With the development of sensor technology, time series data gradually exhibits high-dimensional and multivariate characteristics. Therefore, accurately revealing the potential causal relationships among variables has ...
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(纸本)9798331540845;9789887581598
With the development of sensor technology, time series data gradually exhibits high-dimensional and multivariate characteristics. Therefore, accurately revealing the potential causal relationships among variables has become crucial for accurate modeling of time series. Due to the limitations of traditional Granger causality models in effectively applying to multivariate time series data, this paper proposes an improved Backward Time-lagged Variable Selection (FS-BTS) optimization method based on Isolation Forest and Maximum Relevance Minimum Redundancy algorithm, and combines it with conditional Granger causality to accurately reveal the implicit topological structure among variables. Specifically, the Isolation Forest algorithm can remove outliers in multivariate time series data, significantly suppressing the influence of outliers on modeling the causal relationships among variables. The introduction of the Maximum Relevance Minimum Redundancy algorithm allows FS-BTSCGCI to adaptively select a subset of features for the target variable, eliminating redundancy among variables. Unlike traditional Backward Time- lagged Variable Selection methods that struggle with eliminating redundancy among variables, this paper improves the Backward Time- lagged Variable Selection method and combines it with conditional Granger causality to enable FS-BTSCGCI to effectively select the optimal explanatory variables for the target variable from multivariate time series data. Compared to other traditional causal analysis models, FS-BTSCGCI has significant advantages in accurately identifying Granger causality relationships among variables. Furthermore, this paper conducts simulation experiments on benchmark and real datasets to validate the effectiveness of the proposed method.
The problem of advanced control systems modeling for feedwater-level control in steam generators of nuclear power plants is considered. To suppress disturbances caused by changes in reactor thermal power and steam flo...
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As a sustainable transformation of urbanization, it is unclear whether China's new-type urbanization (NTU) can promote the collaborative governance of carbon reduction (CR) and pollution control (PC). Based on the...
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As a sustainable transformation of urbanization, it is unclear whether China's new-type urbanization (NTU) can promote the collaborative governance of carbon reduction (CR) and pollution control (PC). Based on the data of China's five major urban agglomerations, this study constructed a systematic framework consisting of synergy measurement, relationship exploration and driver analysis to reveal the synergy between CR and PC in the process of NTU. The results indicated that with the steady growth of CR and PC, the coupling coordination degree of the two increased from 0.6457 to 0.9030 in 2014-2022, upgrading from primary synergy to excellent synergy. From the perspective of decoupling, all urban agglomerations improved the relationship between NTU and CR/ PC with 89 and 92 cities exhibiting the state of strong decoupling, respectively, but there were differences in the decoupling types of cities within each subsystem of NTU. Overall, NTU and its subsystems significantly drove the synergy between CR and PC, while the driving effects presented a spatial heterogeneity. The Yangtze River Delta was the only urban agglomeration that fulfilled the driving effects of all subsystems of NTU. These findings provide theoretical and empirical values for taking advantage of NTU to mitigate climate change and optimize air quality.
This article introduces a method for enhancing territorial risk management through dysfunctional analysis supported by the SADT tool. It discusses the importance of functional analysis in identifying risks within a te...
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This article introduces a method for enhancing territorial risk management through dysfunctional analysis supported by the SADT tool. It discusses the importance of functional analysis in identifying risks within a territory, emphasizing the need for a comprehensive approach to understanding the various functions and activities within a territory. By applying these principles to emergency response plans the article highlights the use of incidence matrices in risk assessment and processmodeling to improve preparedness and response to natural or anthropogenic disasters. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)
In order to ensure the safety and manufacturing efficiency of pharmaceuticals, rigorous quality control measures are necessary. However, existing quality control systems often struggle with industrial process complexi...
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With the impact of global climate change and the continuous increase in energy consumption, the thermal energy regulation of indoor environments has become an important issue in the field of architectural design. This...
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With the impact of global climate change and the continuous increase in energy consumption, the thermal energy regulation of indoor environments has become an important issue in the field of architectural design. This article aims to explore the application of intelligent control systems based on BIM (Building Information modeling) visualization in indoor thermal energy regulation, and propose a dynamic building design scheme to optimize indoor comfort and energy efficiency. The study pointed out the shortcomings of existing indoor environmental conditioning systems in terms of flexibility and energy efficiency. On this basis, an intelligent control system based on BIM visualization was designed, whose framework and functions include real-time data collection, analysis, and feedback to enhance the system's control capabilities. Real time monitoring and optimization adjustment of indoor environment have been achieved through BIM visualization design method. The system test results show that the intelligent control system significantly improves efficiency in thermal energy regulation and reduces energy consumption. In order to further promote the energy-saving design of dynamic buildings, this project studied the process of dynamic building design, including the integration of environmental analysis and user needs. At the same time, a method for dynamic building environment optimization control was proposed, which achieved comprehensive management of building energy consumption through joint simulation of daylighting energy consumption. This study indicates that dynamic building design can effectively respond to environmental changes and improve the overall energy efficiency of buildings.
The quality of the surface mount technology (SMT) process directly impacts product efficiency and reliability. Solder paste printing and reflow soldering processes are vital for assembling high-quality electronic comp...
