The congestion control (CC) algorithm is expected to achieve consistent high performance under different network environments. Traditionally, classic CCs are designed with the methodology of inferring path conditions ...
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ISBN:
(纸本)9798400711961
The congestion control (CC) algorithm is expected to achieve consistent high performance under different network environments. Traditionally, classic CCs are designed with the methodology of inferring path conditions to guide the rate adjustment. However, this methodology suffers from wrong path condition inferences in certain cases, which mislead the rate adjustment and lead to performance degradation. To avoid wrong path condition inferences, we develop the projection-based introspective method and design the introspective congestion control (ICC) algorithm in this paper. Specifically, the rate adjustment rules are designed to possess a specialized profile such that the projection of the profile can be distinguished under unchanged path conditions. In this way, the projection, which can be distinguished from the time series of delay signals in the frequency domain, facilitates ICC to extract more information for path condition inferences. Consequently, with the introspection on the projection, ICC can avoid being misled by wrong path condition inferences and thus achieve consistent high performance under different conditions. The advantages of ICC are confirmed through extensive experiments conducted on various locally emulated scenarios, global testbeds over the Internet, and the Alipay platform.
network slicing has been introduced as a promising paradigm for 5G/B5G technology. The slice admission control process is one of the first steps in network slicing. The decisions taken in the slice admission control p...
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This article proposes a fixed-time adaptive controller based on disturbance observer to optimize the disturbance resistance and dynamic performance of full state feedback system. Firstly, a fixed-time prescribed perfo...
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This paper introduces a novel design approach for a backstepping control strategy specifically aimed at optimizing the performance of Voltage Source Converters (VSC) and Pulse Width Modulation (PWM) rectifiers. The ba...
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Conventional multiple-point active noise control (ANC) systems require placing error microphones within the region of interest (ROI), inconveniencing users. This paper designs a feasible monitoring microphone arrangem...
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ISBN:
(纸本)9798350344868;9798350344851
Conventional multiple-point active noise control (ANC) systems require placing error microphones within the region of interest (ROI), inconveniencing users. This paper designs a feasible monitoring microphone arrangement placed outside the ROI, providing a user with more freedom of movement. The soundfield within the ROI is interpolated from the microphone signals using a physics-informed neural network (PINN). PINN exploits the acoustic wave equation to assist soundfield interpolation under a limited number of monitoring microphones, and demonstrates better interpolation performance than the spherical harmonics method in simulations. An ANC system is designed to take advantage of the interpolated signal to reduce noise signal within the ROI. The PINN-assisted ANC system reduces noise more than that of the multiple-point ANC system in simulations.
Federated Learning (FL) diverges from traditional Machine Learning (ML) models decentralizing data utilization, addressing privacy concerns. This approach involves iterative model updates, where individual devices com...
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ISBN:
(纸本)9798350363074;9798350363081
Federated Learning (FL) diverges from traditional Machine Learning (ML) models decentralizing data utilization, addressing privacy concerns. This approach involves iterative model updates, where individual devices compute gradients based on local data, share updates with a central server, and receive an improved global model. High-performance Computing (HPC) systems enhance FL efficiency by leveraging parallel processing. In this study, we aim to explore FL efficiency using four aggregation methods on three datasets across six clients, assess metrics like global model accuracy and communication efficiency, and evaluate FL on HPC. We employ Flower, a versatile FL framework, in our experiments. Our chosen datasets include MNIST, Digits, and Semeion Handwritten Digit, distributed among two clients each. We utilize NVIDIA GPUs for computation, with aggregation methods such as FedAvg, FedProx, FedOpt, and FedYogi. Metrics include Convergence Time, Global Model Accuracy, Communication Efficiency, and HPC Throughput. The results will provide insights into FL performance, especially in HPC environments, impacting convergence, communication, and resource utilization.
Recently, some pioneering works tend to apply more complex modules to enhance the segmentation performance. However, this is not conducive to real-world clinical settings due to limited computational resources. To add...
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Recently, some pioneering works tend to apply more complex modules to enhance the segmentation performance. However, this is not conducive to real-world clinical settings due to limited computational resources. To address this challenge, we propose a more optimized high-performance lightweight U-shaped image segmentation model: Multi-axis Attention Mechanism and Aggregate Supervision Enhanced Image Segmentation network (MARNet). It integrates the Enhanced Multi-axis Attention (EMIA) module and the Multi-featiture Aggregate Supervision Fusion Module (ARFM). EMIA utilizes memory units, residual connections, and element-wise operations to obtain enhanced image multiview edge information while reducing the parameters of the model's self-attention mechanism. The ARFM module effectively fuses multi-scale features by adapting to different scales and utilizing auxiliary masks. Our test results on the ISIC2017 and ISIC2018 datasets show that MARnet outperforms existing methods while maintaining minimal computational requirements, demonstrating its great potential in the field of medical image segmentation.
As a typical nonlinear link, actuator saturation exists in most network security protection systems. It easily disrupts the closed-loop performance of the system, leading to instability. The event triggering control p...
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PLC control programs are vulnerable to real-time threats, where attackers can disrupt the backhaul/front-end network of industrial production by creating numerous loops or I/O operations, leading to severe consequence...
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ISBN:
(纸本)9781728190549
PLC control programs are vulnerable to real-time threats, where attackers can disrupt the backhaul/front-end network of industrial production by creating numerous loops or I/O operations, leading to severe consequences. Therefore, formal verification of PLC control logic at the binary level is essential. In this study, we introduce a framework designed for formal verification of PLC control logic at the binary level. Our verification framework is based on simulation execution, which extracts the core control logic from PLC binary code. Initially, we develop an efficient framework for automating the parsing of PLC programs at the binary level and constructing their control flow graphs (CFGs). Next, we devise an algorithm to transform the reversed PLC assembly program into an smv model, a widely accepted formal verification tool. Subsequently, we generate real-time requirements relevant to industrial production and perform formal verification on the constructed models. To assess the real-time performance of our framework in safeguarding PLC systems, we implement a prototype and evaluated it across various representative ICS scenarios. The evaluation results demonstrate the capability of our proposed approach to effectively detect synchronization threats in PLC logic control programs.
Transiently chaotic neural network (TCNN) and its improved versions have been proven to have search abilities for combinatorial optimization problem (COP). However, the TCNN may be able to maintain its solving ability...
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ISBN:
(纸本)9798350321050
Transiently chaotic neural network (TCNN) and its improved versions have been proven to have search abilities for combinatorial optimization problem (COP). However, the TCNN may be able to maintain its solving ability while the chaotic dynamics are cut. In this paper, the mechanism of the continuous-time Hopfield neural network (CHNN) and the TCNN for COP are analyzed qualitatively from the view of the energy function. It is believed that the "annealing" progress, i.e., the dynamic relaxation of the energy function, helps the improvement of the TCNN over the CHNN. Another Hopfield network with a linear activation function and annealing strategy for the COP is proposed in this paper. Simulations on the TSP show that the improved network performs as well as TCNN but is much more efficient. The performance of the TCNN is improved when linear activation functions are used. Compared with the traditional sigmoid activation function, the improved network is more suitable for hardware implementation.
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