A uniform triangular array (UTA) is proposed for physics-based 2D direction-of-arrival (DOA) estimations of unknown incoming signals. Three capacitively loaded top-hat antennas are used as array elements. Unlike conve...
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Space-air-ground integrated networks (SAGINs) offer seamless coverage and have emerged as a promising solution for high-speed railway (HSR) communications, which traverse various environments. This paper investigates ...
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The wide applications of Electric Vehicles (EVs) for integration with renewable energy sources need high-power, isolated bidirectional converters (IBC). Bi-directional converters often suffer from high switching power...
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With flexibility in maneuverability and remarkable adaptability, airborne bistatic radar system can obtain excellent detection performance for high-speed target by employing coherent integration. However, range migrat...
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With flexibility in maneuverability and remarkable adaptability, airborne bistatic radar system can obtain excellent detection performance for high-speed target by employing coherent integration. However, range migration (RM) and Doppler frequency migration (DFM) could become serious issues due to the relative motion characteristics of airborne platforms and high-speed target. Meanwhile, various unpredictable factors such as atmospheric turbulence and mechanical issues, etc., resulting in additional motion errors, would have further negative impacts on motion state and flight trajectory of airborne platforms. This phenomenon would cause serious consequence on coherent integration and target detection. Thus, we make contributions to tackle these limitations and enhance coherent integration and detection performance. First, we establish signal model with high-speed target in 3-D space for airborne bistatic radar system, along with motion error model which simultaneously includes translational error and rotational error. Next, we articulate range history's mathematical expression and further derive echo signal model. We then propose an improved generalized radon Fourier transform (IGRFT) method. More specifically, the purpose of IGRFT is achieving joint search for the parameters of the target motion and the parameters of motion error, to ensure high precision parameter estimation and high gain integration. However, the computational complexity surges due to the increasing of search dimensionality. To devise computationally feasible methods for practical applications, we split the high-dimensional maximization process into two disjoint problems by sequentially searching motion parameters and then motion error parameters, and this method is named generalized Radon transform (GRT)-IGRFT. Numerical simulations show that the proposed algorithms can correctly estimate parameters and achieve signal integration and target detection. Finally, we present performance analysis of
This paper proposes a voltage source converter (VSC) -based AC-DC hybrid distribution system (HDS) resilient model to mitigate power outages caused by wildfires. Before a wildfire happens, the public-safety power shut...
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This paper proposes a voltage source converter (VSC) -based AC-DC hybrid distribution system (HDS) resilient model to mitigate power outages caused by wildfires. Before a wildfire happens, the public-safety power shutoff (PSPS) strategy is applied to actively cut some vulnerable lines which may easily cause wildfires, and reinforce some lines that are connected to critical loads. To mitigate load shedding caused by active line disconnection in the PSPS strategy, network reconfiguration is applied before the wildfire occurrence. During the restoration period, repair crews (RCs) repair faulted lines, and network reconfiguration is also taken into consideration in the recovery strategy to pick up critical loads. Since there exists possible errors in the wildfire prediction, several different scenarios of wildfire occurrence have been taken into consideration, leading to the proposition of a stochastic multi-period resilient model for the VSC-based AC-DC HDS. To accelerate the computational performance, a progressive hedging algorithm has been applied to solve the stochastic model which can be written as a mixed-integer linear program. The proposed model is verified on a 106-bus AC-DC HDS under wildfire conditions, and the result shows the proposed model not only can improve the system resilience but also accelerate computational speed.
Accumulated Chest X-Ray images have widely been used in the task of automating the diagnosis of thoracic diseases. Multiple studies on using single classifiers for this task and also some studies on the fusion of clas...
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ISBN:
(纸本)9798331529710
Accumulated Chest X-Ray images have widely been used in the task of automating the diagnosis of thoracic diseases. Multiple studies on using single classifiers for this task and also some studies on the fusion of classifiers for the same purpose have been previously done and all have reached promising results. On furthering the classifier fusion approach, we have implemented a variation of methods including those used in the SynthEnsemble [1] paper on the ChestX-ray14 dataset [2], while focusing on the balance of classifier diversity and accuracy and using the same pre-trained single classifiers from SynthEnsemble as grounds for comparison. We have conducted all the same decision fusion methods, such as weighted averaging and using stochastic global optimization methods for classifier weight optimization, and have also employed new and additional methods, such as various heuristic and intelligent weight optimization methods, with regards to optimizing the number of base classifiers for reducing the computational cost and tuning the accumulated diversity of the fused network. The proposed approach, in summary, is a comparison between a brute-force based global heuristic search as a classifier weight optimizer, and genetically inspired stochastic weight optimizations added to an ordered weighted averaging fusion method, complemented with minimizing the set of base classifiers in accordance to their accuracy-diversity trade-off. Throughout this study, we use various metrics of model evaluation through all of which we have reached better or at least similar performance results while lowering the diversity of the set of chosen classifiers, reducing the number of base classifiers from six to four, and decreasing the execution time by a notable 98.7%. Even notwithstanding the improved performance of our proposed fusion methods, which is greatly dependent on the choice of base classifiers and is strictly limited to those used in [1] for accurate comparison, we can draw the
This paper investigates the cooperative adaptive optimal output regulation problem of continuous-time linear multi-agent *** the multi-agent system dynamics are uncertain,solving regulator equations and the correspond...
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This paper investigates the cooperative adaptive optimal output regulation problem of continuous-time linear multi-agent *** the multi-agent system dynamics are uncertain,solving regulator equations and the corresponding algebraic Riccati equations is challenging,especially for high-order *** this paper,a novel method is proposed to approximate the solution of regulator equations,i.e.,gradient descent *** is worth noting that this method obtains gradients through online data rather than model information.A data-driven distributed adaptive suboptimal controller is developed by adaptive dynamic programming,so that each follower can achieve asymptotic tracking and disturbance ***,the effectiveness of the proposed control method is validated by simulations.
Accurately predicting the Remaining Useful Life(RUL)of lithium-ion batteries is crucial for battery management *** learning-based methods have been shown to be effective in predicting RUL by leveraging battery capacit...
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Accurately predicting the Remaining Useful Life(RUL)of lithium-ion batteries is crucial for battery management *** learning-based methods have been shown to be effective in predicting RUL by leveraging battery capacity time series ***,the representation learning of features such as long-distance sequence dependencies and mutations in capacity time series still needs to be *** address this challenge,this paper proposes a novel deep learning model,the MLP-Mixer and Mixture of Expert(MMMe)model,for RUL *** MMMe model leverages the Gated Recurrent Unit and Multi-Head Attention mechanism to encode the sequential data of battery capacity to capture the temporal features and a re-zero MLP-Mixer model to capture the high-level ***,we devise an ensemble predictor based on a Mixture-of-Experts(MoE)architecture to generate reliable RUL *** experimental results on public datasets demonstrate that our proposed model significantly outperforms other existing methods,providing more reliable and precise RUL predictions while also accurately tracking the capacity degradation *** code and dataset are available at the website of github.
Climate-induced extreme weather events, such as floods and heatwaves, pose significant challenges to the resilience of urban power distribution grids. This paper examines the outage and restoration dynamics of Pittsbu...
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Time difference of arrival (TDOA) positioning results obtained using commonly adopted algebraic methods lack uncertainty information. In this letter, we propose to incorporate interval computation into TDOA-based hype...
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