Internet of Things(IoT)with e-learning is widely employed to collect data from various smart devices and share it with other ones for efficient e-learning *** the same time,machine learning(ML)and data mining approach...
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Internet of Things(IoT)with e-learning is widely employed to collect data from various smart devices and share it with other ones for efficient e-learning *** the same time,machine learning(ML)and data mining approaches are presented for accomplishing prediction and classification *** this motivation,this study focuses on the design of intelligent machine learning enabled e-learner non-verbal behaviour detection(IML-ELNVBD)in IoT *** proposed IML-ELNVBD technique allows the IoT devices such as audio sensors,cameras,*** are then connected to the cloud server for further *** addition,the modelling and extraction of behaviour take ***,extreme learning machine sparse autoencoder(ELM-SAE)model is employed for the detection and classification of non-verbal ***,the Ant Colony Optimization(ACO)algorithm is utilized to properly tune the weight and bias parameters involved in the ELM-SAE *** order to ensure the improved performance of the IML-ELNVBD model,a comprehensive simulation analysis is carried out and the results highlighted the betterment compared to the recent models.
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
A new family of continuous distributions which ensure model flexiblity, is introduced based on the Fréchet distribution and Topp Leone-G family. Two special sub-models of the new family are discussed. We provide ...
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When faced with problems involving inference in discrete domains, solutions often involve appeals to conditional independence structure or mean-field approximations. We argue that this is insufficient for a number of ...
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When faced with problems involving inference in discrete domains, solutions often involve appeals to conditional independence structure or mean-field approximations. We argue that this is insufficient for a number of interesting Bayesian problems, including mixture assignment posteriors and probabilistic relational models (e.g. the stochastic block model). These posteriors exhibit no conditional independence structure, precluding the use of graphical model methods, yet exhibit dependency between every single element of the posterior, making mean-field methods a poor fit. We propose using an expressive yet tractable approximation inspired by tensor factorization methods, alternately known as the tensor train or the matrix product state, and which can be construed of as a direct extension of the mean-field approximation to higher-order dependencies. We give a comprehensive introduction to the application of matrix product state in probabilistic inference, and illustrate how to efficiently perform marginalization, conditioning, sampling, normalization, some expectations, and approximate variational inference in our proposed model.
Esterel is a concurrent synchronous language for developing reactive systems. As an alternative to the classical software and hardware synthesis paths, the reactive processing approach uses a specialized processor wit...
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ISBN:
(纸本)1595931082
Esterel is a concurrent synchronous language for developing reactive systems. As an alternative to the classical software and hardware synthesis paths, the reactive processing approach uses a specialized processor with an instruction set tailored to Esterel. A principal difficulty when compiling onto a reactive processor is the faithful, efficient implementation of concurrency. This paper presents a novel reactive processor architecture based on multi-threading, which allows the arbitrary nesting of preemption and concurrency, and is scalable to very high degrees of concurrency. Copyright 2006 ACM.
Graph edit distance is one of the most flexible mechanisms for error-tolerant graph matching. Its key advantage is that edit distance is applicable to unconstrained attributed graphs and can be tailored to a wide vari...
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The concurrent synchronous language Esterel allows programmers to treat reactive systems in an abstract, concise manner. An Esterel program is typically first translated into other, non-synchronous high-level language...
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ISBN:
(纸本)159593149X
The concurrent synchronous language Esterel allows programmers to treat reactive systems in an abstract, concise manner. An Esterel program is typically first translated into other, non-synchronous high-level languages, such as VHDL or C, and then compiled further into hardware or software. Another approach that has been proposed recently is the direct execution of Esterel-like instructions with a customized processor, which promises the flexibility of a software solution with an efficiency close to a hardware implementation. However, the instruction sets and implementations of the processor architectures proposed so far still have some limitations regarding their completeness, efficiency, and adherence to the original Esterel semantics. This paper presents a novel reactive processor architecture, the Kiel Esterel Processor, which addresses these shortcomings. In particular, it provides a complete, semantically accurate implementation of the Esterel preemption primitives, most of which can be expressed directly with a single machine instruction. One advantage of the reactive processors - in addition to their high execution speed compared to traditional software implementations - is that control-flow is preserved while compiling Esterel into machine code, and that the execution platform has a very predictable timing behavior. This paper presents a precise and very efficient Worst Case Reaction Time (WCRT) analysis, which is geared towards the Kiel Esterel Processor, but which could be adapted to other reactive processors as well. Copyright 2005 ACM.
We study a facility location problem motivated by requirements pertaining to the distribution of charging stations for electric vehicles: Place a minimum number of battery charging stations at a subset of nodes of a n...
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Electronics densification is continuing at an unrelenting pace at the server, rack, and facility level. With increasing facility density levels, airflow management has become a major challenge and concern. In an effor...
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Electronics densification is continuing at an unrelenting pace at the server, rack, and facility level. With increasing facility density levels, airflow management has become a major challenge and concern. In an effort to deal with the resulting thermal management challenges, manufacturers are increasingly turning to liquid cooling as a practical solution. The majority of manufacturers have turned to liquid-cooled enclosed racks or rear-door heat exchangers, in which chilled water is delivered to the racks. Some manufacturers are now looking to cold plate cooling solutions that take the heat directly off problem components, such as the CPUs, to get it directly out of the facility. This paper describes work done at the Pacific Northwest National Labs (PNNL) under a Department of Energy-funded program titled "Energy Smart Data Center." An 8.2 kW (27,980 Btu/h) rack of HP rx2600 2U servers has been converted from air-cooling to liquid spray cooling (CPUs only). The rack has been integrated into PNNL's main cluster and subjected to a suite of acceptance tests. Under the testing, the spray-cooled CPUs ran an average of 10°C (18°F) cooler than the air-cooled CPUs. Other peripheral devices, such as the memory D1MMs, ran an average of 8°C (14.4°F) cooler, and the power pod board was measured at 15°C(27°F) cooler. Since installation in July 2005, the rack has been undergoing a one-year uptime and reliability investigation. As part of the investigation, the rack has been subjected to monthly robustness testing and ongoing performance evaluation while running applications such as High Performance Linpack, parts of the NASA NPB-2 Benchmark Suite, and NW Chem. The rack has undergone three months' worth of robustness testing with no major events. Including the robustness testing, the rack uptime is at 95.54% over 299 days. While undergoing application testing, no computational performance differences have been observed between the liquid-cooled and standard air-cooled racks. A sm
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