Arrival Rate Research Articles

Design of Bayesian stochastic networks for numerical treatment of Williamson fluid stretching flow model with mixed convected heat generation

In the presented article, a stochastic network paradigm through Bayesian Regularization backpropagation neural network (BRB-NN) is designed to interpret the dynamics of the Williamson fluid stretching flow model with mixed convected heat generation (WF-SFM). The governing nonlinear PDEs system of WF-SFM is reduced to a set of ODEs by incorporating appropriate transformations. The reference datasets for the anticipated BRB-NN approach are created with Adams solver for numerical solutions of WF-SFM by varying material variable W e , buoyancy factor λ , temperature characteristic parameter ε , thermal relaxation variable γ , heat source factor δ , and Prandtl numbers P r . The knacks of artificial intelligence (AI) based BRB-NN procedure is then employed on the generated dataset for WF-SFM. The bias training, biased testing, and validation of BRB-NN are conducted to compute the approximate numerical results for WF-SFM for sundry scenarios, and outputs are in good agreement in reference data that validate the worthy performance of the proposed BRB-NN which is further justified by results of absolute error, mean squared error, error histogram illustrations and regression measures. The viable performance in terms of mean square error (MSE) is achieved at levels ranging from E − 11 to E − 13 , consistently for all scenarios of WF-SFM. The accuracy and the justification of performance are effectively proven by the low level of MSE, to optimal regression metric index as well as distribution of instances error on histograms with negligible magnitude.

Sibyl: Forecasting Time-Evolving Query Workloads

Database systems often rely on historical query traces to perform workload-based performance tuning. However, real production workloads are time-evolving, making historical queries ineffective for optimizing future workloads. To address this challenge, we propose SIBYL, an end-to-end machine learning-based framework that accurately forecasts a sequence of future queries, with the entire query statements, in various prediction windows. Drawing insights from real-workloads, we propose template-based featurization techniques and develop a stacked-LSTM with an encoder-decoder architecture for accurate forecasting of query workloads. We also develop techniques to improve forecasting accuracy over large prediction windows and achieve high scalability over large workloads with high variability in arrival rates of queries. Finally, we propose techniques to handle workload drifts. Our evaluation on four real workloads demonstrates that SIBYL can forecast workloads with an 87.3% median F1 score, and can result in 1.7× and 1.3× performance improvement when applied to materialized view selection and index selection applications, respectively.

Security synchronization problem for stochastic complex networks via event-triggered impulsive control with actuation delays

This study focused on the security synchronization problem for stochastic complex networks (SCNs) via event-triggered impulsive control (ETIC) with actuation delays. Firstly, incorporating the network topology and the Lyapunov function theory, a novel event-triggered mechanism (ETM) is devised, which accounts for actuation delays; Secondly, an ETM-based quantizer is introduced to optimize network resources, meantime, building upon this foundation, the Lipschitz–Razumikhin method is integrated with graph theory to establish a positive lower bound on the minimum event interval time, thereby preventing Zeno behavior; Additionally, some sufficient conditions are obtained to achieve global asymptotic synchronization of SCNs under quantization and deception attacks; Finally, a Chua’s circuit numerical simulation is employed to demonstrate the effectiveness of our theoretical findings.

A reliable neural network framework for the Zika system based reservoirs and human movement

The motive of current investigations provides the numerical solutions of the neuro computing solver based on the Levenberg-Marquardt backpropagation neural network approach (LMB) to solve the Zika virus system of reservoirs and human movement. The mathematical form of the human movement model is based on ten different classes, which makes the model nonlinear. The solution of this nonlinear mathematical model is presented by using the stochastic LMB neural network process. A dataset is designed based on the Adam solver in order to reduce the mean square error by dividing the data as training, endorsement, and testing with the use of 75%, 10% and 15%. Ten numbers of neurons have been taken by using the log-sigmoid transfer in the hidden layer for solving the human movement model. The precision of the proposed solver is authenticated by using the valuation of reference and proficient outcomes, while the reducible absolute error presents the correctness of the scheme. Moreover, the statistical performances based different proportional schemes have been used to authenticate the reliability of the scheme.

