This study introduces a data-driven approach for state and output feedback control addressing the constrained output regulation problem in unknown linear discrete-time systems. Our method ensures effective tracking pe...
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This study introduces a data-driven approach for state and output feedback control addressing the constrained output regulation problem in unknown linear discrete-time systems. Our method ensures effective tracking performance while satisfying the state and input constraints, even when system matrices are not available. We first establish a sufficient condition necessary for the existence of a solution pair to the regulator equation and propose a data-based approach to obtain the feedforward and feedback control gains for state feedback control using linear programming. Furthermore, we design a refined Luenberger observer to accurately estimate the system state, while keeping the estimation error within a predefined set. By combining output regulation theory, we develop an output feedback control strategy. The stability of the closed-loop system is rigorously proved to be asymptotically stable by further leveraging the concept of λ-contractive sets.
Text classification is a challenging task in the field of Natural Language Processing (NLP), and significant progress has been made using deep learning methods. Traditional deep-learning approaches for text classifica...
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Dear Editor,This letter is concerned with the problem of time-varying formation tracking for heterogeneous multi-agent systems(MASs) under directed switching networks. For this purpose, our first step is to present so...
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Dear Editor,This letter is concerned with the problem of time-varying formation tracking for heterogeneous multi-agent systems(MASs) under directed switching networks. For this purpose, our first step is to present some sufficient conditions for the exponential stability of a particular category of switched systems.
Raman random fiber laser(RRFL) possesses rich physical properties of spectral, temporal, and spatial domains due to its unique feedback mechanism and complex nonlinear effects. Characterizing and controlling the micro...
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Raman random fiber laser(RRFL) possesses rich physical properties of spectral, temporal, and spatial domains due to its unique feedback mechanism and complex nonlinear effects. Characterizing and controlling the microscopic evolution dynamics of RRFL are crucial to driving breakthrough advances in fields such as inertial confinement fusion and fundamental physics. In this work, a novel experimental and theoretical analysis of the evolution of the temporal spectral correlations of the RRFL in the transition and steady states is conducted. In the transitional state, the microscopic dynamics of the RRFL excitation process is revealed comprehensively: the temporal-correlation growth curve contrasts with that of resonant-cavity lasers, and the formation and degradation of spectral correlation are observed. In the steady state, the overall spectrum is characterized by partial correlation, and the correlation characteristics of RRFL mainly originate from the spectral random spikes, which offers a novel dimension for the precise control of RRFL correlation. This work provides new insights into underlying physical properties of continuous broadband lasers, offering key guidance for laser design, control, and applications.
With the rise of encrypted traffic,traditional network analysis methods have become less effective,leading to a shift towards deep learning-based *** these,multimodal learning-based classification methods have gained ...
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With the rise of encrypted traffic,traditional network analysis methods have become less effective,leading to a shift towards deep learning-based *** these,multimodal learning-based classification methods have gained attention due to their ability to leverage diverse feature sets from encrypted traffic,improving classification ***,existing research predominantly relies on late fusion techniques,which hinder the full utilization of deep features within the *** address this limitation,we propose a novel multimodal encrypted traffic classification model that synchronizes modality fusion with multiscale feature ***,our approach performs real-time fusion of modalities at each stage of feature extraction,enhancing feature representation at each level and preserving inter-level correlations for more effective *** continuous fusion strategy improves the model’s ability to detect subtle variations in encrypted traffic,while boosting its robustness and adaptability to evolving network *** results on two real-world encrypted traffic datasets demonstrate that our method achieves a classification accuracy of 98.23% and 97.63%,outperforming existing multimodal learning-based methods.
Emotion recognition using biological brain signals needs to be reliable to attain effective signal processing and feature extraction techniques. The impact of emotions in interpretations, conversations, and decision-m...
