The water supply network is an essential infrastructure for urban life, and designing a scientific and reasonable water supply network can not only reduce construction and operation and maintenance costs but also enha...
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ISBN:
(纸本)9798350321050
The water supply network is an essential infrastructure for urban life, and designing a scientific and reasonable water supply network can not only reduce construction and operation and maintenance costs but also enhance the reliability of the system. However, the design of the system involves complex constraints, as well as optimizing the pipe network is a nonlinear problem that significantly impacts construction investments. Traditional optimization methods have demonstrated poor performance in pipe network optimization due to low convergence accuracy and weak global search capabilities. To address these shortcomings, this paper proposes an Improved Sparrow Search Algorithm (ISSA) that introduces a Levy flight strategy to increase spatial search diversity and eliminate the small search space in the late iteration period and random walk around the optimal solution. The improved algorithm is applied to Hanoi, New York, and ZJ pipe networks, showing that it can efficiently find the best cost design scheme while reducing calculation cost and realizing rapid optimization of the combination optimization problem.
This work focuses on enhancing the operational safety, cybersecurity, computational efficiency, and closed-loop performance of large-scale nonlinear processes with input delays. This is achieved by employing a decentr...
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ISBN:
(纸本)9798350382662;9798350382655
This work focuses on enhancing the operational safety, cybersecurity, computational efficiency, and closed-loop performance of large-scale nonlinear processes with input delays. This is achieved by employing a decentralized model predictive controller (MPC) with encrypted networked communication. Within this decentralized setup, the nonlinear process is partitioned into multiple subsystems, each controlled by a distinct Lyapunov-based MPC. These controllers take into account the interactions between subsystems by utilizing full state feedback. To address the performance degradation associated with input delays, we integrate a predictor with each LMPC to compute the states after the input delay period. The LMPC process model is initialized with these predicted states. Furthermore, to enhance cybersecurity, all signals transmitted to and received from each subsystem are encrypted. Guidelines are established to implement this proposed control structure in any nonlinear system with input delays. The simulation results, conducted on a nonlinear chemical process network, illustrate the effective closed-loop performance of the decentralized MPCs alongside the predictor with encrypted communication when dealing with input delays in a large process.
Currently, most deep denoising networks introduce more parameters, leading to performance degradation, and no research has focused on negative images. Thus, we propose a Dual-Branch Attention Dense Residual network Ba...
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Currently, most deep denoising networks introduce more parameters, leading to performance degradation, and no research has focused on negative images. Thus, we propose a Dual-Branch Attention Dense Residual network Based on Negative Image (NDARNet) for image denoising tasks in this paper. NDARNet chooses to widen the network instead of deepening it. It has two parallel branches: one processes the original noisy image, and the other processes its negative image. The negative image inverts pixel values, highlight features that are not very visible in the original noisy image. We also designed an Attention Residual Multi-Scale Block (ARMSB) and an Attention Residual Dilated Block (ARDB) for efficient feature extraction in both branches. We use many long skip connections in the network. Furthermore, our method supports blind denoising. Extensive experiments show that NDARNet achieves favorable PSNR and SSIM results at different noise levels compared to other common and advanced methods.
This paper develops a distributed control protocol for the distributed optimal consensus problem of multi-agent systems. The dynamics of multi-agent systems are heterogeneous linear. Considering that multi-agent syste...
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ISBN:
(纸本)9789819743988;9789819743995
This paper develops a distributed control protocol for the distributed optimal consensus problem of multi-agent systems. The dynamics of multi-agent systems are heterogeneous linear. Considering that multi-agent systems often face various external disturbances in practical applications, which may seriously affect the performance and stability of the system. The control protocol proposed in this paper pays special attention to dealing with external disturbances in linear dynamic systems. Theoretical results show that the distributed control protocol is robust under the external disruption and drives the states of each agent to the auxiliary variable in a finite time. Further, with the aid of Lyapunov method, the states of close-loop system globally converge to the optimal solution of the non-smooth optimization problem. Finally, the simulation results demonstrate the effectiveness of proposed control protocol.
