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.
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.
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.
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.
Spectroscopic ellipsometry has been widely used as one of the metrology methods of choice in various industries: microelectronics, photovoltaic, optoelectronics, flat panel display, etc. We present an example of the c...
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Spectroscopic ellipsometry has been widely used as one of the metrology methods of choice in various industries: microelectronics, photovoltaic, optoelectronics, flat panel display, etc. We present an example of the characterization of dielectric multilayer structures on the substrates with unintended surface modifications involving macroscopic roughness. We assume that under our inspection conditions, the effect of macrorough surfaces can be treated as the presence of a specially designed overlayer on top of the ordinary substrate. A systematic procedure was then proposed to simulate the dielectric response of the overlayer. This approach is quite useful in a practical sense and provides more accurate process monitoring and control in a production environment.
In crowdsourcing, quality control is commonly achieved by having workers examine items and vote on their correctness. To minimize the impact of unreliable worker responses, a delta-margin voting process is utilized, w...
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ISBN:
(纸本)9798400705540
In crowdsourcing, quality control is commonly achieved by having workers examine items and vote on their correctness. To minimize the impact of unreliable worker responses, a delta-margin voting process is utilized, where additional votes are solicited until a predetermined threshold delta for agreement between workers is exceeded. The process is widely adopted but only as a heuristic. Our research presents a modeling approach using absorbing Markov chains to analyze the characteristics of this voting process that matter in crowdsourced processes. We provide closed-form equations for the quality of resulting consensus vote, the expected number of votes required for consensus, the variance of vote requirements, and other distribution moments. Our findings demonstrate how the threshold.. can be adjusted to achieve quality equivalence across voting processes that employ workers with varying accuracy levels. We also provide efficiency-equalizing payment rates for voting processes with different expected response accuracy levels. Additionally, our model considers items with varying degrees of difficulty and uncertainty about the difficulty of each example. Our simulations, using real-world crowdsourced vote data, validate the effectiveness of our theoretical model in characterizing the consensus aggregation process. The results of our study can be effectively employed in practical crowdsourcing applications.
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.
To address the continuous growth of equipment scale and the number of measurement points in autonomous and controllable intelligent substations, while existing data collection strategies, equipment modeling methods, a...
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With the development of smart grids, distribution network dispatch systems are facing increasingly complex challenges in multi-source dataprocessing and security management. This article proposes a secure control met...
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