Extensive Simulations Research Articles

Online updating Huber robust regression for big data streams

ABSTRACT Big data streams have garnered significant attention in multiple industries. However, the immense volume and the presence of outliers in high-velocity streaming data pose great challenges to its analysis. To address these concerns, this paper introduces a novel Online Updating Huber Robust Regression algorithm. By efficiently capturing the salient features of new data subsets, a computationally efficient online updating estimator is proposed without the need for storing historical data. Furthermore, by incorporating Huber regression into its framework, the estimator exhibits robustness to heavy-tailed, heterogeneous as well as outlier-contaminated data. Theoretically, the proposed online updating estimator is asymptotically equivalent to an Oracle estimator derived from the entire dataset. Extensive numerical simulations and a real-world data analysis have been conducted to demonstrate the effectiveness and practicality of the proposed method.

Full duplex based collision detection to enhance the V2X sidelink autonomous mode

The Third Generation Partnership Project (3GPP) recently introduced the fifth-generation (5G) new radio (NR) sidelink to enable vehicle-to-everything (V2X) communications supporting advanced safety services. Nevertheless, improvements over the previous generation still pose challenges to meet the reliability and latency requirements of V2X communications, particularly in the allocation of distributed resources, i.e., Mode 2. In Mode 2, vehicles autonomously select radio resources for their message transmissions and can maintain the selected resources for a given reservation period to efficiently handle periodic data traffic. However, potential collisions during this period may remain undetected due to half-duplex communications and unacknowledged broadcast transmissions, resulting in persistent message losses and posing a threat to road safety. This paper aims to improve the 5G NR-V2X sidelink for systems beyond 5G-Advanced by exploiting full-duplex transceivers. We propose a novel medium access control (MAC) scheme where vehicles can detect collisions while transmitting, dynamically adapt the collision detection threshold according to the measured channel load, and react to detected collisions through appropriate resource reselection and retransmission procedures. Extensive simulations conducted under various settings show that this MAC scheme brings substantial performance gains in terms of reliability and latency, compared to the current legacy Mode 2 procedure and a benchmark full-duplex scheme from the literature.

Real-time energy management simulation for enhanced integration of renewable energy resources in DC microgrids

The presented work addresses the growing need for efficient and reliable DC microgrids integrating renewable energy sources. However, for the sake of practicality, implementing complex control strategies can increase system complexity. Thus, efficient methodologies are required to provide efficient energy management of microgrids while increasing the integration of renewable energy sources. The primary contribution of this work is to investigate the issues related to operating a DC microgrid with conventional control designed to power DC motors using readily available, non-advanced control strategies with the objective of achieving stable and reliable grid performance without resorting to complex control schemes. The proposed microgrid integrates a combination of uncontrollable renewable distributed generators (DGs) alongside controllable DGs and energy storage systems, including batteries and supercapacitors, connected via DC links. The Incremental Conductance (InCond) algorithm is employed for maximum power point tracking to maximize power output from the PV system. The energy management strategy prioritizes the solar system as the primary source, with the battery and supercapacitor acting as backup power sources to ensure overall system reliability and sustainability. The effectiveness of the microgrid under various operating conditions is evaluated through extensive simulations conducted using MATLAB. These simulations explore different power generation scenarios, including normal operation with varying load levels and operation under Standard Test Conditions (STC). Moreover, fault analysis of the DC microgrid is performed to examine system reliability. The system performance is evaluated using real-time simulation software (OPAL-RT) to validate the effectiveness of the approach under real-time conditions. This comprehensive approach demonstrates the efficacy of operating a DC microgrid with conventional controllers, ensuring grid stability and reliability across various operating conditions and fault scenarios while prioritizing the use of renewable energy sources. The results illustrated that system efficiency increases with load, but fault tolerance measures, can introduce trade-offs between reliability and peak efficiency.

