In today's 5G era, the amount of data generated by the Internet of Things (IoT) devices is enormous. Data is processed and stored in the cloud under a traditional cloud computing architecture, and real-time proces...
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Sleep staging serves as a fundamental assessment for sleep quality measurement and sleep disorder diagnosis. Although current deep learning approaches have successfully integrated multimodal sleep signals, enhancing t...
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Sleep staging serves as a fundamental assessment for sleep quality measurement and sleep disorder diagnosis. Although current deep learning approaches have successfully integrated multimodal sleep signals, enhancing the accuracy of automatic sleep staging, certain challenges remain, as follows: 1) optimizing the utilization of multi-modal information complementarity, 2) effectively extracting both long- and short-range temporal features of sleep information, and 3) addressing the class imbalance problem in sleep data. To address these challenges, this paper proposes a two-stream encode-decoder network, named TSEDSleepNet, which is inspired by the depth sensitive attention and automatic multi-modal fusion (DSA2F) framework. In TSEDSleepNet, a two-stream encoder is used to extract the multiscale features of electrooculogram (EOG) and electroencephalogram (EEG) signals. And a self-attention mechanism is utilized to fuse the multiscale features, generating multi-modal saliency features. Subsequently, the coarser-scale construction module (CSCM) is adopted to extract and construct multi-resolution features from the multiscale features and the salient features. Thereafter, a Transformer module is applied to capture both long- and short-range temporal features from the multi-resolution features. Finally, the long- and short-range temporal features are restored with low-layer details and mapped to the predicted classification results. Additionally, the Lovász loss function is applied to alleviate the class imbalance problem in sleep datasets. Our proposed method was tested on the Sleep-EDF-39 and Sleep-EDF-153 datasets, and it achieved classification accuracies of 88.9% and 85.2% and Macro-F1 scores of 84.8% and 79.7%, respectively, thus outperforming conventional traditional baseline models. These results highlight the efficacy of the proposed method in fusing multi-modal information. This method has potential for application as an adjunct tool for diagnosing sleep disorde
This paper proposes a novel adaptive synthetic inertia (SI) control scheme for the battery energy storage system (BESS) in the wind farm. The proposed approach dynamically adjusts the amplitude of the direct current (...
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作者：
Samant, Indu SekharPanda, SubhasisRout, Pravat Kumar
Department of Computer Science Engineering Odisha India
Department of Electrical Engineering Odisha India
Department of Electrical and Electronics Engineering Odisha India
Smart grids are advanced power systems that have the potential to bring enormous benefits to power consumers and providers. Smart grids utilize advanced communication and information technologies to deliver power more...
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作者：
Lv, ChengDepartment of Computer Science
School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing100044 China
In response to the shortcomings of the SPOC course "Introduction to Computational Thinking"at Beijing University of Civil engineering and Architecture, the teaching team has transformed and improved the cour...
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Smart meters are an important component of the smart grid, and the large-scale deployment of meters on the user side generates a large amount of data that brings huge expenses to the smart grid. In addition, attackers...
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The hope for a futuristic global quantum internet that provides robust and high-capacity quantum information transfer lies largely on qudits,the fundamental quantum information carriers prepared in high-dimensional su...
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The hope for a futuristic global quantum internet that provides robust and high-capacity quantum information transfer lies largely on qudits,the fundamental quantum information carriers prepared in high-dimensional superposition ***,preparing and manipulating N-dimensional flying qudits as well as subsequently establishing their entanglement are still challenging tasks,which require precise and simultaneous maneuver of 2(N-1)parameters across multiple degrees of ***,using an integrated approach,we explore the synergy from two degrees of freedom of light,spatial mode and polarization,to generate,encode,and manipulate flying structured photons and their formed qudits in a four-dimensional Hilbert space with high quantum fidelity,intrinsically enabling enhanced noise resilience and higher quantum data *** four eigen spin–orbit modes of our qudits possess identical spatial–temporal characteristics in terms of intensity distribution and group velocity,thereby preserving long-haul coherence within the entirety of the quantum data transmission *** leveraging the bi-photon entanglement,which is well preserved in the integrated manipulation process,we present versatile spin–orbit cluster states in an extensive dimensional Hilbert *** cluster states hold the promise for quantum error correction which can further bolster the channel robustness in long-range quantum communication.
The paper presents the implementation of a Switched Capacitor Power Amplifier (SCPA) to be integrated into a Narrowband Internet of Things (NB-IoT) Transceiver. The SCPA is designed to operate at a frequency of 0.9GHz...
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Utilizing student-led case studies as an innovative educational tool can effectively introduce real-world practices into the power engineering classroom. By assigning students to draft a story around a case study, the...
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Knowledge Graphs (KGs) are potent frameworks for knowledge representation and reasoning. Nevertheless, KGs are inherently incomplete, leaving numerous uncharted relationships and facts awaiting discovery. Deep learnin...
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Knowledge Graphs (KGs) are potent frameworks for knowledge representation and reasoning. Nevertheless, KGs are inherently incomplete, leaving numerous uncharted relationships and facts awaiting discovery. Deep learning methodologies have proven effective in enhancing KG completion by framing it as a link prediction task, where the goal is to discern the validity of a triple comprising a head, relation, and tail. The significance of structural information in assessing the validity of a triple within a KG is well-established. However, quantifying this structural information poses a challenge. We need to pinpoint the metric that encapsulates the structural information of a triple and smoothly incorporate this metric into the link prediction learning process. In this study, we recognize the critical importance of the intersection among the k-hop neighborhoods of the head, relation, and tail when determining the validity of a triple. To address this, we introduce a novel randomized algorithm designed to efficiently generate intersection features for candidate triples. Our experimental results demonstrate that a straightforward fully-connected network leveraging these intersection features can surpass the performance of established KG embedding models and even outperform graph neural network baselines. Additionally, we highlight the substantial training time efficiency gains achieved by our network trained on intersection features. Copyright 2024 by the author(s)
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