A double stochastic SIS network epidemic model with nonlinear contact rate and limited medical resources

A double stochastic SIS network epidemic model with nonlinear contact rate and limited medical resources

Heavy traffic analysis of multi-class bipartite queueing systems under FCFS

This paper examines the performance of multi-class, multi-server bipartite queueing systems, where each arriving customer is compatible with only a subset of servers. We focus on the system’s performance under a first-come, first-served-assign longest idle server service discipline. In this discipline, an idle server is matched with the compatible customer who has been waiting the longest, and a customer who can be served by multiple idle servers is routed to the server that has been idle for the longest period. We analyse the system under conventional heavy-traffic conditions, where the traffic intensity approaches one from below. Building upon the formulation and results of Afèche et al. (Oper Res 70(1):363–401, 2022), we generalize the model by allowing the vector of arrival rates to approach the heavy-traffic limit from an arbitrary direction. We characterize the steady-state waiting times of the various customer classes and demonstrate that a much wider range of waiting time outcomes is achievable. Furthermore, we establish that the matching probabilities, i.e. the probabilities of different customer classes being served by different servers, do not depend on the direction along which the system approaches heavy traffic. We also investigate the design of compatibility between customer classes and servers, finding that a service provider who has complete control over the matching can design a delay-minimizing matching by considering only the limiting arrival rates. When some constraints on the compatibility structure exist, the direction of convergence to heavy-traffic affects which compatibility structure minimizes delay. Additionally, we discover that the bipartite matching queueing system exhibits a form of Braess’s paradox, where adding more connectivity to an existing system can lead to higher average waiting times, despite the fact that neither customers nor servers act strategically.

Working condition recognition of fused magnesium furnace based on stochastic configuration networks and reinforcement learning

Automatic and accurate recognition of abnormal working conditions of fused magnesia furnace is of great significance to the safe and reliable production of fused magnesia. Aiming at the defects of manual judgment of abnormal working conditions in the production process of fused magnesium furnace and the existing recognition method of abnormal working conditions based on machine learning, this paper proposes a working condition recognition model for fused magnesium furnace based on stochastic configuration networks and reinforcement learning. Firstly, a hybrid data augmentation method of generative and non-generative is used to obtain high-quality sample data with salient features. Secondly, based on ResNeXt, multi-scale local features are extracted under the condition of limited parameter quantity by controlling the cardinality. Combining a mixed model that enjoys the benefit of both self-attention and convolution (ACmix) and bidirectional feature pyramid network (BiFPN), the extracted local feature maps of different scales are cross-scale fused and focused, and more differentiated detailed feature information of the region of interest is retained. Thirdly, based on Transformer, the working condition recognition network of fused magnesium furnace is constructed to improve the global correlation between adjacent local features in the spatial dimension. The fused features are sent to stochastic configuration networks to establish a classification criterion for working condition recognition of fused magnesium furnace with generalization ability. Finally, reinforcement learning is used to evaluate the credibility of uncertain recognition results of samples in real time, and a self-optimizing adjustment action strategy at the Transformer encoding layer is defined. Build a library of Transformer models with different encoding layers, which is adapt to the different feature extraction requirements of multi-modal working samples. The experimental results show that the method in this paper has better recognition performance and generalization ability than other algorithms.

Analysis of the impact of corrective actions for stochastic project networks

In project management, a project plan is constructed that assigns a planned start time to each project activity. Based on this plan, the total planned project duration and cost can be determined. However, during project execution, deviations from the plan are inevitable due to uncertainty and variability. When these deviations endanger the timely completion of projects, the project manager should take corrective actions to get the project back on track. In this study, corrective actions are modelled as modifications of the original activity duration distributions (i.e., reduced mean and/or standard deviation) to account for the uncertain nature of their impact. Further, an analytical procedure is developed to rank activities according to their expected impact on the project duration distribution when they are controlled by a corrective action. This activity ranking is used to determine the number of actions that should be taken and to select the set of activities that will be controlled. A computational experiment on a large set of project networks with varying network complexity and network structures has been conducted. These experiments have shown that taking actions on a relatively small subset of activities, rather than on the entire set of project activities, proves more efficient, when the subset of activities is carefully selected. More precisely, the efficiency of the corrective actions process depends on both the number of actions and the activity selection criterion (activity ranking).

Radio-astronomical image reconstruction with a conditional denoising diffusion model

Context. Reconstructing sky models from dirty radio images for accurate source extraction, including source localization and flux estimation, is a complex yet critical task, and has important applications in galaxy evolution studies at high redshift, particularly in deep extragalactic fields using for example the Atacama Large Millimetre Array (ALMA). With the development of large-scale projects, such as the Square Kilometre Array (SKA), we anticipate the need for more advanced source-extraction methods. Existing techniques, such as CLEAN and PyBDSF, currently struggle to effectively extract faint sources, highlighting the necessity for the development of more precise and robust methods. Aims. The success of the source-extraction process critically depends on the quality and accuracy of image reconstruction. As the imaging process represents an “information-lossy” operator, the reconstruction is characterized by uncertainty. The current study proposes the application of stochastic neural networks for the direct reconstruction of sky models from “dirty” images. This approach allows us to localize radio sources and to determine their fluxes with corresponding uncertainties, providing a potential advancement in the field of radio-source characterization. Methods. We used a dataset of 10 164 images simulated with the CASA tool based on the ALMA Cycle 5.3 antenna configuration. We applied conditional denoising diffusion probabilistic models (DDPMs) to directly reconstruct sky models from these dirty images, and then processed these models using Photutils to extract the coordinates and fluxes of the sources. To test the robustness of the proposed model, which was trained on a fixed water vapor value, we examined its performance under varying levels of water vapor. Results. We demonstrate that the proposed approach is state of the art in terms of source localisation, achieving over 90% completeness at a signal-to-noise ratio (S/N) of as low as 2. Additionally, the described method offers an inherent measure of prediction reliability thanks to the stochastic nature of the chosen model. In terms of flux estimation, the proposed model surpasses PyBDSF in terms of performance, accurately extracting fluxes for 96% of the sources in the test set, a notable improvement over the 57% achieved by CLEAN+ PyBDSF. Conclusions. Conditional DDPMs are a powerful tool for image-to-image translation, yielding accurate and robust characterization of radio sources, and outperforming existing methodologies. While this study underscores the significant potential of DDPMs for applications in radio astronomy, we also acknowledge certain limitations that accompany their use, and suggest directions for further refinement and research.

Periodic Self-Triggered Impulsive Synchronization of Hybrid Stochastic Complex-Valued Delayed Networks

This paper studies the synchronization problem for hybrid stochastic delay complex-valued networks with time-varying multi-links (HSDCNT) via periodic self-triggered impulsive control (PSIC). Therein, PSIC combines with the benefits of impulsive control and periodic self-triggered strategy. Moreover, time delays, stochastic disturbances, and time-varying multi-links are considered simultaneously. Particularly, the synchronization for HSDCNT is studied on the complex space and no split real and imaginary parts. Actually, different from the previous PSIC-based results, this paper extends the real-valued neural networks to HSSs, delayed systems, and complex-valued networks, which is significant and challenging. Then, as a practical application, we consider the synchronization of a kind of Cohen-Grossberg neural network in the complex space. Finally, a numerical example is utilized for demonstration.

Deep-Learning-Aided Voltage-Stability-Enhancing Stochastic Distribution Network Reconfiguration

Power distribution networks are approaching their voltage stability boundaries due to the severe voltage violations and the inadequate reactive power reserves caused by the increasing renewable generations and dynamic loads. In the broad endeavor to resolve this concern, we focus on enhancing voltage stability through stochastic distribution network reconfiguration (SDNR), which optimizes the (radial) topology of a distribution network under uncertain generations and loads. We propose a deep learning method to solve this computationally challenging problem. Specifically, we build a convolutional neural network model to predict the relevant voltage stability index from the SDNR decisions. Then we integrate this prediction model into successive branch reduction algorithms to reconfigure a radial network with optimized performance in terms of power loss reduction and voltage stability enhancement. Numerical results on two IEEE network models verify the significance of enhancing voltage stability through SDNR and the computational efficiency of the proposed method.

Exponential Synchronization of Delayed Stochastic Complex Dynamical Networks via Hybrid Impulsive Control

Exponential Synchronization of Delayed Stochastic Complex Dynamical Networks via Hybrid Impulsive Control

Online Self-Learning Stochastic Configuration Networks for Nonstationary Data Stream Analysis

Online Self-Learning Stochastic Configuration Networks for Nonstationary Data Stream Analysis

Overview and Classification of Evaluation Metrics of Appointment Scheduling Systems

Introduction: This study reviews the several metrics used to evaluate the performance of appointment scheduling systems.Material and Methods: The articles in English were searched using PubMed, Scopus, and Web of Science databases and Google scholar search engine until July 23, 2023. We used queueing theory to classify evaluation metrics.Results: Out of 23403 articles, 75 papers were prepared for detailed analysis. We classify evaluation metrics of appointment scheduling system along with their definition and frequency of use. A total of 24 measures containing twelve (%50), seven (%29), and five (%21) were related to the categories of arrivals (patient), queue (at clinic), and server (physician) were found, respectively.Conclusion: To the best of our knowledge, this paper was one of the first studies collecting and classifying all evaluation metrics of appointment scheduling system in order to help other researchers. Most metrics pertained to patients which may highlight the importance of the patient’s perspective in evaluating appointment scheduling systems.

Learning-Based Cloud Server Configuration for Energy Minimization Under Reliability Constraint

Cloud computing has attracted wide attention from both academia and industry, since it can provide flexible and on-demand hardware and software resources as services. Energy consumption of cloud servers is the main concern of cloud service providers since reducing energy consumption can bring them a lower operation cost (and hence a higher profit) and alleviate carbon footprints to the environment. Typically, the common power management techniques for enhancing energy efficiency would make cloud servers more vulnerable to soft errors and hence adversely impact the quality of services. Thus, reliability cannot be ignored in the design of methodologies for improving the energy efficiency of cloud servers. In this article, we aim to minimize the energy consumption of cloud servers under the soft-error reliability constraint by configuring the size and speed of servers. Specifically, we first derive the expected reliability based energy consumption of cloud servers to formulate the reliability-constrained energy minimization problem. We then leverage the reinforcement learning technique to obtain an optimal server configuration solution that maximizes system energy efficiency while maintaining the system reliability constraint. Finally, we perform extensive simulation experiments to analyze the relationship between system energy consumption and server configuration under varying arrival rates and execution requirements of service requests. Comparative experiments are also performed to validate the efficacy of the proposed learning-based server configuration scheme. Results show that compared to a benchmark method, the energy saved by the proposed scheme can reach up to 31.5%.

Novel Speed-Flow Model Based on Queuing Theory and Its Potential Application in Highway Capacity Guidelines

Speed-flow relationships are incorporated in many highway capacity guidelines such as the U.S. Highway Capacity Manual (HCM) and the German Highway Capacity Manual (HBS). The speed-flow relationship can be used to predict the operating speed depending on the traffic volume. Based on traffic-flow theory, many models for the speed-flow relationship under fluid and congested traffic conditions have been developed. However, most of the speed-flow relationships used in guidelines are either based on empirical regression analysis and thus lack a theoretical background or include model parameters that do not have a realistic meaning. In this paper, a speed-flow relationship is derived based on the queuing theory in a form that the main model parameters can be expressed as real traffic-flow parameters, which can be obtained by field measurements. These parameters are the free-flow speed, the potential capacity of the imbedded queuing system, and a parameter for the stochastics of the queuing system. By varying the model parameters, different geometric, traffic, and control conditions can be considered. Even the impact of new technologies such as connected and automated vehicles can be accounted for by adjusting those parameters.

Analysis of local density during football stadium access: Integrating pedestrian flow simulations and empirical data

This study analyzes numerically the access of football fans to a typical football stadium through pedestrian flow simulations. With this aim, we introduce a novel framework to address the difficulty of simulating pedestrian dynamics in highly complex geometries with multiple accesses. The framework consists of a combination of the Social Force Model (SFM) and Computational Fluid Dynamics tools to calculate multiple desired velocity maps. The introduction of this framework allows the predefinition of the desired velocity fields for the complex geometries in both the interior and surrounding area of the stadium. We validate the results of the proposed framework using actual entry-rate data from the turnstiles of three gates for 15 matches provided by the football club. Remarkably, the validation process allows us to describe the temporal evolution of the spatial distribution of pedestrians during their arrival with a single set of parameters valid for all the analyzed matches. Finally, we assess the potential fatality risk based on the local pedestrian density varying the total number of attendees and their arrival rate. The outcome evidences permanent clogs emerge at the entrance in the most extreme cases with high attendance or low standard deviation of the arrival rate, indicating the possibility of fatality occurrence.

Quality assessment and community detection methods for anonymized mobility data in the Italian Covid context

We discuss how to assess the reliability of partial, anonymized mobility data and compare two different methods to identify spatial communities based on movements: Greedy Modularity Clustering (GMC) and the novel Critical Variable Selection (CVS). These capture different aspects of mobility: direct population fluxes (GMC) and the probability for individuals to move between two nodes (CVS). As a test case, we consider movements of Italians before and during the SARS-Cov2 pandemic, using Facebook users’ data and publicly available information from the Italian National Institute of Statistics (Istat) to construct daily mobility networks at the interprovincial level. Using the Perron-Frobenius (PF) theorem, we show how the mean stochastic network has a stationary population density state comparable with data from Istat, and how this ceases to be the case if even a moderate amount of pruning is applied to the network. We then identify the first two national lockdowns through temporal clustering of the mobility networks, define two representative graphs for the lockdown and non-lockdown conditions and perform optimal spatial community identification on both graphs using the GMC and CVS approaches. Despite the fundamental differences in the methods, the variation of information (VI) between them assesses that they return similar partitions of the Italian provincial networks in both situations. The information provided can be used to inform policy, for example, to define an optimal scale for lockdown measures. Our approach is general and can be applied to other countries or geographical scales.

ActiveGuardian: An accurate and efficient algorithm for identifying active elephant flows in network traffic

Active elephant flows, which indicate the real-time data transmission status, are of primary interest in network management and various applications. However, existing network measurement efforts mainly focus on finding elephant flows, and limited works on identifying active elephant flows suffer from low accuracy and heavy overheads. To address this issue, this paper proposes ActiveGuardian to identify active elephant flows with high accuracy, low memory, and high throughput. The key idea is to intelligently separate potential elephant flows from mice flows, and guard and report the information of active elephant flows in every time window. To obtain high accuracy, we devise a filtering module that adaptively filters unnecessary flows with low arrival rates, by applying an Adaptive Counter Update strategy. To achieve high memory utilization, we design a Leapfrog Hashing algorithm for the guarding module to effectively solve hash collisions. Lastly, we perform theoretical derivation on the false positive and the error bound of ActiveGuardian, and experimental evaluations on its performance with real network traffic traces. The experimental results show that ActiveGuardian achieves higher accuracy (99.65%) with identical memory sizes, and higher throughput (26.53Mps) than the state-of-the-art solutions.

Quantized adaptive practical fixed-time synchronization of stochastic complex networks with actuator faults

This paper investigates the practical fixed-time synchronization for stochastic complex networks (CNs) under quantized adaptive fault-tolerant control scheme. More accurate upper-bound estimates of stochastic settling time are obtained and novel residual sets are derived via proposed new lemmas for practical fixed-time stability in probability (PFTP). Quantized adaptive control scheme is designed based on the improved lemmas, which can deal with the unknown actuator faults and disturbances in CNs. Moreover, the proposed control strategy can make the stochastic CNs achieve practical fixed-time synchronization in probability (PFSP). In addition, the effectiveness of proposed method is confirmed by an example of Chua’s circuit.