Solar power is mostly influenced by solar irradiation,weather conditions,solar array mismatches and partial shading ***,before installing solar arrays,it is necessary to simulate and determine the possible power *** p...
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Solar power is mostly influenced by solar irradiation,weather conditions,solar array mismatches and partial shading ***,before installing solar arrays,it is necessary to simulate and determine the possible power *** power point tracking is needed in order to make sure that,at any time,the maximum power will be extracted from the photovoltaic ***,maximum power point tracking is not a suitable solution for mismatches and partial shading *** overcome the drawbacks of maximum power point tracking due to mismatches and shadows,distributed maximum power point tracking is util-ized in this *** solar farm can be distributed in different ways,including one DC-DC converter per group of modules or per *** this paper,distributed maximum power point tracking per module is implemented,which has the highest *** technology is applied to electric vehicles(EVs)that can be charged with a Level 3 charging station in<1 ***,the problem is that charging an EV in<1 hour puts a lot of stress on the power grid,and there is not always enough peak power reserve in the existing power grid to charge EVs at that ***,a Level 3(fast DC)EV charging station using a solar farm by implementing distributed maximum power point tracking is utilized to address this ***,the simulation result is reported using MATLAB®,LTSPICE and the System Advisor *** results show that the proposed 1-MW solar system will provide 5 MWh of power each day,which is enough to fully charge~120 EVs each ***,the use of the proposed photovoltaic system benefits the environment by removing a huge amount of greenhouse gases and hazardous *** example,instead of supplying EVs with power from coal-fired power plants,1989 pounds of CO_(2) will be eliminated from the air per hour.
We construct a predictor-feedback cooperative adaptive cruise control (CACC) design with integral action, which achieves simultaneous compensation of long, actuation and communication delays, for platoons of heterogen...
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We construct a predictor-feedback cooperative adaptive cruise control (CACC) design with integral action, which achieves simultaneous compensation of long, actuation and communication delays, for platoons of heterogeneous vehicles whose dynamics are described by a third-order linear system with input delay. The key ingredients in our design are an underlying predictor-feedback law that achieves actuation delay compensation and an integral term of the difference between the delayed (by an amount equal to the respective communication delay) and current speed of the preceding vehicle. The latter, essentially, creates a virtual spacing variable, which can be regulated utilizing only delayed position and speed measurements from the preceding vehicle. We establish individual vehicle stability, string stability, and regulation for vehicular platoons, under the control design developed. The proofs rely on combining an input-output approach (in the frequency domain), with derivation of explicit solutions for the closed-loop systems, and they are enabled by the actuation and communication delays-compensating property of the design. We demonstrate numerically the control and model parameters' conditions of string stability, while we also present simulation results, in realistic scenarios, including a scenario in which the leading vehicle's trajectory is obtained from NGSIM data. All case studies confirm the effectiveness of the design developed. IEEE
In multiuser multiple-input multiple-output (MU-MIMO) systems, the selection of a subset of users to achieve the maximum sum rate is critical when resources are limited. In addition, designing suitable precoder and de...
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Classification and regression algorithms based on k-nearest neighbors (kNN) are often ranked among the top-10 Machine learning algorithms, due to their performance, flexibility, interpretability, non-parametric nature...
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Classification and regression algorithms based on k-nearest neighbors (kNN) are often ranked among the top-10 Machine learning algorithms, due to their performance, flexibility, interpretability, non-parametric nature, and computational efficiency. Nevertheless, in existing kNN algorithms, the kNN radius, which plays a major role in the quality of kNN estimates, is independent of any weights associated with the training samples in a kNN-neighborhood. This omission, besides limiting the performance and flexibility of kNN, causes difficulties in correcting for covariate shift (e.g., selection bias) in the training data, taking advantage of unlabeled data, domain adaptation and transfer learning. We propose a new weighted kNN algorithm that, given training samples, each associated with two weights, called consensus and relevance (which may depend on the query on hand as well), and a request for an estimate of the posterior at a query, works as follows. First, it determines the kNN neighborhood as the training samples within the kth relevance-weighted order statistic of the distances of the training samples from the query. Second, it uses the training samples in this neighborhood to produce the desired estimate of the posterior (output label or value) via consensus-weighted aggregation as in existing kNN rules. Furthermore, we show that kNN algorithms are affected by covariate shift, and that the commonly used sample reweighing technique does not correct covariate shift in existing kNN algorithms. We then show how to mitigate covariate shift in kNN decision rules by using instead our proposed consensus-relevance kNN algorithm with relevance weights determined by the amount of covariate shift (e.g., the ratio of sample probability densities before and after the shift). Finally, we provide experimental results, using 197 real datasets, demonstrating that the proposed approach is slightly better (in terms of F-1 score) on average than competing benchmark approaches for mit
This study introduces an adaptive integral sliding mode disturbance observer (AISMDOB)-based robust bidirectional platoon control method, aiming to ensure mesh stability in vehicular systems. Most existing platoon con...
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This study introduces an adaptive integral sliding mode disturbance observer (AISMDOB)-based robust bidirectional platoon control method, aiming to ensure mesh stability in vehicular systems. Most existing platoon control studies only focus on error propagation stability in either the longitudinal or lateral direction, neglecting the uncertainties in kinematics and dynamics of vehicular systems. The study proposes new coupled spacing error dynamics derived from vehicle kinematics and extended look-ahead-based coupled spacing errors to ensure both the longitudinal and lateral error propagation stability (that is, mesh stability) and are subsequently utilized to develop the novel AISMDOB, which improves the existing integral sliding mode disturbance observers (ISMDOBs) by incorporating adaptive estimation of unknown disturbance bounds while preserving their advantages. The AISMDOB-based platoon control method is then proposed using both robust kinematic and dynamic controllers to effectively compensate for the kinematic disturbances and dynamic model uncertainties, thereby reducing chattering phenomenon and ensuring the asymptotic convergence of spacing and velocity errors. Additionally, the proposed method can prevent cutting-corner behaviors during cornering maneuvers by utilizing the coupled spacing error dynamics. Simulation and experimental results verify the effectiveness of the proposed method through comparison with ISMDOB-based, sliding mode control (SMC)-based, and previous extended look-ahead-based methods. IEEE
Use of multi-path network topologies has become a prominent technique to assert timeliness in terms of age of information (AoI) and to improve resilience to link disruptions in communication systems. However, establis...
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Network games provide a framework to study strategic decision making processes that are governed by structured interdependencies among agents. However, existing models do not account for environments in which agents s...
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This paper presents a lightweight and accurate convolution neural network (CNN) based on encoder in vision transformer structure, which uses multigroup convolution rather than multilayer perceptron and multiheaded sel...
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This paper first determines the generalized optical orthogonal code (GOOC) parameters to minimize the bit error probability in fiber-optic code division multiple access systems. The systems use on-off keying as the mo...
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True-time-delay (TTD) arrays can implement frequency-dependent rainbow beams and enable fast beam alignment in wideband millimeter-wave (mmWave) systems. In this paper, we consider 3D rainbow beam training with planar...
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