This article presents the results of developing a model for assessing water quality using the artificial intelligence method. The presented model is based on linear regression, which, when evaluated, revealed a statis...
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A miniature robotic blimp, as one type of lighter-than-air aerial vehicle, has attracted increasing attention in the science and engineering field for its long flight duration and safe aerial locomotion. While a varie...
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The development of microengineered hydrogels has opened up unlimited possibilities for designing complex structures at the microscale. In this study, we constructed an origami-inspired tubular structure with controlle...
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The development of microengineered hydrogels has opened up unlimited possibilities for designing complex structures at the microscale. In this study, we constructed an origami-inspired tubular structure with controlled mechanical buckling based on optically induced electrokinetics(OEK). By inducing a stress gradient in the thickness, a tubular structure can be formed from a poly(ethylene glycol) diacrylate(PEGDA) hydrogel film of various shapes that have been custom fabricated. To achieve an ideal three-dimensional(3D) structure, the amplitude of the tubular structure can be controlled by adjusting the aspect ratios or polymerization time. Furthermore, the tubular structure can be manipulated for the collection and transportation of *** summary, we provide an effective method for designing 3D structures at the micro-nano scale. This forming method holds great potential for achieving various functions in tissue engineering, drug packaging, and transportation in the future.
Mobile robots are becoming increasingly ubiquitous in modern society, requiring more human-like interaction capabilities, such as following operator instructions and collaborating with humans. Conventional robot progr...
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Detecting surrounding situations and reacting accordingly to avoid collisions remains a challenging task for autonomous driving. This task requires predicting the trajectories of surrounding agents and assessing the p...
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Detecting surrounding situations and reacting accordingly to avoid collisions remains a challenging task for autonomous driving. This task requires predicting the trajectories of surrounding agents and assessing the potential risk of future situations, which can be difficult to achieve solely through onboard vehicle devices. Therefore, this paper proposes a cooperative architecture for trajectory prediction and risk assessment conducted on roadside devices (RSUs) to assist Connected and Autonomous Vehicles (CAVs). Firstly, we develop a segmentbased prediction model (SegNet) tailored to hub signalized intersections. Intersections are divided into multiple segments, and the Curvilinear coordinates are utilized to indicate the geometric road features. The model leverages individual interaction cues in the ego segment and group features in the merging segments, while also incorporating traffic signal information to generate multimodal prediction results. In terms of risk assessment, we utilize the prediction results to provide hierarchical assistance, such as risk values, risk maps, and reference trajectories. Offline experimental results demonstrate that our SegNet model achieves competitive and well-balanced performance compared to stateof-the-art methods on the CitySim Database, with more accurate and smooth prediction trajectories. Through real-time CARLA and SUMO co-simulation, the performance of assisted CAVs indicates that they can safely and effectively navigate with the support of the proposed architecture. IEEE
Ensuring airport security has always been crucial for national security. While advancements in security technology have improved the detection of non-metallic and other materials, concerns around health and privacy vi...
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The integration of robots into environments shared by humans has been enhanced through the use of redundant robots capable of executing primary tasks and secondary objectives such as obstacle avoidance and null-space ...
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Modern acoustic emission (AE) diagnostic systems are sensitive instruments for detecting developing defects. A significant limitation of the AE method is the difficulty of isolating signals against the background of i...
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In this paper, the cooperative output regulation(COR) problem of a class of unknown heterogeneous multi-agent systems(MASs) with directed graphs is studied via a model-free reinforcement learning(RL) based fully distr...
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In this paper, the cooperative output regulation(COR) problem of a class of unknown heterogeneous multi-agent systems(MASs) with directed graphs is studied via a model-free reinforcement learning(RL) based fully distributed eventtriggered control(ETC) strategy. First, we consider the scenario that the exosystem is accessible globally to all agents, an internal model-based augmented algebraic Riccati equation(AARE) is constructed, and its solution is learned by the proposed model-free RL algorithm via online input-output data. Further, for the scenario that the exosystem is accessible only to its adjacent followers, the distributed observers are designed for each agent to get the state of the exosystem, and an internal modelbased fully distributed adaptive ETC protocol is then synthesized to construct the corresponding AARE, and the feedback gain matrix is learned in a model-free fashion. The model-free RL-based control protocol proposed in this paper can not only remove the prior knowledge of agents' dynamics, but also release the dependence on global information by the adaptive event-triggered mechanism(ETM) and the new graph-based Lyapunov function. Finally, simulation results are illustrated to show the feasibility and effectiveness of the proposed control scheme.
Grading of onion is important for the purpose of quality as well as market value, and it has, in the past, used traditional methods. Onion grading has thus been automated, which has been a focus of numerous researcher...
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