Xiangyang Liang

To address the high-dimensional, non-convex optimization challenge of tuning fractional-order PID (FOPID) controllers for Automatic Voltage Regulator (AVR) systems, we propose a Chaotic-Enhanced Phototropic Growth Algorithm (C-PGA). The standard Phototropic Growth Algorithm (PGA) suffers from premature convergence due to insufficient population diversity; thus, we integrate a Logistic chaotic map for optimized population initialization and dynamic perturbations during stagnation, enhancing global exploration and local exploitation. A weighted multi-objective function incorporating integral time absolute error (ITAE), overshoot, and steady-state error guides the optimization process. MATLAB/Simulink simulations demonstrate that the C-PGA-tuned FOPID controller achieves near-zero overshoot, improved transient responses, satisfactory stability under ± 50% parameter perturbations, and effective disturbance rejection. Wilcoxon rank-sum tests confirm its competitive performance compared to classical (PSO, GA) and modern (RIME, DBO) algorithms, indicating its potential as an effective solution for AVR voltage regulation in a simplified linear benchmark.

While the Transmission Control Protocol (TCP) is essential for congestion control by adjusting packet sending rates, it falls short of resolving the buffer bloat problem in critical routers. In response, Active Queue Management (AQM) mechanisms, notably Random Early Detection (RED), have been proposed to construct a feedback system, TCP/RED, for congestion control. However, existing AQM controllers like RED lack comprehensive optimization of control parameters for adapting to dynamic network conditions effectively. In this study, we propose a novel heuristic algorithm (AOMOA), which combines the global exploration of Aquila Optimizer (AO) with the local exploitation of Math Optimizer (MO), to optimize AQM controller parameters within the TCP/RED feedback system. AOMOA leverages chaotic-subpopulation and dynamic k-worst shift strategies to ensure a balance between exploration and exploitation, thereby mitigating premature convergence. Additionally, we analyze RED's intrinsic flaw and, therefore introduce a Proportional-Integral-Derivative (PID) adjuster into RED, RED-PID, to overcome the limitation according to theory analysis. To optimize RED-PID parameters, we present an optimization model ensuring stability and sensitivity in congestion control. Comprehensive simulations demonstrate that RED-PID, optimized by AOMOA, outperforms the standard RED controller, showcasing superior congestion control performance.

The combination of unmanned aerial vehicles (UAVs) and deep learning has potential applicability in various complex search and rescue scenes. However, due to the presence of environmental occlusions such as trees, the performance of UAVs mounted with different optical payloads in detecting missing persons is poor. To the best of our knowledge, currently available non-occluded human target datasets are insufficient to address the challenges of automatic recognition for partially occluded human targets. To address this problem, we collected a UAV-based infrared thermal imaging dataset for outdoor, partially occluded person detection (POP). POP is composed of 8768 labeled thermal images collected from different environmental scenes. After training with popular object detection networks, our dataset performed stable average precision for partially occluded person detection and short response time. In addition, high precision of object detection by POP trained networks was not attenuated until the occlusion rate exceeded 70%. We expected POP would extend present methodologies for the search of human objects under complex occluded circumstances.

A solution to address the issues of environmental light interference in Remote Photoplethysmography (rPPG) methods is proposed in this paper. First, signals from the face’s region of interest (ROI) and background noise signals are simultaneously collected, and the two signals are processed by a differential to obtain a more accurate rPPG signal. This method effectively suppresses background noise and enhances signal quality. Secondly, the singular spectrum analysis algorithm (SSA) is enhanced to further improve the accuracy of heart rate detection. The algorithm’s parameters are adaptively optimized by integrating the spectral and periodic characteristics of the heart rate signal. Experimental results demonstrate that the method proposed in this paper effectively mitigates the effects of lighting changes on heart rate detection, thereby enhancing detection accuracy. Overall, the experiments indicate that the proposed method significantly improves the effectiveness and accuracy of heart rate detection, achieving a high level of consistency with existing contact-based detection methods.

In this paper, we address the challenge of task offloading for mobile edge computing in the Internet of Vehicles (IoV). We employ a closed queuing network model to analyze the optimal server load percentage, focusing on the response time of tasks. We evaluate the delay of task offloading by constructing a closed queuing network model and applying the analysis of this network. Additionally, we analyze the system performance by varying the number of vehicles, the number of edge servers, and the edge server load percentage. The results of the study show that there exists an optimal edge server load percentage during task offloading, and this value makes it possible to compute the minimum average response time spent for a task during offloading while ensuring fairness in offloading delays. Increasing the number of edge servers affects the selection of the optimal load percentage, which in turn reduces the minimum response time. Moreover, as the number of vehicles increases, the average response time of tasks in the system increases accordingly. This paper provides a solution for delay-oriented task offloading in the IoV.