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检索条件"任意字段=Conference on Data Analysis and Modeling for Process Control II"

共 4764 条记录，以下是71-80 订阅

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Machine Learning modeling in Industrial processes for Visual analysis 25th

Machine Learning Modeling in Industrial Processes for Visual...

引用

25th International conference on Engineering Applications of Neural Networks (EANN)

作者： Moran, Antonio Alonso, Serafin Fuertes, Juan J. Prada, Miguel A. Roca, Lidia Dominguez, Manuel Univ Leon Escuela Ingn SUPPRESS Res Grp Campus Vegazana Leon 24007 Spain CIEMAT Plataforma Solar Almeria Ctra Senes S-N Almeria 04200 Spain

ISBN: (纸本)9783031624940;9783031624957

The use of visualization tools makes it easy to analyze the operation of an industrial process. Given the large number of variables involved in today's industrial systems, it is necessary to use techniques that reduce both the size of the data and the number of samples. In addition, since industrial systems involve similar processes running in parallel, this information can be added to analyze the processes. This paper proposes the use of a variant of self-organizing maps, Env-SOM, which allows conditioning the projection of these maps based on a set of variables. This variant is applied to operational data from AQUA-SOL ii pilot plant located at the Plataforma Solar de Almer ' ia (PSA), which consists of several flat-plate collector loops that share a common water distribution pipe. Projecting the data in a conditional manner visualization maps are generated based on differences that allow to determine the existing differences between the heating loops.

关键词： Environmental conditions data mining Exploratory analysis Self-Organizing Maps envSOM

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A Heterogeneous Effects analysis Method of Highway Crash Factors Based on Causal Framework

A Heterogeneous Effects Analysis Method of Highway Crash Fac...

引用

2024 International conference on Smart Transportation Interdisciplinary Studies, ICSTIS 2024

作者： Liang, Xiaoxi Li, Shuang Xu, Nuo Guo, Xiucheng Pu, Ziyuan Southeast University School of Transportation China Southeast University School of Transportation Hangzhou Ins China

The analysis of heterogeneous effects on traffic crashes is crucial for understanding their causal mechanisms and enhancing targeted safety management strategies. However, current methodologies for modeling crash heterogeneous effects lack smooth methods for selecting optimal controls. This study proposes an intuitive variable selection method to improve heterogeneity analysis of crash data, as well as performance evaluation and validation tests. The method utilizes causal discovery algorithms to obtain causal diagrams for selecting confounders, moderators, and neutral control factors in observational collision data. The effectiveness and performance of these methods are assessed through the quality of Heterogeneous Treatment Effects (HTE) estimation. Using a real-world highway crash data, the proposed variable selection process based on causal framework is illustrated. Results indicate that most HTE estimation models perform well in terms of goodness-of-fit and robustness when employing the graphical variable selection method. Notably, models based on adjusted causal diagram and forest-based double-robust learning estimators perform the best across all model. This approach overcomes the challenges in selecting control and moderator factors in crash heterogeneity analysis, leading to more accurate effect estimation results. This study contributes to encouraging discussions on the causal pathways of crash occurrence and provides recommendations for optimizing road safety modeling, analysis and management in the field of crash analysis. © 2025 SAE International.

关键词： Highway accidents

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Potential of Digital Technologies and Artificial Intelligence in the Management of Personalized Learning 5

Potential of Digital Technologies and Artificial Intelligenc...

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5th International conference on control in Technical Systems, CTS 2023

作者： Kotova, Elena E. Saint Petersburg Electrotechnical University 'LETI' Saint Petersburg Russia

ISBN: (纸本)9798350304664

The development of digital technologies opens up new opportunities for managing personalized learning using intelligent data analysis methods. The purpose of a comprehensive analysis of the data obtained in the learning process is to create a controlled learning environment that takes into account the individual needs, goals and personal characteristics of students. In the course of our study, various approaches to the organization of the educational process based on the use of intellectual technologies were analyzed. Based on the study of best practices in the use of new digital tools for modeling learning processes based on data analysis, the report presents the developed automated system of intellectual support for personalized learning. © 2023 IEEE.

关键词： data mining

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Application of Computer Big data Information processing Technology in the Operation and Accounting System of Enterprise Accounting Fund

Application of Computer Big Data Information Processing Tech...

