To address the issues of frequent data conversion, serial processes, and lack of effective technical status control during multi-disciplinary cross-platform satellite collaboration, this paper investigates the integra...
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ISBN:
(数字)9798350363609
ISBN:
(纸本)9798350363616
To address the issues of frequent data conversion, serial processes, and lack of effective technical status control during multi-disciplinary cross-platform satellite collaboration, this paper investigates the integrated collaborative design model based on the 3D Experience platform. It explores how to achieve concurrent collaborative design and joint simulation analysis of multi-disciplinary CAD models from different platforms in a unified environment. An integrated collaborative environment is constructed, allowing seamless integration of CAD data from various sources within the same platform, enabling real-time collaborative workflows and precise iteration control. The research methodology covers the entire process from heterogeneous CAD model integration, data management, real-time collaboration, to joint simulation analysis. This approach optimizes the design process and ensures data validity, real-time collaboration, and accuracy of modifications. Additionally, this study demonstrates the potential of the 3DEXPERIENCE platform in improving design efficiency, reducing design conflicts, and enhancing design quality.
In this paper, the common problems of electrical engineering under automation environment are systematically analyzed, and some new solutions are put forward. First of all, this paper reviews the research status of th...
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ISBN:
(数字)9798350378917
ISBN:
(纸本)9798350378924
In this paper, the common problems of electrical engineering under automation environment are systematically analyzed, and some new solutions are put forward. First of all, this paper reviews the research status of this field both at home and abroad. Secondly, this paper introduces the application of Matra displacement model in the field of power system in detail, and summarizes the views of domestic and foreign experts on the development process and characteristics of this technology research. Then, starting from the theoretical basis and basic concepts, this paper expounds the important role of software in the modeling of computer simulation system and analyzes the impact on the system. Finally, the transformation of electrical automation is realized by MATLAB, and the corresponding results are given. The test results show that the static deviation data range is $0.084-0.096$, while the dynamic deviation data range is 0.089-0.097; communication compatibility is as high as $\mathbf{1 0 0 \%}$ and as low as 96%.
Monitoring techniques play an important role in ensuring consistent product quality and safe operation in the process industry. data-based models such Principal Component analysis (PCA) are utilized as they are comput...
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ISBN:
(纸本)9781665441971
Monitoring techniques play an important role in ensuring consistent product quality and safe operation in the process industry. data-based models such Principal Component analysis (PCA) are utilized as they are computationally efficient, and can handle high dimensional data. Most conventional techniques assume that processdata generally follow a Gaussian distribution, are decorrelated, and contain a moderate level of noise. When practical data violate these assumptions, wavelet-based models such as multiscale principal component analysis (MSPCA) can be utilized in order to address these violations. Statistical hypothesis testing methods, such as the generalized likelihood ratio (GLR) technique, have been incorporated with different models in order to enhance fault detection performance. As literature has seen limited integration of multiscale multivariate models with hypothesis testing methods, an objective of this work is to develop and evaluate the performance of different multiscale multivariate fault algorithms, to determine and establish the proper method of integration of both techniques. Two illustrative examples will be utilized: one using simulated synthetic data, and the other using the benchmark Tennessee Eastman process. The results demonstrate that the improved MSPCA-based GLR technique that was developed in this work is able to provide better detection results, with lower missed detection rates, and ARL(1) values than the other techniques.
An innovatively designed open-center pressure control valve with fast response, excellent flow-through capability, and outstanding reliability is utilized in gearshift hydraulic systems for heavy-duty vehicle automati...
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To solve the difficulty of rapid and accurate detection of component content in the rare earth extraction process, a component content modeling method combining mechanism model and error compensation model based on ju...
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One of the key challenges of knowledge base question answering (KBQA) is the multi-hop reasoning. Since in different hops, one attends to different parts of question, it is important to dynamically represent the quest...
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Quality control mathematical model is a mathematical model based on the principle of statistical processcontrol. Its application in modern quality management provides a scientific basis for quality management. The es...
Quality control mathematical model is a mathematical model based on the principle of statistical processcontrol. Its application in modern quality management provides a scientific basis for quality management. The establishment process includes three parts: one is to make statistical analysis from a large number of actual data, the other is to make use of statistical data to establish a mathematical model. Based on support vector machine regression, logistic regression and neural network in machine learning, the mathematical modeling of quality control is studied in this paper. Experimental results show that machine learning based quality control mathematical model can achieve the highest accuracy of 94.4%. It is concluded that machine learning has high application potential in quality management and can provide a scientific basis for quality management.
