Joint communication-caching-computing resource allocation in wireless inland waterway communications enables resource-constrained unmanned surface vehicles(USVs) to provision computation-intensive and latencysensitive...
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Joint communication-caching-computing resource allocation in wireless inland waterway communications enables resource-constrained unmanned surface vehicles(USVs) to provision computation-intensive and latencysensitive tasks forward beyond fifth-generation(B5G) and sixth-generation(6G) era. The power of such resource allocation cannot be fully studied unless bidirectional data computation is properly managed. A novel intelligent reflecting surface(IRS)-assisted hybrid UAV-terrestrial network architecture is proposed with bidirectional tasks. The sum of uplink and downlink bandwidth minimization problem is formulated by jointly considering link quality, task execution mode selection, UAVs trajectory, and task execution latency constraints. A heuristic algorithm is proposed to solve the formulated challenging problem. We divide the original challenging problem into two subproblems, i.e., the joint optimization problem of USVs offloading decision, caching decision and task execution mode selection, and the joint optimization problem of UAVs trajectory and IRS phase shift-vector design. The Karush–Kuhn–Tucker conditions are utilized to solve the first subproblem and the enhanced differential evolution algorithm is proposed to solve the latter one. The results show that the proposed solution can significantly decrease bandwidth consumption in comparison with the selected advanced algorithms. The results also prove that the sum of bandwidth can be remarkably decreased by implementing a higher number of IRS elements.
Integrated sensing and communication (ISAC) is a promising technique to increase spectral efficiency and support various emerging applications by sharing the spectrum and hardware between these functionalities. Howeve...
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Integrated sensing and communication (ISAC) is a promising technique to increase spectral efficiency and support various emerging applications by sharing the spectrum and hardware between these functionalities. However, the traditional ISAC schemes are highly dependent on the accurate mathematical model and suffer from the challenges of high complexity and poor performance in practical scenarios. Recently, artificial intelligence (AI) has emerged as a viable technique to address these issues due to its powerful learning capabilities, satisfactory generalization capability, fast inference speed, and high adaptability for dynamic environments, facilitating a system design shift from model-driven to data-driven. Intelligent ISAC, which integrates AI into ISAC, has been a hot topic that has attracted many researchers to investigate. In this paper, we provide a comprehensive overview of intelligent ISAC, including its motivation, typical applications, recent trends, and challenges. In particular, we first introduce the basic principle of ISAC, followed by its key techniques. Then, an overview of AI and a comparison between model-based and AI-based methods for ISAC are provided. Furthermore, the typical applications of AI in ISAC and the recent trends for AI-enabled ISAC are reviewed. Finally, the future research issues and challenges of intelligent ISAC are discussed.
The current-loop control of a permanent magnet synchronous motor(PMSM) system suffers from periodic and aperiodic disturbances, which result in current ripples and degrade control performance. This paper presents an e...
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The current-loop control of a permanent magnet synchronous motor(PMSM) system suffers from periodic and aperiodic disturbances, which result in current ripples and degrade control performance. This paper presents an enhanced equivalent-input-disturbance(EID) approach to reject periodic and aperiodic disturbances in the current loop of a *** additional quasi-resonant compensators(QRCs) are integrated into a conventional EID estimator with a low-pass filter(LPF) to handle the disturbances. The configuration of an enhanced EID(EEID)-based control system for PMSM current loop is explained. An analysis in the frequency domain shows the mechanism of the presented method for rejecting periodic and aperiodic disturbances simultaneously. It reveals that the sensitivity reduction of the system for aperiodic disturbances is mainly determined by the bandwidth of the LPF and that for periodic disturbances is determined by the parameters of the *** stability of the system is analyzed using the Nyquist stability criterion. The design algorithm for system parameters is provided. Compared to the conventional EID approach, the proposed method provides an additional degree of freedom to deal with periodic disturbances. The design of the QRCs is independent of each other, which makes the proposed method flexible and easy to implement. The effectiveness and the superiority of the EEID approach are validated by simulation results of a PMSM system.
The research and development efforts for the sixth generation(6G) wireless communication are underway to fulfill the growing demands for wireless communications. 6G is expected to become the key enabler for diverse ne...
The research and development efforts for the sixth generation(6G) wireless communication are underway to fulfill the growing demands for wireless communications. 6G is expected to become the key enabler for diverse new applications and services [1]. To achieve the excellent performance, 6G will implement extremely large-scale antenna arrays(ELAAs), terahertz [2], and new types of antennas with metamaterials, leading to a paradigm shift for the electromagnetic characteristics.
Protein acetylation refers to a process of adding acetyl groups(CH3CO-)to lysine residues on protein *** one of the most commonly used protein post-translational modifications,lysine acetylation plays an important rol...
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Protein acetylation refers to a process of adding acetyl groups(CH3CO-)to lysine residues on protein *** one of the most commonly used protein post-translational modifications,lysine acetylation plays an important role in different *** our study,we developed a humanspecific method which uses a cascade classifier of complexvalued polynomial model(CVPM),combined with sequence and structural feature descriptors to solve the problem of imbalance between positive and negative *** gene expression programming and differential evolution are utilized to search the optimal CVPM *** also made a systematic and comprehensive analysis of the acetylation data and the prediction *** performances of our proposed method are 79.15%in Sp,78.17%in Sn,78.66%in ACC 78.76%in F1,and 0.5733 in MCC,which performs better than other state-of-the-art methods.
As a crucial storage and buffering apparatus for balancing the production and consumption of byproduct gases in industrial processes, accurate prediction of gas tank levels is essential for optimizing energy system sc...
