In this paper, a statistical approach for the assessment of the effectiveness level for the tuning parameters of the model-free adaptive control (MFAC) method is presented with the compact form dynamic linearization (...
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ISBN:
(纸本)9798350376357;9798350376340
In this paper, a statistical approach for the assessment of the effectiveness level for the tuning parameters of the model-free adaptive control (MFAC) method is presented with the compact form dynamic linearization (CFDL) data model in single-input single-output unknown nonlinear systems. This is a first step in the parameter tuning of the MFAC data-driven methodology. The statistical approach is accomplished using the tool of N-way analysis of variance (ANOVA), the result of which is the determination of the most statistically significant parameters on the system performance. The process of assessing the effective parameters is data-based and no prior mathematical model assumptions are required. Two linear and nonlinear sample systems are considered and ANOVA tests are performed for different control performance assessment indexes. Finally, the simulation results are summarized and comparison results are provided by trial and error variation of the MFAC tuning parameters.
This paper presents a comprehensive analytical review of contemporary mathematical models of information influence and control in social networks, emphasizing the integration of agent-level factors such as trust, repu...
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The blast furnaces of Anshan Iron and Steel have completed large-scale modernization, and a large amount of information technology has been popularized and applied to the process of blast furnaces. This paper takes th...
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The blast furnaces of Anshan Iron and Steel have completed large-scale modernization, and a large amount of information technology has been popularized and applied to the process of blast furnaces. This paper takes the Anshan Iron and Steel blast furnace group as the research background. Based on big data and industrial Internet technology, combining the smelting process mechanism of blast furnace production and using artificial intelligence, cloud analysis, and other technologies, the data management platform was used to effectively integrate the data of each process of the blast furnace and design the data asset catalogue. The big data application platform for the intensive control of the blast furnace was established. The data were in multidimensional in-depth mining, and the intelligent application model of the blast furnace was established. The visual intelligent monitoring of the safe production and operation of the blast furnace was realized, and the production operation of the blast furnace was guided. The overall information and intelligent level of production operation and management of the blast furnace have been improved.
Epileptic seizure propagation is a dynamic process that can be triggered by local abnormal discharges, leading to widespread network abnormalities in the brain. Understanding the causal relationship between the change...
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Epileptic seizure propagation is a dynamic process that can be triggered by local abnormal discharges, leading to widespread network abnormalities in the brain. Understanding the causal relationship between the changes in brain network characteristics and the diverse propagation dynamics of epileptic seizures is crucial. We gather stereo-EEG data from 17 patients with temporal lobe epilepsy and utilize cross-channel phase amplitude coupling to extract the dynamic functional networks. Further, the patterns of brain network changes during seizure in patients with different surgeries are assessed using Hidden Markov Model. And characteristics of state transitions under different seizure periods are explored. Results show that the frequency of state transitions increases with seizures, and all epilepsy patients have a main state network with weakly connected network structure centered on the epileptogenic zone. The occupancy ratio of main state is inversely proportional to state transition frequency, where the emergence of strongly connected networks facilitates the seizure propagation. Variability in state characteristics is observed cross patients with different surgeries. The heterogeneous epileptor network model driven by the state transition is developed to simulate seizure propagation. Results show that state transition frequency and relationships affect seizure onset time and spread range. Under the main state network, seizures occur only in the epileptogenic zone and do not propagate to surrounding regions. Additionally, increasing the proportion of the main state network delays the onset of seizures. This suggests that the characteristics of the state network and its transitions may play a role in controlling the propagation of epileptic seizures.
The concentration of carbon monoxide (CO) emissions is intricately linked to the operational status and combustion efficiency of municipal solid waste incineration (MSWI) processes, which are characterized by complex,...
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The concentration of carbon monoxide (CO) emissions is intricately linked to the operational status and combustion efficiency of municipal solid waste incineration (MSWI) processes, which are characterized by complex, dynamic, and time-varying behaviors. In order to tackle the challenge of predicting CO emissions, this article introduces a novel method based on nested dual-window drift detection (NDWDD). Initially, a typical sample pool (TSP) is generated using the $k$ -means algorithm. An offline prediction model combining long short-term memory (LSTM) with a feature space drift detection model based on robust principal component analysis (RPCA) is then developed. The control limit for error space prediction accuracy is set using the fast Hoeffding drift detection method (FHDDM). The NDWDD employs a unique combination of external feature space drift detection and nonparametric drift detection within the internal error space, using a nested mechanism to enhance detection efficiency and reduce the influence of inherent noise factors in industrial processes. Finally, the dual-space drift sample collection facilitates updates to the TSP, historical prediction models, RPCA model, and FHDDM control limits. Experimental results from a Beijing MSWI power plant demonstrate that the proposed method can predict CO emissions both robustly and effectively.
