This paper focuses on the stability analysis of a formation shape displayed by a team of mobile robots that uses heterogeneous sensing mechanism. Depending on the convenience and reliability of the local information, ...
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Automated extraction of cerebrovascular is of great importance in understanding the mechanism, diagnosis, and treatment of many cerebrovascular pathologies. However, segmentation of cerebrovascular networks from magne...
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Day-ahead solar irradiance forecast holds important value for optimizing energy utilization within the power system and ensuring stable grid scheduling. The forecast outputs of numerical weather prediction (NWP) are w...
Day-ahead solar irradiance forecast holds important value for optimizing energy utilization within the power system and ensuring stable grid scheduling. The forecast outputs of numerical weather prediction (NWP) are widely acknowledged as one of the indispensable data sources for day-ahead solar irradiance forecast tasks. In previous studies, post-processing methods have generally been employed as correction models to enhance the accuracy of NWP solar irradiance forecasts. However, irradiance sequences contain complex mixed patterns and exhibit various seasonal periodic differences. Based on the analysis of NWP global horizontal irradiance (GHI) error characteristics in this study, errors in NWP GHI forecasts also show obvious seasonal variations. Given these issues, it is challenging for a single correction model to achieve good correction performance and strong seasonal robustness. Therefore, this paper proposes a hybrid model comprising representation learning module, feature sparse activation module, and encoder-decoder-based correction module to address the aforementioned problems. A contrastive-learning-based representation learning module named CoST is introduced to learn disentangled seasonal features and trend features of irradiance sequences. A learnable mixture-of-experts (MoE) layer is adopted to sparsely activate the seasonal-trend features that contribute more to improving correction accuracy. The encoder-decoder-based correction module takes the sparsely activated seasonal-trend features as inputs, achieving the final corrected NWP GHI forecasts. The correction performance of the proposed method was validated on both publicly available datasets and actual field dataset. The results for various datasets show that our proposed CoST-MoELSTM model achieves the highest improvement for NWP forecasts, with increases of 29.82 %, 36.54 %, and 26.58 %. Additionally, we conducted a detailed analysis of the correction performance of CoST-MoELSTM across different
Due to the black-box characteristics of deep learning based semantic encoders and decoders, finding a tractable method for the performance analysis of semantic communications is a challenging problem. In this paper, w...
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— In this letter, we investigate the formation control problem of mobile robots moving in the plane where, instead of assuming robots to be simple points, each robot is assumed to have the form of a disk with equal r...
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Renewable energy sources, elastic loads, and purposeful manipulation of meter readings challenge the monitoring and control of today’s power systems (PS). In this context, fast and robust state estimation (SE) is tim...
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Realistic animation of various interactions between multiple fluids, possibly undergoing phase change, is a challenging task in computer graphics. The visual scope of multi-phase multi-fluid phenomena covers complex t...
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Realistic animation of various interactions between multiple fluids, possibly undergoing phase change, is a challenging task in computer graphics. The visual scope of multi-phase multi-fluid phenomena covers complex tangled surface structures and rich color variations, which can greatly enhance visual effect in graphics applications. Describing such phenomena requires more complex models to handle challenges involving the calculation of interactions, dynamics and spatial distribution of multiple phases, which are often involved and hard to obtain real-time performance. Recently, a diverse set of algorithms have been introduced to implement the complex multi-fluid phenomena based on the governing physical laws and novel discretization methods to accelerate the overall computation while ensuring numerical stability. By sorting through the target phenomena of recent research in the broad subject of multiple fluids, this state-of-the-art report summarizes recent advances on multi-fluid simulation in computer graphics.
Effect of friction on the motion control processes in mechatronic systems is considered. The analysis of modern friction modeling methods is carried out. Features of processes at low speeds: accuracy, oscillations and...
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In this paper, the measurement-based characteristic curves, reactive power-voltage ( Q-V ) curve and reactive power-active power-voltage ( Q-P-V ) curve, are developed for voltage stability and control at the point of...
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This book shows how supervisory control theory (SCT) supports the formulation of various control problems of standard types, like the synthesis of controlled dynamic invariants by state feedback, and the resolution of...
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ISBN:
(数字)9783319774527
ISBN:
(纸本)9783319774510
This book shows how supervisory control theory (SCT) supports the formulation of various control problems of standard types, like the synthesis of controlled dynamic invariants by state feedback, and the resolution of such problems in terms of naturally definable control-theoretic concepts and properties, like reachability, controllability and observability. It exploits a simple, abstract model of controlled discrete-event systems (DES) that has proved to be tractable, appealing to control specialists, and expressive of a range of control-theoretic ideas. It allows readers to choose between automaton-based and dually language-based forms of SCT, depending on whether their preference is for an internal-structural or external-behavioral description of the problem.;The monograph begins with two chapters on algebraic and linguistic preliminaries and the fundamental concepts and results of SCT are introduced. To handle complexity caused by system scale, architectural approaches—the horizontal modularity of decentralized and distributed supervision and the vertical modularity of hierarchical supervision—are introduced. Supervisory control under partial observation and state-based supervisory control are also addressed; in the latter, a vector DES model that exploits internal regularity of algebraic structure is proposed. Finally SCT is generalized to deal with timed DES by incorporating temporal features in addition to logical ones.
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