Synchronization Algorithm Research Articles

Multi‐graph representation spatio‐temporal attention networks for traffic forecasting in the cinematic metaverse

AbstractThe cinematic metaverse aims to create a virtual space with the context of a film. Users can enter this space in the form of avatars, experiencing the cinematic plot firsthand in an immersive manner. This requires us to design a rational computation resource allocation and synchronization algorithm to meet the demands of multi‐objective joint optimization, such as low latency and high throughput, which ensures that users can seamlessly switch between virtual and real worlds and acquire immersive experiences. Unfortunately, the explosive growth in the number of users makes it difficult to jointly optimize multiple objectives. Predicting traffic generated by the users' avatars in the cinematic metaverse is significant for the optimization process. Although graph neural networks‐based traffic prediction models achieve superior prediction accuracy, these methods rely only on physical distances‐based topological graph information, while failing to comprehensively reflect the real relationships between avatars in the cinematic metaverse. To address this issue, we present a novel Multi‐Graph Representation Spatio‐Temporal Attention Networks (MGRSTANet) for traffic prediction in the cinematic metaverse. Specifically, based on multiple topological graph information (e.g., physical distances, centerity, and similarity), we first design Multi‐Graph Embedding (MGE) module to generate multiple graph representations, thus reflecting on the real relationships between avatars more comprehensively. The Spatio‐Temporal Attention (STAtt) module is then proposed to extract spatio‐temporal correlations in each graph representations, thus improving prediction accuracy. We conduct simulation experiments to evaluate the effectiveness of MGRSTANet. The experimental results demonstrate that our proposed model outperforms the state‐of‐the‐art baselines in terms of prediction accuracy, making it appropriate for traffic forecasting in the cinematic metaverse.

Advancing Convergence Speed of Distributed Consensus Time Synchronization Algorithms in Unmanned Aerial Vehicle Ad Hoc Networks

Time synchronization is a critical prerequisite for unmanned aerial vehicle ad hoc networks (UANETs) to facilitate navigation and positioning, formation control, and data fusion. However, given the dynamic changes in UANETs, improving the convergence speeds of distributed consensus time synchronization algorithms with only local information poses a major challenge. To address this challenge, this study first establishes a convex model on the basis of graph theory and relevant theories of random matrices to approximate the original problem. Subsequently, three acceleration schemes for consensus algorithms are derived by minimizing the Frobenius norm of the iteration matrix. Additionally, this study provides a new upper bound for constant communication weights and discusses the limitations of existing metrics used to measure the convergence speeds of consensus algorithms. Finally, the proposed schemes are compared with existing ones through simulation. Our results indicate that the three proposed schemes can achieve faster convergence while maintaining high-precision synchronization in scenarios with static or known topological structures of networks. In scenarios where the topological structure of a UANET is time-varying and unknown, the scheme proposed in this paper achieves the fastest convergence speed.

Twin-Tool Orientation Synchronous Smoothing Algorithm of Pinch Milling in Nine-Axis Machine Tools.

Pinch milling is a new technique for slender and long blade machining, which can simultaneously improve the machining quality and efficiency. However, two-cutter orientation planning is a major challenge due to the irregular blade surfaces and the structural constraints of nine-axis machine tools. In this paper, a method of twin-tool smoothing orientation determination is proposed for a thin-walled blade with pinch milling. Considering the processing status of the two cutters and workpiece, the feasible domain of the twin-tool axis vector and its characterization method are defined. At the same time, an evaluation algorithm of global and local optimization is proposed, and a smoothing algorithm is explored within the feasible domain along the two tool paths. Finally, a set of smoothly aligned tool orientations are generated, and the overall smoothness is nearly globally optimized. A preliminary simulation verification of the proposed algorithm is conducted on a turbine blade model and the planning tool orientation is found to be stable, smooth, and well formed, which avoids collision interference and ultimately improves the machining accuracy of the blade with difficult-to-machine materials.