In this paper we provide an analytical solution to an H-2 optimal control problem, that applies whenever the process corresponds to a uniformly damped network of masses and springs. The solution covers both stable and...
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ISBN:
(纸本)9798331540920;9783907144107
In this paper we provide an analytical solution to an H-2 optimal control problem, that applies whenever the process corresponds to a uniformly damped network of masses and springs. The solution covers both stable and unstable systems, and illustrates analytically how damping affects the levels of achievable performance. Furthermore, the resulting optimal controllers can be synthesised using passive damped mass-spring networks, allowing for controller implementations without an energy source. We investigate the impact of both positive and negative damping through a small numerical example.
In the context of nonlinear and uncertain manipulator systems, achieving fast and accurate desired trajectories for each joint of the manipulator is crucial. Our study aims to improve current control methods by combin...
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Given the increasing use of wind energy in power systems globally, Wind Farm systems (WFS) need to actively engage in the operation of electric networks by using a suitable generation control strategy. The present art...
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This paper focuses on the critical task of abnormal detection in the Open Radio Access network (O-RAN) environment. Utilizing real-world O-RAN data, we propose a novel abnormal detection method that integrates explain...
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ISBN:
(纸本)9798350386851;9798350386844
This paper focuses on the critical task of abnormal detection in the Open Radio Access network (O-RAN) environment. Utilizing real-world O-RAN data, we propose a novel abnormal detection method that integrates explainable Artificial Intelligence (AI) with statistical learning techniques. Our approach classifies O-RAN devices based on their operational locations and utilizes historical data for AI training, emphasizing the identification of deviations in networkperformance metrics such as Radio Resource control (RRC) connections. Through field testing, the feasibility of our method has been validated. This study provides a scalable and transparent abnormal detection solution for network management in O-RAN systems, highlighting the importance of applying explainable AI in the field of telecommunications.
In this work, we provide an experimental validation of the recent concepts of closed-loop state and input sensitivity in the context of robust flight control for a quadrotor (UAV) equipped with the popular PX4(1) cont...
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ISBN:
(纸本)9798350357899;9798350357882
In this work, we provide an experimental validation of the recent concepts of closed-loop state and input sensitivity in the context of robust flight control for a quadrotor (UAV) equipped with the popular PX4(1) controller. Our objective is to experimentally assess how the optimization of the reference trajectory w.r.t. these sensitivity metrics can improve the closedloop system performance against model uncertainties commonly affecting the quadrotor systems. To accomplish this, we present a series of experiments designed to validate our optimization approach on two distinct trajectories, with the primary aim of assessing its precision in guiding the quadrotor through the center of a window at relatively high speeds. This approach provides some interesting insights for increasing the closedloop robustness of the robot state and inputs against physical parametric uncertainties that may degrade the system's performance.
In this paper, we present a contraction-guided adaptive partitioning algorithm for improving interval-valued robust reachable set estimates in a nonlinear feedback loop with a neural networkcontroller and disturbance...
ISBN:
(纸本)9798350301243
In this paper, we present a contraction-guided adaptive partitioning algorithm for improving interval-valued robust reachable set estimates in a nonlinear feedback loop with a neural networkcontroller and disturbances. Based on an estimate of the contraction rate of over-approximated intervals, the algorithm chooses when and where to partition. Then, by leveraging a decoupling of the neural network verification step and reachability partitioning layers, the algorithm can provide accuracy improvements for little computational cost. This approach is applicable with any sufficiently accurate open-loop interval-valued reachability estimation technique and any method for bounding the input-output behavior of a neural network. Using contraction-based robustness analysis, we provide guarantees of the algorithm's performance with mixed monotone reachability. Finally, we demonstrate the algorithm's performance through several numerical simulations and compare it with existing methods in the literature. In particular, we report a sizable improvement in the accuracy of reachable set estimation in a fraction of the runtime as compared to state-of-the-art methods.
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