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

共 28200 条记录，以下是131-140 订阅

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LEAD: Latency-Efficient Application Deployment for Microservices Architecture 29

LEAD: Latency-Efficient Application Deployment for Microserv...

引用

29th international conference on automation and computing (ICAC)

作者： Mirzaei, Mohammad-Ali Poorsistani, Parsa Javadi, Seyyed Ahmad Amirkabir Univ Technol Dept Comp Engn Tehran Iran Amirkabir Univ Technol Dept Math & Comp Sci Tehran Iran

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.

关键词： Cloud computing Microservices architecture Tail-latency Resource management Container placement

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Coffee Bean Grading Based on Weight Estimation Using Densenet121 Model 7

Coffee Bean Grading Based on Weight Estimation Using Densene...

引用

2023 7th international conference On computing, Communication, control And automation, ICCUBEA 2023

作者： Bipin Nair, B.J. Abrav Nanda, K.M. Shalwin, A.S. Likith Rao Mohethe, G. Raghavendra, V. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science Mysuru India

ISBN: (纸本)9798350304268

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 have emerged as a powerful tool for automating coffee bean grading. This study uses the Densenet121 model to analyze a dataset of 363 images of manually graded coffee beans to recognize the different grades based on their features and patterns. The results show that the deep learning model achieves an accuracy of 81.89%, demonstrating its potential for revolutionizing the coffee industry by offering speed, accuracy, cost-effectiveness, scalability, and consistency in quality production. This study highlights the promising potential of deep learning models in image analysis and grading for various industries, with further research and development. © 2023 IEEE.

关键词： classification coffee bean Deep Learning grading image processing

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A Reinforcement Adversarial Framework Targeting Endogenous Functional Safety in ICS: Applied to Tennessee Eastman process 16

A Reinforcement Adversarial Framework Targeting Endogenous F...

引用

16th international conference on Computer and automation Engineering (ICCAE)

作者： Wu, Xinyi Ding, Yulong Yang, Shuang-Hua Southern Univ Sci & Technol Dept Comp Sci & Engn Next Generat Ind Internet Shenzhen Key Lab Safety & Secur Shenzhen Peoples R China Southern Univ Sci & Technol Shenzhen Key Lab Safety & Secur Next Generat Ind Internet Shenzhen Peoples R China Univ Reading Dept Comp Sci Reading Berks England

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.

关键词： endogenous functional safety deep reinforcement learning industrial control systems

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Sparse Gaussian process Regression for Residual Dynamics Learning in Multi-rotor Aerial Vehicles control 20

Sparse Gaussian Process Regression for Residual Dynamics Lea...

引用

IEEE 20th international conference on automation Science and Engineering (CASE)

作者： Kulathunga, Geesara Hanheide, Marc Klintchik, Alexandr Univ Lincoln Lincoln Inst Agrifood Technol Lincoln LN1 England Univ Lincoln Sch Comp Sci Lincoln LN6 England

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.

关键词： Gaussian distribution

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Deep Convolutional Neural Networks for Intelligent Emotional computing: A Multidimensional Analysis and Practical Application of Emotional Features in Digital Learning Interfaces 2

Deep Convolutional Neural Networks for Intelligent Emotional...

引用

2nd international conference on Artificial Intelligence and automation control, AIAC 2024

作者： Zhang, Naixin Leong, Waiyie Zhang, Tong Chengdu Jincheng College Inti International University Sichuan Chengdu China Inti International University Faculty of Engineering and Quantity Surveying Nilai Malaysia The Affiliated Hospital of Southwest Jiao Tong University The Third People's Hospital of Chengdu Department of Cardiology Chengdu China

ISBN: (纸本)9798331529147

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 interfaces. A multi-channel, multi-scale convolutional neural network structure is designed to effectively integrate multimodal emotional features such as facial expressions, physiological signals, behavioral data, and learning environments. Experimental validation on both self-built datasets and publicly available datasets demonstrates that the proposed model achieves superior performance in emotion recognition tasks, with an average accuracy rate of 85.2%, significantly outperforming traditional methods based on handcrafted features and shallow networks. © 2024 IEEE.

关键词： Contrastive Learning

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A Method for Analyzing Data in Measuring Devices Using a Specialized computing System for Synthesizing and Evaluating Statistical Hypotheses 5

A Method for Analyzing Data in Measuring Devices Using a Spe...

引用

5th international conference on control Systems, Mathematical Modeling, automation and Energy Efficiency, SUMMA 2023

作者： Tarasov, Ilya RTU MIREA Laboratory of Specialized Computer Systems Moscow Russia

ISBN: (纸本)9798350309126

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 difference between the method is the use of the error probability distribution function with an independent parameter "distribution scale"and the search for a combination of process model parameters at which the posterior probability function takes the maximum value. The method is characterized by high resistance to impulse noise, as a result of which it can be used in measuring systems. A special feature of the method is the large number of calculations required to generate hypotheses and test their probability. To solve this problem, a parallel computing system architecture has been proposed that generates hypotheses and evaluates them using independent processing elements. © 2023 IEEE.