Detection of phase modulation disorder when processing a sequence of ultra-wideband quasi-radio signals against a background of white Gaussian noise

In conditions of a complex electromagnetic environment in communication and data transmission systems, a change in the type of modulation occurs when one or more signal parameters change abruptly, therefore the task of determining the type of signal modulation can be reduced to the task of detecting a disorder, that is, detecting the fact of a change in the type of modulation of the signal and estimation of the disorder time. The purpose of the work is to synthesize an algorithm for detecting phase modulation disorder when processing a sequence of ultra-wideband quasi-radio signals with unknown amplitude and initial phase against a background of white Gaussian noise and to analyze the effectiveness of the resulting algorithm based on the results of statistical modeling. The work synthesizes an algorithm for detecting a phase modulation disorder of a received sequence of ultra-wideband quasi-radio signals against a background of white Gaussian noise. To analyze the effectiveness of the resulting algorithm, statistical modeling of algorithm for detecting a phase modulation disorder is carried out. The dependences of the probability of detection of a disorder on the signal-to-noise ratio and the length of the sequence of received signals at a fixed moment of disorder are obtained. A comparison of the detection characteristics of phase modulation disorder of ultra-wideband quasi-radio signals and narrowband radio signals is made. It is shown that when a low-order phase modulation disorder is detected, the required values of the probability of detection are achieved at low signal-to-noise ratios, while with an increase in the modulation order and the use of its modifications, the efficiency indicators of the algorithm will deteriorate. The introduction of algorithms for detecting a disorder in radio engineering applications will allow optimizing synchronization algorithms and adaptation to interference conditions with a concomitant change in the type of modulation, as well as reducing the amount of transmitted service information in telecommunication systems.

A Systematic Optimization Method for Permanent Magnet Synchronous Motors Based on SMS-EMOA.

The efficient design of Permanent Magnet Synchronous Motors (PMSMs) is crucial for their operational performance. A key design parameter, cogging torque, is significantly influenced by various structural parameters of the motor, complicating the optimization of motor structures. This paper proposes an optimization method for PMSM structures based on heuristic optimization algorithms, named the Permanent Magnet Synchronous Motor Self-Optimization Lift Algorithm (PMSM-SLA). Initially, a dataset capturing the efficiency of motors under various structural parameter scenarios is created using finite element simulation methods. Building on this dataset, a batch optimization solution aimed at PMSM structure optimization was introduced to identify the set of structural parameters that maximize motor efficiency. The approach presented in this study enhances the efficiency of optimizing PMSM structures, overcoming the limitations of traditional trial-and-error methods and supporting the industrial application of PMSM structural design.

FPGA implementation of joint synchronization algorithm based on short training sequence

Synchronization processing is a vital component of the Orthogonal Frequency Division Multiplexing (OFDM) receiver, playing a decisive role in determining the performance of the OFDM receiver. Based on the repetitive structure of the Short Training Sequence (STS) in the IEEE 802.11 physical layer protocol data unit, this paper proposes and implements a joint synchronization algorithm to address issues related to data packet detection and coarse frequency offset correction during the synchronization process. Through software simulation and Field-Programmable Gate Array (FPGA) board-level validation, it is concluded that the algorithm ensures performance while, to some extent, conserving FPGA resources.

The Influence of Solar Irradiance and Temperature on The Pll-Synchronized Inverter Within Grid-Connected PV Systems

This study assesses the influence of solar irradiance and temperature on αβPLL synchronized inverters by investigating their intricate relationship within grid-connected PV systems. The intermittent nature of the DC side of these systems can significantly impact their performance. However, the extent of this impact remains uncertain when employing the stationary reference frame PLL (αβPLL) algorithm for synchronization with the utility grid. Employing simulation in MATLAB 2020a and a Model Based Design approach in the Simscape toolbox of SIMULINK, we conducted this investigation through two test scenarios: one with constant solar irradiance and varying temperature, and the other with varying solar irradiance and constant temperature. Our results indicate that fluctuations in solar irradiance and temperature have minimal effects on the output parameters of grid-connected PV systems when synchronized with the stationary reference frame PLL (αβPLL) algorithm. This conclusion is supported by the total harmonic distortion values, which all fall within the specified limits of IEEE Standard 929- 2000. These findings underscore the potential of αßPLL synchronization as a promising technique, capable of effectively synchronizing grid inverters even in the presence of DC input distortion. This proficiency in grid integration holds promise for enhancing the integration of renewable energy sources into the grid, thereby bolstering power availability for household distribution.

