For accurate and efficient pin-by-pin core calculation of SP3 equations, a simplified two-node Coarse Mesh Finite Difference (CMFD) method with the nonlinear iterative strategy is proposed. In this study, the two-node...
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This paper focuses on developing an optimal energy storage strategy for smart home energy management using adaptive dynamic programming (ADP) and blockchain technique. The ADP-based self-learning algorithm is develope...
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ISBN:
(数字)9798350373691
ISBN:
(纸本)9798350373707
This paper focuses on developing an optimal energy storage strategy for smart home energy management using adaptive dynamic programming (ADP) and blockchain technique. The ADP-based self-learning algorithm is developed to generate an iterative control sequence for the battery as the electricity storage equipment. The algorithm takes real-time electricity prices, load demand, and battery power efficiency into account to establish an optimal performance index function. The goal is to minimize total electricity costs while maximizing the battery’s lifespan. Additionally, the blockchain technique is employed to ensure transparent, tamper-proof and secure transaction data. A numerical simulation is provided to demonstrate the effectiveness of the proposed algorithm.
In nonlinear dynamical system identification, a flexible statistical model is commonly used for identification to compensate for non-linear elements. The flexibility in the statistical model induces a high sensitivity...
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Cloth-like items are ubiquitous in both daily life and industrial production, but their softness makes them difficult to manipulate. This paper focuses on solving the problem of automatic manipulation especially in th...
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Thermal Diffusion Coefficient (TDC) is one of the most important parameters in subchannel analysis codes. Abundant research results demonstrate that TDC is mainly determined by the structures of fuel assembly, especia...
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To reduce the trigger voltage and enhance holding voltage of traditional electrostatic discharge (ESD) protection device, a novel RC-Coupled SCR (RCSCR) is proposed, Compared to the traditional LVTSCR, the proposed RC...
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Bayesian inference has provided the continual learning (CL) with an elegant framework where past experiences and new knowledge are consolidated into the posterior constantly. Typical approaches rely on Bayesian neural...
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ISBN:
(数字)9781665410205
ISBN:
(纸本)9781665410212
Bayesian inference has provided the continual learning (CL) with an elegant framework where past experiences and new knowledge are consolidated into the posterior constantly. Typical approaches rely on Bayesian neural networks whose parameters are updated by variational inference, namely, maximizing the evidence lower bound of log-likelihood. In this paper, we discuss the effects of local reparameterization on the optimization of such networks in the context of CL. The empirical results show that it does not only increase the inference speed of neural networks, but also enhance the CL performance in some scenarios. Additionally, motivated by the observation that variance matrices have low-rank structures, we propose the d-tied variational continual learning (d-tied-VCL) to improve the parameter efficiency of variational continual learning (VCL). Experiments on random classification, per-muted MNIST, and split CIFAR100 show that even VCL with rank-1 variance matrices achieves competitive performance.
Plastic deformation is driven by the movement of dislocations on a microscopic scale, and the mobility of these dislocations determine the strength of the material. Forest dislocations and dislocation loops are often ...
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