BER and outage throughput analysis of DPIM/DHPIM coded QPSK-OFDM based outdoor optical wireless communications

With the growing demand for fast and reliable communication systems, particularly in outdoor environments, it is essential to investigate advanced encoding techniques. Digital Pulse Interval Modulation (DPIM) and Dual Header Pulse Interval Modulation (DHPIM) emerge as promising alternatives to traditional line coding methods, providing enhanced spectral efficiency and resistance to signal disruptions. This paper presents the performance of a Quadrature Phase-Shift Keying (QPSK) Orthogonal Frequency Division Multiplexing (OFDM)-based Optical Wireless Communication (OWC) system using these advanced encoding schemes. The analysis includes simulation results on QPSK-OFDM-based transmitter design, free space optical channel modeling based on Gaussian and log-normal distribution atmospheric turbulence, and recovery of input digital stream using the mentioned line coding techniques. The results demonstrate optimal bit error rate (BER) values for the QPSK-OFDM-based OWC system. The primary innovation of this research lies in the encoding schemes and their performance in outdoor optical wireless communication systems under turbulent conditions. Through extensive simulations and analysis, detailed insights into the bit error rate and outage throughput characteristics of DPIM/DHPIM coded QPSK-OFDM are provided, offering a unique perspective on the performance of these schemes in real-world scenarios. The findings not only highlight the optimal BER values achievable with the QPSK-OFDM-based OWC system but also offer insights into the system's ability to overcome transmission challenges under various atmospheric conditions.

Improved fault detection and classification in PV arrays using stockwell transform and data mining techniques

The growing integration of photovoltaic (PV) systems into the power grid necessitates reliable fault detection and classification mechanisms to ensure operational efficiency and safety. Fault detection in photovoltaic (PV) arrays is crucial for maintaining optimal system performance and ensuring the reliability of solar power generation. This paper proposes a novel approach for fault detection in PV arrays by employing the Stockwell transform in combination with various data mining techniques. The Stockwell transform is an advanced time-frequency analysis tool that allows for enhanced feature extraction from time-series data. By applying the Stockwell transform to the PV array's operational data, valuable frequency-domain information is extracted, enabling the identification of subtle fault signatures. To effectively detect and classify various faults, different data mining techniques, such as support vector machines, decision trees, random forests, and k-nearest neighbours, are applied to the transformed data. Each technique's effectiveness in identifying faults is evaluated and compared, enabling the selection of the most suitable algorithm for the specific application. Experimental results demonstrate the effectiveness of the proposed fault detection approach, exhibiting high accuracy, sensitivity, and specificity in identifying various types of faults in PV arrays. Extensive simulations and experimental validations were conducted on various fault conditions, including partial shading, open-circuit faults, and degradation. The results demonstrate the proposed method's superior performance, achieving an accuracy of 99.61 %, precision of 99.75 % and F1 score of 98.73 %. These metrics significantly surpass traditional fault detection techniques, highlighting the method's potential for real-world deployment. The approach not only enhances the reliability of PV systems but also contributes to reducing maintenance costs and improving system efficiency. The combination of the Stockwell transforms with data mining techniques proposed here provides a robust and efficient framework for early detection of faults, enabling timely maintenance and minimizing energy losses.

Optimal nonlinear Fractional-Order Proportional-Integral-Derivative controller design using a novel hybrid atom search optimization for nonlinear Continuously stirred Tank reactor

Atom search optimization (ASO) algorithm derived from physics molecular dynamics. Lennard-Jones(L-J) and bond length potential of molecules are used to derive the model for optimization. In this paper, ASO is used for developing a nonlinear Fractional Order Proportional Integral Derivative controller (NL-FOPID) for Continuously Stirred Tank Reactor (CSTR). The convergence characteristics of ASO was improved by proposing a novel hybridization approach. The proposed hybridization approach called Hybrid ASO(HASO) guides the Atom search algorithm to optimally replace the atoms that goes out of the boundary of the search space. The designed algorithm is implemented to optimize various unimodal and multi model standard benchmark functions. From results obtained from this extensive simulation, it is indicated that proposed approach increased the convergence rate and also improved the optimization effort of conventional ASO. The proposed algorithm also tested with controller design for nonlinear CSTR. The NLPID and NL FOPID designed by HASO was better than conventional controllers found in the literature.

