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检索条件"任意字段=International Conference on Process Automation, Control and Computing"

共 28211 条记录，以下是1-10 订阅

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Proceedings of 2011 international conference on process automation, control and computing, PACC 2011

Proceedings of 2011 International Conference on Process Auto...

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2011 international conference on process automation, control and computing, PACC 2011

ISBN: (纸本)9781612847641

The proceedings contain 163 papers. The topics discussed include: 6.6KV source changeover scheme using PLC in LHS;a compressed domain dual video watermarking for real-time applications;a defense technique for jamming attacks in wireless sensor networks based on SI;a differential evolution algorithm for image fusion;a disconnected 1-safe petri net whose reachability tree is homomorphic to a complete Boolean lattice;a distributed algorithm for dynamic spectrum access using primary-prioritised Markov model;a knowledge management framework for agile software development teams;a low order transfer function model for MIMO ALSTOM gasifier;a multiple model adaptive control strategy for model predictive controller for interacting non linear systems;and a novel approach for scheduling and routing of the self guided vehicles in mesh topology using velocity control and alternate path techniques.

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international Workshop on Artificial Intelligence for IT Operations, AIOps 2021, 3rd Workshop on Smart Data Integration and processing, STRAPS 2021, international Workshop on AI-enabled process automation, AI-PA 2021 and Scientific Satellite Events held in conjunction with 19th international conference on Service-Oriented computing, ICSOC 2021

International Workshop on Artificial Intelligence for IT Ope...

引用

international Workshop on Artificial Intelligence for IT Operations, AIOps 2021, 3rd Workshop on Smart Data Integration and processing, STRAPS 2021, international Workshop on AI-enabled process automation, AI-PA 2021 and Scientific Satellite Events held in conjunction with 19th international conference on Service-Oriented computing, ICSOC 2021

ISBN: (纸本)9783031141348

The proceedings contain 32 papers presendted at a virtual meeting. The special focus in this conference is on Artificial Intelligence for IT Operations. The topics include: A Taxonomy of Anomalies in Log Data;little Help Makes a Big Difference: Leveraging Active Learning to Improve Unsupervised Time Series Anomaly Detection;MMRCA: MultiModal Root Cause Analysis;IAD: Indirect Anomalous VMMs Detection in the Cloud-Based Environment;comparing Graph Data Science Libraries for Querying and Analysing Datasets: Towards Data Science Queries on Graphs;gamification for Healthier Lifestyle – User Retention;streaming and Visualising Neuronal Signals for Understanding Pain;graph Analytics Workflows Enactment on Just in Time Data Centres Position Paper;say No2Ads: Automatic Advertisement and Music Filtering from Broadcast News Content;data Centred Intelligent Geosciences: Research Agenda and Opportunities Position Paper;Design Patterns in an ERP Environment;towards a Semantics-Based Search Engine for Smart Contract Information;a Blockchain-Based Autonomous System for Primary Financial Market;multi-agent Reinforcement Learning for Task Allocation in Cooperative Edge Cloud computing;occupancy Estimation from Thermal Images;constraint-Aware Trajectory for Drone Delivery Services;seamless Synchronization for Collaborative Web Services;Crowd4ME: A Crowdsourcing-Based Micro-expression Collection Platform;AI-Enabled processes: The Age of Artificial Intelligence and Big Data;hybrid Recommendation of Movies Based on Deep Content Features;continuously Testing Distributed IoT Systems: An Overview of the State of the Art;towards Scalable Blockchains Using Service-Oriented Architectures;snapshot of Research Issues in Service Robots;automatic Image Annotation Using Quantization Reweighting Function and Graph Neural Networks;interactive process Improvement Using Simulation of Enriched process Trees;a Reproducible Approach for Mining Business Activities from Emails for process Analytics;human-in

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Machine Learning Approaches for process automation: A Case Study Using Q-Learning and Supervised Learning 4

Machine Learning Approaches for Process Automation: A Case S...

引用

4th international conference on Power, control and computing Technologies, ICPC2T 2025

作者： Azad, S.M.A.K. Suresh, P. Sireesha, R. Department of Electrical and Electronics Engineering Sri Venkanteswara College of Engineering India

ISBN: (纸本)9798331530402

process automation is critical in modern industries, providing systems for precise control of variables such as temperature, pressure, and flow. Traditional control methods like PID controllers require manual tuning and often struggle with nonlinear dynamic systems. This paper presents a comparative study of two machine learning techniques namely Q-Learning and Decision Trees applied to industrial process control, specifically temperature regulation. Through a Python-based simulation, we evaluate the effectiveness of both techniques in real-time optimization. The results demonstrate that Q-Learning continuously adapts to system changes, optimizing control strategies, while Decision Trees provide reliable predictions based on historical data, reducing error margins. Our study shows that machine learning has the potential to revolutionize process automation by minimizing human intervention, enhancing efficiency, and improving system robustness. © 2025 IEEE.