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2024 Asia-Pacific conference on Software Engineering, Social Network analysis and Intelligent Computing, SSAIC 2024

作者： Li, Baqun Hunan Open University Hunan Network Engineering Vocational College Hunan Changsha China

ISBN: (纸本)9798350393682

Based on the theory of process reengineering and internal control combined with the latest RPA and OCR technology, this paper optimizes and reconstructs the intelligent account reimbursement system, intelligent budget system, interactive communication platform and financial sharing system. The system uses SAP HANA view modeling to construct real-time analysis model. In this way, enterprises can meet the real-time data analysis requirements. Finally, the financial early warning model is constructed by data mining algorithm. This helps enterprises identify and reduce financial risks. You find that the intelligent system can realize the electronation of financial data and paperless vouchers. This system can improve the efficiency of financial work and promote the sharing and integration of resources. © 2024 IEEE.

关键词： Intelligent systems

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Dynamic modeling and Transmission Error analysis of Cycloid Drive Based on Variable Clearance 12

Dynamic Modeling and Transmission Error Analysis of Cycloid ...

引用

IEEE 12th data Driven control and Learning Systems conference (DDCLS)

作者： Zhou, Yunchen Han, Zhenhua Wang, Hao Du, Youwu Li, Rirong Jiangsu Univ Technol Sch Mech Engn Changzhou 213001 Peoples R China

ISBN: (纸本)9798350321050

In order to precisely construct the nonlinear dynamic analysis model of the N-cycloidal pin reducer in the transmission process, the paper employs the Load tooth contact analysis (LTCA) to determine the time-varying meshing clearance and the time-varying torsional stiffness, and it takes into account the influence of the revolution displacement and meshing damping of the cycloid gear pair. Based on the lumped parameter method build a six degree of freedom (DOF) nonlinear dynamic analysis model. The dynamic differential equation is deduced using the general Lagrange function. Then, using the fourth-order Runge Kutta method to calculates the dynamic response. The transmission errors (TE) under various examples of profile modification are calculated and analyzed through the result of dynamic response. The results show that the transmission error value and the fluctuation increase with the meshing clearance, leading to a decrease in reducer stability.

关键词： Dynamic modeling Meshing clearance Torsional stiffness Transmission error

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A Variational Bayesian Dictionary Learning for process Monitoring 12

A Variational Bayesian Dictionary Learning for Process Monit...

引用

IEEE 12th data Driven control and Learning Systems conference (DDCLS)

作者： Zhang, Qi Xie, Lei Xu, Weihua Su, Hongye Zhejiang Univ State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China

ISBN: (纸本)9798350321050

Industrial process data acquisition is inevitably disturbed by noise, and data contamination makes process data carry too much redundant information, which will greatly limit the interpretation capability of data-driven modeling approaches. Given that sparse representations can effectively handle noise, a process monitoring method for variational Bayesian dictionary learning (VBDL) is developed in this work. Conventional dictionary learning requires a priori knowledge of the assumed noise variance and sparsity levels, which is not available in real industrial processes. The derived VBDL is built on a Bernoulli distribution with the beta distribution as the conjugate prior, the number of dictionary atoms and their relative importance can be inferred nonparametrically with the iterative update of the variational inference. Since the collected data exhibits temporal correlation, the large noise interference makes dynamic analysis infeasible. A low-rank vector autoregression is developed to dynamically analyze the reconstructed samples, thereby improving the robustness of the model to noise. To illustrate the feasibility and efficacy, the proposed algorithm is verified by a numerical simulation and the CSTR simulation.

关键词： Beta-Bernoulli process dictionary learning process monitoring probabilistic model variational Bayesian

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Long Short-term Memory modeling method with monotonicity analysis as constraints base on Spearman coefficient 12

Long Short-term Memory modeling method with monotonicity ana...

引用

IEEE 12th data Driven control and Learning Systems conference (DDCLS)

作者： Zhan, Zhiyong Zhou, Yang Jia, Li Zhao, Yilin Shanghai Univ Sch Math Engn & Automat Shanghai 200444 Peoples R China

ISBN: (纸本)9798350321050

This paper proposes a new method of monotonicity, which is used to solve the overfitting problem of the Long-Short-Term Memory (LSTM) model. The main contribution of this paper is applying the monotonicity as priori knowledge to the modeling process. This study uses scatter plots to describe bivariate variables and the Spearman coefficient to extract the monotonicity of data. To exclude most noise point, the scatter diagram is filtered by a binary 0-1 liner program. Base on the monotonicity of data have known, an optimization problem with constraint is proposed to obtain the LSTM neural network model. An experiment of ethylene cracking show that the proposed method can achieve a good predicting performance and less overfitting effects.

