In this work, we address the problem of the economic operation of wastewater treatment plants by proposing a data-driven economic predictive control approach. First, we propose a deep input-output Koopman modeling fra...
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ISBN:
(纸本)9798350382662;9798350382655
In this work, we address the problem of the economic operation of wastewater treatment plants by proposing a data-driven economic predictive control approach. First, we propose a deep input-output Koopman modeling framework, which is able to predict the overall economic operational cost for the water treatment process based on input data and partial state measurements. Subsequently, based on the learned model, a convex economic model predictive control (EMPC) strategy is developed. This control strategy improves the overall operational performance in a computationally efficient manner. The simulation results validate the effectiveness of our proposed approach and demonstrate its superiority over a benchmark EMPC method.
This study introduces three novel dynamic interval-valued principal component analysis (DIPCA) methods: dynamic centers PCA (D-CPCA), dynamic vertices PCA (D-VPCA), and dynamic complete information PCA (D-CIPCA). Thes...
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ISBN:
(纸本)9798350373981;9798350373974
This study introduces three novel dynamic interval-valued principal component analysis (DIPCA) methods: dynamic centers PCA (D-CPCA), dynamic vertices PCA (D-VPCA), and dynamic complete information PCA (D-CIPCA). These methods advance traditional interval-valued PCA (IPCA) by integrating dynamic aspects of industrial processes, thus addressing both data uncertainties and temporal correlations. The DIPCA methods were validated using real-world data from the Ain El Kebira cement plant. Results indicate significant improvements in fault detection accuracy, achieving lower false alarm rates and higher reliability compared to classical IPCA methods. Furthermore, an enhanced combined index for interval-valued data was developed, providing a single, comprehensive statistical measure for streamlined process monitoring.
The Problem Frames (PF) approach has gained significant attention and recognition in the field of requirements engineering. However, most of the existing work focuses on modeling normal behaviors in systems and lacks ...
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ISBN:
(纸本)9798331539894;9798331539887
The Problem Frames (PF) approach has gained significant attention and recognition in the field of requirements engineering. However, most of the existing work focuses on modeling normal behaviors in systems and lacks safety considerations and analysis. Therefore, it is crucial to integrate problem-oriented requirements analysis methods with modern safety analysis approaches. In this paper, we propose an approach which combines the PF requirements analysis method with the System-Theoretic processanalysis (STPA), to identify hazardous control actions, loss scenarios, and safety requirements, and formally validate the identified results using the model-checker UPPAAL. The aim is to incorporate safety requirements early in the requirements modeling phase. A case study of an insulin pump control system demonstrates the applicability and viability of this method.
This paper proposes a Fast Geometric Constructive Neural Network (FastGCNet) that aims to provide a fast learning paradigm for data-driven modelling in resource-constrained Industrial Internet of Things (IIoT) devices...
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This paper proposes a Fast Geometric Constructive Neural Network (FastGCNet) that aims to provide a fast learning paradigm for data-driven modelling in resource-constrained Industrial Internet of Things (IIoT) devices. First, by integrating the concept of simulated annealing with the block incremental method, we have developed a dynamic block incremental approach and incorporated it into a compact angle control strategy, resulting in a compact angle control strategy with variable block incremental. This strategy allows multiple new nodes (i.e., node block) to be added to the hidden layer simultaneously, which in turn greatly accelerates the convergence efficiency of the network. Second, by combining the dynamic block increment method with the Grenville iterative method, we propose a fast iterative method that reduces the computational consumption of evaluating the output weights each time a new node block is added. Specifically, when adding a new node block, the method is able to obtain the output weights of the whole network after adding a new node block on the basis of the old output weights by iterative updating, which avoids the recalculation of the output weights of the whole network, and thus greatly reduces the computational consumption in the process of network construction. Thirdly, this paper demonstrates that FastGCNet has universal approximation property through theoretical analyses. The results from four benchmark datasets and a real ore grinding process show that FastGCNet can effectively reduce the computational consumption in the modelling process while maintaining considerable accuracy.
The online quality monitoring of a process with low volume data is a very challenging task and the attention is most often placed in detecting when some of the underline (unknown) process parameter(s) experience a per...
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The online quality monitoring of a process with low volume data is a very challenging task and the attention is most often placed in detecting when some of the underline (unknown) process parameter(s) experience a persistent shift. Self-starting methods, both in the frequentist and the Bayesian domain aim to offer a solution. Adopting the latter perspective, we propose a general closed-form Bayesian scheme, where the testing procedure is built on a memory-based control chart that relies on the cumulative ratios of sequentially updated predictive distributions. The theoretic framework can accommodate any likelihood from the regular exponential family and the use of conjugate analysis allows closed form modeling. Power priors will offer the axiomatic framework to incorporate into the model different sources of information, when available. A simulation study evaluates the performance against competitors and examines aspects of prior sensitivity. Technical details and algorithms are provided as .
This article aims to develop a total quality management (TQM)-based Industry 4.0 maturity model. The reviewed literature showed that there is no consensus on Industry 4.0 maturity modeling and models are insufficient ...
