Numerical Experiments Research Articles

Enhancing vehicular platoon stability in the presence of communication Cyberattacks: A reliable longitudinal cooperative control strategy

This study proposes a reliable longitudinal distributed control strategy for connected and automated vehicles (CAVs) in the presence of communication cyberattacks. The proposed strategy is designed to utilize dependable measurements from onboard sensors to evaluate the reliability of vehicle-to-vehicle (V2V) information and enhance the platoon string stability in an integrated manner. Specifically, this paper proposes a real-time reliability evaluation mechanism for the ego vehicle based on sensing and communication information from multiple predecessors, employing an upper-tailed chi-squared test. The mechanism is then incorporated into a distributed multi-predecessor linear feedback controller to adaptively weigh the preceding vehicles’ information, ensuring robustness against cyberattacks and maintaining the string stability of the vehicular platoon. To better understand the whole framework, sufficient conditions of true string stability and pseudo string stability are theoretically derived, unveiling the disturbance amplification jointly impacted by measurement accuracy of onboard sensors, cyberattack severity in V2V communication, and control parameters. Numerical experiments are conducted to validate the controller across various types of communication cyberattacks. The results suggest that the proposed control strategy can significantly alleviate the impact of cyberattacks and simultaneously dampen traffic oscillations effectively.

Fighting against de-pooling effect of airport advertising spaces: A supply chain perspective

Being a scarce resource, airport advertising spaces/billboards have a fixed capacity and the demand largely depends on the firm’s marketing efforts. Besides selling the limited capacity directly to its clients, it is common for an airport advertising firm to sell a portion of the capacity, at a discount price, through advertising agencies. By splitting the capacity into two channels, the airport advertising firm encounters a capacity de-pooling effect and it forms into a competition and collaboration relationship with the agencies. In this paper, we investigate the problem of how to fight against the de-pooling effect from a supply chain perspective. Specifically, we study the interactive decisions between an airport advertising firm and an agency firm. The airport advertising firm determines the maximal capacity to sell to the agency firm, and then the two firms compete on marketing efforts with the objective of maximizing their respective profits. We characterize the effect of capacity de-pooling and devise a coordination contract to fight against such effect. Numerical experiments are conducted to evaluate the potential benefit of the contract.

Surface accuracy prediction method for mesh reflector antenna considering uncertainty factors in manufacturing process

The cable-net structure of mesh reflector antenna meets the requirement of surface accuracy by optimizing the shape and tension. However, in the process of manufacturing and assembly, there are inevitable random errors and structure gravity, which lead to the displacement of the nodes in surface, the surface accuracy of the antenna decreases after fabrication eventually. It is challenging to analyze the uncertainty factors and the effects on the surface accuracy. Therefore, this paper analyzes the uncertainty factors of the antenna during fabrication and proposes a method to predict the surface accuracy of the antenna after manufacturing and assembly. Firstly, based on the equilibrium equation of the cable-net structure, the mechanism of node displacement induced by uncertainty factors leading to the reduction of surface accuracy is analyzed. Secondly, based on the structure characteristics and manufacturing conditions of the antenna, the uncertainty factors including cable-net manufacturing and assembly errors, cable-truss assembly errors and gravity are analyzed qualitatively and quantitatively, the distributions of the uncertainty factors are given. Thirdly, based on Monte Carlo method, the mean and interval of the surface accuracy of different dimension parameters are predicted, the empirical prediction formulas of the surface accuracy mean and interval are fitted. Finally, the accuracy and effectiveness of the proposed formulas are verified by several numerical simulations and surface accuracy measurement experiments. The prediction method can effectively guide the design and craft of large-aperture mesh reflector antenna.