A thorough understanding of complex process-structure-property (P-S-P) relationships in additive manufacturing (AM) has long been pursued due to its paramount importance in achieving AM process optimization and qualit...
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A thorough understanding of complex process-structure-property (P-S-P) relationships in additive manufacturing (AM) has long been pursued due to its paramount importance in achieving AM process optimization and quality control. Physics-based modeling and experimental approaches are usually time-consuming and/or costly. With the increasing availability of digital AM data and rapid development of data-driven modeling techniques, especially machine learning (ML), data-driven AM modeling is emerging as an effective approach towards this end. It allows for automatic discovery of patterns and trends in the AM data, construction of quantitative models of P-S-P relationships over the parameter space and prediction at unseen points without having to perform new physical modeling or experiments. A proliferation of researches on data-driven modeling of process, structure and property in AM have been witnessed in recent years. In this context, this paper aims to provide a systematic review of existing data-driven AM modeling with respect to different quantities of interest (QoI) along the process-structure-property chain. Specifically, this paper provides a summary of important information (i.e., input features, QoI-related output, data source and data-driven models) on existing data-driven AM modeling, as well as an in-depth analysis on relevant success achieved so far. Based on the comprehensive review, this paper also critically discusses the major limitations faced today and identifies some research directions that are promising for significantly advancing data-driven AM modeling in the future.
We are on the cusp of holistically analyzing a variety of data being collected in every walk of life in diverse ways. For this, current analytics and science are being extended (Big data Analytics/Science) along with ...
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Nowadays flue gas desulphurization (FGD) technologies have been extensively applied in the coal-fired electricity-generating plants. As emission standards for sulfur dioxide (SO2) have become more stringent in recent ...
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ISBN:
(纸本)9798350387780;9798350387797
Nowadays flue gas desulphurization (FGD) technologies have been extensively applied in the coal-fired electricity-generating plants. As emission standards for sulfur dioxide (SO2) have become more stringent in recent years, there is a real need to develop more advanced modeling techniques such that the FGD process can be identified accurately, which in turn provides a reliable foundation for FGD processcontrol and optimization. However, FGD process has characteristics of large time delay, strongly nonlinear dynamics, making traditional identification models become ineffectual. To address this issue, this paper proposes a hybrid model consisting of information-theoretic technique and convolution neural network (CNN). To verify the proposed identification approach, a real FGD system of a 600MW coal-fired power station is selected as case analysis. Experimental results indicate that our model achieves satisfactory identification result and performs better than other popular FGD models appeared in previous studies, demonstrating the effectiveness and superiority of the proposed MI-CNN model.
Most developments of the sequential probability ratio test (SPRT) control chart assume that the underlying process comes from a Normal distribution with known mean and standard deviation. Nevertheless, the true values...
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Most developments of the sequential probability ratio test (SPRT) control chart assume that the underlying process comes from a Normal distribution with known mean and standard deviation. Nevertheless, the true values of the process parameters are usually inaccessible in production settings, and they must be approximated from a set of Phase-I data. In certain areas, the processdata can be positively skewed, which in turn affect the performance of control charts designed under the Normal distribution. In this paper, we provide a thorough analysis on the performances of the SPRT chart with estimated process parameters under the influence of Weibull distributed data. The unconditional properties of the expected value and standard deviation of the time to signal are evaluated using Monte Carlo simulation to facilitate comparisons between the Normal and Weibull distributions. Results show that both the in-control and out-of-control performances of the SPRT chart deteriorate when Weibull data are used. However, the optimal design of the SPRT chart with estimated process parameters seems to reverse the effect for large process mean shifts.
modeling the different dynamics in parallel flow regenerative kilns for the production of quicklime is one of the steps towards obtaining the digital twins of the kilns. This paper shows a data-driven modeling approac...
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