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Online parameter estimation for cyber-physical production systems based on mixed integer nonlinear programming, process mining and black-box optimization techniques

https://doi.org/10.1515/auto-2017-0124
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/9781118625590openalex-snapshot
Applied Regression Analysis
inferredcites →https://doi.org/10.1007/978-3-540-75183-0_24openalex-snapshot
Fuzzy Mining – Adaptive Process Simplification Based on Multi-perspective Metrics
inferredcites →https://doi.org/10.1007/978-3-642-25566-3_40openalex-snapshot
Sequential Model-Based Optimization for General Algorithm Configuration
inferredcites →https://doi.org/10.1007/s12532-018-0144-7openalex-snapshot
RBFOpt: an open-source library for black-box optimization with costly function evaluations
inferredcites →https://doi.org/10.1016/j.protcy.2014.09.083openalex-snapshot
Why Cyber-physical Production Systems Need a Descriptive Engineering Approach – A Case Study in Plug & Produce
inferredcites →https://doi.org/10.1016/s0169-023x(03)00066-1openalex-snapshot
Workflow mining: A survey of issues and approaches
inferredcites →https://doi.org/10.1023/a:1008306431147openalex-snapshot
Efficient Global Optimization of Expensive Black-Box Functions
inferredcites →https://doi.org/10.1109/87.595926openalex-snapshot
Self-tuning PID control of a plant with under-damped response with specifications on gain and phase margins
inferredcites →https://doi.org/10.1109/coase.2016.7743550openalex-snapshot
An agent approach to flexible automated production systems based on discrete and continuous reasoning
inferredcites →https://doi.org/10.1109/indin.2016.7819329openalex-snapshot
Optimizing modular and reconfigurable cyber-physical production systems by determining parameters automatically
inferredcites →https://doi.org/10.1109/induscon.2010.5739892openalex-snapshot
Automatic configuration (Plug & Produce) of Industrial Ethernet networks
inferredcites →https://doi.org/10.1109/tii.2017.2718729openalex-snapshot
Automatic Parameter Estimation for Reusable Software Components of Modular and Reconfigurable Cyber-Physical Production Systems in the Domain of Discrete Manufacturing
inferredcites →https://doi.org/10.1109/tkde.2004.47openalex-snapshot
Workflow mining: discovering process models from event logs
inferredcites →https://doi.org/10.1145/2464576.2501592openalex-snapshot
An evaluation of sequential model-based optimization for expensive blackbox functions
inferredcites →https://doi.org/10.2307/1270576openalex-snapshot
Applied Regression Analysis
inferredcites →https://doi.org/10.24406/publica-fhg-388128openalex-snapshot
Automatic parameterization of automation software for plug-and-produce
inferredcites →https://doi.org/10.5220/0001504902550262openalex-snapshot
COMMUNICATION, CONFIGURATION, APPLICATION - The Three Layer Concept for Plug-and-Produce
inferredcites →https://doi.org/10.7551/mitpress/3206.001.0001openalex-snapshot
Gaussian Processes for Machine Learning

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4690886openalex-extracted
Process Mining in Manufacturing and Logistics: A Systematic Mapping and New Taxonomy Proposal
inferred← citeshttps://doi.org/10.1007/978-3-662-59084-3openalex-snapshot
Machine Learning for Cyber Physical Systems
inferred← citeshttps://doi.org/10.1007/978-3-662-62746-4openalex-snapshot
Machine Learning for Cyber Physical Systems
inferred← citeshttps://doi.org/10.1007/978-3-662-62746-4_11openalex-snapshot
Hardening Deep Neural Networks in Condition Monitoring Systems against Adversarial Example Attacks
inferred← citeshttps://doi.org/10.1080/01430750.2022.2031288openalex-snapshot
Predicting the renewable energy consumption in 2026 by using a recursive moving average model
inferred← citeshttps://doi.org/10.1109/ict4s58814.2023.00025openalex-snapshot
ReThink Your Processes! A Review of Process Mining for Sustainability
inferred← citeshttps://doi.org/10.1109/indin.2018.8472060openalex-snapshot
Generation of Adversarial Examples to Prevent Misclassification of Deep Neural Network based Condition Monitoring Systems for Cyber-Physical Production Systems
inferred← citeshttps://doi.org/10.1109/indin51400.2023.10218171openalex-snapshot
Generation of Synthetic Data to Improve Security Monitoring for Cyber-Physical Production Systems
inferred← citeshttps://doi.org/10.1109/indin51773.2022.9976111openalex-snapshot
Process mining in industrial control systems
inferred← citeshttps://doi.org/10.1109/indin51773.2022.9976140openalex-snapshot
Cyberattack Impact Reduction using Software-Defined Networking for Cyber-Physical Production Systems
inferred← citeshttps://doi.org/10.1109/tase.2024.3386313openalex-snapshot
Automatic Optimization of Tolerance Ranges for Model-Driven Runtime State Identification
inferred← citeshttps://doi.org/10.1145/3777518.3777520openalex-snapshot
Process Mining for Robust Heat Integration through Process- and Product-Centric Energy Demand Modeling
inferred← citeshttps://doi.org/10.1515/auto-2018-0108openalex-snapshot
Model-based routing in flexible manufacturing systems
inferred← citeshttps://doi.org/10.1515/auto-2018-0117openalex-snapshot
Effiziente Initialisierung von Steuerungsparametern für Cyber-Physische Produktionssysteme via Multi-Ebenen-Optimierung
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