Joint optimization of UAV aided covert edge computing via a deep reinforcement learning framework

In this work, we consider an Unmanned Aerial Vehicle (UAV) aided covert edge computing architecture, where multiple sensors are scattered with a certain distance on the ground. The sensor can implement several computation tasks. In an emergency scenario, the computational capabilities of sensors are often limited, as seen in vehicular networks or Internet of Things (IoT) networks. The UAV can be utilized to undertake parts of the computation tasks, i.e., edge computing. While various studies have advanced the performance of UAV-based edge computing systems, the security of wireless transmission in future 6G networks is becoming increasingly crucial due to its inherent broadcast nature, yet it has not received adequate attention. In this paper, we improve the covert performance in a UAV aided edge computing system. Parts of the computation tasks of multiple ground sensors are offloaded to the UAV, where the sensors offload the computing tasks to the UAV, and Willie around detects the transmissions. The transmit power of sensors, the offloading proportions of sensors and the hovering height of the UAV affect the system covert performance, we propose a deep reinforcement learning framework to jointly optimize them. The proposed algorithm minimizes the system average task processing delay while guaranteeing that the transmissions of sensors are not detected by the Willie under the covertness constraint. Extensive simulations are conducted to verify the effectiveness of the proposed algorithm to decrease the average task processing delay with comparison with other algorithms.

QRAGE-Simultaneous multiparametric quantitative MRI of water content, T1, T2*, and magnetic susceptibility at ultrahigh field strength.

To introduce quantitative rapid gradient-echo (QRAGE), a novel approach for the simultaneous mapping of multiple quantitative MRI parameters, including water content, T1, T2*, and magnetic susceptibility at ultrahigh field strength. QRAGE leverages a newly developed multi-echo MPnRAGE sequence, facilitating the acquisition of 171 distinct contrast images across a range of inversion and TE points. To maintain a short acquisition time, we introduce MIRAGE2, a novel model-based reconstruction method that exploits prior knowledge of temporal signal evolution, represented as damped complex exponentials. MIRAGE2 minimizes local Block-Hankel and Casorati matrices. Parameter maps are derived from the reconstructed contrast images through postprocessing steps. We validate QRAGE through extensive simulations, phantom studies, and in vivo experiments, demonstrating its capability for high-precision imaging. In vivo brain measurements show the promising performance of QRAGE, with test-retest SDs and deviations from reference methods of < 0.8% for water content, < 17 ms for T1, and < 0.7 ms for T2*. QRAGE achieves whole-brain coverage at a 1-mm isotropic resolution in just 7 min and 15 s, comparable to the acquisition time of an MP2RAGE scan. In addition, QRAGE generates a contrast image akin to the UNI image produced by MP2RAGE. QRAGE is a new, successful approach for simultaneously mapping multiple MR parameters at ultrahigh field.

Cloud-fog architecture-based control of smart island microgrid in master-slave organization using disturbance observer-based hybrid backstepping sliding mode controller

Distributed control is an effective method to coordinate the microgrid with various components, and also in a smart microgrid, communication graph layouts are essential since changing the topology unexpectedly could disrupt the operation of the distributed controllers, and also an imbalance may occur between the production and load. Hence, reducing the exchanged data between units and system operator is essential in order to reduce the transmitted data volume and computational burden. For this purpose, an islanded microgrid with multiple agents which is using cloud-fog computing is proposed here, in order to reduce the computing burden on the central control unit as well as reducing data exchange among units. To balance the production power and loads in a smart island with a stable voltage/frequency, a hybrid backstepping sliding mode controller (BSMC) with disturbance observer (DO) is suggested to control voltage/frequency and current in the MG-based master-slave organization. Therefore, this paper proposes a DO-driven BSMC for controlling voltage/frequency, and power of energy sources within a Master-Slave organization; in addition, the study proposes a clod-fog computing for enhancing performance, reducing transferred data volume, and processing information on time. In the extensive simulations, the suggested controller shows a reduction in steady-state error, a fast response, and a lower total harmonic distortion (THD) for nonlinear and linear loads less than 0.33 %. The fog layer serves as a local processing level, so it reduces the exchanged data between cloud and fog nodes.

