In increasingly complex air combat, machine learning method such as deep reinforcement learning (DRL) for air combat decision-making control has become a research hotspot. In order to prevent air combat agents from fa...
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In order to reduce the coupling between dense antenna arrays in multiple input multiple output (MIMO) systems, this paper proposes a method to reduce the coupling between microstrip antenna arrays by utilizing a defec...
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People-centric activity recognition is one of the most critical technologies in a wide range of real-world applications,including intelligent transportation systems, healthcare services, and brain-computer interfaces....
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People-centric activity recognition is one of the most critical technologies in a wide range of real-world applications,including intelligent transportation systems, healthcare services, and brain-computer interfaces. Large-scale data collection and annotation make the application of machine learning algorithms prohibitively expensive when adapting to new tasks. One way of circumventing this limitation is to train the model in a semi-supervised learning manner that utilizes a percentage of unlabeled data to reduce the labeling burden in prediction tasks. Despite their appeal, these models often assume that labeled and unlabeled data come from similar distributions, which leads to the domain shift problem caused by the presence of distribution gaps. To address these limitations, we propose herein a novel method for people-centric activity recognition,called domain generalization with semi-supervised learning(DGSSL), that effectively enhances the representation learning and domain alignment capabilities of a model. We first design a new autoregressive discriminator for adversarial training between unlabeled and labeled source domains, extracting domain-specific features to reduce the distribution gaps. Second, we introduce two reconstruction tasks to capture the task-specific features to avoid losing information related to representation learning while maintaining task-specific consistency. Finally, benefiting from the collaborative optimization of these two tasks, the model can accurately predict both the domain and category labels of the source domains for the classification task. We conduct extensive experiments on three real-world sensing datasets. The experimental results show that DGSSL surpasses the three state-of-the-art methods with better performance and generalization.
The subversive nature of information war lies not only in the information itself, but also in the circulation and application of information. It has always been a challenge to quantitatively analyze the function and e...
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The subversive nature of information war lies not only in the information itself, but also in the circulation and application of information. It has always been a challenge to quantitatively analyze the function and effect of information flow through command, control, communications, computer, kill, intelligence,surveillance, reconnaissance (C4KISR) system. In this work, we propose a framework of force of information influence and the methods for calculating the force of information influence between C4KISR nodes of sensing, intelligence processing,decision making and fire attack. Specifically, the basic concept of force of information influence between nodes in C4KISR system is formally proposed and its mathematical definition is provided. Then, based on the information entropy theory, the model of force of information influence between C4KISR system nodes is constructed. Finally, the simulation experiments have been performed under an air defense and attack scenario. The experimental results show that, with the proposed force of information influence framework, we can effectively evaluate the contribution of information circulation through different C4KISR system nodes to the corresponding tasks. Our framework of force of information influence can also serve as an effective tool for the design and dynamic reconfiguration of C4KISR system architecture.
A pivotal problem in the Internet of Things (IoT) is resource allocation, where the goal is to optimize allocation strategies of IoT resources. In general, resource allocation problems are formulated as constrained op...
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Machine learning has become important for anomaly detection in water quality prediction. Data anomalies are often caused by the difficulties of analysing large amounts of data, both technical and human, but approaches...
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The advancement of terahertz (THz) communication technology drives the evolution of wireless communication systems,offering novel pathways and technical means for the development of future 6G communication *** wireles...
The advancement of terahertz (THz) communication technology drives the evolution of wireless communication systems,offering novel pathways and technical means for the development of future 6G communication *** wireless communication systems are often constrained by bandwidth limitations of electronic devices in high frequency ***,THz communication technology leverages the characteristics of electromagnetic waves to transcend these limitations,enabling communication athigher frequencies and wider bandwidths.
In scenarios such as vehicle radiation monitoring and unmanned aerial vehicle radiation detection,rapid measurements using a NaI(Tl)detector often result in low photon counts,weak characteristic peaks,and significant ...
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In scenarios such as vehicle radiation monitoring and unmanned aerial vehicle radiation detection,rapid measurements using a NaI(Tl)detector often result in low photon counts,weak characteristic peaks,and significant statistical *** issues can lead to potential failures in peak-searching-based identification *** address the low precision associated with short-duration measurements of radionuclides,this paper proposes an identification algorithm that leverages heterogeneous spectral transfer to develop a low-count energy spectral identification *** experiments demonstrated that transferring samples from 26 classes of simulated heterogeneous gamma spectra aids in creating a reliable model for measured gamma *** only 10%of target domain samples used for training,the accuracy on real low-count spectral samples was 95.56%.This performance shows a significant improvement over widely employed full-spectrum analysis methods trained on target domain *** proposed method also exhibits strong generalization capabilities,effectively mitigating overfitting issues in low-count energy spectral classification under short-duration measurements.
The growing focus on enhancing color quality in liquid crystal displays(LCDs)and organic light-emitting diodes(OLEDs)has spurred significant advancements in color-conversion ***,color conversion is also important for ...
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The growing focus on enhancing color quality in liquid crystal displays(LCDs)and organic light-emitting diodes(OLEDs)has spurred significant advancements in color-conversion ***,color conversion is also important for the development and commercialization of *** article provides a comprehensive review of different types of color conversion methods as well as different types of color conversion *** summarize the current status of patterning process,and discuss key strategies to enhance display ***,we speculate on the future prospects and roles that color conversion will play in ultra-high-definition micro-and projection displays.
Traffic classification is a crucial task for network *** of the most difficult challenges is to accurately identify the traffic of unknown applications as well as discriminate the known *** current learning-based clas...
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Traffic classification is a crucial task for network *** of the most difficult challenges is to accurately identify the traffic of unknown applications as well as discriminate the known *** current learning-based classifiers can achieve high classification accuracy for the known traffic[1,2],but are infeasible toclassifyeither the unknown/unseen/unlabeled application or zero-day application traffic[3],which is known as identification of unknown applications(IUA).Although the clustering-based methods can identify the unknown traffic,they need lots of human intervention to thefeaturechoice,hyperparameter configuration and known/unknown traffic division[4].
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