This paper evaluates the accuracy of a commercialized high impedance fault (HIF) detection relay module for distinguishing HIFs in a real distribution network. Actual HIFs which are modeled based on real-world measure...
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In this work, a broadband single pole triple throw (SP3T) switch that operates between a frequency range of 0.02 - 6 GHz is reconfigured by means of wireless power transfer (WPT) through an energy harvesting (EH) circ...
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In this study, a robust Linear Quadratic Regulator (LQR) controller was developed and evaluated for the Bebop 2 quadrotor drone. Polytopic uncertainties in the dynamic model were taken into account in the control desi...
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ISBN:
(数字)9798331538606
ISBN:
(纸本)9798331538613
In this study, a robust Linear Quadratic Regulator (LQR) controller was developed and evaluated for the Bebop 2 quadrotor drone. Polytopic uncertainties in the dynamic model were taken into account in the control design, ensuring robustness. The drone's localization was achieved using fiducial markers and a Kalman Filter for state estimation, integrating visual and positional data. The controller design utilized Linear Matrix Inequalities (LMIs) to optimize performance while accounting for system constraints. A circular trajectory in three-dimensional space was employed to assess the controller's effectiveness in simulation. Results demonstrated precise trajectory tracking, stability, and robust performance despite parameter uncertainties. These findings highlight the applicability of the proposed framework for real-world scenarios involving underactuated UAVs.
We overview our previously reported systems for detection of COVID-19 infection from digital holographic reconstructed red blood cells. The overviewed systems use time-varying information of the samples to classify sp...
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We overview deep learning based optical signal detection in turbid water using multidimensional integral imaging. Overviewed method substantially improves the performance of optical signal detection in comparison to o...
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One often overlooked aspect of calculating minimum energy path for autonomous electric vehicles (AEVs) is consideration of electric motor's physical limitations. This aspect becomes significantly complex when cons...
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ISBN:
(数字)9798350317664
ISBN:
(纸本)9798350317671
One often overlooked aspect of calculating minimum energy path for autonomous electric vehicles (AEVs) is consideration of electric motor's physical limitations. This aspect becomes significantly complex when considering other constraints during the speed planning stage. One such limitation is the electric motor’s ability to extract maximum energy during regenerative braking (RB). This paper incorporates maximum current curve (MCC) and other motor limitations for solving optimum speed planning of AEVs using mixed-integer linear programming (MILP). The proposed approach not only determines the most suitable speed profile for a predefined route but also streamlines the integration of RB in obtaining optimal speed profile. This involves operating the motor based on its MCC and analyzing its impact on selecting different speed trajectories. The proposed method is compared to a base case model that does not incorporate MCC into its MILP formulation. The results are validated through MATLAB/Simulink simulation for a route under study and it is shown that the proposed method consumes 2.3% less energy throughout the route when compared to the base case scenario.
Development of Brain computer Interface (BCI) has been rapid since the mid 1990‘s. There are three criteria for BCI, (i) comfortability and possession of a suitable signal acquisition device, (ii) system validation a...
Development of Brain computer Interface (BCI) has been rapid since the mid 1990‘s. There are three criteria for BCI, (i) comfortability and possession of a suitable signal acquisition device, (ii) system validation and dissemination, and (iii) reliability and potentiality. As there are no BCI possessing the optimal criteria, it was essential to consider building a new one. Thereby, the paper investigates building BCI based on the utilization of EEG signals to translate brainwave patterns into actionable commands. The primary objective is to enhance communication capabilities for individuals afflicted with neurological disorders, empowering them to command external devices and engage more effectively with their surroundings. We built our model on EEG online dataset for the purpose of feature extraction and classification. Statistical features and Discrete Wavelet Transform (DWT) have been applied for feature selection. Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) were the classifiers involved. Results showed that the proposed architecture of MLP and RBF were able to classify the EEG signals into two classes (open eye and closed eye). Results also showed that the proposed approach, which is based on the combination of statistical features and DWT for features selection using AF3 and AF4 channels by the application of MLP, has 98% succession rate. BCI system based on Arduino circuit has been built after the classification Further algorithms and system evaluation need to be considered as future work.
Functional magnetic resonance imaging (fMRI), as a non-invasive method to reveal brain function alterations, frequently yields time series with unequal lengths in real-world scenarios, which may arise from factors suc...
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We demonstrate an adaptive learning framework, termed dynamic synthesis network (DSN), which dynamically synthesizes model weights and adapts to different scattering conditions. The efficiency of DSN is demonstrated i...
We demonstrate Computational Miniature Mesoscope to achieve large field-of-view, high-resolution, single-shot fluorescence imaging by jointly designing miniature optics and a deep learning algorithm in a miniaturized ...
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