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

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Proactive Algorithms for Stabilizing the State of Technological Processes Based on Metric Machine Learning Algorithms

Proactive Algorithms for Stabilizing the State of Technologi...

引用

international Russian automation conference (RusAutoCon)

作者： A. Musaev A. Makshanov D. Grigoriev St. Petersburg State Institute of Technology (Technical University) Saint-Petersburg Russia Department of Computing Systems and Computer Science Admiral Makarov State University of Maritime and Inland Shipping Saint-Petersburg Russia Center for econometrics and business analytics (CEBA) St. Petersburg State University (SPBU) Saint-Petersburg Russia

ISBN: (数字)9798350349818

ISBN: (纸本)9798350349825

The task of proactive stabilization of technological processes (TP), the evolution of the state of which is described by non-stationary random processes, is considered. Metric precedent analysis technologies, which belong to the class of machine learning tasks, are proposed as a mathematical tool. The training data used is a data set containing large arrays of retrospective data obtained during the monitoring of the state of the TP during its previous operation. Basic mathematical models of ongoing processes and the algorithmic apparatus of precedent data analysis used are presented. The results of numerical studies of technologies for forecasting the state of non-stationary TPs and the effectiveness of proactive stabilization algorithms built on their basis are given.

关键词： Measurement Machine learning algorithms Statistical analysis Heuristic algorithms Process control Training data Predictive models Random processes Forecasting Monitoring

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computing Midcurve with Multi-Layer and Convolutional Neural Networks

Computing Midcurve with Multi-Layer and Convolutional Neural...

引用

Computational, Communication and Information Technology (ICCCIT), international conference on

作者： Prashanth Sreenivasan Yogesh H. Kulkarni

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512972

Midcurve computation: the dimension reduction of 2D thin polygon shapes to 1D curves is important for several Computer-Aided Design (CAD) and Computer-Aided Engineering (CAE) applications, particularly in finite element analysis and robot path planning. We advance the state-of-the-art by building on previous work on MidcurveNN, through two new neural architectures: a multi-layer dense network and a CNN-based architecture with skip connections. Through comparative analysis, we demonstrate that while the dense network shows limited improvements, the CNN-based architecture with skip connections achieves a tenfold reduction in average loss compared to existing methods. The proposed approach improves the quality of the generated Midcurves.

关键词： Dimensionality reduction Solid modeling Design automation Shape Architecture Buildings Noise Path planning Convolutional neural networks Robots

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RoboKube: Establishing a New Foundation for the Cloud Native Evolution in Robotics

RoboKube: Establishing a New Foundation for the Cloud Native...

引用

international conference on Autonomous Robots and Agents, ICARA

作者： Yu Liu Aitor Hernandez Herranz Roberto C. Sundin Ericsson Research Stockholm Sweden

ISBN: (数字)9798350394245

ISBN: (纸本)9798350394252

Cloud native technologies have been observed to expand into the realm of Internet of Things (IoT) and Cyber-physical Systems, of which an important application domain is robotics. In this paper, we review the cloudification practice in the robotics domain from both literature and industrial perspectives. We propose RoboKube, an adaptive framework that is based on the Kubernetes (K8s) ecosystem to set up a common platform across the device-cloud continuum for the deployment of cloudified Robotic Operating System (ROS) powered applications, to facilitate the cloud native evolution in robotics. We examine the process of modernizing ROS applications using cloud-native technologies, focusing on both the platform and application perspectives. In addition, we address the challenges of networking setups for heterogeneous environments. This paper intends to serves as a guide for developers and researchers, offering insights into containerization strategies, ROS node distribution and clustering, and deployment options. To demonstrate the feasibility of our approach, we present a case study involving the cloudification of a teleoperation testbed.

关键词： Cloud computing automation Adaptive systems Service robots Reviews Operating systems Ecosystems

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Visual Inertial Odometry using Focal Plane Binary Features (BIT-VIO)

Visual Inertial Odometry using Focal Plane Binary Features (...