Network-based representations and dynamic discrete choice models for multiple discrete choice analysis

In many choice modeling applications, consumer demand is frequently characterized as multiple discrete, which means that consumer choose multiple items simultaneously. The analysis and prediction of consumer behavior in multiple discrete choice situations pose several challenges. In this paper, to address this, we propose a random utility maximization (RUM) based model that considers each subset of choice alternatives as a composite alternative, where individuals choose a subset according to the RUM framework. While this approach offers a natural and intuitive modeling approach for multiple-choice analysis, the large number of subsets of choices in the formulation makes its estimation and application intractable. To overcome this challenge, we introduce directed acyclic graph (DAG) based representations of choices where each node of the DAG is associated with an elemental alternative and additional information such as the number of selected elemental alternatives. Our innovation is to show that the multi-choice model is equivalent to a recursive route choice model on the DAG, leading to the development of new efficient estimation algorithms based on dynamic programming. In addition, the DAG representations enable us to bring some advanced route choice models to capture the correlation between subset choice alternatives. Numerical experiments based on synthetic and real datasets show many advantages of our modeling approach and the proposed estimation algorithms.

Взаимосвязь окружной скорости и потерь энергии в проточной части центростремительной турбины с частичным облопачиванием рабочего колеса

Low-consumption turbomachines are devices that play an important role in the drive of various units in the field of shipbuilding, aircraft engineering and other branches of heavy engineering. They have some advantages over a high-average power turbine. The largest number of low-consumption turbomachines are made partial, i.e. with partial intake. The principle of a turbine with partial flapping of the impeller is considered as one of the types of partial turbomachines. The influence of the main velocity characteristic of the turbine stage on the loss of kinetic energy in the stage is investigated. The simulation of gas dynamic processes occurring in the turbine stage was carried out using the ANSYS Workbench software package. With the help of this complex, a three-dimensional geometric model of the turbine stage with varying degrees of impeller damping was created. By applying the finite element method, a computational grid, a computational model are generated, and boundary conditions of a numerical experiment are set. The result of the numerical experiment is graphs of the dependence of kinetic energy loss on the circumferential velocity (speed characteristics of the turbine stage). This dependence can be represented not only graphically, but also with the help of mathematical apparatus. An example of such an apparatus is the polynomial dependence. The considered mathematical design can be used in order to optimize mathematical models of gas flow in the flow part of a low-flow turbine. Cubic two-parameter polynomials of kinetic energy losses in the flow part of the nozzle and impeller are obtained, and an assessment of its applicability in the current mathematical model is given.

Enhancing registration systems: Implementing database systems in digital boarding schools

Islamic boarding schools, known as pondok pesantren are specialized schools for Islamic education in Indonesia. With the community's increasing interest in religious education, some pesantren have begun to offer online learning opportunities. Pondok Pesantren Bayt Tamyiz is a Islamic boarding school that has started developing online pesantren education under the name Tamyiz Digittren. This program aims to enhance understanding and translation of the Qur'an using the Tamyiz method. The pondok pesantren requires significant changes in its administrative systems, including the registration process. Information system development is often challenging for the pesantren staffs and software developers. One of the fundamental difficulties lies in the user requirement process. Typically, pesantren need help to provide the information system they need. Such difficulties hinder the development process as developers must conduct numerous experiments. This research aims to design and implement a suitable database for the new student registration system with minimal information and limited time development. The research follows a database development method divided into three stages: Requirement Collection and Analysis, Conceptual Database Design, and Logical Database Design. Database development utilizes MySQL as the Database Management System (DBMS) and PHP as the programming language. This research results in a database design capable of storing and managing student data, payments, and other registration information. We expect the results of this database development to be effective in supporting the registration system and in the future development of information systems at Pondok Pesantren Bayt Tamyiz.

A hybrid algorithm for quadratically constrained quadratic optimization problems

Quadratically Constrained Quadratic Programs (QCQPs) are an important class of optimization problems with diverse real-world applications. In this work, we propose a variational quantum algorithm for general QCQPs. By encoding the variables in the amplitude of a quantum state, the requirement for the qubit number scales logarithmically with the dimension of the variables, which makes our algorithm suitable for current quantum devices. Using the primal-dual interior-point method in classical optimization, we can deal with general quadratic constraints. Our numerical experiments on typical QCQP problems, including Max-Cut and optimal power flow problems, demonstrate better performance of our hybrid algorithm over classical counterparts.