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This article aims to develop a total quality management (TQM)-based Industry 4.0 maturity model. The reviewed literature showed that there is no consensus on Industry 4.0 maturity modeling and models are insufficient in creating value-focused actions. About 49 recommended actions prepared by ISO (International Organization of Standardization) international experts (ISO/TC 176) that are published in ISO 9000:2015 are considered as variables of the proposed Industry 4.0 maturity model. According to this consideration, a prescriptive questionnaire was constructed with these variables via reviewing TQM/Quality and Industry 4.0 related literature. The proposed model was analyzed with confirmatory factor analysis and the final 32 variables (questions) were evaluated for model fit, reliability, and validity. Analyses were done with SPSS and AMOS software. The validated model was applied to a manufacturer located in Gaziantep/T & uuml;rkiye to create a roadmap to the decision makers of the company. The analysis showed that the model based on indicated TQM actions proposed by ISO 9000:2015 is reliable and valid in the context of measuring Industry 4.0 maturity and can create effective transition road maps to stakeholders. This study includes a novel model which uses globally accepted ISO 9000:2015 recommended actions. Besides, the prescriptive structure of the constructed questionnaire is another originality. Organizations can use the proposed model as a guide for transition to Industry 4.0 in the context of TQM.
Quantum computing, with its unique parallel computing ability and efficient optimization algorithm, provides new possibilities for the optimization of complex systems. This paper aims to explore the modeling and optim...
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Context: Automated analysis of code review comments (CRCs) can aid in highlighting frequently discussed issues by reviewers from large repositories. Topic modeling is a promising approach to analyzing large natural la...
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ISBN:
(纸本)9783031783852;9783031783869
Context: Automated analysis of code review comments (CRCs) can aid in highlighting frequently discussed issues by reviewers from large repositories. Topic modeling is a promising approach to analyzing large natural language repositories. However, CRCs contain natural language text and code references;thus, data pre-processing and topic modeling approaches must be carefully selected. Objective: This work aims to discuss the various decisions taken and considerations involved in the analysis of CRCs. Method: We utilized 5,560 CRCs from an open-source system to study the decisions and considerations faced during the analysis of CRCs using topic modeling, followed by an evaluation of the interpretability of identified themes by a domain expert. Results: We report several observations and challenges in improving the quality of the identified themes, including choices regarding the pre-processing, topic modeling parameters, embedding model, and objective measures of coherence used, which impact the subjective interpretability of the identified themes. Conclusions: This work offers unique considerations, and the impact of these decisions can facilitate future studies in conducting topic modeling-based analyses of CRCs. Future studies can utilize the technical demonstrator to explore the interpretability of the topics generated from CRCs.
The modeling and simulation of dynamical systems is a necessary step for many control approaches. Using classical, parameter-based techniques for modeling of modern systems, e.g., soft robotics or human-robot interact...
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The modeling and simulation of dynamical systems is a necessary step for many control approaches. Using classical, parameter-based techniques for modeling of modern systems, e.g., soft robotics or human-robot interaction, are often challenging or even infeasible due to the complexity of the system dynamics. In contrast, data-driven approaches need only a minimum of prior knowledge and scale with the complexity of the system. In particular, Gaussian process dynamical models (GPDMs) provide very promising results for the modeling of complex dynamics. However, the control properties of these GP models are just sparsely researched, which leads to a "blackbox " treatment in modeling and control scenarios. In addition, the sampling of GPDMs for prediction purpose respecting their nonparametric nature results in non-Markovian dynamics making the theoretical analysis challenging. In this article, we present approximated GPDMs, which are Markov and analyze their control theoretical properties. Among others, the approximated error is analyzed and conditions for boundedness of the trajectories are provided. The outcomes are illustrated with numerical examples that show the power of the approximated models while the computational time is significantly reduced.
Modern rail and particularly automated train systems utilize train control schemes which rely on continuous onboard-wayside wireless communication in the UHF/SHF frequency bands. The knowledge of radio propagation pro...
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ISBN:
(纸本)9780791887776
Modern rail and particularly automated train systems utilize train control schemes which rely on continuous onboard-wayside wireless communication in the UHF/SHF frequency bands. The knowledge of radio propagation process and propagation environment are essential for specification, design, installation, and optimization of the cited wireless communication systems. To this end, radio propagation prediction models are applied which are normally characterized by the radio environment as a function of frequency and distance between transmitter and receiver along with electromagnetic characteristics of the propagation environment. These radio propagation models typically predict received power level or path loss profile for specific transmitter and receiver location. A railway tunnel offers a common and complex radio propagation scenario for automated train control applications with strict radio-based data communication subsystem requirements. In this research, special attention has been given to the case of theoretical modeling of the UHF/SHF radio propagation process inside curved multi-section inhomogeneous tunnels. The theoretical results are compared to the measurement data collected through an extensive field validation campaign conducted in the Toronto Transit Commission (TTC) subway tunnels.
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