关键词： Three term control systems

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Optimization technology of ship real-time automation control system based on edge computing 2

Optimization technology of ship real-time automation control...

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2nd international conference on Big Data, Computational Intelligence, and Applications, BDCIA 2024

作者： Wang, Guangfu Wei, Jian CSSC System Engineering Research Institute Beijing100094 China

ISBN: (纸本)9781510689053

The rapid advancements in edge computing have opened new avenues for optimizing real-time control systems in maritime environments. Traditional centralized systems face challenges such as high latency, limited scalability, and dependency on stable network connections, which are particularly critical for ship automation systems. To address these challenges, this study proposes an innovative optimization framework for real-time ship automation control systems based on edge computing. The framework employs a multi-layered architecture, integrating edge, fog, and cloud layers to ensure efficient data processing and task execution. A dynamic task scheduling algorithm is developed to prioritize critical operations, reduce latency, and optimize energy consumption. Additionally, the framework incorporates fault-tolerant mechanisms, including predictive fault detection and redundancy strategies, to enhance system reliability. Scalability is achieved through adaptive resource allocation, allowing the system to handle increasing complexity without significant performance degradation. Experimental results demonstrate the framework's effectiveness in reducing task latency by up to 30%, improving energy efficiency by over 25%, and maintaining high task success rates even under fault conditions. These findings highlight the potential of the proposed framework to improve operational efficiency, reliability, and sustainability in real-time ship automation systems, addressing the unique demands of dynamic and resource-constrained maritime environments. © 2025 SPIE.

关键词： Edge computing

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Serverless computing and DevOps: A Synergistic Approach to Modern Software Development 1st

Serverless Computing and DevOps: A Synergistic Approach to M...

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2024 international conference on Computational Intelligence and Generative AI

作者： Puppala, Ramya Goutham, Potharlanka Rohan, Shaik Abdul Sainadh, Jaladi Teja Krishna David, Thalathoti Jason Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Guntur Andhra Pradesh India

ISBN: (纸本)9783031766121;9783031766107;9783031766091

This discussion explores the evolution of serverless computing, highlighting its origins, rise in importance, and the challenges it presents. Serverless computing represents a significant shift from traditional, hardware-dependent environments to cloud-based, code-centric architectures. The concept of serverless emerged with the introduction of platforms like Google App Engine and AWS Lambda, freeing developers from server management concerns and enabling them to focus solely on code development. The integration of serverless computing with DevOps practices is examined, emphasizing the role of Infrastructure as Code (IaC), version control, continuous integration (CI), and continuous deployment (CD). These practices are essential for streamlining the development and deployment of serverless applications, enhancing consistency, and promoting collaboration between development and operations teams. Furthermore, the discussion addresses the specific challenge of cold starts in serverless computing and how DevOps practices can help mitigate their impact. Strategies such as proactive monitoring, scaling policies, scheduled warm-up, provisioned concurrency, performance testing, automation, and continuous optimization are presented as solutions to minimize cold start delays. Lastly, the importance of Testing automation within the CI/CD pipeline is highlighted. Through rapid feedback, comprehensive testing, consistency, regression testing, and scalability, Testing automation ensures the quality and reliability of software in a fast-paced development environment, aligning with the principles of DevOps.

关键词： Serverless computing Function-as-a-service Infrastructure as code Continuous integration Continuous delivery Continuous deployment Cold starts Limited state management

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Robotic process automation Activity Recommendation using NLP Techniques 5

Robotic Process Automation Activity Recommendation using NLP...

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5th international conference on Advanced Research in computing, ICARC 2025

作者： Herath, Tharindu Asanka, Dinesh University of Kelaniya Department of Industrial Management Dalugama Sri Lanka

ISBN: (纸本)9798331530983

The integration of Natural Language processing (NLP) into Robotic process automation (RPA) has revolutionized the automation of complex business processes by addressing challenges such as interpreting unstructured data and reducing human intervention. This research focuses on enhancing RPA systems by developing NLP models to recommend UiPath activities based on textual process descriptions. Leveraging pre-trained transformer models, including BERT, DistilBERT, TinyBERT, and ALBERT, fine-tuned on a structured and diverse dataset, the study incorporates linguistic variability to improve generalization. A robust evaluation framework based on precision, recall, and F1-score underscores the performance of these models. Key contributions include the creation of high-accuracy NLP models, a comprehensive evaluation methodology, and a representative dataset designed for UiPath activity recommendation. The findings highlight NLP's potential to streamline automation workflows, reduce manual effort, and enhance scalability, ultimately bridging the gap between unstructured text and automation tasks, and laying the groundwork for advanced, autonomous RPA systems. © 2025 IEEE.