Robust Platoon Control of Mixed Autonomous and Human-Driven Vehicles for Obstacle Collision Avoidance: A Cooperative Sensing-Based Adaptive Model Predictive Control Approach

Obstacle detection and platoon control for mixed traffic flows, comprising human-driven vehicles (HDVs) and connected and autonomous vehicles (CAVs), face challenges from uncertain disturbances, such as sensor faults, inaccurate driver operations, and mismatched model errors. Furthermore, misleading sensing information or malicious attacks in vehicular wireless networks can jeopardize CAVs’ perception and platoon safety. In this paper, we develop a two-dimensional robust control method for a mixed platoon, including a single leading CAV and multiple following HDVs that incorporate robust information sensing and platoon control. To effectively detect and locate unknown obstacles ahead of the leading CAV, we propose a cooperative vehicle–infrastructure sensing scheme and integrate it with an adaptive model predictive control scheme for the leading CAV. This sensing scheme fuses information from multiple nodes while suppressing malicious data from attackers to enhance robustness and attack resilience in a distributed and adaptive manner. Additionally, we propose a distributed car-following control scheme with robustness to guarantee the following HDVs, considering uncertain disturbances. We also provide theoretical proof of the string stability under this control framework. Finally, extensive simulations are conducted to validate our approach. The simulation results demonstrate that our method can effectively filter out misleading sensing information from malicious attackers, significantly reduce the mean-square deviation in obstacle sensing, and approach the theoretical error lower bound. Moreover, the proposed control method successfully achieves obstacle avoidance for the mixed platoon while ensuring stability and robustness in the face of external attacks and uncertain disturbances.

Performance and Design Optimization of Solar Collectors

The objective of the present work was to conduct a comparative analysis of solar collectors. The experimental set-up consists of two flat plate solar collectors, the first collector was with a fixed surface tilted at 30о towards the South, and the second one was equipped with dual-axis rotation system. A programmable chronological tracker was used to control the motion of the moving collector. An experimental programme was performed to investigate the effect of the sun tracking system usage on the collector efficiency. The study includes development of a 3-D mathematical model of the system and extensive numerical simulation, based on the computational fluid dynamics (CFD) approach. The main aim of the modelling is to provide information on heat transfer, and to numerically simulate the heat transfer performances and the energy capture capabilities of the fixed and moving collectors in various operating modes. The comparative analysis shows that there is a good agreement between the experimental and numerically predicted results for different running conditions, and the presented modelling approach can be used for further investigations, including more complex problems.

Learning-based joint recommendation, caching, and transmission optimization for cooperative edge video caching in Internet of Vehicles

In an era dominated by multimedia information, achieving efficient video transmission in the Internet of Vehicles (IoV) is crucial because of the inherent bandwidth constraints and network volatility within vehicular environments. In this paper, we propose a cooperative edge video caching framework designed to enhance video delivery efficiency in IoV by integrating joint recommendation, caching, and transmission optimization. Leveraging deep reinforcement learning with the discrete soft actor–critic algorithm, our methodology dynamically adapts to fluctuating network conditions and diverse user preferences, aiming to optimize content delivery efficiency and quality of experience. The proposed approach combines recommendation and caching strategies with transmission optimization to provide a comprehensive solution for high-performance video services. Extensive simulation results demonstrate that our framework significantly outperforms traditional baseline methods, achieving superior outcomes in terms of service utility, delivery rate, and delay reduction. These results highlight the robust potential of our solution to facilitate seamless and high-quality video experiences in the complex and dynamic landscape of vehicular networks, advancing the capabilities of IoV content delivery.

A Vortex Electromagnetic Wave Beam Control Method based on Amplitude-Modulated Concentric Circular Array

Abstract In order to enhance the design efficiency of vortex electromagnetic (EM) wave radar detection systems, a method for generating and controlling vortex EM waves based on amplitude-modulated concentric circular array (CCA) is proposed. This method allows for precise control of the vortex EM waves, ensuring accurate main lobe direction while effectively reducing sidelobes. Based on approximate orthogonal transformation, formulas for calculating the excitation amplitudes of each sub-circular array are derived and the time complexity of this method is negligible compared to search-based algorithms. Extensive simulation experiments are conducted and compared with the results obtained from the genetic algorithm (GA) approach, providing robust evidence of the effectiveness of this method(SLL &lt; -30dB).

