐varying equivalent circuit parameters considering field mutual leakage reactance. Then, a 300MVar condenser is taken as the research object, and the proposed method is used to simulate the different operating conditions of the condenser and verified by the finite element method and experiment. The results show that the method improves the calculation accuracy of the equivalent circuit parameter model, reduces the calculation time, and applies to different operating conditions.

Codesign of reactor-oriented hardware and software for cyber-physical systems

Modern cyber-physical systems often make use of heterogeneous systems-on-chip with reconfigurable logic to provide adequate computing power and flexible I/O. However, modeling, verifying, and implementing the computations spanning CPUs and reconfigurable logic is still challenging. The hardware and software components are often designed by different teams and at different levels of abstraction, making it hard to reason about the resulting computation. We propose to lift both hardware and software design to the same level of abstraction by using the Lingua Franca coordination language. Lingua Franca is based on a sparse synchronous model that allows modeling concurrency and timing while keeping a sequential model for the actual computation. We define hardware reactors as a subset of the reactor model of computation underlying Lingua Franca. We also present and evaluate reactor-chisel, a hardware runtime implementing the semantics of hardware reactors, and an extension to the Lingua Franca compiler enabling reactor-oriented hardware-software codesign.

Research on Optimization Design of Electric Motor Based on Finite Element Calculation

With the widespread application of permanent magnet synchronous motors in the new energy vehicle market, the demand for verification and optimization of the design rationality of permanent magnet synchronous motors is gradually increasing. This article first establishes a three-phase four pole permanent magnet synchronous motor model based on the motor parameters provided by the motor manufacturer. Then, finite element simulation calculations are carried out on this model to solve the three-phase induced electromotive force, three-phase magnetic flux, cogging torque, iron core loss, and eddy current loss of the permanent magnet synchronous motor when it is unloaded. The rationality of the motor design is verified through the solution results. Finally, the average core loss of the permanent magnet synchronous motor is reduced by 3.6% through parameterized simulation, and its operating efficiency is improved. The optimization design is carried out on it.

Rethinking Digital Customer Complaints’ Problem Solving: The Synchronous Model

Abstract In the current business environment, problem-solving remains a critical and highly relevant skill for achieving customer satisfaction especially in the context of digitalization and industry 4.0 and a strong requirement from IATF 16949. In the present paper, a comprehensive analysis of existing problem-solving models is conducted, examining their strengths, limitations, and applicability in the manufacturing companies. By drawing insights from traditional methodologies, as already addressed in the scientific literature, such as Six-Step Model A3 methodology, 8 Disciplines, Six Sigma, the Cynefin Framework, Kepner-Tregoe Methodology and 5P (Principles) for Problem Solving, a new and integrative model designed to address the shortcomings identified in current approaches is created. This innovative framework, named “Synchronous Model” (Greek word for modern) synthesizes key elements from diverse models, offering a more adaptable solution to complex and modern problem-solving challenges. Emphasizing its potential for business digital transformation, we discuss how the proposed model fosters efficiency, agility, and strategic decision-making within organizational settings. This research not only contributes to the theoretical understanding of problem-solving methodologies but also provides valuable insights for practitioners seeking innovative approaches to address challenges in their respective industries, for which the current methods are either not sufficient or too complex. The integration of “Synchronous Model” is meant to elevate problem-solving practices, offering a transformative impact on business operations and strategic decision-making, all in the benefit of the customer.

Adaptive low voltage crossing strategy for symmetrical faults of networked inverters

Abstract Due to the inability of grid-following control methods to compensate for the comprehensive reduction in system inertia, strength, and short-circuit capacity caused by the decreased proportion of synchronous machines, the grid exhibits insufficient resilience to disturbances, leading to severe stability risks in terms of rotor angle, frequency, and voltage. Grid-forming inverters have emerged to address this issue. During symmetrical faults, the low-voltage ride-through of grid-forming inverters often involves freezing the reactive power loop, resulting in a direct output of higher voltage and reactive current. However, an increase in reactive power command leads to a sharp rise in the internal potential magnitude of grid-forming inverters. Upon clearance of the grid fault, the excessively high internal potential may cause a rapid, short-term increase in terminal voltage, potentially resulting in overvoltage, which is detrimental to the operation of grid-connected equipment. In response to this issue, this paper proposes an adaptive low-voltage ride-through control strategy for grid-forming inverters under symmetrical faults controlled by a virtual synchronous machine control method. Initially, a virtual synchronous machine control model is established. Limiting current is achieved through the construction of a fixed virtual impedance loop. The issues associated with fixed virtual impedance current limiting are analyzed, leading to the proposal of an adaptive virtual impedance control strategy. Finally, the feasibility of the proposed method is verified through simulation experiments.

