This thesis summarizes the construction of a digital financial management platform in the operational management process of aerospace enterprises, covering comprehensive budgeting, contract management, and full life-c...
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The improvement of people's living standards, every family has a private car, and large trucks used in industry are the reason for the continuous increase in the traffic flow, and traffic congestion has become an ...
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Accurate estimation of multiple quality variables is critical for building industrial soft sensor models, which have long been confronted with data efficiency and negative transfer issues. Methods sharing backbone par...
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Accurate estimation of multiple quality variables is critical for building industrial soft sensor models, which have long been confronted with data efficiency and negative transfer issues. Methods sharing backbone parameters among tasks address the data efficiency issue;however, they still fail to mitigate the negative transfer problem. To address this issue, a balanced mixture-of-experts (BMoE) is proposed in this work, which consists of a multigate mixture-of-experts module and a task gradient balancing (TGB) module. The mixture-of-experts module aims to portray task relationships, while the TGB module balances the gradients among tasks dynamically. Both of them cooperate to mitigate the negative transfer problem. Experiments on the typical sulfur recovery unit demonstrate that BMoE models task relationship and balances the training process effectively, and achieves better performance than baseline models significantly.
The accurate prediction of time series data in the industrial production process can provide important guidance for the scheduling and decision-making of industrial systems, and is also an important part of predictive...
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ISBN:
(纸本)9798350334722
The accurate prediction of time series data in the industrial production process can provide important guidance for the scheduling and decision-making of industrial systems, and is also an important part of predictive control technology. In this paper, a time series prediction model which introduces the graph neural network (GCN) is proposed. This model mainly consists of a time series feature extraction module and a relational modeling module. In the time series feature extraction module, the Att-LSTM model is proposed to extract the feature information of time series data. In the relational modeling module, a novel M-GCN network is proposed to model the relevance among different time series nodes. In addition, based on the time series prediction model, a time series multi-classification model is also proposed. The proposed model can predict the energy consumption conditions of the data center accurately. The experimental results demonstrate that the propose model can provide a desirable performance superior to some traditional models in accuracy and robustness.
GPT-3 and several other language models (LMs) can effectively address various natural language processing (NLP) tasks, including machine translation and text summarization. Recently, they have also been successfully e...
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ISBN:
(纸本)9783031342400;9783031342417
GPT-3 and several other language models (LMs) can effectively address various natural language processing (NLP) tasks, including machine translation and text summarization. Recently, they have also been successfully employed in the business process management (BPM) domain, e.g., for predictive process monitoring and process extraction from text. This, however, typically requires fine-tuning the employed LM, which, among others, necessitates large amounts of suitable training data. A possible solution to this problem is the use of prompt engineering, which leverages pre-trained LMs without fine-tuning them. Recognizing this, we argue that prompt engineering can help bring the capabilities of LMs to BPM research. We use this position paper to develop a research agenda for the use of prompt engineering for BPM research by identifying the associated potentials and challenges.
As the infrastructure industry continues to evolve toward digitalization, ongoing development of spatial and temporal data-based intelligent sensing guarantees safe operation. However, blast furnace ironmaking process...
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As the infrastructure industry continues to evolve toward digitalization, ongoing development of spatial and temporal data-based intelligent sensing guarantees safe operation. However, blast furnace ironmaking processes (BFIP) encounter a tricky dilemma in this revolution. data-driven multivariate statistical analysis always fails for expected diagnosis performance due to complex dynamic, nonlinear, and nonstationary characteristics. To address this issue, we propose a novel method named modified mixed kernel-aided canonical stationary variate analysis (M2KCSVA). To start with, the past and future matrices and multiview nonlinear mapping of mixed kernel are properly considered to explore canonical stationary variables (CSVs) for both temporal correlation and weak stationarity. Especially, efficiency improvement procedures based on singular value decomposition and iterative modeling flow are deployed to reduce the computational cost and estimate accurate CSVs. In addition, we retain the smooth information without autocorrelation in the residuals for further analysis using stationary subspace analysis to generate static stationary variables. The corresponding two statistics and exponential difference contributions are computed for simultaneous fault detection and identification with an intuitive interpretation of dynamic and static stationary information. Experiments through an actual BFIP demonstrate that M2KCSVA surpasses comparison methods in terms of efficiency and accuracy.
With the rapid development of social media, short videos have become an important carrier of the information dissemination. For example, the dissemination network analysis of short videos is an important research bran...
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Improving the quality of the educational process, bringing it to modern requirements is a complex task, the solution of which requires a systematic approach from higher education institutions and the development of a ...
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While real-world problems are often challenging to analyze analytically, deep learning excels in modeling complex processes from data. Existing optimization frameworks like CasADi facilitate seamless usage of solvers ...
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While real-world problems are often challenging to analyze analytically, deep learning excels in modeling complex processes from data. Existing optimization frameworks like CasADi facilitate seamless usage of solvers but face challenges when integrating learned process models into numerical optimizations. To address this gap, we present the Learning for CasADi (L4CasADi) framework, enabling the seamless integration of PyTorch-learned models with CasADi for efficient and potentially hardware-accelerated numerical optimization. The applicability of L4CasADi is demonstrated with two tutorial examples: First, we optimize a fish's trajectory in a turbulent river for energy efficiency where the turbulent flow is represented by a PyTorch model. Second, we demonstrate how an implicit Neural Radiance Field environment representation can be easily leveraged for optimal control with L4CasADi. L4CasADi, along with examples and documentation, is available under MIT license at https://***/Tim-Salzmann/l4casadi
This paper develops computationally efficient data-driven model predictive control (MPC) for Agile quadrotor flight. Agile quadrotors in high-speed flights can experience high levels of aerodynamic effects. modeling t...
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ISBN:
(纸本)9798350328066
This paper develops computationally efficient data-driven model predictive control (MPC) for Agile quadrotor flight. Agile quadrotors in high-speed flights can experience high levels of aerodynamic effects. modeling these turbulent aerodynamic effects is a cumbersome task and the resulting model may be overly complex and computationally infeasible. Combining Gaussian process (GP) regression models with a simple dynamic model of the system has demonstrated significant improvements in control performance. However, direct integration of the GP models to the MPC pipeline poses a significant computational burden to the optimization process. Therefore, we present an approach to separate the GP models to the MPC pipeline by computing the model corrections using reference trajectory and the current state measurements prior to the online MPC optimization. This method has been validated in the Gazebo simulation environment and has demonstrated of up to 50% reduction in trajectory tracking error, matching the performance of the direct GP integration method with improved computational efficiency.
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