Self-stabilizing synchronous unison in directed networks

Self-stabilization is a general paradigm that characterizes the ability of a distributed system to recover from transient faults. Since its introduction by Dijkstra in 1974, self-stabilization has been successfully applied to efficiently solve many networking tasks. However, most of the literature focuses on bidirectional networks. Now, in today's networks such as WSNs, some communication channels may be one-way only. Considering such network topologies, a.k.a. directed graphs, makes self-stabilization more complicated, and sometimes even impossible. In this paper, we investigate the gap in terms of requirements and efficiency when considering a directed graph instead of an undirected one as network topology for a self-stabilizing algorithm. Our case study is a variant of a synchronous unison algorithm proposed by Arora et al.; the synchronous unison being a clock synchronization problem.

The application of improved densenet algorithm in accurate image recognition

Image recognition technology belongs to an important research field of artificial intelligence. In order to enhance the application value of image recognition technology in the field of computer vision and improve the technical dilemma of image recognition, the research improves the feature reuse method of dense convolutional network. Based on gradient quantization, traditional parallel algorithms have been improved. This improvement allows for independent parameter updates layer by layer, reducing communication time and data volume. The introduction of quantization error reduces the impact of gradient loss on model convergence. The test results show that the improvement strategy designed by the research improves the model parameter efficiency while ensuring the recognition effect. Narrowing the learning rate is conducive to refining the updating granularity of model parameters, and deepening the number of network layers can effectively improve the final recognition accuracy and convergence effect of the model. It is better than the existing state-of-the-art image recognition models, visual geometry group and EfficientNet. The parallel acceleration algorithm, which is improved by the gradient quantization, performs better than the traditional synchronous data parallel algorithm, and the improvement of the acceleration ratio is obvious. Compared with the traditional synchronous data parallel algorithm and stale synchronous parallel algorithm, the optimized parallel acceleration algorithm of the study ensures the image data training speed and solves the bottleneck problem of communication data. The model designed by the research improves the accuracy and training speed of image recognition technology and expands the use of image recognition technology in the field of computer vision.Please confirm the affiliation details of [1] is correct.The relevant detailed information in reference [1] has been confirmed to be correct.

Nearest advocate: a novel event-based time delay estimation algorithm for multi-sensor time-series data synchronization

Estimating time delays in event-based time-series is a crucial task in signal processing as it affects the data quality and is a prerequisite for many subsequent analyses. In particular, data acquired from wearable devices often suffer from a low timestamp precision or clock drift. Current state-of-the-art methods such as Pearson Cross-Correlation are sensitive to typical data quality issues, e.g. misdetected events, and Dynamic Time Warping is computationally expensive. To overcome these limitations, we propose Nearest Advocate, a novel event-based time delay estimation method for multi-sensor time-series data synchronisation. We evaluate its performance using three independent datasets acquired from wearable sensor systems, demonstrating its superior precision, particularly for short, noisy time-series with missing events. Additionally, we introduce a sparse variant that balances precision and runtime. Finally, we demonstrate how Nearest Advocate can be used to solve the problem of linear as well as non-linear clock drifts. Thus, Nearest Advocate offers a promising opportunity for time delay estimation and post-hoc synchronization for challenging datasets across various applications.