Mobile-YOLO: An Accurate and Efficient Three-Stage Cascaded Network for Online Fiberglass Fabric Defect Detection

The tolerance for different defects in the production process of fiberglass fabric varies. For acceptable defects, only relevant information needs to be recorded and printed, while for intolerable defects, the loom needs to be stopped promptly for handling. To address the issue of inefficient production of fiberglass fabric due to this situation, this paper proposes a task-modularized cascade network model for quality inspection of fiberglass fabric. The three stages cascaded detection network is composed of the improved MobilenetV3-small and the enhanced You Only Look Once v8 Nano (YOLOv8n). For MobilenetV3_small, the Squeeze- and-Excitation(SE) module in the Block layer with a 5×5 convolutional kernel is replaced with the Efficient Channel Attention (ECA) mechanism, enhancing channel information while making the model more lightweight. For YOLOv8n, a Global Attention Module (GAM) is added to the network, enhancing the ability to capture global information of input data, making the model more perceptual. Additionally, a Similarity Attention Module (SimAM) is added between the neck and head networks, enabling the network to better capture relevant information in the images, improving the quality of feature maps and enhancing network performance. Finally, a large number of defect images of fiberglass fabric were collected from an industrial site, and different datasets for the three stages were created. Numerous experiments were conducted, and when using all three stages for detection, the accuracy rate reaches 99.4%, with a parameter count of 16.8M and Frames Per Second (FPS) of 186. The cascade network has been successfully applied in the industrial field of fiberglass fabric and meets industrial production requirements.

Structure-preserving algorithms for guiding center dynamics based on the slow manifold of classical Pauli particle

The classical Pauli particle (CPP) serves as a slow manifold, substituting the conventional guiding center dynamics. Based on the CPP, we utilize the averaged vector field (AVF) method in the computations of drift orbits. Demonstrating significantly higher efficiency, this advanced method is capable of accomplishing the simulation in less than one-third of the time of directly computing the guiding center motion. In contrast to the CPP-based Boris algorithm, this approach inherits the advantages of the AVF method, yielding stable trajectories even achieved with a tenfold time step and reducing the energy error by two orders of magnitude. By comparing these two CPP algorithms with the traditional RK4 method, the numerical results indicate a remarkable performance in terms of both the computational efficiency and error elimination. Moreover, we verify the properties of slow manifold integrators and successfully observe the bounce on both sides of the limiting slow manifold with deliberately chosen perturbed initial conditions. To evaluate the practical value of the methods, we conduct simulations in non-axisymmetric perturbation magnetic fields as part of the experiments, demonstrating that our CPP-based AVF method can handle simulations under complex magnetic field configurations with high accuracy, which the CPP-based Boris algorithm lacks. Through numerical experiments, we demonstrate that the CPP can replace guiding center dynamics in using energy-preserving algorithms for computations, providing a new, efficient, as well as stable approach for applying structure-preserving algorithms in plasma simulations.

Robust optimization model for relief supplies distribution considering fairness

AbstractThe emergency management agency (EMA) needs to distribute limited relief supplies efficiently. However, it is difficult to develop a reliable system for relief supplies distribution owing to the uncertainties in emergencies. This paper investigates a robust multiperiod relief supplies distribution problem that considers the uncertainties of transportation time, the amount of donation amount, and the secondary disasters. First, a satisfaction model is constructed by considering the relief supplies and the transportation time. The negative effect of the delay in transportation time is considered in the satisfaction model. Second, based on the satisfaction model, a comprehensive fairness strategy is constructed that considers both vertical fairness and horizontal fairness. Finally, a relief supplies distribution model is built for the EMA that considers the actions of the non‐governmental organization (NGO) and donors. Both the utility and the fairness are considered in the objective of the proposed model. In numerical experiments, the Wenchuan earthquake is conducted to illustrate the applicability of the model and provide implications for decision‐makers. The results show the benefits of considering both vertical fairness and horizontal fairness. Then the suitable threshold is given by the analysis of the time delay. Finally, managerial insights and recommendations for the EMA and the NGO derived from the numerical experiments are presented. The findings in this paper help improve the reliability of the relief supplies distribution system.

