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检索条件"任意字段=10th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2021"

共 359 条记录，以下是311-320 订阅

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Framework for learning a Hand Intent Recognition Model from sEMG for FES-based control 10

Framework for Learning a Hand Intent Recognition Model from ...

引用

10th ieee RAS/EMBS International conference for Biomedical Robotics and Biomechatronics (BioRob)

作者： Das, Neha Endo, Satoshi Kavianirad, Hossein Hirche, Sandra Tech Univ Munich Sch Computat Informat & Technol Chair Informat Oriented Control D-80992 Munich Germany

ISBN: (纸本)9798350386523;9798350386530

Stroke survivors and individuals with neuromuscular disorders often experience motor function impairments, particularly during hand movements crucial for activities of daily living (ADL). Functional Electrical Stimulation (FES) has emerged as a potential assistive and rehabilitative technique to address these limitations. However, accurately determining user intent during FES poses a significant challenge. this work proposes a framework for rapidly learning a model of the user's hand intent from surface electromyography (sEMG) signals, specifically for continuous FES-based control of the ipsilateral hand. the framework systematically collects data from expected volitional and FES-evoked hand motions, followed by training a logistic regression model for intent classification. the study demonstrates that the proposed model can learn from limited data and compares favorably to deep neural nets trained on the same dataset. this model is able to recognize user intent with high accuracy even during concurrent FES stimulation.

关键词： Logistic regression

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A Machine learning Classifier for Detection of Performance Issues in Industrial Closed-Loop PID controllers 10

A Machine Learning Classifier for Detection of Performance I...

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10th International conference on control, Decision and Information Technologies (CoDIT)

作者： Yagci, Mehmet Forsman, Krister Boling, Jari M. Neste Oyj Turku Finland Abo Akad Univ Lab Proc & Syst Engn Turku Finland Perstorp AB Perstorp Sweden NTNU Dept Chem Engn Throndheim Norway Univ Turku Dept Mech & Mat Engn Turku Finland

ISBN: (纸本)9798350373981;9798350373974

the tight production objectives and dynamically evolving conditions within industries necessitate the diligent monitoring and evaluation of control assets' operational health. During the past decades, a lot of data-driven methods, using fundamentals of signal processing and process control or machine learning, have been developed to detect performance issues in control loops. One of those, machine learning based methods, has also become popular in recent years. However, the complexity of algorithms used, incapability of predicting more than "good" or "bad" or need for process excitations limits the practical use of these methods in large industrial scale. In this paper, an easy-to-use classifier has been developed which is based on only routine industrial closed-loop data available and common for many control systems. the developed classifier is able to classify the control loops as acceptable, aggressive tuning, sluggish tuning, stiction and external disturbance, which account for almost all common and major problems experienced in closed-loop PID controllers. the features calculated and given to the classifier are immensely easy-to-obtain metrics based on histograms of control error, auto-correlation function, and impulse response. the developed classifier has an 88% training and 85% test accuracy. the classifier has also been tested with a set of industrial loops assessed extensively by process control engineers and able to predict the true class of 88% of the loops, with a 3% false negative rate.

关键词： control performance monitoring CPM PID machine learning classification fault detection

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An Edge Video Analysis Solution For Intelligent Real-Time Video Surveillance systems 10

An Edge Video Analysis Solution For Intelligent Real-Time Vi...

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10th ieee International conference on Cloud Networking (ieee CloudNet)

作者： Silva, Alessandro Bonfim, Michel Rego, Paulo A. L. Univ Fed Ceara Fortaleza Ceara Brazil

ISBN: (纸本)9781665435383

Video Analytics has played an essential role in the most varied public safety sectors, mainly when applied to Intelligent Video Surveillance systems. In this scenario, Edge Video Analytics seeks to migrate part of the workload of the Video Analysis process to devices close to the data source to reduce transmission overhead on the network and overall latency. therefore, this work proposes an Edge Video Analytics architecture for real-time video monitoring systems. Such architecture divides the analysis process into functional and independent modules, being flexible to support analytics or network functions. We developed a proof of concept to validate the proposed architecture, focusing on detecting and recognizing license plate characters in the edge. In this scenario, between the detection and recognition modules, we used Deep learning to implement a module responsible for discard plates with distorted identification text to reduce network utilization. Conducted experiments demonstrate that the architecture meets its objectives by reducing an average of 25.64% of the network traffic in its frames flow due to a resolution quality control and 56.65% in its license plate flow due to the filtering step proposed.

关键词： Videos-Analysis Docker Deep learning

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An Introduction to Machine learning in a Nutshell 10

An Introduction to Machine Learning in a Nutshell

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10th International conference on System Modeling and Advancement in Research Trends, SMART 2021

作者： Kaur, Mansirat Shukla, Anand Kumar Kaur, Saranjeet Chandigarh University University Institute of Computing Punjab Mohali India

ISBN: (纸本)9781665439701

Deep learning, Machine learning and Artificial intelligence are buzzwords that has piqued the curiosity of many researchers. One of the most major and notable advancements in the field of computer science has been the ability to the computer systems that they can act, think and make decisions like human being. