Arrival Rate Research Articles

Adaptive event-triggered impulsive control for stochastic complex networks with delays under deception attacks

Adaptive event-triggered impulsive control for stochastic complex networks with delays under deception attacks

Multi-level optimizing of parameters in stochastic configuration networks based on cloud model and nutcracker optimization algorithm

As a state-of-the-art neural network model, stochastic configuration networks (SCNs) are widely employed in diverse fields due to their exceptional approximation capabilities. Similar to other neural network models, an excessive number of parameters can potentially compromise the generalization ability of SCNs, including hyper-parameters such as the stochastic scale factors (λ) and the maximum number of nodes (Lmax), as well as model parameters like input weight (w) and input bias (b). To tackle this issue, this study proposes a multi-level parameter optimization approach, termed stochastic configuration network with cloud models (CMSCNs). Firstly, the optimal parameter range is determined based on the concept of “cloud droplet” from the cloud model. Herein, mathematical expectation (Ex) is substituted by a polynomial function constructed with λ as the dependent variable and other parameters as independent variables. Secondly, we employ the nutcracker optimization algorithm (NOA) to optimize w and b, using residuals as evaluation indices to identify their optimal combination. Thirdly, singular value decomposition (SVD) is integrated to compress the network structure of CMSCNs for enhanced computational efficiency. Finally, 18 public real datasets and submergence depth data from an oil well are utilized to assess the performance of CMSCNs. The experimental results demonstrate that our proposed method offers improved generalizability and stability while also exhibiting significant potential in practical applications.

Applying queueing theory to evaluate wait-time-savings of triage algorithms

In the past decade, artificial intelligence (AI) algorithms have made promising impacts in many areas of healthcare. One application is AI-enabled prioritization software known as computer-aided triage and notification (CADt). This type of software as a medical device is intended to prioritize reviews of radiological images with time-sensitive findings, thus shortening the waiting time for patients with these findings. While many CADt devices have been deployed into clinical workflows and have been shown to improve patient treatment and clinical outcomes, quantitative methods to evaluate the wait-time-savings from their deployment are not yet available. In this paper, we apply queueing theory methods to evaluate the wait-time-savings of a CADt by calculating the average waiting time per patient image without and with a CADt device being deployed. We study two workflow models with one or multiple radiologists (servers) for a range of AI diagnostic performances, radiologist’s reading rates, and patient image (customer) arrival rates. To evaluate the time-saving performance of a CADt, we use the difference in the mean waiting time between the diseased patient images in the with-CADt scenario and that in the without-CADt scenario as our performance metric. As part of this effort, we have developed and also share a software tool to simulate the radiology workflow around medical image interpretation, to verify theoretical results, and to provide confidence intervals for the performance metric we defined. We show quantitatively that a CADt triage device is more effective in a busy, short-staffed reading setting, which is consistent with our clinical intuition and simulation results. Although this work is motivated by the need for evaluating CADt devices, the evaluation methodology presented in this paper can be applied to assess the time-saving performance of other types of algorithms that prioritize a subset of customers based on binary outputs.

Stochastic configuration networks for short-term power load forecasting

The accuracy of power load forecasting can enhance the operational efficacy and economic efficiency of the power system. This paper utilizes the stochastic configuration network (SCN) and improved marine predator algorithm (IMPA) for power systems short-term load forecasting. First, the daily load data is clustered using the kernel fuzzy C-means (KFCM), and the clustering results are utilized as training input data for the load forecasting model. Then, load forecasting is performed using SCN. Next, IMPA is developed by integrating the opposite-based differential evolution (ODE) and normal mutation perturbation strategy for the purpose of preventing the algorithm from converging to local minima. Finally, IMPA is employed to optimize the critical parameters of the load forecasting model. The experimental results clearly demonstrate that the proposed algorithm performs favorably in terms of the prediction accuracy compared to other modeling algorithms.

An aggregate matching and pick-up model for mobility-on-demand services

This paper presents an Aggregate Matching and Pick-up (AMP) model to delineate the matching and pick-up processes in mobility-on-demand (MoD) service markets by explicitly considering the matching mechanisms in terms of matching intervals and matching radii. With passenger demand rate, vehicle fleet size and matching strategies as inputs, the AMP model can well approximate drivers’ idle time and passengers’ waiting time for matching and pick-up by considering batch matching in a stationary state. Properties of the AMP model are then analyzed, including the relationship between passengers’ waiting time and drivers’ idle time, and their changes with market thickness, which is measured in terms of the passenger arrival rate (demand rate) and the number of active vehicles in service (supply). The model can also unify several prevailing inductive and deductive matching models used in the literature and spell out their specific application scopes. In particular, when the matching radius is sufficiently small, the model reduces to a Cobb–Douglas type matching model proposed by Yang and Yang (2011) for street-hailing taxi markets, in which the matching rate depends on the pool sizes of waiting passengers and idle vehicles. With a zero matching interval and a large matching radius, the model reduces to Castillo model developed by Castillo et al. (2017) that is based on an instant matching mechanism, or a bottleneck type queuing model in which passengers’ matching time is derived from a deterministic queue at a bottleneck with the arrival rate of idle vehicles as its capacity and waiting passengers as its customers. When both the matching interval and matching radius are relatively large, the model also reduces to the bottleneck type queuing model. The performance of the proposed AMP model is verified with simulation experiments.

Robust multi-target regression with improved stochastic configuration networks and its applications

To improve the accuracy and robustness of stochastic configuration networks (SCNs) for resolving multi-target regression tasks, this paper proposes a robust modeling approach based on improved stochastic configuration networks. A parallel implementation of SCN models is designed to incrementally generate the hidden nodes, which enhances the diversity of hidden layer mapping through information superposition and spanning connection. We employ an elastic net regularization model to sparsely constrain the model parameters to characterize the correlation among multiple targets. Then, the mixture Laplace distributions are used as the prior distribution of each target modeling error, and the output weights of the SCN model are re-evaluated by maximizing a posteriori estimation to enhance model’s robustness with respect to some uncertainties presented in training samples. The modelling performance of the proposed solution is tested on six standard datasets and the historical data of a municipal solid waste incineration process. The experimental results show that the proposed modeling technique has advantages in terms of both the prediction accuracy and the robustness.

Stochastic network to model the global spreading of respiratory diseases: From SARS-CoV-2 to pathogen X pandemic

The recent COVID-19 pandemic has underscored the vulnerability of global health systems. Emerging in November 2019 in Hubei, China, COVID-19 has had far-reaching consequences, affecting every corner of the globe. The impact has been particularly severe, causing widespread collapse of public health systems and contraction of the world economy. The imposition of stringent sanitary restrictions by the majority of countries, in response to SARS-CoV-2, disrupted various economic sectors on a massive scale. The existing gap between developed and underdeveloped countries further complicates the global scenario, raising uncertainties. This concern is amplified when considering the potential threat of other infectious diseases with dynamics akin to SARS-CoV-2, such as a new recombining H5N1 flu strain. Such a strain, if easily transmissible among humans, could lead to another pandemic. In this study, we introduce a stochastic network model designed to assess control strategies on a global scale. This model enables us to project how new variants, evading immunity, might respond to either a coordinated global response from governments or a complete lack of coordination. Our connectivity model between countries is based on a network of contacts derived from actual commercial air connectivity data. The disease dynamics within each country are simulated using a population-based approach with differential equations. The epidemiological model is fine-tuned using real SARS-CoV-2 data reported by various countries from 2019 to 2023.

Node-to-node clustering asymptotic synchronized discrete stochastic neural networks in time and space with Bernoulli switching delay

The article proposes a new approach to synchronizing space–time discrete stochastic neural networks with random switching delays. This is achieved by employing a control in the boundary, which is based on node-to-node clustering and controlling theories. The design of the control in the boundary for synchronizing space–time discrete stochastic neural networks in the form of clusters is based on the establishment of several significant sequential inequalities and the creation of cluster information. Also, an executable computer algorithm has been developed to streamline the implementation of the findings presented in this paper. The current study represents a pioneering approach in considering spatial discrete factors, providing a solid foundation for future research and offering theoretical and practical guidance.

A Comprehensive examination of utilizing neutrosophic parameters in manufacturing industry through queueing approach

Queueing theory plays a pivotal role in analyzing the dynamics of manufacturing systems subject to random arrivals and service times. The M/G/1 queueing model, which represents a single-server queue with general distributions for inter-arrival and service times, is fundamental in this regard. However, traditional queueing models often fail to account for the inherent uncertainties encountered in real-world manufacturing scenarios. Neutrosophic theory provides a robust framework for modeling indeterminacy, ambiguity, and inconsistency in queueing parameters, thereby offering a more adaptable and precise depiction of system behavior. This research investigates the utilization of Neutrosophic sets in defining arrival rates, service times, and queue length distributions within the M/G/1 framework tailored to manufacturing settings. By employing numerical examples and comparative analyses, the study explores the effects of Neutrosophic parameters on key performance indicators such as mean waiting time, queue length distribution, and server utilization, considering distributions like Exponential, Erlang, and Deterministic. Additionally, it delves into the implications of integrating Neutrosophic parameters into queueing theory, providing valuable insights into improved decision-making and system optimization across various manufacturing operational contexts.

Modular stochastic configuration network with attention mechanism for soft measurement of water quality parameters in wastewater treatment processes

Due to the complex and multi-distributed nature of wastewater treatment process data, stochastic configuration networks (SCNs) face difficulties such as large network structure and complex construction process when dealing with data modeling tasks. To address these challenges, this paper proposes a modular stochastic configuration network with attention mechanism, which integrates the advantages of modular partitioning, attention mechanism, and SCN. The model employs a fuzzy mean clustering strategy to simplify and decompose the complex multi-case and multi-distribution nonlinear modeling task, thereby reducing the modeling complexity of a single model. Furthermore, the model utilizes a stochastic configuration algorithm to learn the subtasks and construct corresponding sub-networks. The output weights are then assigned to each sub-network based on the attention mechanism to obtain the model’s final output. The validity of the proposed method is validated through two benchmark experiments. Then the model is applied to measure effluent ammonia nitrogen. The results indicate that compared with the classical randomized and modularized network models, the proposed model exhibits certain advantages in terms of learning accuracy and generalization performance.

Advanced Queueing and Location-Allocation Strategies for Sustainable Food Supply Chain

Background: This study presents an integrated multi-product, multi-period queuing location-allocation model for a sustainable, three-level food supply chain involving farmlands, facilities, and markets. The model employs M/M/C/K queuing systems to optimize the transportation of goods, enhancing efficiency and sustainability. A mixed-integer nonlinear programming (MINLP) approach is used to identify optimal facility locations while maximizing profitability, minimizing driver waiting times, and reducing environmental impact. Methods: The grasshopper optimization algorithm (GOA), a meta-heuristic algorithm inspired by the behavior of grasshopper swarms, is utilized to solve the model on a large scale. Numerical experiments demonstrate the effectiveness of the proposed model, particularly in solving large-scale problems where traditional methods like GAMS fall short. Results: The results indicate that the proposed model, utilizing the grasshopper optimization algorithm (GOA), effectively addresses complex and large-scale food supply chain problems. Compared to GAMS, GOA achieved similar outcomes with minimal differences in key metrics such as profitability (with a gap ranging from 0.097% to 1.11%), environmental impact (0.172% to 1.83%), and waiting time (less than 0.027%). In large-scale scenarios, GOA significantly reduced processing times, ranging from 20.45 to 64.78 s. The optimization of processing facility locations within the supply chain, based on this model, led to improved balance between cost (up to $74.2 million), environmental impact (122,112 hazardous units), and waiting time (down to 11.75 h). Sensitivity analysis further demonstrated that increases in truck arrival rates and product value had a significant impact on improving supply chain performance.

Privacy-preserving coordination of power and transportation networks using spatiotemporal GAT for predicting EV charging demands

The gradual replacement of conventional-fuel vehicles by electric vehicles (EVs) in recent years provides a growing incentive for the collaborative optimization of power distribution networks (PDNs) and urban transportation networks (UTNs). However, the implementation of a centralized optimization model for PDNs and UTNs is currently unrealistic due to the requirement for establishing information privacy barriers between the two independently operated networks. The present work proposes a predict-then-optimize (PTO) framework that combines Spatiotemporal Graph Attention Network (STGAT) with enhanced queueing theory to achieve accurate prediction of EV charging demands. Building upon this, it efficiently coordinates optimization between the PDN and UTN, while preserving the information privacy of both independently operated networks. Specifically, the proposed STGAT model predicts vehicle flow by simultaneously exploiting the dynamic temporal and spatial dependencies in the historical traffic data of the target UTN. It then converts the predicted flow into spatiotemporal EV charging demands using an enhanced queuing model that considers the service capacity constraints of charging stations (CSs) and the behavior of EV users. Subsequently, the predicted EV charging demands are integrated into a multi-period PDN scheduling model. The results of numerical computations based on an IEEE 33-bus PDN and real-world traffic flow datasets demonstrate that the scheduling results provided by the proposed approach differ by only 0.5% compared to results obtained when applying actual EV charging demands.

Space-time graph path planner for unsignalized intersection management with a V2V agent coordination architecture

Reducing traffic congestion and increasing passenger safety are important objectives for emerging automated transportation systems. Autonomous intersection management systems (AIMS) enable large scale optimization of vehicle trajectories with connected and autonomous vehicles (CAVs). We propose a novel approach for computing the fastest waypoint trajectory in intersections using graph search in a discretized space-time graph that produces collision-free paths with variable vehicle speeds that comply with traffic rules and vehicle dynamical constraints. To assist our planner algorithm in decentralized scenarios, we also propose a multi-agent protocol architecture for vehicle coordination for trajectory planning using a vehicle-to-vehicle (V2V) network. The trajectories generated allow a much higher evacuation rate and congestion threshold, with lower O(N) algorithm runtime compared to the state of the art conflict detection graph platoon path planning method, even for large scenarios with vehicle arrival rate of 1/s and thousands of vehicles.

Estimating common whelk (Buccinum undatum) abundance using baited remote underwater video (BRUV)

Abstract Baited static gear fisheries targeting benthic invertebrates have expanded at a global scale. While improvements have been made in the monitoring and management of these fisheries, reliable survey methods for stock assessments of many key species are lacking. In this study we examine the viability of a baited remote underwater video (BRUV) system for obtaining abundance indices for a data-limited stock (common whelk, Buccinum undatum), and compare this method to catch per unit effort (CPUE) from commercial fishing. BRUVs capable of collecting 15 hours of timelapse footage were deployed on pot strings in two distinct whelk fishing sites, with replication over a spring-neap tidal cycle. Three potential BRUV metrics—the maximum (MaxN) and mean (MeanN) abundance and time of first arrival (T1)—were calculated for each deployment, and significant linear relationships were identified between MaxN, MeanN, and CPUE across sites. Temporal variability in BRUV indices driven by tidal dynamics was minimal, although results suggest high current speeds may reduce abundance estimates, and a potential method for predicting density on the seabed using arrival rates is also demonstrated. BRUVs are a valuable tool in developing stock assessment surveys for static gear fisheries.

Comparative investigation of heat extraction performance in 3D self-affine rough single fractures using CO2,N2O and H2O as heat transfer fluid

The type and thermophysical properties of heat transfer fluids significantly impact the heat extraction rate of Enhanced Geothermal Systems (EGS). Studies based on field-scale stochastic discrete fracture network geometric models have certain limitations and uncertainties. To compare the heat extraction performance of heat transfer fluids H2O, CO2, and N2O in a core-scale (φ50 × 100 mm) rough single fracture in hot dry rock, we first constructed the fracture geometry with rock surface roughness characteristics using a fractal self-affine function, with a joint roughness coefficient (JRC) of 12.38. Then, at temperatures ranging from 37 to 300°C and a pressure of 30 MPa, we established a thermo-hydraulic (TH) coupling model based on the thermophysical parameter equations of the three heat transfer fluids. Finally, we conducted a comparative study of the changes in the outlet mean temperature and heat extraction rate after flowing through the fracture, as well as the temperature distribution on the fracture surface, under conditions of constant inlet flow velocity with varying inlet temperature and constant inlet temperature with varying inlet flow velocity. The results indicate that (1) with the increase in inlet flow velocity and temperature, the outlet mean temperature and heat extraction rate of CO2 and N2O at the fracture outlet are essentially the same and lower than those of H2O. (2) When the inlet flow velocity increases from 0.005 m/s to 0.025 m/s, the thermal convection effect from the rock matrix to the fluid in the fracture predominates, and flow velocity is the main factor affecting the heat transfer process between the fluid and the rock matrix. When the inlet temperature increases from 37°C to 57°C, the thermal conduction effect from the rock matrix to the fluid in the fracture predominates, and temperature is the main factor affecting the heat transfer process between the fluid and the rock matrix.(3)When maintaining constant inlet temperature, increasing inlet flow velocity from 0.005 m/s to 0.025 m/s results in CO2 experiencing a maximum heat extraction rate increase of 118.85 W. In contrast, N2O experiences 117.05 W, and H2O experiences 266.4 W.(4) When the inlet flow rate is constant, the maximum increase in heat extraction rate for CO2 is 15.34 W as the inlet temperature increases from 37 °C to 57 °C; for N2O, it is 13.9 W; and for H2O, it is 45.45 W.

Optimized memory allocation in edge-PLCs using Deep Q-Networks and bidirectional LSTM with Quantum Genetic Algorithm

This paper offers a fresh perspective to memory allocation in edge-PLCs within industrial IoT environments, leveraging a hybrid a structure that incorporates Deep Q-Networks (DQN) and Bidirectional Long Short-Term Memory Networks (BiLSTM), complemented by Quantum Genetic Algorithm Optimization. In addressing the dynamic challenges of memory management, our method combines the reinforcement learning capabilities of DQN with the temporal contextualization provided by BiLSTM networks. The DQN component learns to optimize memory allocation policies based on immediate rewards and feedback, while the BiLSTM network captures long-term dependencies in data, enhancing predictive modeling for future data arrival rates. Moreover, we introduce Quantum Genetic Algorithm Optimization, a cutting-edge approach that infuses quantum-inspired principles into the traditional genetic algorithm framework, to further refine the memory allocation process. By leveraging principles of quantum computing, this optimization algorithm explores the solution space more efficiently, allowing for faster convergence and improved performance. Through simulation experiments, we exhibit the potency of our hybrid approach in reducing data loss probability and enhancing system performance in edge-PLCs. Our findings underscore the significance of integrating advanced machine learning techniques with quantum-inspired optimization algorithms to address complex challenges in industrial IoT environments, offering a promising avenue for enhancing memory allocation efficiency in edge computing systems.

Temporal online self-learning stochastic configuration networks: A study on strip deviation prediction

Stochastic configuration networks (SCNs), with the rapid learning abilities and universal approximation properties, have garnered growing interest in the industrial domain in recent years. In the hot rolling process, strip deviation affects product quality and production safety. In this paper, a novel model named temporal online self-learning stochastic configuration networks (TOSL-SCNs) designed for early prediction of strip deviation in the hot rolling process is introduced. To address the challenges of time series prediction, conventional SCNs are extended to a two-dimensional format, enabling effective processing of time series data. Given the dynamic nature of industrial environments, the model incorporates an online self-learning capability to adapt to nonstationary data. Additionally, to ensure the stability of the model’s predictive performance, a data buffer strategy is introduced to dynamically refresh the training dataset. This paper demonstrates the capabilities of TOSL-SCNs in time series modeling, online self-learning, and data buffer strategy from multiple perspectives. The case study on strip deviation prediction highlights the necessity of these capabilities in industrial applications.

Take-away and sit-down service operations under inequity aversion

Profit contributed by take-away service has become an increasingly essential element of the restaurant operating revenue. Since take-away service usually relies on the third-party platform and there are many differences between the cost of take-away service and that of sit-down service. We focus on the restaurant which provides both sit-down service and take-away service, the service system is modeled as a two-stage tandem queueing system. We study the restaurant’s optimal capacity level for each stage. Besides, as there exist price/waiting time difference between the two services, inequity aversion is also investigated in our model. We study symmetrical and asymmetrical inequity aversion. We find that the optimal service capacity consists of two parts, base capacity and safety capacity. And the loss resulted from waiting time lag is equal to the waste of resources caused by fluctuations in arrival rate. Further, when customers really long for the restaurant, even if high price will lead to severe inequity aversion, restaurant can always earn more by raising the price in service channel with high revenue. While when customers are indifferent of the restaurant and the others, the price gap is meant to result in revenue decrease. In addition, reduction in customers’ susceptibility can help to enhance operation profit in general. Market environment plays a decisive role in choosing the optimal service level.

Computational Modelling of 2004 Off West Coast of Sumatra Earthquake-Tsunami Wave Propagation Using Two-Dimensional Cellular Automata in the Eastern Indian Coast

ABSTRACT The ability to detect destructive tsunamis and spare lives from serious harm has made the computation of tsunami wave dynamics increasingly important. The tremendous loss of life and property brought on by the 26th of December, 2004 tsunami has raised the demand for accurate and timely propagation of tsunami wave simulations. These days, a variety of early warning technologies and models are utilized to predict tsunami waves. The propagation phase of a tsunami wave is simulated using two-dimensional cellular automata of rectangular lattices in eight directions from this research. Modelling of the Sumatra-Andaman earthquake in 2004 to simulate the propagation of tsunami waves to strike the northern coasts of India. The earliest tsunami arrival time, travel time, and wave spread rate are determined from this study, and the results are verified using historical data.

Optimized Backstepping Cooperative Control for Output-Constrained Stochastic Nonlinear Network Systems via a Multibridge-Hole Function.

In this article, a new leader-following tracking control approach is investigated for stochastic multiagent systems with multibridge-hole output constraints. The multibridge-hole output constraints mean that the output of the system is constrained in some intervals and unconstrained in other intervals. The constrained and unconstrained intervals can be set arbitrarily. By designing a new shift function to construct the barrier Lyapunov function, the optimal controller is constructed by combining the backstepping technique with the adaptive dynamic programming technique. The model network is used to estimate the unknown disturbances and uncertainty terms in the system. The critic network and the actor network are constructed such that the designed controller adheres to the Bellman optimality principle and gives the optimal solution of the system. The proposed control method is versatile and compatible with various types of output constrained control problems, such as unconstrained control problems, constrained control problems, and delay constrained problems without changing the structure of the controller. Finally, some simulation results are given to verify the effectiveness of the method.