The impacts incurred by floods regularly affect the planets population, inflicting social and economic problems. Optimal control strategies based on reservoir management may aid in controlling floods and mitigating th...
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The impacts incurred by floods regularly affect the planets population, inflicting social and economic problems. Optimal control strategies based on reservoir management may aid in controlling floods and mitigating the resulting damage. To this end, an accurate dynamic representation of water systems is needed. In practice, flood control strategies rely on hydrological forecasting models obtained fromconceptual or data-drivenmethods. Encouraged by recent works, this research proposes a novel surrogate model for water flow in a river channel based on physics-informed neural networks (PINNs). This approach achieved promising results regarding the assimilation of real-data measurements and the parameter identification of differential equations that govern the underlying dynamics. This article investigates PINN performance in a simulated environment built directly from a configuration of the Saint-Venant equations. The objective is to create a suitable model with high prediction accuracy and scientifically consistent behavior for use in real-Time applications. The experiments revealed promising results for hydrological modeling and presented alternatives to solve the main challenges found in conventional methods while assisting in synthesizing real-world representations. Impact Statement-The research seeks to contribute to the hydrological modeling area with a surrogate model based on physicsinformed neural networks (PINNs) to water flow in a watershed. In practice, thesemodels use conceptual or *** models to reach the precision provided by themethodology use large numbers of physical parameters. These parameters can demand deep knowledge about the environment and are possibly hard to identify in a complex basin. On the other hand, while data-driven methods do not require such knowledge about the dynamic system, they depend on a reliable and useful database to guarantee the accuracy of system *** introduce PINNs as a viable solution for
The large-scale neural networks have brought incredible shocks to the world,changing people’s lives and offering vast ***,they also come with enormous demands for computational power and storage pressure,the core of ...
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The large-scale neural networks have brought incredible shocks to the world,changing people’s lives and offering vast ***,they also come with enormous demands for computational power and storage pressure,the core of its computational requirements lies in the matrix multiplication units dominated by multiplication *** address this issue,we propose an area-power-efficient multiplier-less processing element(PE)*** to implementing the proposed PE,we apply a powerof-2 dictionary-based quantization to the model and effectiveness of this quantization method in preserving the accuracy of the original model is *** hardware design,we present a standard and one variant‘bi-sign’architecture of the *** evaluation results demonstrate that the systolic array that implement our standard multiplier-less PE achieves approximately 38%lower power-delay-product and 13%smaller core area compared to a conventional multiplication-and-accumulation PE and the bi-sign PE design can even save 37%core area and 38%computation ***,the applied quantization reduces the model size and operand bit-width,leading to decreased on-chip memory usage and energy consumption for memory ***,the hardware schematic facilitates expansion to support other sparsity-aware,energy-efficient techniques.
This paper proposes a method for estimating the moving velocity of underwater vehicles by utilizing the Doppler shift in positioning signals employed for time-of-arrival (ToA) acoustic positioning. The Doppler shift g...
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During COVID-19 pandemic several countries have implemented quarantines in response to the virus’ spread, which was considered an emergency. Thailand was under a state of emergency from January to December 2020, whic...
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Propranolol hydrochloride can be considered a persistent and bioaccumulative pharmaceutical in the *** drug and its by-products are potentially toxic and have adverse effects,since these compounds have been associated...
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Propranolol hydrochloride can be considered a persistent and bioaccumulative pharmaceutical in the *** drug and its by-products are potentially toxic and have adverse effects,since these compounds have been associated with endocrine-disrupting effects,reproductive deficiencies,embryo abnormalities and pericardial ***_(2)–La 0.05%–carboxymethyl-β-cyclodextrin(CMCD)nanoparticles were successfully prepared by a simple two-step method,which consists of sonification and *** characterization analyses reveal that lanthanum is dispersed on the semiconductor surface,probably forming Ti–O–La bonds,which can induce oxygen vacancies and surface defects that effectively restrain the recombination of photogenerated electron/holes *** efficiency of TiO_(2)–La 0.05%–CMCD samples in degradation of propranolol under UV-light irradiation is higher than that of pristine TiO_(2) within 20 min reaction,probably due to complex formation between theβ-blocker and the oligosaccharide,which allows us to propose a photocatalytic mechanism based on the formation of intermediates and competition of these compounds to the radicals and CMCD cavities.
The accuracy of prosodic structure prediction is crucial to the naturalness of synthesized speech in Mandarin text-to-speech system, but now is limited by widely-used sequence-to-sequence framework and error accumulat...
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Inflammatory bowel disease (IBD) is a chronic inflammatory disease. Complex pathogenesis behind disease formation and progression necessitated the development of new approaches to identify disease related genes and af...
This study delves into the utilization of Generative Adversarial Networks (GANs) for generating subject-specific time series sensor data, offering an innovative alternative to traditional metamodel-based simulations. ...
ISBN:
(纸本)9798350369663
This study delves into the utilization of Generative Adversarial Networks (GANs) for generating subject-specific time series sensor data, offering an innovative alternative to traditional metamodel-based simulations. We undertake an in-depth analysis of DoppelGANger, a prominent GAN variant for time series data and metadata generation, evaluating its efficiency and efficacy. The sensor data for this investigation was sourced from the National Health and Nutrition Examination Survey, which served as the foundational training set. We scrutinized the synthesized sensor data corresponding to various physical attributes, focusing on the temporal and multi-dimensional statistical properties. Our empirical findings underscore the potential of GANs to adeptly capture the time-dependent correlations and the intricate statistical characteristics inherent in multi-dimensional data. This insight into GANs' capabilities is a crucial step towards more sophisticated synthetic data generation, with significant implications for future applications in wearable technology and personalized health monitoring systems.
Automatic speaker verification (ASV) plays a critical role in security-sensitive environments. Regrettably, the reliability of ASV has been undermined by the emergence of spoofing attacks, such as replay and synthetic...
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As the implementation of MW order photovoltaic power plants (PV) gathers a significant number of specialists, their connection to the medium voltage (MV) distributors involves problems related to the following aspects...
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