In this contribution, the Dynamic Mode Decomposition with control (DMDc) is used to derive a surrogate model of a continuous PHA biopolymer production process based on a recently published complex process model. Here,...
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In this contribution, the Dynamic Mode Decomposition with control (DMDc) is used to derive a surrogate model of a continuous PHA biopolymer production process based on a recently published complex process model. Here, snapshot simulation data of the original model is processed to obtain a linear surrogate model formulation using delay coordinates. The quality of the surrogate is statistically validated within simulation studies. Additionally, the influence of the of the order of delay coordinates is investigated. It is shown, that the highly nonlinear dynamics of the PHA-manufacturing process can be approximated accurately by the DMD-based model even for large variations of initial conditions and control variables. This offers the opportunity to apply well-studied and established tools from robust and optimal control in future investigations. Copyright (c) 2025 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)
automation technologies such as robotic processautomation (RPA) and intelligent automation (IA) are essential for managing rising healthcare costs and ensuring sustainable health services. Although these solutions ha...
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Factory automation is evolving from a hierarchical structure towards an interconnected architecture in which systems at different levels can communicate seamlessly with each other. This requires an appropriate replace...
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ISBN:
(纸本)9798350325621
Factory automation is evolving from a hierarchical structure towards an interconnected architecture in which systems at different levels can communicate seamlessly with each other. This requires an appropriate replacement of proprietary fieldbus systems in the automation sector. A promising solution to implement cross-plane end-to-end real-time capable communication between computing nodes is proposed in literature. The usage of Open Platform Communication Unified Architecture (OPC UA) as the information model with the Publish-Subscribe (PubSub) communication mechanism in combination with Time Sensitive Networking (TSN) is presented to fulfill the requirements for message transmission. In contrast to the state of the art of configuring fieldbus systems, the configuration of such a real-time communication node has to be done manually. This process is complex, as deep knowledge in different domains is required. In this work we elaborate the requirements for such a communication node and present the resulting implementation challenges, which can be used as the basis for a configuration tool.
Due to the current day's financial landscape, which is data-driven and changing rapidly, the latest use of technology has become an order of the day to further increase efficiency in the decision-making process wi...
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In cloud computing, microservices architecture has become the preferred choice for many applications. Accordingly, several small and decoupled containerized services hosted on different servers communicate through the...
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ISBN:
(纸本)9798350360882;9798350360899
In cloud computing, microservices architecture has become the preferred choice for many applications. Accordingly, several small and decoupled containerized services hosted on different servers communicate through the network. Therefore, communication latency significantly impacts end-to-end latency. Kubernetes, the de facto standard for container orchestration, cannot reduce this overhead due to its lack of awareness of service interactions. We present LEAD, a Latency-Efficient Application Deployment framework that integrates with Kubernetes without modifying its core components. LEAD considers the inter-service relationships and resource constraints, improving service placement to reduce end-to-end latency. The idea behind LEAD is straightforward: keep the cooperating services close to each other to exploit faster in-node communication and automate this process. The proposed idea is realized by leveraging a scoring algorithm and monitoring framework to achieve dynamic improvement of service placement. Our experimental results show an average 20% improvement in the 99th percentile latency compared to Kubernetes default scheduler.
Coffee bean grading is a crucial process in the coffee industry, traditionally done manually by human experts, which is time-consuming, expensive, and prone to errors. To address these challenges, deep learning models...
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Endogenous Safety and Security (ESS) of Industrial control Systems (ICS) has gained great attention with the advent of Industry 4.0. However, with rising cyber threats, most current research has focused mainly on cybe...
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ISBN:
(纸本)9798350370058;9798350370164
Endogenous Safety and Security (ESS) of Industrial control Systems (ICS) has gained great attention with the advent of Industry 4.0. However, with rising cyber threats, most current research has focused mainly on cybersecurity aspects. Our study aims to fill this research gap by focusing on the endogenous functional safety of ICS, with a particular emphasis on key control parameters "setpoints". We propose a reinforcement adversarial framework to investigate the functional security issues arising from unexpected operations and malicious tampering against setpoints. In this framework, a deep reinforcement learning(DRL) agent interacts with a custom input rule model, which serves as both a dynamic validator and an adversary, aiming to explore previously unforeseen behaviors. Explored unexpected behaviors are continuously updated to the input rule model, enhancing system adaptability and robustness. Our study employed the Tennessee Eastman process as a case study, using the proximal policy optimization(PPO) algorithm with Beta and Gaussian distributions. Our approach exhibited significant advantages in exploration efficiency over baseline methods such as random agents and simulated annealing. These findings underscore DRL's important role in augmenting ICS functional safety, thereby enhancing system resilience and security in Industry 4.0.
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:
(纸本)9798350358513;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 trajectorytracking approaches in terms of tracking reference trajectory while avoiding collisions. Our approach demonstrates superior performance, achieving shorter flight times without sacrificing computational efficiency.
This study explores an intelligent emotional computing method baed on deep convolutional neural networks (DCNNs) aimed at accurately identifying and analyzing learners' emotional states within digital learning int...
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The article examines the application of a method for processing experimental data in measurement systems by generating hypotheses about the presence of a given set of parameters and estimating their probability. The d...
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