The proceedings contain 17 papers. The special focus in this conference is on Practice of Enterprise modeling. The topics include: Fostering Digital Progression of Society: Exploratory Case Studies of Third Place for ...
ISBN:
(纸本)9783031779077
The proceedings contain 17 papers. The special focus in this conference is on Practice of Enterprise modeling. The topics include: Fostering Digital Progression of Society: Exploratory Case Studies of Third Place for Services;using Enterprise modeling for Dealing with Complexity of Elderly Care in Sweden;evaluation of Categorization Patterns for Conceptual modeling of IoT Applications;SmartCML: A Visual modeling Language to Enhance the Comprehensibility of Smart Contract Implementations;assessing Model Quality Using Large Language Models;grass-Root Enterprise Modelling: How Large Language Models Can Help;investigating the Effectiveness of Feedback-Driven Exercises on Deadlock Detection Skills in Conceptual Modelling;knowledge Graphs as a Scholarly data Fabric: A data Silo Transformation Pipeline with Visualization Semantics;enriching Business process Event Logs with Multimodal Evidence;towards Timeline-Based Layout for process Mining;Conceptualisation and (Meta)modelling of Problem-Solution Chains in Early Business-IT Alignment and System Design;SymboleoAC: An Access control Model for Legal Contracts;Functional Security in Automation: The FAST Approach;Configuration of Software Product Lines Driven by the Softgoals: The TEAEM Approach;the Dual Nature of Organizational Policies.
Hospitals currently face numerous challenges in managing their pharmacy operations efficiently. While Business process Reengineering (BPR) has been proposed as a solution, its implementation in healthcare is more comp...
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Musicians spend more time practicing than performing live, but the process of rehearsal has been understudied. This paper introduces a dataset for using AI and machine learning to address this gap. The project observe...
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ISBN:
(数字)9783031564352
ISBN:
(纸本)9783031564345;9783031564352
Musicians spend more time practicing than performing live, but the process of rehearsal has been understudied. This paper introduces a dataset for using AI and machine learning to address this gap. The project observes the progression of pianists learning new repertoire over long periods of time by recording their rehearsals, generating a comprehensive multimodal dataset, the Rach3 dataset, with video, audio, and MIDI for computational analysis. This dataset will help investigating the way in which advanced students and professional classical musicians, particularly pianists, learn new music and develop their own expressive interpretations of a piece.
With the continuous spread and globalization of infectious diseases, infectious disease prevention and control has become an important task for both countries and the world. Traditional infectious disease monitoring m...
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ISBN:
(纸本)9798350385113;9798350385106
With the continuous spread and globalization of infectious diseases, infectious disease prevention and control has become an important task for both countries and the world. Traditional infectious disease monitoring methods often rely on manual collection and analysis of data, which is inefficient and easily limited by human errors. However, the development of artificial intelligence and big data technology has provided new opportunities for infectious disease prevention and control. This article introduces the application of artificial intelligence in infectious disease monitoring. By using artificial intelligence algorithms and models, real-time monitoring and analysis of infectious disease data can be carried out, predicting the spread trend and risk of infectious diseases. This helps to detect and report infectious disease outbreaks, thus taking corresponding prevention and control measures and reducing the spread and impact of the epidemic. At the same time, this article explores the application of big data in infectious disease prevention and control. By using big data technology, in-depth mining and analysis of patient case data, virus gene sequences, transmission chain information, and other data can be carried out to discover the characteristics and patterns of infectious diseases, and be used to build an infectious disease warning system to predict and prevent the occurrence of infectious diseases in advance. Finally, this article discusses the application cases of artificial intelligence and big data in infectious disease prevention and control. By combining artificial intelligence and big data technology, the entire process of monitoring and management of infectious diseases can be achieved.
The power oscillation has a bad impact on the safety and economy of power plants and power grids, and the causes and solutions of the power oscillation problem of secondary reheat units are not mature. In order to qui...
