The rapid growth of renewable energy resources penetration is bringing more challenges to power system planning and operation. Relevant renewable energy integration studies, such as the capability and dynamic performa...
The rapid growth of renewable energy resources penetration is bringing more challenges to power system planning and operation. Relevant renewable energy integration studies, such as the capability and dynamic performance of inverter-based resources’ primary frequency response and fast frequency response, require high-resolution renewable generation output data that are representative of renewable energy resources. This paper focuses on creating synthetic but realistic solar irradiance data and proposes a long short-term memory-based generative adversarial network to generate high-resolution (second-level) solar irradiance sequences from low-resolution (minute-level) measurements. Combined with a classifier to recognize the solar irradiance patterns, the proposed model is trained using multi-loss functions to accurately capture the temporal correlations among both high-resolution and low-resolution sequences. Verification of the proposed approach is performed on the data set of the Oahu Solar Measurement Grid collected through the National Renewable Energy laboratory. The results of the case studies demonstrate the proposed approach’s capability to capture the statistical characteristics of different solar irradiance patterns and to generate high-quality synthetic solar irradiance sequences in high resolution.
We present advancements in novel rf signal processing techniques which combine the benefits of high-Q, high-linearity, and large dynamic range of acoustic wave devices with the tunability and nonlinear functionality o...
We present advancements in novel rf signal processing techniques which combine the benefits of high-Q, high-linearity, and large dynamic range of acoustic wave devices with the tunability and nonlinear functionality of spin wave devices. The design and implementation of two such magneto-acoustic devices, a tunable high-overtone bulk acoustic resonator (HBAR) and a rf signal correlator are discussed. The devices are implemented on yttrium iron garnet (YIG), chosen for its low damping of both spin and acoustic waves and magnetoelastic properties for coupling them.
Soft robotics has gained considerable attention in recent years for its structural flexibility and inherent safety in environmental interactions. To address the pitfalls of pneumatic actuation systems, namely sluggish...
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With the rapid expansion of mobile internet usage, the prevalence of the Android operating system on smartphones is steadily growing. However, improper utilization of the Intent mechanism within Android applications c...
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Amyloid proteins are associated with a broad spectrum of neurodegenerative ***,it remains a grand challenge to extract molecular structure information from intracellular amyloid proteins in their native cellular *** a...
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Amyloid proteins are associated with a broad spectrum of neurodegenerative ***,it remains a grand challenge to extract molecular structure information from intracellular amyloid proteins in their native cellular *** address this challenge,we developed a computational chemical microscope integrating 3D midinfrared photothermal imaging with fluorescence imaging,termed Fluorescence-guided Bond-Selective Intensity Diffraction Tomography(FBS-IDT).Based on a low-cost and simple optical design,FBS-IDT enables chemical-specific volumetric imaging and 3D site-specific mid-IR fingerprint spectroscopic analysis of tau fbrils,an important type of amyloid protein aggregates,in their intracellular ***-free volumetric chemical imaging of human cells with/without seeded tau fibrils is demonstrated to show the potential correlation between lipid accumulation and tau aggregate ***-resolved mid-infrared fingerprint spectroscopy is performed to reveal the protein secondary structure of the intracellular tau fibrils.3D visualization of theβ-sheet for tau fibril structure is achieved.
With the CMOS technology advancing and the complexity of circuits growing, the demand for analog/mixed-signal design automation tools is increasing quickly. Although some tools have been developed to tackle this chall...
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Reconfigurable intelligent surfaces are arrays capable of anomalously reflecting electromagnetic waves due to modifying the reflected wave phase controlled by switching devices. This new technology has been studied ba...
Reconfigurable intelligent surfaces are arrays capable of anomalously reflecting electromagnetic waves due to modifying the reflected wave phase controlled by switching devices. This new technology has been studied based on reconfigurable reflect arrays and metasurfaces for the introduction of its use in the Fifth and Sixth generations of mobile networks. So in this work, we design a 1-bit reconfigurable intelligent surface capable of modifying the beamforming reflected on its surface controlled at 2.45 GHz by commercial PIN diodes. Simulations carried out by ANSYS HFSS demonstrated that the structure generated two switching states shifted by one hundred and eighty degrees and modified the reflected beam anomalously in a array of one hundred elements.
Virtual coupling technologies have been widely studied in recent years, due to their merits of flexibility and efficiency. However, to operate a virtually coupled train set (VCTS) as a single train, the synchronous op...
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Our work focuses, on designing and proposing a force estimation sensor system for Geological phenomena data collection and analysis, based on Biomimetic. In this sense, biomimetic term, is used, as proposed by Schmitt...
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Graph Neural Networks (GNNs) have drawn tremendous attention due to their unique capability to extend Machine Learning (ML) approaches to applications broadly-defined as having unstructured data, especially graphs. Co...
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Graph Neural Networks (GNNs) have drawn tremendous attention due to their unique capability to extend Machine Learning (ML) approaches to applications broadly-defined as having unstructured data, especially graphs. Compared with other Machine Learning (ML) modalities, the acceleration of Graph Neural Networks (GNNs) is more challenging due to the irregularity and heterogeneity derived from graph typologies. Existing efforts, however, have focused mainly on handling graphs' irregularity and have not studied their heterogeneity. To this end we propose H-GCN, a PL (Programmable Logic) and AIE (AI Engine) based hybrid accelerator that leverages the emerging heterogeneity of Xilinx Versal Adaptive Compute Acceleration Platforms (ACAPs) to achieve high-performance GNN inference. In particular, H-GCN partitions each graph into three subgraphs based on its inherent heterogeneity, and processes them using PL and AIE, respectively. To further improve performance, we explore the sparsity support of AIE and develop an efficient density-aware method to automatically map tiles of sparse matrix-matrix multiplication (SpMM) onto the systolic tensor array. Compared with state-of-the-art GCN accelerators, H-GCN achieves, on average, speedups of 1.1~2.3x.
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