H-infinity control for nonlinear systems with uncertain, time-varying delays stands as a vital control methodology for managing the complexities inherent in such systems. This approach is frequently employed to addres...
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ISBN:
(纸本)9798331540845;9789887581598
H-infinity control for nonlinear systems with uncertain, time-varying delays stands as a vital control methodology for managing the complexities inherent in such systems. This approach is frequently employed to address challenges associated with uncertain systems experiencing time delays. In practical applications, the implementation of H-infinity control often leads to enhancements in system stability and robustness. Rooted in the H-infinity norm, this control design method aims to optimize the stability margin of the system. Typically, the steps involved in this process encompass system modeling, performance index selection, controller design, and subsequent performance analysis. Given the inherent complexity of such systems, it is essential to employ appropriate mathematical tools and control strategies to ensure that the controller maintains optimal performance across varying operational conditions. H-infinity control emerges as a potent solution capable of addressing a wide array of complex systems, including nonlinear systems plagued by uncertain time-varying delays. Through prudent controller design, significant improvements in system performance and stability can be achieved, thereby enabling the system to operate seamlessly despite the presence of uncertainty and time delay.
Gaussian process state space models are becoming common tools for the analysis and design of nonlinear systems with uncertain dynamics. When designing control policies for these systems, safety is an important propert...
ISBN:
(纸本)9798350301243
Gaussian process state space models are becoming common tools for the analysis and design of nonlinear systems with uncertain dynamics. When designing control policies for these systems, safety is an important property to consider. In this paper, we provide safety guarantees by computing finite-horizon forward reachable sets for Gaussian process state space models. We use data-driven reachability analysis to provide exact probability measures for state trajectories of arbitrary length, even when no data samples are available. We investigate two numerical examples to demonstrate the power of this approach, such as providing highly non-convex reachable sets and detecting holes in the reachable set.
Abstract: analysis of the features of modern power-supply systems and the increasing requirements of consumers to electric-power-quality indicators (EPQIs) at the point of connection of electrical installations (elect...
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Hip exoskeletons offer significant potential for enhancing human movement, especially for those with mobility impairments. However, optimizing their performance typically involves lengthy discrete and continuous optim...
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Hip exoskeletons offer significant potential for enhancing human movement, especially for those with mobility impairments. However, optimizing their performance typically involves lengthy discrete and continuous optimization methods. To address this, we propose a novel approach using machine learning to predict controller parameter classes, aiming to improve the tuning process. Our method relies on subject-specific anthropometric data to predict optimal controller parameters for hip exoskeletons. Through a machine learning framework, we develop predictive models to determine the most effective parameter settings tailored to individual users. By employing feature engineering, data synthesis techniques, and model training, we enhance the initialization of Bayesian Human-in-the-loop (HIL) optimization. Results indicate that our machine learning models can predict control parameter classes with 75% accuracy, leading to a 9.98% improvement in optimized exoskeleton performance for users.
In response to the challenge of diagnosing incipient faults in industrial processes, a fault diagnosis method based on moving window cumulative sum principal component analysis (MWSUM-PCA) and reconstructed contributi...
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ISBN:
(纸本)9798350387780;9798350387797
In response to the challenge of diagnosing incipient faults in industrial processes, a fault diagnosis method based on moving window cumulative sum principal component analysis (MWSUM-PCA) and reconstructed contribution plot is proposed in this paper. Based on sliding window, the statistics are updated by iterative calculation using real-time data. The average expected difference reconstruction contribution plot algorithm is used for fault identification. The difference between the contribution value of sliding window data and its expected value is used for reconstruction to reduce the impact of noise on the identification results. Through simulation experiments on the Tennessee Eastman (TE) process, and comparison with the results of PCA and K-nearest neighbor (KNN) PCA algorithms, the experimental results comprehensively demonstrate the effectiveness of the proposed method.
Neural network soft sensor methods are widely used in industrial process quality prediction. Currently, gate-controlled neural networks with long short-term memory capabilities are effective in dealing with non-linear...
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ISBN:
(纸本)9798350387780;9798350387797
Neural network soft sensor methods are widely used in industrial process quality prediction. Currently, gate-controlled neural networks with long short-term memory capabilities are effective in dealing with non-linear time series data in industry to predict important variables. However, the actual industrial processes are complex and involve numerous variables. The simple gated recurrent GRU unit cannot effectively handle features with varying information loads in neural networks. Therefore, this paper proposes a GRU-based time-space reconstruction neural network (TSR-GRU). The network can reconstruct the features from two dimensions of time and space, and it can adaptively assign different weights to different features to enhance the feature extraction ability of the network. Finally, the network proposed in this paper is applied to the dome temperature prediction of hot blast stove, which verifies the effectiveness of the proposed model.
