The study examines the multifaceted determinants influencing a project's community utility, including technological refinement, team dynamics, market feasibility, and funding sources. Crowdfunding, a prominent pat...
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RISC-V architectures are increasingly utilized in security-critical embedded systems, with OpenTitan standing out as a prominent open-source silicon Root-of-Trust. OpenTitan delivers essential functionalities, such as...
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The increasing pervasiveness of digital infrastructures, also extending into marine domains, makes Underwater Wireless Sensor Networks (UWSNs) an essential tool for the development of novel marine sustainability and m...
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Sideslip angle and vehicle velocity are crucial for both traditional and autonomous vehicles. They play essential roles in chassis stability control, as well as in tasks such as path planning and tracking control. How...
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Sideslip angle and vehicle velocity are crucial for both traditional and autonomous vehicles. They play essential roles in chassis stability control, as well as in tasks such as path planning and tracking control. However, these states cannot be directly measured by onboard sensors, therefore various vehicle state estimation algorithms have been developed. Most of these algorithms assume that the noise characteristics are known, ignoring the impact of missing measurement data, and cannot simultaneously handle the effects of colored noise and white noise. To address these issues, we propose a fault-tolerant extended Kalman filter network (FTEKFNet), which integrates both physics-based and data-driven methods for vehicle state estimation. Based on the Fault Tolerant Extended Kalman Filter (FTEKF) iterative framework, a pre-trained artificial neural network is utilized to directly predict the Kalman gain, and it is combined with FTEKF to form FTEKFNet. Experimental results under different conditions demonstrate that FTEKFNet can simultaneously deal with unknown noise and data loss problems and has good adaptability to color noise. The estimation performance of the proposed algorithm is better than the traditional FTEKF and EKF methods. IEEE
Relative overgeneralization (RO) occurs in cooperative multi-agent learning tasks when agents converge towards a suboptimal joint policy due to overfitting to suboptimal behaviors of other *** methods have been propos...
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Relative overgeneralization (RO) occurs in cooperative multi-agent learning tasks when agents converge towards a suboptimal joint policy due to overfitting to suboptimal behaviors of other *** methods have been proposed for addressing RO in multi-agent policy gradient (MAPG) methods although these methods produce state-of-the-art *** address this gap, we propose a general, yet simple, framework to enable optimistic updates in MAPG methods that alleviate the RO *** approach involves clipping the advantage to eliminate negative values, thereby facilitating optimistic updates in *** optimism prevents individual agents from quickly converging to a local ***, we provide a formal analysis to show that the proposed method retains optimality at a fixed *** extensive evaluations on a diverse set of tasks including the Multi-agent MuJoCo and Overcooked benchmarks, our method outperforms strong baselines on 13 out of 19 tested tasks and matches the performance on the rest. Copyright 2024 by the author(s)
In this study, the problem of measuring noise pollution distribution by the intertial-based integrated navigation system is effectively suppressed. Based on nonlinear inertial navigation error modeling, a nested dual ...
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In this study, the problem of measuring noise pollution distribution by the intertial-based integrated navigation system is effectively suppressed. Based on nonlinear inertial navigation error modeling, a nested dual Kalman filter framework structure is developed. It consists of unscented Kalman filter (UKF)master filter and Kalman filter slave filter. This method uses nonlinear UKF for integrated navigation state estimation. At the same time, the exact noise measurement covariance is estimated by the Kalman filter dependency filter. The algorithm based on dual adaptive UKF (Dual-AUKF) has high accuracy and robustness, especially in the case of measurement information interference. Finally, vehicle-mounted and ship-mounted integrated navigation tests are conducted. Compared with traditional UKF and the Sage-Husa adaptive UKF (SH-AUKF), this method has comparable filtering accuracy and better filtering stability. The effectiveness of the proposed algorithm is verified.
Microgrids equipped with hybrid energy storage systems (ESSs) are increasingly critical for balancing the intermittency of renewable energy sources and the fluctuations in demand. This article introduces a novel multi...
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Epilepsy disease is a neurological condition marked by recurring seizures that has a big effect on people's life. Effective management and therapy depend on a prompt and correct diagnosis. The traditional methods,...
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In this paper,we revisit the semi-global weighted output average tracking problem for a discrete-time multi-agent system subject to input saturation and external *** multi-agent system consists of multiple heterogeneo...
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In this paper,we revisit the semi-global weighted output average tracking problem for a discrete-time multi-agent system subject to input saturation and external *** multi-agent system consists of multiple heterogeneous linear systems as leader agents and multiple heterogeneous linear systems as follower *** design both the state feedback and output feedback control protocols for each follower *** particular,a distributed state observer is designed for each follower agent that estimates the state of each leader *** the output feedback case,state observer is also designed for each follower agent to estimate its own *** these estimates,we design low gain-based distributed control protocols,parameterized in a scalar low gain *** is shown that,for any bounded set of the initial conditions,these control protocols cause the follower agents to track the weighted average of the outputs of the leader agents as long as the value of the low gain parameter is tuned sufficiently *** results illustrate the validity of the theoretical results.
Instance selection (IS) serves as a vital preprocessing step, particularly in addressing the complexities associated with high-dimensional problems. Its primary goal is the reduction of data instances, a process that ...
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