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ISBN:
(纸本)9798350377477;9798350377460
The power oscillation has a bad impact on the safety and economy of power plants and power grids, and the causes and solutions of the power oscillation problem of secondary reheat units are not mature. In order to quickly solve the power oscillation problem of the secondary reheat unit, a simulation and analysis method of the power oscillation of the secondary reheat unit considering the characteristics of the valve adjustment was proposed. By analyzing the influence of operating parameters and adjusting parameters on the characteristics of the valve, the modular modeling of the secondary reheat unit is carried out, and the power model considering the secondary reheating process and the actuator model considering the adjusting characteristics of the valve are proposed, then the optimized adjusting parameters are proposed based on this. The simulation analysis was carried out with reference to the actual power oscillation case, which verified the correctness of the theoretical analysis and the effectiveness of the proposed method are provided an effective decision-making reference for the cause finding of the power oscillation of the secondary reheat unit.
Business process simulation (BPS) is a versatile technique for estimating process performance across various scenarios. Traditionally, BPS approaches employ a control-flow-first perspective by enriching a process mode...
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ISBN:
(纸本)9798350365030
Business process simulation (BPS) is a versatile technique for estimating process performance across various scenarios. Traditionally, BPS approaches employ a control-flow-first perspective by enriching a process model with simulation parameters. Although such approaches can mimic the behavior of centrally orchestrated processes, such as those supported by workflow systems, current control-flow-first approaches cannot faithfully capture the dynamics of real-world processes that involve distinct resource behavior and decentralized decision-making. Recognizing this issue, this paper introduces AgentSimulator, a resource-first BPS approach that discovers a multi-agent system from an event log, modeling distinct resource behaviors and interaction patterns to simulate the underlying process. Our experiments show that AgentSimulator achieves state-of-the-art simulation accuracy with significantly lower computation times than existing approaches while providing high interpretability and adaptability to different types of process-execution scenarios.
In this article, a learning-based controller is proposed to realize motion policy learning based on intuitive human demonstrations. The position, velocity, and force data during the demonstration are collected as inpu...
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In this article, a learning-based controller is proposed to realize motion policy learning based on intuitive human demonstrations. The position, velocity, and force data during the demonstration are collected as input features without any physical contact with the human demonstrator, and an algorithm is designed to automatically label the data in combination with motion and force data. After the learning process, the robot can complete the assembly according to the human demonstrations, and the proposed controller will generate different angular acceleration commands as control inputs to help finish the manipulation well. Finally, a comprehensive analysis, including Lyapunov stability and Lipschitz constraint, is also provided to guarantee the stability and security of this learning-based controller. Sufficient experiments based on the real robot system verify the effectiveness of the proposed method.
This study undertakes a comprehensive analysis of topic modeling techniques-N on-negative Matrix Factorization (NMF), Latent Dirichlet Allocation (LDA), and Correlation Explanation (Corex) enhanced by BERT's depth...
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On the basis of a component-level approach, mathematical and simulation models are created for various modules to examine the operation of the APU (Auxiliary Power Unit) and the ATS (Air Turbine Starter) in civil airc...
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The Behavioral Toolbox is a collection of Matlab functions for modeling, analysis, and design of dynamical systems using the behavioral approach to systems theory and control. It implements newly emerged direct data-d...
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The Behavioral Toolbox is a collection of Matlab functions for modeling, analysis, and design of dynamical systems using the behavioral approach to systems theory and control. It implements newly emerged direct data-driven methods as well as classical parametric representations of linear time-invariant systems. At the core of the toolbox is a nonparameteric representation of the finitehorizon behavior by an orthonormal basis. The current version has education and research goals and isn't intended for handling "big data". The paper presents five problems-checking systems equality, interconnection of systems, errors-in-variables least-squares smoothing, missing input estimation, and data-driven forecasting-and describes their solution by the methods in the toolbox.
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