Graphene metamaterial (GMM) based dual mode, tunable, and broadband THz absorber with triple circular split ring structure

In this study we investigated a novel approach to designing a graphene metamaterial (GMM) based terahertz (THz) absorber with dual-mode functionality, tunability, and broadband absorption capabilities. The study leverages the unique properties of graphene, a material known for its exceptional electronic and optical characteristics, combined with metamaterials to achieve efficient THz absorption. Here we performed extensive simulation on four different types of configurations and found the optimized structure has the highest bandwidth of 3.8 THz and absorption over 90 %. The absorber is designed to operate in two distinct modes, enhancing its versatility for different applications in the THz spectrum. Moreover, the tunability of the absorber is a significant feature, allowing for dynamic adjustment of the absorption frequency, which is crucial for applications in THz imaging, sensing, and communication systems. The broadband nature of the absorber ensures effective performance over a wide range of frequencies, addressing the need for flexible and high-performance devices in emerging THz technologies. This work represents a significant advancement in the field of THz metamaterials, with potential implications for the development of next-generation THz devices.

Studying Binary Formation under Dynamical Friction Using Hill’s Problem

Using the equations of motion from Hill’s problem, with added accelerations for different forms of dynamical friction, we provide the (to-date) broadest scale-free study of friction-driven binary formation in gaseous disks and stellar clusters. We focus mainly on binary formation between stellar-mass black holes in active galactic nuclei (AGNs), considering both gas dynamical friction (GDF) from AGN disks and stellar dynamical friction (SDF) from the nuclear star cluster. We first find simple, dimensionless friction coefficients that approximate the effects of standard models for GDF and SDF. We perform extensive simulations of Hill’s problem under such friction, and we present a picture of binary formation through encounters between single stars on nearby orbits, as a function of friction parameter, eccentricity, and inclination. Notably, we find that the local binary formation rate is a linear function of the friction coefficient so long as the friction is weak. Due to the dimensionless nature of our model problem, our findings are generalizable to binary formation at all scales (e.g., intermediate-mass black holes in a star cluster, planetesimals in a gaseous disk).

The impact of dynamic reward on cooperation in the spatial public goods game

Debates persist regarding how different forms of rewards influence public cooperation. In many real-world scenarios, individuals who consistently honor multiple commitments receive higher credit scores than those who fulfill only one commitment. Inspired by this, herein dynamic reward is introduced into the spatial public goods game, wherein the probability of receiving a reward increases with the number of consecutive cooperations by individuals. To illustrate the role of the dynamic reward mechanism, fixed reward is used as a comparison to investigate the impact of dynamic reward on the evolution of cooperation. Through extensive simulations, we find that the phases presented by dynamic reward are more than those by fixed reward. Moreover, it is more conducive to cooperation than fixed reward, indicating that dynamic reward can more effectively resolve social dilemmas. Additionally, the higher initial probability of reward promotes the earlier emergence of cooperation. This research broadens the form of reward, enhancing our understanding of dynamic reward in promoting prosocial behaviors in the spatial public goods game.

Securing IoT: Mitigating Sybil Flood Attacks with Bloom Filters and Hash Chains

In the evolving landscape of the Internet of Things (IoT), ensuring the security and integrity of data transmission remains a paramount challenge. Routing Protocol for Low-Power and Lossy Networks (RPL) is commonly utilized in IoT networks to facilitate efficient data routing. However, RPL networks are susceptible to various security threats, with Sybil and flood attacks being particularly detrimental. Sybil attacks involve malicious nodes generating multiple fake identities to disrupt network operations, while flood attacks overwhelm network resources by inundating them with excessive traffic. This paper proposes a novel mitigation strategy leveraging Bloom filters and hash chains to enhance the security of RPL-based IoT networks against sybil and flood attacks. Extensive simulation and performance analysis demonstrate that this solution significantly reduces the impact of sybil and flood attacks while maintaining a low power consumption profile and low computational overhead.