引用

IEEE international conference on Robotics and automation (ICRA)

作者： Matthew Lisondra Junseo Kim Riku Murai Kourosh Zareinia Sajad Saeedi Toronto Metropolitan University (TMU) Department of Computing Imperial College London

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Focal-Plane Sensor-Processor Arrays (FPSP)s are an emerging technology that can execute vision algorithms directly on the image sensor. Unlike conventional cameras, FPSPs perform computation on the image plane – at individual pixels – enabling high frame rate image processing while consuming low power, making them ideal for mobile robotics. FPSPs, such as the SCAMP-5, use parallel processing and are based on the Single Instruction Multiple Data (SIMD) paradigm. In this paper, we present BIT-VIO, the first Visual Inertial Odometry (VIO) which utilises SCAMP-5. BIT-VIO is a loosely-coupled iterated Extended Kalman Filter (iEKF) which fuses together the visual odometry running fast at 300 FPS with predictions from 400 Hz IMU measurements to provide accurate and smooth trajectories. Project Page: https://***/view/bit-vio/home

关键词： Visualization Single instruction multiple data Robot vision systems Trajectory Odometry Kalman filters Velocity measurement

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Determination of Current Transducer Phase Delay and its Effect on PWM-Induced Losses Calculation in Laminated Ferromagnetic Materials

Determination of Current Transducer Phase Delay and its Effe...

引用

international conference on Electrical Machines, ICEM

作者： Igor Sirotić Stjepan Stipetić Marinko Kovačić Dept. of electrical machines drives and automation Faculty of Elec. Eng. and Computing Zagreb Croatia

ISBN: (数字)9798350370607

ISBN: (纸本)9798350370614

Current transducers introduce a phase delay depending on the frequency of excitation, which affects the calculation of PWM-induced power loss in ferromagnetic materials. It is therefore necessary to compensate for this delay. The measurement setup is presented, the compensation characteristics were determined and applied to the results of DC bias measurements performed on a ring core sample of ferromagnetic material. Significant effect of compensation is found for some cases with higher frequency of excitation current.

关键词： Transducers Phase measurement Current measurement Measurement uncertainty Ferromagnetic materials Attenuation measurement Pulse width modulation Loss measurement Delays Frequency measurement

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A Novel Distributed Bernoulli Filter with Adaptive Event-Triggered Communication

A Novel Distributed Bernoulli Filter with Adaptive Event-Tri...

引用

international conference on Information Fusion

作者： Litao Zheng Yunze Cai Feng Yang Lihong Shi Department of Automation Shanghai Jiao Tong University Shanghai China School of Automation Northwestern Polytechnical University Xi’an China

ISBN: (数字)9781737749769

ISBN: (纸本)9798350371420

—paper addresses communication bandwidth reduction and energy efficiency enhancement of a peer-to-peer sensor network for distributed target detection and tracking. A distributed Bernoulli filter with event-triggered communication is developed where each node broadcasts only local posteriors that achieve significant information gain. Specifically, for the cases where the Bernoulli density is no-target or single-target, the corresponding event-triggered strategies are constructed, respectively, in which the information discrepancy is measured via the Jeffreys divergence, and the triggering threshold is determined by the local information confidence coefficient. In addition, the presented method is combined with flooding protocol for internode communication, and weighted conservative fusion approaches are used to fuse the target existence probabilities and spatial distributions. Finally, simulation results demonstrate the effectiveness and superiority of the proposed approach.

关键词： Target tracking Protocols Graphical models Fuses Simulation Object detection Information filters Energy efficiency Peer-to-peer computing Floods

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Optimized Machine Learning: Training and Classification Performance Using Quantum computing 6

Optimized Machine Learning: Training and Classification Perf...

引用

6th IEEE international conference on computing, Communication and automation, ICCCA 2021

作者： Nivelkar, Mukta Bhirud, S.G. Department of Computer Engineering and It Mumbai India

ISBN: (纸本)9781665414739

Quantum computing hardware in great extent to optimize algorithm performance of various domains of classical and high-performance computing. Optimization in any category of algorithm results in improved performance and new method suggestion for existing standards. Initially machine learning from classical to parallel computing have given very good platform for researcher's to optimize algorithm performance if data size will be huge and complex. Quantum mechanics such as superposition and entanglement will make quantum computing more successful over classical standard. Quantum Computers operates on quantum superposition and entanglement mechanism of quantum physics. Quantum machine learning integrates machine learning and quantum computing together for exponential speedup. Classical machines are enables to perform several tasks because of computational complexity and operational ability of hardware. Quantum computing definitely justice the operational ability for complex task and pattern formation. Quantum machine learning will optimize existing machine learning methods based upon performance outcome of algorithm. This paper discussed optimization of supervised machine learning using variational quantum circuits. Discussion extended to various methods and subroutines to justify classifier's performance for big data processing on quantum hardware. © 2021 IEEE.