关键词： Natural language processing systems

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Soft sensor-based inline geometry control of hole-rolled polygon hubs 11

Soft sensor-based inline geometry control of hole-rolled pol...

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11th Vienna international conference on Mathematical Modelling, MATHMOD 2025

作者： Arne, Viktor Spies, Daniel Groche, Peter Institute for Production Engineering and Forming Machines Technical University of Darmstadt Germany

New machine concepts, such as the 3D Servo Press, make it possible to implement and study new forming processes like the flexible hole-rolling. In combination with the ongoing development of models, new control concepts can be developed and thus increase productivity of manufacturing systems. As part of this work, a soft sensor was developed to predict the part geometry of a polygon profile during the process of hole rolling based on the process forces and the ram position. This prediction was used in an experimental investigation to set up an inline geometry control to compensate for the geometric error due to roller deflection during the process. Copyright © 2025 The Authors.

关键词： process control

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Architectural Elements of Decentralized process Management Systems 22nd

Architectural Elements of Decentralized Process Management S...

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22nd international conference on Service Oriented computing

作者： Grunert, Kai Shoferi, Janis Joderi Gold, Lucas Rieder, Wolf Kuepper, Axel Tech Univ Berlin Serv Ctr Networking Berlin Germany

ISBN: (纸本)9789819608041;9789819608058

In a decentralized process Management System, several process engines cooperate to execute a single process instance by using direct Machine-to-Machine communication and local coordination of the process flow. In this paper, we analyze the software architecture elements of a decentralized process Management System. We explain the involved components, connectors, data, and the relationships between them. We also describe the state transitions of decentralized processes during execution.

关键词： decentralized process Management System Distributed process Engines Distributed and Heterogeneous Workflow Enactment Service Business process Management System process automation process Execution System process Engine

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Empirical Modeling of Unsteady Bulging in Continuous Slab Casters 11

Empirical Modeling of Unsteady Bulging in Continuous Slab Ca...

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11th Vienna international conference on Mathematical Modelling, MATHMOD 2025

作者： Landauer, Julian Gasparini, Luca Kugi, Andreas Steinboeck, Andreas Christian Doppler Laboratory for Intelligent Process Control for High-Quality Steel Products Automation and Control Institute TU Wien Austria Automation and Control Institute TU Wien Austria

In the continuous casting of steel slabs, a constant mold level is crucial for the quality of the final products. However, mold level fluctuations may be caused by unsteady bulging, i.e., a time-varying bending of the solidified strand shell between the rolls of the roll array due to variations in the strand shell thickness. A control-oriented model of unsteady bulging is needed to design controllers that should suppress unsteady bulging systematically. Most existing models use complex, detailed, nonlinear formulations, typically requiring high computational costs. Therefore, these models are unsuitable for closed-loop simulations or applications in real-Time control. As an alternative, this work presents a novel empirical model that allows parameterization based on usually available measurements from industrial continuous slab casters. The model facilitates fast dynamic simulations of unsteady bulging and its results accurately match measurements. Copyright © 2025 The Authors.

关键词： Continuous casting

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Industrial IoT Smart Manufacturing Systems based on Cloud computing Energy Efficiency Analysis using Machine Learning Algorithms 3

Industrial IoT Smart Manufacturing Systems based on Cloud Co...

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Chinnasamy, P. Khan, Patan Feridoz Sasikala, C. Swathi, Mavalluru Kumar, M. Prema Yadav, Ajay Kumar Kalasalingam Academy of Research and Education School of Computing Department of Computer Science and Engineering Tamilnadu India Mohan Babu University School of Computing Department of Computer Science and Engineering Andhra Pradesh Tirupati India Dept of Cse Andhra Pradesh Ananthapuramu India Audisankara College of Engineering and Technology Andhra Pradesh India Dept. of Ece Andhra Pradesh Bhimavaram India United Institute of Management Department of Computer Application Prayagraj India

ISBN: (纸本)9798331509675

With open connectivity and emergent computing, the Industrial Internet of Things (IIoT) combined with cloud computing resources provides significant breakthroughs in the field of industrial automation. These developments lead to the evolution of supplemental services, which improve automation in the production and industrial sectors. This research proposes novel technique in smart manufacturing system based on cloud computing with IIoT in energy efficiency using machine learning. Here the smart manufacturing process has been analysed based on IIoT with cloud computing network, where the energy efficiency is analysed using virtual machine-based reinforcement markov chain based linear regression. Experimental analysis has been carried out in terms of latency, energy efficiency, task response time, accuracy. proposed technique obtained 97% of Latency, 95% of Energy efficiency, 98% of Accuracy, 94% of Task response time. © 2025 IEEE.

关键词： Smart manufacturing

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