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As a crucial storage and buffering apparatus for balancing the production and consumption of byproduct gases in industrial processes, accurate prediction of gas tank levels is essential for optimizing energy system scheduling. Considering that the continuous switching of the pressure and valve status(mechanism knowledge) would bring about multiple working conditions of the equipment, a multi-condition time sequential network ensembled method is proposed. In order to especially consider the time dependence of different conditions, a centralwise condition sequential network is developed, where the network branches are specially designed based on the condition switching sequences. A branch combination transfer learning strategy is developed to tackle the sample imbalance problem of different condition data. Since the condition or status data are real-time information that cannot be recognized during the prediction process, a pre-trained and ensemble learning approach is further proposed to fuse the outputs of the multi-condition networks and realize a transient-state involved prediction. The performance of the proposed method is validated on practical energy data coming from a domestic steel plant, comparing with the state-of-the-art algorithms. The results show that the proposed method can maintain a high prediction accuracy under different condition switching cases, which would provide effective guidance for the optimal scheduling of the industrial energy systems.
Human emotions are intricate psychological phenomena that reflect an individual’s current physiological and psychological *** have a pronounced influence on human behavior,cognition,communication,and ***,current emot...
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Human emotions are intricate psychological phenomena that reflect an individual’s current physiological and psychological *** have a pronounced influence on human behavior,cognition,communication,and ***,current emotion recognition methods often suffer from suboptimal performance and limited scalability in practical *** solve this problem,a novel electroencephalogram(EEG)emotion recognition network named VG-DOCoT is proposed,which is based on depthwise over-parameterized convolutional(DO-Conv),transformer,and variational automatic encoder-generative adversarial network(VAE-GAN)***,the differential entropy(DE)can be extracted from EEG signals to create mappings into the temporal,spatial,and frequency information in *** enhance the training data,VAE-GAN is employed for data augmentation.A novel convolution module DO-Conv is used to replace the traditional convolution layer to improve the network.A transformer structure is introduced into the network framework to reveal the global dependencies from EEG *** the proposed model,a binary classification on the DEAP dataset is carried out,which achieves an accuracy of 92.52%for arousal and 92.27%for ***,a ternary classification is conducted on SEED,which classifies neutral,positive,and negative emotions;an impressive average prediction accuracy of 93.77%is *** proposed method significantly improves the accuracy for EEG-based emotion recognition.
To study the effect of different deposition temperatures on the optical properties of porous SiC films,single crystal Si was used as the substrate,a layer of anodic aluminum oxide(AAO)film was transferred on the Si su...
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To study the effect of different deposition temperatures on the optical properties of porous SiC films,single crystal Si was used as the substrate,a layer of anodic aluminum oxide(AAO)film was transferred on the Si substrate by chemical method,and then a layer of SiC was deposited on anodic aluminum oxide(AAO)template to prepare porous fluorescent SiC film by magnetron *** deposition temperature was ranged from 373 to 873 *** thickness of the porous SiC film coated on the AAO surface was around 283 *** is found that the porous SiC with the deposition temperature of 873 K has the strongest photoluminescence(PL)intensity excited by 375 nm *** time-resolved PL spectra prove that the PL is mainly from intrinsic light emitting of *** the optimized process,porous amorphous SiC film may have potential applications in the field of warm white LEDs.
For a sub-connected hybrid multiple-input multiple-output(MIMO) receiver with K subarrays and N antennas, there exists a challenging problem of how to rapidly remove phase ambiguity in only single time-slot. A directi...
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For a sub-connected hybrid multiple-input multiple-output(MIMO) receiver with K subarrays and N antennas, there exists a challenging problem of how to rapidly remove phase ambiguity in only single time-slot. A direction of arrival(DOA) estimator of maximizing received power(Max-RP) is proposed to find the maximum value of K-subarray output powers, where each subarray is in charge of one sector, and the center angle of the sector corresponding to the maximum output is the estimated true DOA. To make an enhancement on precision, Max-RP plus quadratic interpolation(Max-RP-QI) method is designed. In the proposed Max-RP-QI, a quadratic interpolation scheme is adopted to interpolate the three DOA values corresponding to the largest three receive powers of *** achieve the Cramer Rao lower bound, a Root-MUSIC plus Max-RP-QI scheme is developed. Simulation results show that the proposed three methods eliminate the phase ambiguity during one time-slot and also show low computational complexities. The proposed Root-MUSIC plus Max-RP-QI scheme can reach the Cramer Rao lower bound,and the proposed Max-RP and Max-RP-QI are still some performance losses 2–4 d B compared to the Cramer Rao lower bound.
Aided by device-to-device(D2D) connections, unmanned aerial vehicle(UAV) can significantly enhance the coverage of wireless communications. In this paper, we consider a data collection system with the assistance of D2...
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Aided by device-to-device(D2D) connections, unmanned aerial vehicle(UAV) can significantly enhance the coverage of wireless communications. In this paper, we consider a data collection system with the assistance of D2D, where two fixed-wing UAVs as aerial base stations cooperatively serve the ground devices. To accommodate more devices, we propose two effective algorithms to establish the multi-hop D2D connections. Then, the user scheduling, UAV trajectory, and transmit power are jointly optimized to maximize the energy efficiency, which is a non-convex problem. Accordingly, we decompose it into three subproblems. The scheduling optimization is first converted into a linear programming. Then, the trajectory design and the transmit power optimization are reformulated as two convex problems by the Dinkelbach method. Finally, an iterative algorithm is proposed to effectively solve the original problem. Simulation results are presented to verify the effectiveness of the proposed scheme.
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