An approach for multiscale two-phase flow simulation in the direct simulation Monte Carlo framework

To simulate multiscale gas flow with solid particles, Burt's model, based on the Direct Simulation Monte Carlo (DSMC) framework, is widely used to predict gas–solid interactions under the assumption of a negligibly small solid particle diameter compared to the local gas mean free path. However, Burt's model could become inaccurate when the solid particle is large relative to the local gas mean free path. This study introduces the Gas–Solid Synchronous (GSS) model, which predicts gas–solid interactions in continuum gas regions without assuming the local gas flow regime around a solid particle. Similar to Burt's model, the GSS model includes gas-to-solid and solid-to-gas interaction models to consider bidirectional interaction between two phases. The GSS gas-to-solid model is established by selecting accurate semi-empirical force and heat transfer models in comparison with DSMC simulation results. The GSS solid-to-gas model is developed based on the principles of momentum and energy conservation and validated against Burt's solid-to-gas model. The results show that Burt's model could overestimate the interphase force and heat transfer rates when its assumption on solid particle diameter does not hold, but it can reproduce non-equilibrium characteristics of two-phase flows where gas velocity distribution functions do not follow the Maxwell–Boltzmann distribution. By contrast, the GSS model can accurately predict gas–solid interaction in continuum gas flows, while it cannot capture the non-equilibrium nature of two-phase flows. The characteristics and limitations of the two models indicate that using a valid model for each gas–solid interaction could be crucial for accurate simulation of multiscale two-phase flows.

MixFormer: End-to-End Tracking With Iterative Mixed Attention.

Visual object tracking often employs a multi-stage pipeline of feature extraction, target information integration, and bounding box estimation. To simplify this pipeline and unify the process of feature extraction and target information integration, in this paper, we present a compact tracking framework, termed as MixFormer, built upon transformers. Our core design is to utilize the flexibility of attention operations, and we propose a Mixed Attention Module (MAM) for simultaneous feature extraction and target information integration. This synchronous modeling scheme allows us to extract target-specific discriminative features and perform extensive communication between target and search area. Based on MAM, we build our MixFormer trackers simply by stacking multiple MAMs and placing a localization head on top. Specifically, we instantiate two types of MixFormer trackers, a hierarchical tracker MixCvT, and a non-hierarchical simple tracker MixViT. For these two trackers, we investigate a series of pre-training methods and uncover the different behaviors between supervised pre-training and self-supervised pre-training in our MixFormer trackers. We also extend the masked autoencoder pre-training to our MixFormer trackers and design the new competitive TrackMAE pre-training technique. Finally, to handle multiple target templates during online tracking, we devise an asymmetric attention scheme in MAM to reduce computational cost, and propose an effective score prediction module to select high-quality templates. Our MixFormer trackers set a new state-of-the-art performance on seven tracking benchmarks, including LaSOT, TrackingNet, VOT2020, GOT-10 k, OTB100, TOTB and UAV123. In particular, our MixViT-L achieves AUC scores of 73.3% on LaSOT, 86.1% on TrackingNet and 82.8% on TOTB.

Indicative meanings of geological sea-level indicators in the Solent region and Sussex coast (south coast of England) and implications for uplift rates

The Solent Region and Sussex coastal plain in southern England have preserved palaeo-sea-level indicators from multiple interglacial periods, with a particularly complete record of deposition throughout the last interglacial. However, as yet, none of the research on these indicators has fully addressed the relationship of the different types of deposits preserved to mean sea-level. In this paper we apply recent approaches to estimating past relative sea-levels based on applying modern analogues to understand the indicative meaning of these indicators. We also apply a synchronous correlation model previously developed on rapidly uplifting coastlines to assess uplift rates. The uplift rates required to match the elevations of sequences suggest a significant decrease in uplift rates between the Late Wolstonian Substage and Ipswichian Stage; that is, the c. 240 and c. 125 ka sea-level highstands, broadly equivalent to marine isotope stages (MIS) 7 and 5e. This coincides in time with the final opening of the Straits of Dover. Supplementary material: Fossil data from sites that have not previously been published are available at https://doi.org/10.6084/m9.figshare.c.7172532

The Effectiveness of Government Policy on MSMEs Through Empirical Data Analysis in Lampung

The purpose of this study is to develop a model of policy synchronization between the central government and local governments and to project strategic goals and objectives to be achieved, as well as future policy directions. The research method used is a quantitative research method with descriptive analysis techniques. The research locus is the Central Government and Banten Province. The research time is May–July 2021. The research stages include primary and secondary data collection, data processing, primary and secondary data analysis, and projection analysis for the years 2021–2025. Based on the results of the analysis, the population policy synchronization model between the central and local governments can be implemented by focusing on government regulations and population control frameworks. This finding is expected to be the basis for stakeholders in synchronizing policies at the regional center so that there are no policy differences between the center and the regions. Central and Regional Government policies must be in line so that it can facilitate the implementation of population control policies in the regions. Keywords: policy synchronization, population, central government, local government

Implementasi Google Cloud Pub/Sub menggunakan Metode Subscription Pull dalam Pengiriman Data Promosi Toko di PT XYZ

As a company adopts a distributed business model, scalability issues often arise in the distribution of data from server to client. PT XYZ, as one of the companies facing this challenge, frequently encounters delays and request blocking when using synchronous communication models such as RESTful API. Additionally, there is resource and time wastage on the server side as it has to handle store requests one by one, resulting in losses for the store due to delayed access to required data for business processes. This research proposes a solution to address these issues by implementing a publish/subscribe communication model. Through the implementation of this architecture, clients and servers are separated by a broker, namely Google Cloud. Pub/sub technology is implemented using the Python programming language with the Waterfall development method. The results of this research include the design of a client-side application capable of receiving data from a previously deployed broker. Test results demonstrate that the client can receive data at any time without disrupting the data fetching process for other clients.

Subnanometric Pt-Ga alloy clusters confined within Silicalite-1 zeolite for n-hexane dehydrogenation

Dehydrogenation of light naphtha followed by catalytic cracking into high-value end products can meet the high demand in the world’s petrochemical market. In this work, at attempting synthesis of novel catalysts for hexane dehydrogenation, a series of bimetallic Pt-Ga clusters encapsulated within Silicalite-1 (S-1) catalysts were synthesized using the ligand-protected direct H2 reduction method. The reduced Pt-Ga@S-1-H catalysts comprised restricted ultra-fine Pt-Ga alloys with an average size 0.86–0.95 nm, mainly corresponding to the Pt3 clusters with coordinate number 1.5–2.1. Ga doping significantly improved the catalytic stability, depending on the Ga content, which ranged from 0.02 to 0.27 wt%. As confirmed by the XPS and EXAFS analysis, after the Ga addition, electron transfer occurred between the Pt-Ga alloys and the skeleton oxygen in zeolite, stabilizing the Pt clusters on the zeolite, thereby inhibiting the growth of Pt crystals. During the n-hexane dehydrogenation, the 0.40Pt0.04 Ga@S-1-H catalyst exhibited a hexene selectivity of 87.1 %, achieving a hexene formation rate of 101.4 molhexene∙gPt-1∙h−1 at 550 °C with a WHSV of 90 h−1. After 1000 min of reaction, this best catalyst experienced only slight deactivation with a deactivation constant of 0.066 h−1. The catalytic activity remained almost unchanged after consecutive five cycles of H2 regeneration. DFT calculation using the synchronous structural models showed that the Gibbs free energy changes (−ΔG) for the first three steps of hexane dehydrogenation over the 0.4Pt0.04 Ga@S-1-H catalyst were much lower than those obtained on the 0.4Pt/S-1-H catalyst, suggesting that the introduction of Ga energetically favored the hexane dehydrogenation.