This paper introduces a data-driven optimization (DDO) method based on novel strategic sampling (SS) considering data correlations for multiperiod optimal power flow (OPF) considering energy storage devices under unce...
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This paper introduces a data-driven optimization (DDO) method based on novel strategic sampling (SS) considering data correlations for multiperiod optimal power flow (OPF) considering energy storage devices under uncertainty (OPF-ESDUU) of uncertain renewable energy and power loads (UREPL). This DDO method depends only on the uncertainty samples to yield an optimal solution that satisfies a specific confidence level, which is effective because of two resounding learning algorithms: Bayesian hierarchical modeling (BHM) and determinantal point process (DPP). Considering both the local bus information and spatial correlations over all buses, BHM learns the convex approximation of AC power flow (CAACPF) more accurately than the existing learning methods, converting the originally non-convex OPF-ESDUU to a convex optimization problem. DPP considers the correlations between samples to find a small set of significant samples by measuring the relative weight of each sample using the random matrix theory, significantly decreasing the data samples required by the existing SS. The experimental analysis in IEEE test cases shows that after considering data correlations, 1) BHM learns CAACPF better with 13-90% accuracy improvement, compared with the existing learning methods, and 2) the proposed DDO performs more efficiently than the existing DDO as DPP-based SS boosts the sampling efficiency by 50% at least.
data-driven predictive models for end-point quality variables are important tools in industrial processmodeling. However, establishing an effective predictive model with limited labeled data remains challenging. Tran...
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ISBN:
(纸本)9798350382662;9798350382655
data-driven predictive models for end-point quality variables are important tools in industrial processmodeling. However, establishing an effective predictive model with limited labeled data remains challenging. Transfer learning (TL) offers a solution by leveraging knowledge from similar but different tasks. This paper introduces a novel TL-based predictive model, domain-adaptation parallel stacked autoencoders (DA-PSAE), which can extract and accumulate knowledge from multiple similar processes. First, a parallel model structure is designed for the simultaneous extraction of static and plastic latent features. Besides, an effective TL-based training strategy is proposed, which utilizes data from multiple similar processes. The proposed model is applied to a sulfur recovery unit composed of four parallel sub-units. Experimental results verify the effectiveness of the proposed model.
Citation analysis is a bibliometric research method that utilizes mathematical, statistical, and logical approaches to analyze citation patterns in scientific journals, papers, and authors. This study employs citation...
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ISBN:
(纸本)9798350366907;9789887581581
Citation analysis is a bibliometric research method that utilizes mathematical, statistical, and logical approaches to analyze citation patterns in scientific journals, papers, and authors. This study employs citation analysis to investigate the emerging subfield of Affective Computing in control science and technology. Beginning with the leading journal in this field, IEEE Transactions on Affective Computing, 15 important journals were identified to map the research landscape. Furthermore, it reveals an overall upward trend in the impact factors of journals within this field from 2013 to 2022. Through the use of the Web of Science database, an automated data retrieval program was developed to collect and process a large amount of empirical data, addressing any anomalies within the dataset. Statistical analysis and examination of empirical data were conducted to observe the evolution of citation pattern among scholars in the field and investigate the relationship between this evolution and the widespread increase in journal impact factors. The research findings indicate that the citation time patterns among scholars remain stable with no significant changes, while the average number of references per article has shown a notable increase.
Knowledge tracing (KT) is a crucial task in intelligent education, focusing on predicting students' performance on given questions to trace their evolving knowledge. The advancement of deep learning in this field ...
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ISBN:
(纸本)9798400704901
Knowledge tracing (KT) is a crucial task in intelligent education, focusing on predicting students' performance on given questions to trace their evolving knowledge. The advancement of deep learning in this field has led to deep-learning knowledge tracing (DLKT) models that prioritize high predictive accuracy. However, many existing DLKT methods overlook the fundamental goal of tracking students' dynamical knowledge mastery. These models do not explicitly model knowledge mastery tracing processes or yield unreasonable results that educators find difficulty to comprehend and apply in real teaching scenarios. In response, our research conducts a preliminary analysis of mainstream KT approaches to highlight and explain such unreasonableness. We introduce GRKT, a graph-based reasonable knowledge tracing method to address these issues. By leveraging graph neural networks, our approach delves into the mutual influences of knowledge concepts, offering a more accurate representation of how the knowledge mastery evolves throughout the learning process. Additionally, we propose a fine-grained and psychological three-stage modelingprocess as knowledge retrieval, memory strengthening, and knowledge learning/forgetting, to conduct a more reasonable knowledge tracing process. Comprehensive experiments demonstrate that GRKT outperforms eleven baselines across three datasets, not only enhancing predictive accuracy but also generating more reasonable knowledge tracing results. This makes our model a promising advancement for practical implementation in educational settings. The source code is available at https://***/JJCui96/GRKT.
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