Proactive deadlock prevention based on traffic classification sub-graphs for triplet-based NoC TriBA-cNoC

Network topology and routing algorithms stand as pivotal decision points that profoundly impact the performance of Network-on-Chip (NoC) systems. As core counts rise, so does the inherent competition for shared resources, spotlighting the critical need for meticulously designed routing algorithms that circumvent deadlocks to ensure optimal network efficiency. This research capitalizes on the Triplet-Base Architecture (TriBA) and its Distributed Minimal Routing Algorithm (DM4T) to overcome the limitations of previous approaches. While DM4T exhibits performance advantages over previous routing algorithms, its deterministic nature and potential for circular dependencies during routing can lead to deadlocks and congestion. Therefore, this work addresses these vulnerabilities while leveraging the performance benefits of TriBA and DM4T. This work introduces a novel approach that merges a proactive deadlock prevention mechanism with Intermediate Adjacent Shortest Path Routing (IASPR). This combination guarantees both deadlock-free and livelock-free routing, ensuring reliable communication within the network. The key to this integration lies in a flow model-based data transfer categorization technique. This technique prevents the formation of circular dependencies. Additionally, it reduces redundant distance calculations during the routing process. By addressing these challenges, the proposed approach achieves improvements in both routing latency and throughput. To rigorously assess the performance of TriBA network topologies under varying configurations, extensive simulations were undertaken. The investigation encompassed both TriBA networks comprising 9 nodes and those with 27 nodes, employing DM4T, IASPR routing algorithms, and the proactive deadlock prevention method. The gem5 simulator, operating under the Garnet 3.0 network model using a standalone protocol for synthetic traffic patterns, was utilized for simulations at high injection rates, spanning diverse synthetic traffic patterns and PARSEC benchmark suite applications. Simulations rigorously quantified the effectiveness of the proposed approach, revealing reductions in average latency 40.17% and 34.05% compared to the lookup table and DM4T, respectively. Additionally, there were notable increases in average throughput of 7.48% and 5.66%.

Dynamic Prediction Using Landmark Historical Functional Cox Regression

Dynamic prediction of survival data in the presence of time-varying covariates is an area of active research. Two common analytic approaches for this type of data are joint modeling of the longitudinal and survival processes and landmarking. However, there has been little work dedicated to densely measured time-varying covariates using either approach. Moreover, the software for joint modeling is slow, especially for large datasets, and rather limited for landmarking. We propose a landmark approach for dynamic prediction of survival outcomes using densely measured longitudinal predictors, which treats the past of the time-varying covariate at each landmark point as a functional predictor. This approach is orders of magnitude faster than existing software for simpler joint models. Our extensive comparative simulation study required 8.4 computation-years, over 99% of which was devoted to fitting and predicting from two joint models. Our landmark approach performs similarly to joint modeling when the joint model is correctly specified and substantially out-performs it when it is not. Methods are motivated by an application predicting time to recovery of Multiple Sclerosis lesions in a large neuroimaging dataset. Supplementary materials for this article are available online.

Power modified XLindley distribution: Statistical properties and applications

This research introduces a novel two-parameter distribution, the power-modified XLindley distribution, developed through the application of power transformation techniques to the existing modified XLindley distribution. This new distribution enhances flexibility and adaptability in statistical modeling. We conduct a thorough examination of its statistical properties, exploring its potential to improve data fitting and modeling accuracy. To assess the effectiveness of the model, we employ multiple estimation techniques and evaluate their performance through extensive simulation experiments. Our findings indicate that the maximum product of the spacings method is particularly effective for parameter estimation. To demonstrate the practical utility of the proposed model, we apply it to two real-world datasets: one related to flood data and the other to reliability engineering. The results underscore the distribution’s superior ability to capture the characteristics of these datasets compared to existing models, highlighting its significance for applications in natural disaster analysis and reliability studies.