The proceedings contain 22 papers. The special focus in this conference is on Subject-Oriented Business process Management. The topics include: data Mesh: How to Implement the Paradigm Shift;improving Agile Maturity i...
ISBN:
(纸本)9783031720406
The proceedings contain 22 papers. The special focus in this conference is on Subject-Oriented Business process Management. The topics include: data Mesh: How to Implement the Paradigm Shift;improving Agile Maturity in data Teams: A Framework for Enhanced Business process Agility;Synergizing data Contracts and BPM to Improve process Interoperability: An analysis of Gaps and Opportunities of data Exchange Agreements in BPM Models;an Aspect-Oriented Extension of the Parallel Activity Specification Schema: A First Draft;how Business processmodeling Can Benefit from Rhetorical Structure Theory;modeling of IoT Systems Behavior: A Subject-Oriented Reference Model;Subject-Oriented modeling Workflow control Patterns with PASS;subject- and process-Oriented Comparison of Multi-factor Authentication Methods;S-BPM as an Alternative to BPMN in the Context of Low-Code: Applicability, User Experience and Transformation from S-BPM to BPMN;facilitating the Preparation of Life Cycle Assessment Through Subject-Oriented processmodeling: A Methodological Framework;a Framework for Sustainable Web Design in the Era of Digital Transformation;green Thoughts and Creative Spaces: An Experimental Study on Influence of Innovation Labs on Productivity and Sustainability of process Teams;towards Resilient Digital Supply Chains;Walking Away from Omelas: Towards a Comprehensive Model for Successful Adoption of Industry 4.0 Technologies in SMEs;interoperable Product Change Management Within Engineering: A Digital Twin Approach;Next-Generation Business process Management (BPM): A Systematic Literature Review of Cognitive Computing and Improvements in BPM;three Stances in Enterprise System Design;process Orientation in Authorities: Opportunities, Challenges and Best Practices;Which Good Practices Minimize Procedural Problems in IT Workflows Between Authorities?;Meta-prompt Engineering in ChatGPT-4 for AI-Generated BPM Reference Models.
This paper introduces a method for modeling residual dynamics between a high-level planner and a low-level controller, using reference trajectory tracking in a cluttered environment as a case study. We aim to mitigate...
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ISBN:
(数字)9798350358513
ISBN:
(纸本)9798350358520
This paper introduces a method for modeling residual dynamics between a high-level planner and a low-level controller, using reference trajectory tracking in a cluttered environment as a case study. We aim to mitigate residual dynamics resulting solely from the kinematical modeling employed in high-level planning. Our high-level planner utilizes a simplified motion model for quadrotor motion. We propose a Sparse Gaussian process Regression-based technique to model residual dynamics. In contrast, data-Driven MPC, a recent technique, targets aggressive maneuvers without obstacle constraints. Our proposed method is compared with data-Driven MPC in estimating residual dynamics error, including obstacle constraints. Comparative analysis indicates that our technique reduces nominal model error by an average factor of 2. Furthermore, we evaluate our complete framework against four other trajectory-tracking approaches in terms of tracking reference trajectory while avoiding collisions. Our approach demonstrates superior performance, achieving shorter flight times without sacrificing computational efficiency.
Fault monitoring plays a crucial role in modern industrial systems. Timely and accurate monitoring is essential to mitigate economic losses and protect life safety. Traditional data-driven methods, such as auto-encode...
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ISBN:
(数字)9798350349252
ISBN:
(纸本)9798350349269
Fault monitoring plays a crucial role in modern industrial systems. Timely and accurate monitoring is essential to mitigate economic losses and protect life safety. Traditional data-driven methods, such as auto-encoder (AE) and variational autoencoder (VAE), are widely utilized in nonlinear process monitoring due to their simplicity and efficiency. However, the interpretability and generalization capabilities of data-driven models are constrained by the absence of physical knowledge. To address this limitation, we have developed a physics-informed variational auto-encoder (PI-VAE) method in this study. This method incorporates physical mechanism information into the model’s loss function through equality constraints. The results obtained from applying the PI-VAE method to a 1000MW ultra-supercritical coal-fired power plant demonstrate its exceptional capability in fault monitoring. By integrating reliable physical data with the VAE model, the model ensures enhanced stability and improved generalization.
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