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Emotion recognition using biological brain signals needs to be reliable to attain effective signal processing and feature extraction techniques. The impact of emotions in interpretations, conversations, and decision-making, has made automatic emotion recognition and examination of a significant feature in the field of psychiatric disease treatment and cure. The problem arises from the limited spatial resolution of EEG recorders. Predetermined quantities of electroencephalography (EEG) channels are used by existing algorithms, which combine several methods to extract significant data. The major intention of this study was to focus on enhancing the efficiency of recognizing emotions using signals from the brain through an experimental, adaptive selective channel selection approach that recognizes that brain function shows distinctive behaviors that vary from one individual to another individual and from one state of emotions to another. We apply a Bernoulli–Laplace-based Bayesian model to map each emotion from the scalp senses to brain sources to resolve this issue of emotion mapping. The standard low-resolution electromagnetic tomography (sLORETA) technique is employed to instantiate the source signals. We employed a progressive graph convolutional neural network (PG-CNN) to identify the sources of the suggested localization model and the emotional EEG as the main graph nodes. In this study, the proposed framework uses a PG-CNN adjacency matrix to express the connectivity between the EEG source signals and the matrix. Research on an EEG dataset of parents of an ASD (autism spectrum disorder) child has been utilized to investigate the ways of parenting of the child's mother and father. We engage with identifying the personality of parental behaviors when regulating the child and supervising his or her daily activities. These recorded datasets incorporated by the proposed method identify five emotions from brain source modeling, which significantly improves the accurac
With the development of deep learning in recent years, code representation learning techniques have become the foundation of many software engineering tasks such as program classification [1] and defect detection. Ear...
With the development of deep learning in recent years, code representation learning techniques have become the foundation of many software engineering tasks such as program classification [1] and defect detection. Earlier approaches treat the code as token sequences and use CNN, RNN, and the Transformer models to learn code representations.
THE development of agriculture faces significant challenges due to population growth, climate change, land depletion, and environmental pollution, threatening global food security [1]. This necessitates the developmen...
THE development of agriculture faces significant challenges due to population growth, climate change, land depletion, and environmental pollution, threatening global food security [1]. This necessitates the development of sustainable agriculture, where a fundamental step is crop breeding to improve agronomic or economic traits, e.g., increasing yields of crops while decreasing resource usage and minimizing pollution to the environment [2].
This study presents an overview on intelligent reflecting surface(IRS)-enabled sensing and communication for the forthcoming sixth-generation(6G) wireless networks, in which IRSs are strategically deployed to proactiv...
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This study presents an overview on intelligent reflecting surface(IRS)-enabled sensing and communication for the forthcoming sixth-generation(6G) wireless networks, in which IRSs are strategically deployed to proactively reconfigure wireless environments to improve both sensing and communication(S&C) performance. First, we exploit a single IRS to enable wireless sensing in the base station's(BS's) non-line-of-sight(NLoS) area. In particular, we present three IRS-enabled NLoS target sensing architectures with fully-passive, semi-passive, and active IRSs, respectively. We compare their pros and cons by analyzing the fundamental sensing performance limits for target detection and parameter estimation. Next, we consider a single IRS to facilitate integrated sensing and communication(ISAC), in which the transmit signals at the BS are used for achieving both S&C functionalities, aided by the IRS through reflective beamforming. We present joint transmit signal and receiver processing designs for realizing efficient ISAC, and jointly optimize the transmit beamforming at the BS and reflective beamforming at the IRS to balance the fundamental performance tradeoff between S&C. Furthermore, we discuss multi-IRS networked ISAC, by particularly focusing on multi-IRS-enabled multi-link ISAC, multi-region ISAC, and ISAC signal routing, respectively. Finally, we highlight various promising research topics in this area to motivate future work.
Optical data storage(ODS)is a low-cost and high-durability counterpart of traditional electronic or mag-netic *** a means of enhancing ODS capacity,the multiple recording layer(MRL)method is more promising than other ...
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Optical data storage(ODS)is a low-cost and high-durability counterpart of traditional electronic or mag-netic *** a means of enhancing ODS capacity,the multiple recording layer(MRL)method is more promising than other approaches such as reducing the recording volume and multiplexing ***,the architecture of current MRLs is identical to that of recording data into physical layers with rigid space,which leads to either severe interlayer crosstalk or finite recording layers constrained by the short working distances of the ***,we propose the concept of hybrid-layer ODS,which can record optical information into a physical layer and multiple virtual layers by using high-orthogonality random *** the virtual layer,32 images are experimentally reconstructed through holog-raphy,where their holographic phases are encoded into 16 printed images and complementary images in the physical layer,yielding a capacity of 2.5 Tbit cm^(-3).A higher capacity is achievable with more virtual layers,suggesting hybrid-layer ODS as a possible candidate for next-generation ODS.
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