Pinning intra-layer synchronization in multiplex networks of nonidentical layers

In this paper, we investigate intra-layer synchronization in multiplex networks via pinning control strategies. All layers have different topologies and different intrinsic node dynamics. Novel sufficient criteria are derived for pinning intra-layer synchronization of multiplex networks, which indicates that the pinned node set can stabilize the multiplex network if, for each layer, the pinned node(s) can access all unpinned nodes. A distributed adaptive algorithm and finite-time pinning algorithm for intra-layer synchronization of multiplex networks are also proposed. Finally, we give some numerical examples to validate the effectiveness of these theoretical results.

Current-Controlled Synchronverter: A Grid Fault Tolerant Grid Forming Inverter

A Synchronverter (SV) equipped with an Output Admittance Synthesizer is modified to avoid the usual transient instability of grid forming inverters in presence large grid disturbances, keeping the injected currents at safe values. The proposal consists of changing the control objective of the SV: instead of controlling the injected active and reactive powers, the injected active and reactive currents are controlled (as in a Grid Following Inverter). The resulting system [called here Current Controlled SV, (CCSV)], injects balanced currents, even against asymmetric faults. The CCSV is capable of operating both in island mode and in grid connected mode, and can switch between these modes without prior synchronization. Like the SV, the CCSV does not use a dedicated algorithm for synchronization (such as a PLL).

Numerical simulation and experimental study of fluid–structure interactions in elastic structures based on the SPH method

The study of fluid–structure interactions in elastic structures is highly important in hydraulic structural engineering. This paper adopts the smooth particle hydrodynamics (SPH) coupling method to simulate dam failure water impact elastic plate flow and solid coupling problems. In addition, a numerical simulation is performed on the study of the free surface flow impact of the elastic plate structure on the main dynamic characteristics, including the impact force distribution and the change process. Additionally, the elastic structure of the vibration amplitude and frequency change rule, as well as the drastic changes in free surface flow, are also carried out. In this paper, the synchronous finite element algorithm is used to analyze the problem to obtain FEM results Additionally, two-dimensional benchmark physical tests are designed for dam-break water flow impacting an elastic plate are conducted, focusing on a comparative analysis of the numerical results of the SPH method with the results of physical tests and verifying the validity, accuracy, and superiority of the SPH coupling method for solving the problem of fluid‒solid coupling in elastic structures. On this basis, to explore the relevant physical quantities that cannot be measured or described during the test process, the flow–solid coupling characteristics of the elastic plate impacted by water flow are further understood by analyzing the nature of the flow field, the change in the flow–solid force of the elastic plate and the change rule of the frequency spectrum. The results show the following: (i) The jumps in velocity between the streams and the strong curvature of the free liquid surface lead to the creation of vortices at the cavities. (ii) The flow solid force under the impact of water flow increases and then decreases, and the smaller the deformation of the elastic structure is, the greater the force is. (iii) The vibration frequency of the fixed end of the elastic structure gradually decreases along the free end frequency, and the larger the deformation of the free end of the elastic plate is, the weaker its vibration response is. In addition, the natural frequency of the elastic plate under water flow impact is slightly lower than that under free vibration of the elastic plate. The related research results provide reference and guidance for the fluid–structure interaction problem.

Research on the key technology of high-precision waveform synchronization identification and measurement.

High-precision waveform identification and measurement are effective for waveform detection and evaluation in signal processing. The accuracy of waveform identification, precision of measurement, and speed of response are important indicators of waveform measurement instruments. To detect the waveform accurately, a hold and attenuation circuit divided into two is designed, and the STM32F4 microcontroller is used to accurately capture and perform spectrum analysis using a high-precision analog-to-digital converter based on fast Fourier transform technology to identify key parameters, such as waveform type, frequency, peak-to-peak value, and duty cycle. To improve the recognition accuracy and response speed, technical solutions, such as high-frequency sampling and over-zero detection, are used to improve the system efficiency. Algorithm simulation, circuit simulation, and physical testing show that the high-precision waveform synchronization recognition circuit and algorithm can accurately recognize various essential waveforms in the voltage and frequency ranges of 50 mV ≤ VPP ≤ 10 V and 1 Hz ≤ f ≤ 50 kHz, respectively, and simultaneously measure important parameters, such as frequency, peak-to-peak value, and duty cycle with an accuracy within ±1%. Intelligent linkage, no intermediate parameter setting, and a response speed of approximately 0.3 s make it suitable for such applications as fast and high-precision waveform intelligent detection and display. The method is highly integrated, simple to operate, cost-effective, and practical.