Emergent behaviours of a non-abelian quantum synchronisation model over the unitary group

Abstract We introduce a new non-abelian quantum synchronisation model over the unitary group, represented as a gradient flow, where state matrices asymptotically converge to a common one up to phase translation. We provide a sufficient framework leading to quantum synchronisation based on Riccati-type differential inequalities. In addition, uniform time-delayed interaction is considered for modelling realistic communication, and we demonstrate that quantum synchronisation is persistent when a small time delay is allowed. Finally, numerical simulation is performed to visualise qualitative behaviours and support theoretical results.

Research on Data-Driven Fault Scenario Simulation Model of Distributed Network

Distribution lines are critical components of today's power system, and can directly impact on power supply security and stability. An effective power system protection system should detect any faults as soon as they occur. There are two steps in fault diagnosis. One is defect classification, which has already achieved excellent accuracy rates. As a result, this study concentrates on the opposite objective, which is fault location. This research introduces a War Strategy Water Wave Optimization (WSWWO) based Deep Q Network for radial distribution networks using synchronous generators for Distributed Generation (DG). The algorithm estimates the fault site by analyzing voltage and current samples at the main feeder head, and scheduled active and reactive power injections by network synchronous generators. A full-order synchronous machine model was utilized to analyze the dynamic behavior of DG plants during fault transients. Here, the Deep Q Network is trained by WSWWO, and hybridized by two optimization algorithms, War Strategy Optimization (WSO) and Water Wave Optimization (WWO). Furthermore, the experimental results revealed that the WSWWO-Deep Q Network outperformed state-of-the-art models in terms of Accuracy and Error, with values of 0.997 and 0.141, respectively.

Grouping Formation and Obstacle Avoidance Control of UAV Swarm Based on Synchronous DMPC

This paper focuses on the grouping formation control problem of unmanned aerial vehicle (UAV) swarms in obstacle environments. A grouping formation and obstacle avoidance control algorithm based on synchronous distributed model predictive control (DMPC) is proposed. First, the UAV swarm is divided into several groups horizontally and into a leader layer and a follower layer vertically. Second, tracking is regarded as the objective, and collision avoidance and obstacle avoidance are considered as constraints. By combining the velocity obstacle method with synchronous DMPC and providing corresponding terminal components, a leader layer control law is designed. The control law can enable the UAV swarm to track the target while avoiding collisions and dynamic obstacles. Then, considering the formation maintenance term, based on different priorities, member-level obstacle avoidance and group-level obstacle avoidance strategies are proposed, and the corresponding follower layer control laws are provided. Furthermore, the stability of the UAV swarm system under the control algorithm is demonstrated based on the Lyapunov theory. Finally, the effectiveness of the designed algorithm and its superiority in obstacle avoidance are verified through simulations.

Enhancing Electric Vehicle Charger Performance with Synchronous Boost and Model Predictive Control for Vehicle-to-Grid Integration

This paper investigates optimizing the power exchange between electric vehicles (EVs) and the grid, with a specific focus on the DC-DC converters utilized in vehicle-to-grid (V2G) systems. It specifically explores using model predictive control (MPC) in synchronous boost converters to enhance efficiency and performance. Through experiments and simulations, this paper shows that replacing diodes with SIC MOSFETs in boost converters significantly improves efficiency, particularly in synchronous mode, by minimizing the deadtime of SIC MOSFETs during switching. Additionally, this study evaluates MPC’s effectiveness in controlling boost converters, highlighting its advantages over traditional control methods. Real-world validations further validate the robustness and applicability of MPC in V2G systems. This study utilizes TMS320F28379D, one of Texas Instruments’ leading digital signal processors, enabling the implementation of MPC with a high PWM frequency of up to 200 MHz. This processor features dual 32-bit CPUs and a 16-bit ADC, allowing for high-resolution readings from sensors. Leveraging digital signal processing technologies and advanced electronic circuits, this study advances the development of high-performance boost converters, achieving power outputs of up to 48 watts and output voltages of 24 volts. Electronic circuits (PCB boards) have been devised, implemented, and evaluated to showcase their significance in advancing efficient V2G integration.