The Response of the Inner Dark Matter Halo to Stellar Bars

Barred galaxies constitute about two-thirds of observed disc galaxies. Bars affect not only the mass distribution of gas and stars but also that of the dark matter. An elongation of the inner dark matter halo is known as the halo bar. We aim to characterize the structure of the halo bars, with the goal of correlating them with the properties of the stellar bars. We use a suite of simulated galaxies with various bar strengths, including gas and star formation. We quantify the strengths, shapes, and densities of these simulated stellar bars. We carry out numerical experiments with frozen and analytic potentials in order to understand the role played by a live responsive stellar bar. We find that the halo bar generally follows the trends of the disc bar. The strengths of the halo and stellar bars are tightly correlated. Stronger bars induce a slight increase in dark matter density within the inner halo. Numerical experiments show that a non-responsive frozen stellar bar would be capable of inducing a dark matter bar, but it would be weaker than the live case by a factor of roughly two.

Trajectory Tracking Control of Unmanned Underwater Vehicle Based on Projected Perpendicular Guidance Method with Disturbance Observer

Unmanned underwater vehicles (UUVs) possess impressive maneuverability and versatility, but controlling them during trajectory tracking can be challenging due to their susceptibility to external disturbances and perturbations in their model parameters. Additionally, the UUV has four degrees of freedom underwater, but only three control inputs, making it a typical underactuated system. To address these issues, this paper introduces a novel optimize sliding mode control (OPSMC) algorithm grounded in projected perpendicular guidance (PPG). The PPG algorithm transforms the three-degree-of-freedom path trajectory control into two-degree-of-freedom heading tracking control and surge velocity tracking control by designing virtual posture angles. Optimized sliding mode control, based on sliding mode control, improves control precision and reduces control input chattering by constructing optimization functions for control inputs. During trajectory tracking, UUVs are susceptible to external environmental disturbances and perturbations in system model parameters. Disturbance observers are employed to estimate these disturbances and perturbations. Finally, MATLAB/Simulink is used for numerical simulation experiments. The simulation results demonstrate that the PPG algorithm effectively enables underactuated UUVs to achieve trajectory tracking control. The designed optimized sliding mode controller and disturbance observer enhance the control precision and robustness of the system.

Study on the Emission of Connected Autonomous Vehicle Considering the Control of Electronic Throttle Opening

Aiming at the networked cruise control scenario of CAV (connected autonomous vehicle) queue, we propose a new networked cruise control strategy for CAV by introducing the average information of ET (electronic throttle) opening of the downstream vehicle group as a feedback signal. By performing linear stability analysis on the new model, we derive its linear stability conditions. Further, we design exhaustive numerical simulation experiments aiming to systematically investigate the effect of the multi-vehicle ahead electronic throttle opening average feedback signal on CAV traffic stability, fuel consumption, and key emission factors, such as CO, HC, and NOx, during the cruise control process. The results show that the feedback signal can not only significantly improve the operational stability of the CAV traffic flow but also significantly improve its fuel consumption and the emission levels of CO, HC, and NOx. When the number of CAV vehicles in the feedback signal is set to three, the levels of CO, HC, and NOx emissions as well as fuel consumption in the road system can reach a stable and optimized state.