关键词： Impulse noise

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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)

作者： Arne, Viktor Spies, Daniel Groche, Peter Tech Univ Darmstadt Inst Prod Engn & Forming Machines 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 (c) 2025 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： Modelling Identification and control methods process supervision Hole-rolling Metal forming

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A Detailed Review on Host Overload Detection Techniques 7

A Detailed Review on Host Overload Detection Techniques

引用

2023 7th international conference On computing, Communication, control And automation, ICCUBEA 2023

作者： Singh Yadav, Deepak Kumar Sinha, Bharati National Institute of Technology Department of Computer Engineering Kurukshetra India

ISBN: (纸本)9798350304268

Cloud computing (CC) is a famous paradigm which provides computing resources on request via internet. Despite its many benefits, such as scalability, cost savings, and flexibility, cloud computing is vulnerable to host overload, which occurs when a host is unable to handle the workload assigned to it. Host overload can lead to performance degradation and even system failure. Thus, to overcome this issues the host overload detection technique is used. Host overload detection refers to the process of identifying when a system or server is experiencing too much demand and is unable to handle the workload efficiently. Overload detection ensures the reliable and efficient operation of systems and applications, minimizing downtime, enhancing user experience, and reducing costs. So, there are plethora of techniques developed by the researchers for host overload detection. Thus, this article provides a detailed overview of the methods used for detecting overload in host for cloud, we have divided host overload detection techniques into three categories i.e. Static Utilization Threshold, Regression based and Adaptive Utilization Threshold based techniques. The strengths and weaknesses of each approach are analyzed, and the challenges and future directions in host overload detection are discussed. © 2023 IEEE.

关键词： Cloud computing Cloud Datacenter Host Overload QoS SLA

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Research and Implementation Based on the eTPU 3

Research and Implementation Based on the eTPU

引用

3rd international conference on Artificial Intelligence, automation, and High-Performance computing, AIAHPC 2023

作者： Wang, Ying Gao, Bo Li, Jinze Hao, Yibo Nation’s Research Laboratory of Computer Application Engineering Xi’an Aeronautics Computing Technique Research Institute AVIC Xi’an China Xi’an XinHang Talent Service Co. Ltd. Xi’an China

ISBN: (数字)9781510666580

ISBN: (纸本)9781510666573

As a special I/O channel in the control system, the eTPU (Enhanced Time processing Unit) cooperates with the host computer to complete the corresponding functions. As the most foundational and important function in the eTPU, which is completed through I/O channel. This paper makes a comprehensive analysis of the thread entry and the pre-defined channel mode in the process of implementing the I/O function. Through the design of thread entry and the optimal selection of the pre-defined channel mode, the I/O function can be efficiently completed. By adopting clever design ideas and optimization, eTPU can provide more accurate and efficient functions. Especially through the implementation part of this article, the usage method of eTPU is more convenient to master. © 2023 SPIE.

关键词： Computer control systems

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Navigating the Intersections of Quantum computing, DNA-Based Data Storage, and Geospatial Data Security: An Overview 8

Navigating the Intersections of Quantum Computing, DNA-Based...

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8th IEEE international conference on computing, Communication, control and automation, ICCUBEA 2024

作者： Bhangale, Prajakta Vaikole, Shubhangi Fr.Conceicao Rodrigues College of Engineering Mumbai Univeristy India Datta Meghe College of Engineering Mumbai India Fr. Concicao Rodrigues Institute of Technology Deaprtment Information Technology Mumbai India

ISBN: (纸本)9798350391770

In recent years, the intersection of quantum computing and bioinformatics has emerged as a captivating frontier, promising to reshape the landscape of biological sequence analysis. DNA's ability to store vast information and its stability make it a candidate for secure geospatial data storage. Quantum computing, exemplified by QKD, transforms encryption, enhancing security. Bio-inspired algorithms, emulating DNA sequences, optimise problem-solving. Combining these with quantum computing improves geospatial data security through protocol enhancements. Quantum bioinformatics explores quantum computing's potential in genetics and medicine, indirectly reinforcing data security. Integration of DNA-based storage with quantum encryption ensures highly secure data transmission. Geospatial data can be encoded in DNA sequences and encrypted for network transmission. This convergence promises enhanced data security, though implementation needs further research. Overall, these innovations hold potential for advancing geospatial data security. © 2024 IEEE.

关键词： Bio-inspired algorithms Data transmission DNA-based data storage Genetic analysis Geospatial data security Personalized medicine Protocol optimization Quantum bioinformatics Quantum computing Quantum encryption

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