Evaluation of CPU Scheduling and Synchronization Algorithms in Distributed Systems

Evaluation of CPU Scheduling and Synchronization Algorithms in Distributed Systems

A Massively Parallel Spatially Resolved Stochastic Cluster Dynamics Method for Simulations of Irradiated Materials

The spatially resolved stochastic cluster dynamics (SRSCD) method is one of the most important methods for simulating the time-evolution of spatially correlated microstructures in irradiated materials. It is a kinetic Monte Carlo-based method for stochastically evolving integer-valued populations of defects within multi finite volume elements according to reaction rates. However, the increasing spatial scale and complexity of the simulated systems have exceeded the capabilities of serial SRSCD. To extend SRSCD to simulate large-scale complex systems, we propose a massively parallel SRSCD method in this paper and implement the program named MISA-SLSCD. It contains four contributions: (1) a dynamic Defect-Reaction Tree data structure to efficiently store and update millions of defect species and reactions; (2) a double-grouping search strategy to speed up the search for defects and reactions; (3) an adaptive synchronous algorithm to balance the accuracy and efficiency of simulations; and (4) an on-demand communication strategy to eliminate communication redundancy. A series of numerical simulation results show that our method has high accuracy in simulating damage accumulation in irradiated materials and obtains a good performance of over 90% parallel efficiency on 32,000 CPU cores with a 32 million-volume-element system. Program summaryProgram Title: MISA-SLSCDCPC Library link to program files:https://doi.org/10.17632/ctmn5f5bzk.1Code Ocean capsule:https://codeocean.com/capsule/8351487Licensing provisions: BSD 3-clauseProgramming language: Fortran90Nature of problem: Behaviours of defects in irradiated materials are typically space-dependent long-term dynamical processes, and spanning multiple orders of magnitude in space (∼nm to m) and time (∼ μs to years). The SRSCD method is an important method for simulating the time-evolution of spatially correlated microstructures in irradiated materials. However, the increasing spatial scale and complexity of the simulated systems have exceeded the capabilities of serial and existing parallel versions. It is necessary to develop new massively parallel method and program to extend SRSCD to simulate large-scale complex systems.Solution method: To extend SRSCD to simulate large-scale complex systems, we propose a massively parallel SRSCD method in this paper and implement the program named MISA-SLSCD. It contains: (1) a dynamic Defect-Reaction Tree data structure to efficiently store and update millions of defect species and reactions; (2) a double-grouping search strategy to speed up the search for defects and reactions; (3) an adaptive synchronous algorithm to balance the accuracy and efficiency of simulations; and (4) an on-demand communication strategy to eliminate communication redundancy.

Demand-adapted train timetabling with coupling-decoupling operations on a bidirectional intercity railway line

Many factors need to be considered when designing the timetable of intercity railway lines, where the passenger demand is variable and unbalanced along time in both directions of the line. This paper introduces a new multi-objective nonlinear integrated optimization model for designing full-day train timetables incorporating coupling-decoupling operations and rolling stock assignment on a bidirectional railway line. The model explicitly considers train capacity constraints and oversaturation while minimizing the total passenger waiting time and operating costs. To solve the problem, a hybrid algorithm governed by a genetic algorithm is proposed. Before evaluating the fitness of each individual, to complete the definition of each chromosome, a truncated branch and cut algorithm is used to solve a capacity and train unit allocation subproblem. A good-quality initial population is partially generated using the spatiotemporal synchronous coupling algorithm. The proposed approach is applied to a real case using the Shanghai-Hangzhou intercity railway line. The results of different experiments show the effectiveness of the model and algorithm, both in terms of passenger convenience and cost savings.

