Couple-Group Consensus of Cooperative-Competitive Heterogeneous Multiagent Systems: A Fully Distributed Event-Triggered and Pinning Control Method

Receive wireless sensor data through IoT gateway using web client based on border gateway protocol

One of the significant topics in the field of the Internet of Things (IoT) pertains to the interaction and information sharing among people. The utilization of the Border Gateway Protocol (BGP) stack enhances the integration of web protocols and sensor networks, leading to greater accessibility. However, the BGP protocol stack introduces substantial overhead to messages transmitted at each layer, resulting in increased data overhead and energy consumption in networks by several orders of magnitude. This paper proposes a method to reduce the overhead on small and medium-sized packets. In multi-temporal networks utilizing BGP, scheduling and aggregating BGP packets at sensor nodes help achieve specific objectives. Various research methodologies and measures are employed to facilitate this, including request classification, BGP response prioritization within the network, determination of maximum acceptable delay, and overall network management. Synchronization and temporal integration of received messages at sensor nodes are performed, considering the maximum allowable delay for each message and the availability of the destination to process the accumulated messages. The evaluation results of the proposed method demonstrate a significant reduction in energy consumption and network traffic, particularly in monitoring applications within multi-stage networks. The protocol stack used is derived from the BGP standard.

Remote intelligent perception system for multi-object detection

Introduction

During the last few years, a heightened interest has been shown in classifying scene images depicting diverse robotic environments. The surge in interest can be attributed to significant improvements in visual sensor technology, which has enhanced image analysis capabilities.

Methods

Advances in vision technology have a major impact on the areas of multiple object detection and scene understanding. These tasks are an integral part of a variety of technologies, including integrating scenes in augmented reality, facilitating robot navigation, enabling autonomous driving systems, and improving applications in tourist information. Despite significant strides in visual interpretation, numerous challenges persist, encompassing semantic understanding, occlusion, orientation, insufficient availability of labeled data, uneven illumination including shadows and lighting, variation in direction, and object size and changing background. To overcome these challenges, we proposed an innovative scene recognition framework, which proved to be highly effective and yielded remarkable results. First, we perform preprocessing using kernel convolution on scene data. Second, we perform semantic segmentation using UNet segmentation. Then, we extract features from these segmented data using discrete wavelet transform (DWT), Sobel and Laplacian, and textual (local binary pattern analysis). To recognize the object, we have used deep belief network and then find the object-to-object relation. Finally, AlexNet is used to assign the relevant labels to the scene based on recognized objects in the image.

Results

The performance of the proposed system was validated using three standard datasets: PASCALVOC-12, Cityscapes, and Caltech 101. The accuracy attained on the PASCALVOC-12 dataset exceeds 96% while achieving a rate of 95.90% on the Cityscapes dataset.

Discussion

Furthermore, the model demonstrates a commendable accuracy of 92.2% on the Caltech 101 dataset. This model showcases noteworthy advancements beyond the capabilities of current models.

Decentralized restoration of distribution systems with coupling neighboring microgrids

In this study, a multi-agent system (MAS) is incorporated in a decentralized strategy to restore distribution systems while taking into account coupling neighboring microgrids (CNMGs). This provides modeling for renewable energy sources (RESs), electric vehicles (EVs), battery storage systems (BSS) and load. The desired and most favorable restoration path is found by the MAS, in which zone agents are dispersed across the distribution system. The MAS can also manage microgrids (MGs) overloaded as the unbalance operation of RESs, BSS, EVs, and load. This is realized by making a bridge between MGs and neighboring non-overloaded MGs. The suggested method adheres to voltage and power flow restrictions while operating according to expert system standards. The recommended approach is put to the test using a 33-bus radial distribution system. MATLAB calculations on agents and power flow are carried out in order to verify the validity of the choices made by agents. The proposed restoration plan is able to obtain the best power supply path with a low number of switching in the event of a fault so that the voltage magnitude is higher than 0.9 p.u. and free capacity is available for the distribution lines. The smart charging strategy of EVs reduces 93% of their turn off compared to the non-smart charging strategy. However, if the CNMG plan is established, all vehicles can be powered.

Performance and robustness analysis of V-Tiger PID controller for automatic voltage regulator

This paper presents a comprehensive study on the implementation and analysis of PID controllers in an automated voltage regulator (AVR) system. A novel tuning technique, Virtual Time response-based iterative gain evaluation and re-design (V-Tiger), is introduced to iteratively adjust PID gains for optimal control performance. The study begins with the development of a mathematical model for the AVR system and initialization of PID gains using the Pessen Integral Rule. Virtual time-response analysis is then conducted to evaluate system performance, followed by iterative gain adjustments using Particle Swarm Optimization (PSO) within the V-Tiger framework. MATLAB simulations are employed to implement various controllers, including the V-Tiger PID controller, and their performance is compared in terms of transient response, stability, and control signal generation. Robustness analysis is conducted to assess the system's stability under uncertainties, and worst-case gain analysis is performed to quantify robustness. The transient response of the AVR with the proposed PID controller is compared with other heuristic controllers such as the Flower Pollination Algorithm, Teaching-Learning-based Optimization, Pessen Integral Rule, and Zeigler-Nichols methods. By measuring the peak closed-loop gain of the AVR with the controller and adding uncertainty to the AVR's field exciter and amplifier, the robustness of proposed controller is determined. Plotting the performance degradation curves yields robust stability margins and the accompanying maximum uncertainty that the AVR can withstand without compromising its stability or performance. Based on the degradation curves, robust stability margin of the V-Tiger PID controller is estimated at 3.5. The worst-case peak gains are also estimated using the performance degradation curves. Future research directions include exploring novel optimization techniques for further enhancing control performance in various industrial applications.

Is exponential stability achievable in singular perturbed delayed systems with time-varying parameters? A comprehensive analysis

The present article conducts an investigation into the phenomenon of exponential stability within singular perturbed delayed systems, incorporating time-varying parameters. Singularly perturbed systems serve as essential tools in modeling intricate systems characterized by multiple time scales, wherein one subsystem exhibits significantly faster evolution than the others. The presence of small delays introduces complexities, influencing both state derivatives and delays, further accentuating the intricacies of the system. Drawing upon the principles of singular perturbation theory, the article introduces a novel approach to analyzing the stability of these complex systems, eschewing the conventional assumption of exponential stability in the fast subsystem. Within the scope of this study, we propose a rigorous stability analysis, utilizing Linear Matrix Inequality (LMI) methods, while considering time-varying parameters that exert substantial influence on the system's dynamics. The proposed methodology enables the exploration of system stability beyond conventional assumptions, imparting valuable insights into the behavior of singular perturbed delayed systems amidst varying conditions. Through extensive numerical simulations, the effectiveness and robustness of the approach are validated, illuminating the stability properties of these intricate systems. Comparative studies with existing techniques, which assume exponential stability in the fast subsystem, demonstrate the distinct advantages and uniqueness of the presented approach. The findings underscore the significance of accounting for time-varying parameters in achieving a comprehensive understanding of the exponential stability inherent in singular perturbed delayed systems. This research makes substantial contributions to the field of system stability analysis, particularly in the context of singular perturbed delayed systems featuring time-varying parameters. The originality of our approach lies in introducing a comprehensive analysis framework that overcomes the limitations of existing methodologies. By integrating a novel stability analysis method based on Linear Matrix Inequalities (LMIs), we offer a fresh perspective on achieving exponential stability in such complex systems. Significantly, our work addresses a critical gap in current literature by challenging the assumption of exponential stability in the fast subsystem, a key feature of singularly perturbed systems. Through a meticulous examination of time-varying parameters, we unveil their profound impact on system dynamics, thus enriching the understanding of stability behaviors. The potential real-world applications of our findings span diverse fields, ranging from engineering to mathematical modeling. Performance metrics are a key focal point of our research. Numerical simulations employing our proposed LMIs serve as a robust benchmark, demonstrating the superior stability achieved in comparison to existing methods. This performance-driven evaluation ensures the practical applicability and reliability of our analysis approach across various scenarios.