Hygrothermal response to air movements in wall junctions: Comparison between two numerical approaches and experiments

Airflow in wall-to-wall junctions is known to have a major hygrothermal impact on building performance. However, current and validated modeling options to simulate such phenomena are limited. This paper develops and compares two numerical models to study the heat and moisture transfer due to air infiltrations through a prefabricated wall-to-wall junction. The first model explicitly accounts for the airflow with a pipe flow approach. The second model is a modification to a typical approach to simulate ventilated cavities in building envelope simulation tools and mimics the effect of the airflow through source terms. Both approaches were introduced in a heat and moisture transfer 2D finite element model. Additionally, laboratory measurements were conducted in a climatic chamber to validate the simulation results. Six scenarios were tested experimentally under steady-state conditions. These datasets were used to calibrate different parameters of the models, such as material properties, the junction air gap thickness, and the magnitude of the heat and moisture source terms. Both sets of numerical results provided reasonable agreement with the measurements. The first approach outputs more accurate temperature and relative humidity values than the second one. However, considering uncertainties, no method predicted a perfect fit with the relative humidity profiles. Close to the junction, the first method estimates better the relative humidity than the second one. This work provides guidelines to better model and account for wall junctions in building envelope simulators.

Modified MF-DFA Model Based on LSSVM Fitting

This paper proposes a multifractal least squares support vector machine detrended fluctuation analysis (MF-LSSVM-DFA) model. The system is an extension of the traditional MF-DFA model. To address potential overfitting or underfitting caused by the fixed-order polynomial fitting in MF-DFA, LSSVM is employed as a superior alternative for fitting. This approach enhances model accuracy and adaptability, ensuring more reliable analysis results. We utilize the p model to construct a multiplicative cascade time series to evaluate the performance of MF-LSSVM-DFA, MF-DFA, and two other models that improve upon MF-DFA from recent studies. The results demonstrate that our proposed modified model yields generalized Hurst exponents h(q) and scaling exponents τ(q) that align more closely with the analytical solutions, indicating superior correction effectiveness. In addition, we explore the sensitivity of MF-LSSVM-DFA to the overlapping window size s. We find that the sensitivity of our proposed model is less than that of MF-DFA. We find that when s exceeds the limited range of the traditional MF-DFA, h(q) and τ(q) are closer than those obtained in MF-DFA when s is in a limited range. Meanwhile, we analyze the performances of the fitting of the two models and the results imply that MF-LSSVM-DFA achieves a better outstanding performance. In addition, we put the proposed MF-LSSVM-DFA into practice for applications in the medical field, and we found that MF-LSSVM-DFA improves the accuracy of ECG signal classification and the stability and robustness of the algorithm compared with MF-DFA. Finally, numerous image segmentation experiments are adopted to verify the effectiveness and robustness of our proposed method.

A numerical investigation on the vibration of a two-deck Euler–Bernoulli beam flooded by a potential flow

Abstract This work concerns the structural vibration of a bladeless wind turbine, modelled by a two-deck Euler–Bernoulli beam, due to a surrounding potential flow. The deflection is governed by the Euler–Bernoulli equation which is studied first by a linear theory and then computed numerically by a finite difference method in space with a collocation method over the arc length, and an implicit Euler method in time. The fluid motion in the presence of gravity is governed by the full Euler equations and solved by the time-dependent conformal mapping technique together with a pseudo-spectral method. Numerical experiments of excitation by a moving disturbance on the fluid surface with/without a stochastic noise are carried out. The random process involved in generating the noise on the water surface is driven by a Wiener Process. A Monte Carlo method is used for stochastic computations. The generated surface waves impinge on the beam causing structural vibration which is presented and discussed in detail. By elementary statistical analysis, the structural response subject to the stochastic hydrodynamic disturbance caused by white noise is found to be Gaussian.