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The quality of the surface mount technology (SMT) process directly impacts product efficiency and reliability. Solder paste printing and reflow soldering processes are vital for assembling high-quality electronic components. Effectively optimizing these process parameters to ensure product consistency and reliability has become a critical issue in the electronics manufacturing services industry. Motivated by realistic needs to enhance the quality of the SMT process, this study integrates a surrogate-based optimization framework combining neural networks and particle swarm optimization (PSO) techniques to minimize experiment counts in SMT processes. Furthermore, a defect detection system utilizing YOLOv4 achieves real-time solder joint quality classification, significantly reducing production downtime and costs. This study proposes a surrogate-based optimization framework to improve the quality and productivity of the SMT production line. It encompasses five stages: domain knowledge, design of experiment, data collection and analysis, modeling, and optimization. Statistical correlation analysis and experimental design are used to reduce experiment counts. Then, neural networks and optimization algorithms are utilized to identify the optimal process parameters in the solder paste printing process. Moreover, this study proposes transfer learning methods for cross-product and line parameter optimization, which not only reduces production changeover time but also offers valuable insights for developing the solder paste printing process. A heat transfer model derived from a single experiment is used to identify parameters for reflow soldering. The target function is then optimized to find the optimal reflow recipe. Additionally, a solder joint defect detection system is established using deep learning and image processing techniques, capable of real-time detection and classification of solder joint defects. To evaluate the validity of the proposed framework, the surrogate
Development of shale gas reservoirs is the fastest growing area on a large scale globally due to their potential reserves. CO2 has a great affinity to be adsorbed on shale organic surface over CH4. Therefore, CO2 inje...
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Development of shale gas reservoirs is the fastest growing area on a large scale globally due to their potential reserves. CO2 has a great affinity to be adsorbed on shale organic surface over CH4. Therefore, CO2 injection into shale reservoirs initiates a potential for enhanced gas recovery and CO2 geological sequestration. The efficiency of CO2 enhanced gas recovery (CO2- EGR) is mainly dominated by several shale properties and engineering design parameters. However, due to the heterogeneity of shale reservoirs and the complexity of modeling the CO2-CH4 displacement process, there are still uncertainties in determining the main factors that control CO2 sequestration and enhanced CH4 recovery in shale reservoirs. Therefore, in view of the previous sensitivity analysis studies, no quantitative framework, accurate CO2-EGR modeling, or design process has been identified. Thus, this work aimed to provide a practical screening tool to manage and predict the efficiency of enhanced gas recovery and CO2 sequestration in shale reservoirs. To meet our objectives, we performed correlation analysis to identify the strength of the relationship between the examined shale properties and engineering design parameters and the efficiency of CO2-EGR. data for this study was gathered across publications on a wide subset of numerical modeling studies and experimental investigations. The sensitivity of data was further improved by a hybrid approach adopted for handling the missing values to avoid bias in our data set. Our results indicate that CO2 flooding might be the best applicable option for CO2 injection in shale reservoirs, whereas the huff-and-puff scenario does not seem to be a viable option. The efficiency of CO2-EGR increases as the pressure difference between injection pressure and reservoir pressure increases. The results show that shallow shale reservoirs with high fracture permeability, total organic content, and CO2-CH4 preferential adsorption capacity are favorable targe
Nonlinearity and uncertainty are major features in control systems. In this context, the present work proposes to merge the brain emotional learning model with the benefits of robust event-driven control to handle unc...
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Nonlinearity and uncertainty are major features in control systems. In this context, the present work proposes to merge the brain emotional learning model with the benefits of robust event-driven control to handle uncertain nonlinear systems. The state-dependent unmodeled dynamics is estimated via the limbic system-inspired learning algorithm and added to the nominal control signal for compensation purposes. Furthermore, aiming at reducing dataprocessing, and inherently, computational cost, the controller is triggered asynchronously driven by events function. Moreover, the closed-loop stability of the proposed control scheme is verified through the Lyapunov formalism, as well as the sampling admissibility to prevent the Zeno phenomena. The performance observed in the numerical results witnesses the effectiveness of the proposed control scheme.
Forced periodic operation is a technique that periodically changes the manipulating variable of a chemical reaction system in order to exploit nonlinear dynamics to improve reactant conversion rate. However, the analy...
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Forced periodic operation is a technique that periodically changes the manipulating variable of a chemical reaction system in order to exploit nonlinear dynamics to improve reactant conversion rate. However, the analysis and design of a periodically operated chemical process is a significant challenge. To resolve this problem, recently, nonlinear frequency response (NFR) based methods have been proposed. However, because of the need to derive the NFR from a first principle model, existing NFR methods can only perform qualitative analysis to simple processes and are often difficult to be applied in engineering practice. This article proposes a novel data driven approach to the analysis and optimal design of forced periodic operation of chemical reactions. From the data generated numerically using the first principle model or experimentally from experimental tests, the approach produces a data-driven NFR model that can readily be used for both quantitative study and optimal design of forced periodic operation of any complexities. This can fundamentally address the challenges faced by the existing NFR methods, and provides an effective approach that can potentially be applied in engineering practice. Simulation studies and experimental works are carried out on the application of the new method to an isothermal continuous stirred tank reactor system and a laboratory-scale carbon dioxide absorption process, respectively. The results verify the effectiveness and advantage of the newly proposed data driven approach and demonstrate the potential of the new approach in engineering applications.
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