The method [K]control of Adaptive Multiple-timescale systems (KAMS) has been used as a method of adaptive control for systems with states that evolve at vastly different rates and with uncertain parameters. Prior rese...
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The popularity of quadrotor Unmanned Aerial Vehicles(UAVs)stems from their simple propulsion systems and structural ***,their complex and nonlinear dynamic behavior presents a significant challenge for control,necessi...
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The popularity of quadrotor Unmanned Aerial Vehicles(UAVs)stems from their simple propulsion systems and structural ***,their complex and nonlinear dynamic behavior presents a significant challenge for control,necessitating sophisticated algorithms to ensure stability and accuracy in *** strategies have been explored by researchers and control engineers,with learning-based methods like reinforcement learning,deep learning,and neural networks showing promise in enhancing the robustness and adaptability of quadrotor control *** paper investigates a Reinforcement Learning(RL)approach for both high and low-level quadrotor control systems,focusing on attitude stabilization and position tracking tasks.A novel reward function and actor-critic network structures are designed to stimulate high-order observable states,improving the agent’s understanding of the quadrotor’s dynamics and environmental *** address the challenge of RL hyper-parameter tuning,a new framework is introduced that combines Simulated Annealing(SA)with a reinforcement learning algorithm,specifically Simulated Annealing-Twin Delayed Deep Deterministic Policy Gradient(SA-TD3).This approach is evaluated for path-following and stabilization tasks through comparative assessments with two commonly used control methods:Backstepping and Sliding Mode Control(SMC).While the implementation of the well-trained agents exhibited unexpected behavior during real-world testing,a reduced neural network used for altitude control was successfully implemented on a Parrot Mambo mini *** results showcase the potential of the proposed SA-TD3 framework for real-world applications,demonstrating improved stability and precision across various test scenarios and highlighting its feasibility for practical deployment.
Detecting P300 event-related potentials (ERPs) from electroencephalography (EEG) finds applications in several domains including brain computer interfaces (BCIs). Due to the low signal-to-noise ratio (SNR), however, d...
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ISBN:
(数字)9798331520526
ISBN:
(纸本)9798331520533
Detecting P300 event-related potentials (ERPs) from electroencephalography (EEG) finds applications in several domains including brain computer interfaces (BCIs). Due to the low signal-to-noise ratio (SNR), however, detecting P300 from single trials has been challenging. As a result, multiple repetitions are typically needed, extending the BCI decision time. To address this problem, in this paper, we present TopoEEG, a novel method that generates a sequence of topography-based images from EEG, to capture both spatial and temporal information. These sequences are subsequently processed using the state-of-the-art video classification technique, TimeSformer. Evaluated using the BCI Competition II (IIb) dataset, results demonstrate that TopoEEG achieves single-trial P300 detection with F1-scores of 0.6830 and 0.7134 within the first 500 ms and 600 ms, respectively. These results highlight TopoEEG's potential to enhance the efficiency and speed of BCIs by reducing the need for multiple repetitions and the time required for decision-making in single-trial P300 detection.
This paper presents novel sub-harmonic synchronous machines having series or parallel permanent magnet structures on rotors for better performance in terms of greater torque density. The stator of the machines has two...
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We investigate the phase transition in developing amorphous hafnium oxides for optoelectronic applications by employing the molecular dynamic simulation. Our study provides a microscopic picture on the macroscopic opt...
We propose data-driven engineering of active light-disorder interactions. Neural networks generate the family of disorders for active multilayer structures having similar modulation sensitivity, enabling the independe...
Prior to the introduction of Graph Neural Networks (GNNs), modelling and analyzing irregular data, particularly graphs, was thought to be the Achilles' heel of deep learning. The core concept of GNNs is to find a ...
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We investigate supersymmetric transformations for engineering the short-range order of material. In crystals and quasicrystals, the weak value momentum of the ground state determines the control of short-range order w...
Weakly supervised video object segmentation (WSVOS) enables the identification of segmentation maps without requiring extensive annotations of object masks, relying instead on coarse video labels indicating object pre...
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In this work, a Voronoi-TCAD co-simulation framework is developed to investigate the impact of intrinsic and extrinsic variation sources on the performance and variability of 2D-layered TFTs. It is found that : i) Hig...
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