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12th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS)

作者： Chen, Xu Ren, Jiayi Zhao, Chunhui Zhejiang Univ Coll Control Sci & Engn Hangzhou 310027 Peoples R China Shenzhen Polytech Univ Inst Intelligence Sci & Engn Shenzhen 518055 Peoples R China

Control systems regulate industrial processes by processing input signals to generate corresponding output signals. The monitoring of control systems ensures process safety. For control systems, we have the following recognition: the future outputs are determined by the output states and input excitations at the past moments, which have different interpretability capacities. It is a significant challenge to decouple the intricate relationship between inputs and outputs to provide comprehensive and interpretable monitoring results for control systems. In this paper, we propose a synergetic decomposition variational information bottleneck (SDVIB) approach to solve this problem from the perspective of information theory. Firstly, a compressed representation strategy is proposed to extract interpretable features from the output and input separately by employing the information bottleneck principle, which can provide initial state information and excitation information, respectively. Secondly, a reconstruction model is established within the framework of variational autoencoder (VAE), which can decouple the remaining interpretable-unrelated features to describe the cross-correlation separately among the input and output variables. In this way, the relationships between input and output variables are decoupled into multiple subspaces with specific physical meanings, which can be described by the constructed monitoring statistics. The effectiveness of the proposed method is validated through a well-known benchmark process. 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/)

关键词： Control system monitoring fault detection input-output dependency interpretable modeling information bottleneck variational inference

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Synergetic Decomposition of input-output dependency for Control System Intelligent Monitoring: A Perspective from Information Theory

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IFAC-PapersOnLine 2024年第4期58卷 312-317页

作者： Xu Chen Jiayi Ren Chunhui Zhao College of Control Science and Engineering Zhejiang University Hangzhou 310027 China Institute of Intelligence Science and Engineering Shenzhen Polytechnic University 518055 Shenzhen

关键词： Control system monitoring fault detection input-output dependency interpretable modeling information bottleneck variational inference

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input-output Directed dependency: Driving-Disentangled Dynamic Mode Decomposition for Control System Representation Learning and Monitoring

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2024年第10期71卷 13428-13438页

作者： Chen, Xu Zhao, Chunhui Ding, Jinliang Zhao, Shunyi Zhejiang Univ Coll Control Sci & Engn Hangzhou 310027 Peoples R China East China Jiaotong Univ Nanchang 330013 Jiangxi Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China Jiangnan Univ Key Lab Adv Proc Control Light Ind Minist Educ Wuxi 214122 Jiangsu Peoples R China

Process monitoring provides a guarantee for the efficient and safe operation of industrial equipment. However, traditional methods rarely consider the dependency relationship between inputs and outputs in industrial control systems, which can lead to misalignment of process state identification. This study solves the problem based on the following recognitions: 1) the inputs of control systems can drive the changes in the output states, termed input-output directed dependency;2) both the inputs and the process inertia are related to the dependency, showing the diversity of dependency relationship;and 3) the dependency can be further disentangled into different subspaces, revealing different types of process variations for fine-grained monitoring. Thereupon, a driving-disentangled dynamic mode decomposition with control method is proposed for control systems input-output directed dependency representation learning and monitoring. The process variables are divided into inputs and outputs. The input-output directed dependency is identified, revealing the response of outputs to inputs during adjustment of the process states, which is quite different from common multivariate correlations. A linearly independent vector SelecTion strategy is designed to construct a transformation matrix, which can further disentangle the input-output dependency into external-driving and self-driving subspaces. Three monitoring statistics are developed for process monitoring. The effectiveness is verified through a gas turbine thermal power experimental rig.

关键词： Dynamic mode decomposition dynamic process monitoring fault detection industrial processes input-output dependency

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IFVM Bridge: A Virtual Machine for IFML Models Execution in Combination with Domain Models 7th

IFVM Bridge: A Virtual Machine for IFML Models Execution in ...

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7th International Conference on Model-Driven Engineering and Software Development (MODELSWARD)

作者： Gotti, Sara Mbarki, Samir Ibn Tofail Univ Fac Sci MISC Lab Kenitra Morocco

ISBN: (纸本)9789897583582

Many software systems are executable on various computing devices. No one denies that each of these devices has its own user interfaces. Nevertheless, this trend of computing everywhere is not accompanied by a solution that can be used to abstractly express the content, user interaction and control behavior of the software application front end without focusing on the implementation platform. Applying the concept of abstract models to user interfaces become a necessity. Accordingly, OMG adopted (in March 2013) the new Interaction Flow Modeling Language (IFML) for abstractly describing the system front end. It ensures executability in order to be mapped into executable applications for different kind of devices. In this paper, we propose a new model driven development approach to execute the logical description of UIs components and their interactions captured with IFML. We define IFVM, a virtual machine for executing IFML models with focus on the content-dependent navigation specification for passing parameters between the ViewElements, and the Data binding specification to specify the source of the published content.

关键词： Interaction Flow Modeling Language IFML Model Execution Mda Bytecode Virtual Machine Model Interpretation Model Compilation User Interfaces Front End Content dependency input-output dependency

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An Inheritance based Service Execution Planning Approach using Bully Election Algorithm 15

An Inheritance based Service Execution Planning Approach usi...

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International Conference on Advanced Research in Computer Science Engineering and Technology (ICARCSET)

作者： Rathore, Maya Suman, Ugrasen Devi Ahilya Univ DAVV Sch Comp Comp Sci & IT Indore Madhya Pradesh India

ISBN: (纸本)9781450334419

Web service composition has gained tremendous interest with emerging application development. Automatic service composition is a key aspect in overcoming runtime problems that arise due to dynamic nature of runtime environment. In SOA, applications are formed through the combinations of independently developed web services that lead to emergence of different dependencies among the component services. The challenge of web service composition is to manage such kind of dependencies among web services when there is large number of services. In this paper, an inheritance based Bully Election approach is proposed for analyzing dependency among services and generating automatic service composition plan. It also identifies the coordinator service on which the execution of other services is dependent. Experimental result shows that the proposed approach is able to reduce the composition time complexity by including only the selected services on the composition plan.

关键词： Web services Automatic dependency extraction Composition plan Service dependency Cyclic dependency Bully Election Algorithm input-output dependency

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