Decentralized micro-energy storage capacity sharing within the residential community: an enhanced uniform price-based bidding framework

“Sharing economy” refers to a transformative socio-economic phenomenon where individuals or institution with idle resources transfer the right to use resources for economic compensation. With the widespread adoption of distributed photovoltaic generation and energy storage (ES) device in residential communities, there is a growing interest in establishing a suitable platform for residential users to share their ES capacity with community shared equipment controllers (CSECs). This paper proposes a local ES capacity sharing market, and presents the market trading process, pricing and allocation rules using an iterative uniform-price bidding mechanism Acknowledging the selfish-interest of both RUs and CSECs, we introduce the resource management organization (RMO) as a regulated third-party organization responsible for administering the market. To evaluate the proposed scheme, we conduct case studies based on real-life data from Pecan Street. The numerical experiment results demonstrate the effectiveness and applicability of our approach.

Valuations of generalized variance swaps under the jump–diffusion model with stochastic liquidity risk

This paper focuses on the pricing problem of generalized variance swaps with jump risk in the underlying asset price under a stochastic liquidity model. We obtain a pricing formula of generalized variance swap in a jump–diffusion model with stochastic liquidity risk by the joint moment generating function (MGF) generated by solving the partial integral differential equation (PIDE). Using asymptotic analysis, we also demonstrate that as the sampling interval approaches zero, the pricing formula of discretely sampled generalized variance swap tends to be that of continuously sampled generalized variance swap. Finally, to verify the feasibility of the pricing formula of the generalized variance swap presented in this paper, we conduct some numerical experiments, including a comparison with the results of Monte Carlo (MC) simulation, the impact of various model parameters on the delivery prices of generalized variance swaps, and empirical research using actual market data.

Dynamic Sensor Selection for Efficient Monitoring of Coupled Multidisciplinary Systems

Abstract Coupled multidisciplinary systems involve different disciplines/subsystems with feedback-coupled interactions, illustrating the complex interdependencies inherent in real-world engineering systems. Effective monitoring of a coupled multidisciplinary system is crucial for real-time assessment of the interactions between various disciplines within the system. This monitoring provides the data necessary for detecting and addressing issues in a timely manner and facilitates adaptive decision-making for taking reliable design or control actions. However, processing and analyzing data in real time is computationally intensive, and limited resources, such as computational power, sensor capabilities, and budget, may constrain the extent to which a system can be monitored comprehensively. To address this, this paper develops a particle-based approach that dynamically selects a subset of sensors that provides the highest information about the state of the system in real time. The proposed approach first predicts the amount of uncertainty in the estimation of the state of the system given noisy measurements from different subsets of available sensors. Then, it selects the sensors that reduce this uncertainty the most, enhancing the precision and efficiency of the monitoring process. The efficacy of the proposed framework is demonstrated via two coupled multidisciplinary systems in the numerical experiments.

Optimal Joint Defense and Monitoring for Networks Security under Uncertainty: A POMDP-Based Approach

The increasing interconnectivity in our infrastructure poses a significant security challenge, with external threats having the potential to penetrate and propagate throughout the network. Bayesian attack graphs have proven to be effective in capturing the propagation of attacks in complex interconnected networks. However, most existing security approaches fail to systematically account for the limitation of resources and uncertainty arising from the complexity of attacks and possible undetected compromises. To address these challenges, this paper proposes a partially observable Markov decision process (POMDP) model for network security under uncertainty. The POMDP model accounts for uncertainty in monitoring and defense processes, as well as the probabilistic attack propagation. This paper develops two security policies based on the optimal stationary defense policy for the underlying POMDP state process (i.e., a network with known compromises): the estimation-based policy that performs the defense actions corresponding to the optimal minimum mean square error state estimation and the distribution-based policy that utilizes the posterior distribution of network compromises to make defense decisions. Optimal monitoring policies are designed to specifically support each of the defense policies, allowing dynamic allocation of monitoring resources to capture network vulnerabilities/compromises. The performance of the proposed policies is examined in terms of robustness, accuracy, and uncertainty using various numerical experiments.