Meetor: A Human-Centered Automatic Video Editing System for Meeting Recordings

Widely adopted digital cameras and smartphones have generated a large number of videos, which have brought a tremendous workload to video editors. Recently, a variety of automatic/semi-automatic video editing methods have been proposed to tackle these issues in some specific areas. However, for the production of meeting recordings, the existing studies highly depend on extra equipment in the conference venues, such as the infrared camera or special microphone, which are not practical. In this paper, we design and implement Meetor, a human-centered automatic video editing system for meeting recordings. The Meetor mainly contains three parts: an audio-based video synchronization algorithm, human-centered video content flaw detection algorithms, and an automatic video editing algorithm. Two main experiments are conducted from both objective and subjective aspects to evaluate the performance of the Meetor. The experimental results on a testbed illustrate that the proposed algorithms could achieve state-of-the-art (SOTA) performance in video content flaw detection. On the other hand, the conducted user study demonstrates that Meetor could generate meeting recordings with a satisfactory quality compared with professional video editors. Moreover, we also present a practical application of the Meetor in a university campus prototype, in which the Meetor is applied in the automatic editing of lecture recordings. All in all, the proposed Meetor can be utilized in practical applications to release the workload of professional video editors.

Performance optimisation techniques for Conflict-free Replicated Data Types (CRDT)

The research relevance is determined by the need for distributed data handling optimisation that does not cause conflicts during replication, since the introduction of such types of data leads to an increase in the need for their optimisation and performance. The study aims to develop techniques to improve the efficiency of various types of data with conflict-free replication. To achieve this goal, the methods of analysis and experimentation were used. The study results demonstrate that the use of certain optimised methods of conflict-free data types in replication leads to a significant improvement in efficiency and reduction of resource costs. The results show that caching and precomputation, asynchronous synchronisation, and local processing are the most effective methods for improving the efficiency of Conflict-free Replicated Data Types. It is established that the choice of a specific method for improving the performance of Conflict-free Replicated Data Types should be justified and based on a careful analysis of the requirements and characteristics of a particular distributed system. This takes into account user needs, data criticality, edit frequency, etc. The research includes the development and optimisation of synchronisation algorithms that significantly improve the performance of these types of data in distributed systems. The work includes simple software implementations for detailed analysis and study of methods for optimising the performance of data types in conflict-free replications. The introduction of distributed computing has made it possible to optimise the processes of data replication and synchronisation, which helps to reduce overheads and improve system performance. It is concluded that the study is of practical importance since the use of optimised conflict-free data types with replication in real distributed systems can improve their efficiency and reliability. The practical significance of the study lies in the possibility of applying optimised methods of data types with conflict-free replication in real distributed systems, which will increase their performance and ensure efficient operation under conditions of high load and limited resource potential

Extensive assessment of virtual synchronous generators in intentional island mode

Virtual Synchronous Generators (VSGs) are considered one of the most effective cutting-edge technologies for seamlessly integrating Renewable Energy Systems (RESs) into power grids. While various VSG control schemes have been proposed and analysed for different technical aspects, not many experimental demonstrations have been published about VSGs operating in Intentional Island Mode (IIM). Inversely, the IIM has become increasingly attractive, nowadays, to energize those remote areas of the world where entire districts are served by a small number of RESs operating off-grid. In this scenario, the main objective of this paper is to present an extensive performance assessment of the VSG operating in IIM. Different working conditions are considered, with special focus on: black start capability, grid-connected-to-IIM transition, grid reconnection, feeding of non-linear and asymmetric local loads, load sharing between multiple VSGs operating in parallel. In order to achieve this objective, an improved structure of VSG is implemented on 8kVA three-phase converters and significant experimental results are presented. Algorithms for black start capability and grid synchronization are illustrated in detail as well. Meaningful information is provided for engineers involved in activity of designing and testing islanded VSGs.