关键词： Learning algorithms

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Session Based Recommender System: A Generative Approach

Session Based Recommender System: A Generative Approach

引用

international conference on Computer and automation Engineering, ICCAE

作者： Y. Rishoban G.P. Seneviratne University Of Colombo School Of Computing University Of Colombo Colombo Sri Lanka

ISBN: (数字)9798350370058

ISBN: (纸本)9798350370164

In the age of information overload and digitalization, recommender systems (RSs) have become increasingly important for informed decision-making and service utilization. Session-based recommender systems (SBRSs) have emerged as a new type of RS in recent years. Unlike traditional RSs like content-based RSs and collaborative filtering-based RSs, which focus on static, long-term user preferences, SBRSs aim to capture dynamic, short-term user preferences to provide timely and accurate recommendations that adapt to evolving user contexts. This research propose novel generative model that capture the patterns among the user's choices and generate the items for the given session. We observed that pattern recognition algorithms only capture past user choices or the choices of similar users. To address this limitation, we propose a non-deterministic predictive model that balances exploration and exploitation.

关键词： Computational modeling Heuristic algorithms Decision making Predictive models Prediction algorithms Information filters Information age

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Building the Future of IoT: Cloud Platforms, Integration Challenges, and Emerging Applications

Building the Future of IoT: Cloud Platforms, Integration Cha...

引用

international conference on Computer and Applications (ICCA)

作者： Manjunatha Sughaturu Krishnappa Deepak Jayabalan Bindu Mohan Harve Vivekananda Jayaram Darshan Mohan Bidkar Prema Kumar Veerapaneni Vedamurthy Yogeshappa Gejjegondanahalli Oracle America Inc California USA Meta Inc. California USA JP Morgan Chase Bank NA Texas USA

ISBN: (数字)9798350367560

ISBN: (纸本)9798350367577

The integration of cloud computing with the Internet of Things (IoT) has fostered scalable, efficient, and cost-effective solutions to meet the substantial data demands of modern IoT applications. Cloud-based IoT platforms provide the critical infrastructure needed to manage, process, and analyze vast amounts of data generated by IoT devices, supporting a range of applications from smart cities to healthcare and industrial automation. This paper presents an in-depth review of cloud-based IoT platform architectures, focusing on essential features like scalability, flexibility, and interoperability. It also delves into the roles of edge and fog computing, which are key to reducing latency and enabling real-time responses in data- sensitive applications. Further, the paper investigates the primary challenges cloud-based IoT systems face, including security risks, data privacy, and regulatory compliance, and assesses current strategies to mitigate these challenges. Case studies of real-world applications demonstrate successful implementations across diverse sectors. The paper also explores future research directions, emphasizing the integration of artificial intelligence and machine learning for predictive analytics, the impact of 5G technology in enhancing IoT performance, and the potential of quantum computing to strengthen data security. This study provides a comprehensive foundation for understanding cloud-based IoT platforms and their transformative potential in driving digital advancements across industries.

关键词： Cloud computing Data privacy Quantum computing Scalability Data security Real-time systems Internet of Things Resource management Interoperability Edge computing

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Text Semantic Analysis and Intelligent Interaction in Parallel Dispatching Systems for Technical Station

Text Semantic Analysis and Intelligent Interaction in Parall...

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Digital Twins and Parallel Intelligence (DTPI), IEEE international conference on

作者： Yuantao Jiao Jian Wang Runmei Li Gang Xiong Shichao Chen School of Automation and Intelligence Beijing Jiaotong University Beijing China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China State Key Laboratory for Multimodal Artificial Intelligence Systems Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350349252

ISBN: (纸本)9798350349269

Technical station dispatching system plays an important role in cargo operation, but due to the large number of dispatching systems and complex operation, dispatchers rely on manual experience to complete the task, and many scenarios cannot be applied in the real system. Therefore, this paper utilizes the data-driven form to establish a parallel dispatching system for technical stations, semantically analyzes text data rich in dispatcher experience, completes the Computational Experiments on the textual semantic data in the artificial dispatching system, and synchronizes the Parallel Execution of data interaction with the real dispatching system.

关键词： Training Analytical models Semantics Dispatching Digital twins Synchronization Feeds

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