关键词： Soft sensing Monotonicity Spearman coefficient LSTM neural network

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Object-centered heuristic A* conformance checking method 2

Object-centered heuristic A* conformance checking method

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2nd International conference on Big data, Computational Intelligence, and Applications, BDCIA 2024

作者： Yang, Xiaolong Wang, Lili College of Mathematics and Big Data Anhui University of Science＆Technology Anhui Huainan232001 China State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines Anhui University of Science and Technology Anhui Huainan232001 China

ISBN: (纸本)9781510689053

Traditional approaches typically flatten the process when checking the conformance of complex processes. However, this flattening approach can result in the loss of dependencies between objects, reducing the accuracy of the conformance check. This paper proposes an object-centered alignment optimization method based on the heuristic A* algorithm to address this issue. This method effectively improves the efficiency of object-centered alignment in complex scenarios by considering both heuristic search and actual cost. The experimental results demonstrate that, in comparison to existing approaches, the proposed method presented in this paper observably enhances the efficiency of alignment in complex graph structures, exhibits superior adaptability, and gains a better understanding of the relationship between object-centric process behavior and data. These results provide new ideas and methods for improving conformance checking, which are of great theoretical and practical significance, especially in dynamic and complex environments where the potential for application has been further validated. © 2025 SPIE.

关键词： Detection and tracking algorithms Mining Alignment modeling process modeling Algorithm development Beam propagation method Mathematical optimization Connectors Medicine Transportation

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Transformer-Based Predictive analysis and Maintenance for Aluminum Foil Mill 43

Transformer-Based Predictive Analysis and Maintenance for Al...

引用

43rd Chinese control conference, CCC 2024

作者： Su, Jiuzheng Hu, Wenshan Liu, Guo-Ping Lei, Zhongcheng Wuhan University School of Electrical Engineering and Automation Wuhan430072 China Southern University of Science and Technology Center for Control Science and Technology Shenzhen518055 China

ISBN: (纸本)9789887581581

The aluminum foil mill is an important industrial production equipment. To reduce operation and maintenance costs and prevent breakdowns in the rolling mill, it is necessary to analyze and predict the data of different sensors of the mill, including voltage, current, temperature and others. In this paper, the problem analysis and modeling of the actual rolling mill production process are carried out, and the typical data of sensors are analyzed based on the Transformer model. Experimental results are compared with other typical time series prediction models. The final results show that the Transformer model has great potential in the predictive maintenance of aluminum foil mill. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Aluminum foil

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Enabling process Mining in the Construction Industry: An Event Log Schema for Change Management process

Enabling Process Mining in the Construction Industry: An Eve...

引用

2023 Canadian Society for Civil Engineering Annual conference

作者： Martinez, Araham Nik-Bakht, Mazdak Concordia Univ Dept Bldg Civil & Environm Engn Montreal PQ Canada Concordia Univ Dept BCEE Complecc Lab Montreal PQ Canada

ISBN: (纸本)9783031621727;9783031621703;9783031621697

Construction has suffered from stagnant productivity for decades, with only 1% growth over the past twenty years compared to the 3.6% growth in the manufacturing industry. One of the main causes of this problem is that the industry still relies on manual and subjective methods for modeling and managing its construction processes, which are error-prone and time-consuming. To tackle this challenge, process mining has proven to be a game-changer in managing processes effectively providing automation capabilities for several industries such as manufacturing, banking, and health care. These industries have adopted the eXtensible Event Stream (XES) and object-centric event logs (OCEL) standards to semantically structure event logs that record timestamped human-machine interactions (HMI) associated with the real execution of business processes. However, one major factor preventing a broader process mining implementation in the construction industry is the lack of a domain-specific framework to facilitate the data integration for the generation of event logs from generic data sources (i.e., relational databases). Thus, this work aims at enabling process mining capabilities in construction organizations through an event log generation framework for construction processes. The main contributions of this research work are (i) an extended event log architecture that facilitates the extraction, transformation, loading (ETL), and querying of construction data;and (ii) the development of a process mining use case related to the construction change management process. In conclusion, this study facilitates the event log generation that enables process mining analysis to get data-driven actionable process performance insights that support construction companies with strategic decision-making across the project lifecycle.

关键词： Event log process mining Change management process

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