The coded Aperture Snapshot Spectral Imaging (CASSI) system has great advantages in dynamically acquiring Hyper-Spectral image (HSI) compared to traditional measurement methods, but there are the following problems. 1...
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Few-shot font generation (FFG) aims to learn the target style from a limited number of reference glyphs and generate the remaining glyphs in the target font. Previous works focus on disentangling the content and style...
The integration of renewable energy sources into the power grid has led to new challenges in maintaining the stability of the system frequency. This paper proposes a novel approach to address the Optimal Demand Side F...
The integration of renewable energy sources into the power grid has led to new challenges in maintaining the stability of the system frequency. This paper proposes a novel approach to address the Optimal Demand Side Frequency control (ODFC) problem using Multi-Agent Deep Deterministic Policy Gradient (MADDPG) method. The proposed method models the ODFC problem as a Markov game, with centralized training based on multi-agent cooperative self-learning and associative storage service. In the decentralized execution stage, each agent independently outputs control actions to the controlled plant using local observations. Numerical simulations show that the proposed method effectively addresses the ODFC problem with superior performance compared to traditional methods.
Multiple types of sensors are utilized for remote monitoring of single industrial process to compensate for the limited accuracy of a single type of sensor. Network slicing technology is considered promising for colle...
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Continual learning aims to learn on a sequence of new tasks while maintaining the performance on previous tasks. Source-free domain adaptation (SFDA), which adapts a pretrained source model to a target domain, is usef...
Continual learning aims to learn on a sequence of new tasks while maintaining the performance on previous tasks. Source-free domain adaptation (SFDA), which adapts a pretrained source model to a target domain, is useful in protecting the source domain data privacy. Generalized SFDA (G-SFDA) combines continual learning and SFDA to achieve outstanding performance on both the source and the target domains. This paper proposes semi-supervised G-SFDA (SSG-SFDA) for domain incremental learning, where a pre-trained source model (instead of the source data), few labeled target data, and plenty of unlabeled target data, are available. The goal is to achieve good performance on all domains. To cope with domain-ID agnostic, SSG-SFDA trains a conditional variational auto-encoder (CVAE) for each domain to learn its feature distribution, and a domain discriminator using virtual shallow features generated by CVAE to estimate the domain ID. To cope with catastrophic forgetting, SSG-SFDA uses soft domain attention to improve the sparse domain attention in G-SFDA. To cope with insufficient labeled target data, SSG-SFDA uses MixMatch to augment the unlabeled target data and better exploit the few labeled target data. Experiments on three datasets demonstrated the effectiveness of SSG-SFDA.
To improve the accuracy of grain yield prediction, a grain yield prediction model based on wavelet transform and long short-term memory (LSTM) is proposed. Firstly, the original data is decomposed by wavelet transform...
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The binary defocusing technique is sensitive to the defocusing degree. The defocusing projection mechanism will introduce high-frequency harmonics at the inappropriate defocused level, leading to limitations in measur...
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Aiming at the semantic complexity and the difficulty of capturing long-distance dependencies in the task of named entity recognition in Chinese resume text, a BLDC (BERT-BiLSTM-Dense-CRF)-based named entity recognitio...
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P systems are distributed parallel computing models in the area of membrane computing, which are inspired by the structure and the functioning of living cells, as well as the organization of cells in tissues, organs, ...
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P systems are distributed parallel computing models in the area of membrane computing, which are inspired by the structure and the functioning of living cells, as well as the organization of cells in tissues, organs, and other higher order structures. P systems with proteins on membranes are a class of P systems, which have proved to be very efficient computing devices. Specifically, it was known that the Quantified satisfiability problem (QSAT) of a Boolean formula can be solved by a semi-uniform family of P systems with proteins on membranes and with membrane division. However, it remains open whether a uniform families of P systems with proteins on membranes can solve in polynomial time exactly the class of problems PSPACE. In this work, we present a uniform solution to QSAT problem by P systems with proteins on membranes in a linear time with respect to both the number $n$ of Boolean variables and the number $m$ of clauses of the instance, which answers the above open problem.
As lithium-ion batteries are widely used in different fields,the thermal effect is of serious *** to achieve accurate temperature estimation in real time is the main challenge of current *** address this problem,we pr...
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As lithium-ion batteries are widely used in different fields,the thermal effect is of serious *** to achieve accurate temperature estimation in real time is the main challenge of current *** address this problem,we propose a realtime distributed moving horizon estimation(RT-DMHE) based on partial differential equations describing thermal dynamics of a lithium-ion battery *** decomposes the real-time centralized moving horizon estimation(RT-CMHE) into multiple local estimators that run in parallel with information exchange from adjacent *** validation shows that the root mean square error of temperature estimates from the proposed RT-DMHE is smaller than that of an existing distributed Kalman filter in ***,compared to the RT-CMHE,the proposed RT-DMHE achieves comparable estimation accuracy while vastly reducing the average computation time per sample.
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