信息工程最新文献

Accurate velocity measurement of unmanned aerial vehicles (UAVs) is essential in various applications. Traditional vision-based methods rely heavily on visual features, which are often inadequate in low-light or feature-sparse environments. This study presents a novel approach to measure the axial velocity of UAVs using motion blur images captured by a UAV-mounted monocular camera. We introduce a motion blur model that synthesizes imaging from neighboring frames to enhance motion blur visibility. The synthesized blur frames are transformed into spectrograms using the Fast Fourier Transform (FFT) technique. We then apply a binarization process and the Radon transform to extract light-dark stripe spacing, which represents the motion blur length. This length is used to establish a model correlating motion blur with axial velocity, allowing precise velocity calculation. Field tests in a hydropower station penstock demonstrated an average velocity error of 0.048 m/s compared to ultra-wideband (UWB) measurements. The root-mean-square error was 0.025, with an average computational time of 42.3 ms and CPU load of 17%. These results confirm the stability and accuracy of our velocity estimation algorithm in challenging environments.

Semantic segmentation is significant to realize the scene understanding of autonomous driving. Due to the lack of annotated real-world data, the technology of domain adaptation is applied so that the model is trained on the synthetic data and inferred on the real data. However, this domain gap leads to aleatoric and epistemic uncertainty. These uncertainties link to the potential safety issue of autonomous driving in normal weather and adverse weather. In this study, we explore the scientific problem that has received sparse attention previously. We postulate that the Dual Attention module can mitigate the uncertainty in the task of semantic segmentation and provide some empirical study to validate it. Furthermore, the utilization of Kullback-Leibler divergence (KL divergence) helps the estimation of aleatoric uncertainty and boosts the robustness of the segmentation model. Our empirical study on the diverse datasets of semantic segmentation demonstrates the effectiveness of our method in normal and adverse weather. Our code is available at: https://github.com/liubo629/Seg-Uncertainty-dual-attention.

This paper presents and examines a COVID-19 model that takes comorbidities and up to three vaccine doses into account. We analyze the stability of the equilibria, examine herd immunity, and conduct a sensitivity analysis validated by data on COVID-19 in Indonesia. The disease-free equilibrium is locally and globally asymptotically stable whenever the basic reproduction number is less than one, while an endemic equilibrium exists and is globally asymptotically stable when the number is greater than one. Subsequently, the model incorporates two effective measures, namely public education and enhanced medical care, to determine the most advantageous approach for mitigating the transmission of the disease. The optimal control model is then determined using Pontryagin’s maximum principle. The integrated control strategy is the best method for reliably safeguarding the general population against COVID-19 infection. Cost evaluations and numerical simulations corroborate this conclusion.

This study addresses the complexities of maritime area information collection, particularly in challenging sea environments, by introducing a multi-agent control model for regional information gathering. Focusing on three key areas—regional coverage, collaborative exploration, and agent obstacle avoidance—we aim to establish a multi-unmanned ship coverage detection system. For regional coverage, a multi-objective optimization model considering effective area coverage and time efficiency is proposed, utilizing a heuristic simulated annealing algorithm for optimal allocation and path planning, achieving a 99.67% effective coverage rate in simulations. Collaborative exploration is tackled through a comprehensive optimization model, solved using an improved greedy strategy, resulting in a 100% static target detection and correct detection index. Agent obstacle avoidance is enhanced by a collision avoidance model and a distributed underlying collision avoidance algorithm, ensuring autonomous obstacle avoidance without communication or scheduling. Simulations confirm zero collaborative failures. This research offers practical solutions for multi-agent exploration and coverage in unknown sea areas, balancing workload and time efficiency while considering ship dynamics constraints.

Water conservation has become a global problem as the population increases. In many densely populated cities in China, leaks from century-old pipe works have been widespread. However, entirely eradicating the issues involves replacing all water networks, which is costly and time-consuming. This paper proposed an AI-enabled water-saving control system with three control modes: time division control, flow regulation, and critical point control according to actual flow. Firstly, based on the current leaking situation of water supply networks in China and the capability level of China’s water management, a water-saving technology integrating PID control and a series of deep learning algorithms was proposed. Secondly, a multi-jet control valve was designed to control pressure and reduce water distribution network cavitation. This technology has been successfully applied in industrial settings in China and has achieved gratifying water-saving results.

The emergence and zoonotic transmission of poxviruses in the Middle East have been recognized as complex public health issues. Poxviruses, a vast family of DNA viruses, can infect many hosts, including animals and humans. The Middle East has had multiple epidemics of poxvirus infections (e.g., Monkeypox, Smallpox, and Camelpox) that have raised concerns owing to their detrimental effects on livestock, wildlife, and sporadic human cases. This review aims to thoroughly examine the complexity of the epidemiological patterns, intricate genetic diversity, and several contributing factors that support the emergence and zoonotic transmission of poxviruses in the Middle East. Several aspects of poxviruses contribute to the emergence of endemics and zoonotic breakouts, such as the complex nature of human-animal interactions, environmental changes, and their subtle capacity for viral adaptability. This review was compiled in the hopes of contributing to the current understanding of poxvirus biology and its implications for human and animal health in the Middle East. We provide a comprehensive overview of the most common poxviruses in the Middle East, including their classification, structure, replication cycle, pathogenesis, route of transmissions, and of how the Middle East has developed ways to mitigate these biological threats.

In this article, a nonlinear optimal control approach is proposed for the dynamic model of 3-DOF four-cable driven parallel robots (CDPR). To solve the associated nonlinear optimal control problem, the dynamic model of the 3-DOF cable-driven parallel robot undergoes approximate linearization around a temporary operating point that is recomputed at each time-step of the control method. The linearization relies on Taylor series expansion and on the associated Jacobian matrices. For the linearized state-space model of the 3-DOF cable-driven parallel robot a stabilizing optimal (H-infinity) feedback controller is designed. To compute the controller’s feedback gains an algebraic Riccati equation is repetitively solved at each iteration of the control algorithm. The stability properties of the control method are proven through Lyapunov analysis. The proposed nonlinear optimal control approach achieves fast and accurate tracking of reference setpoints under moderate variations of the control inputs and a minimum dispersion of energy.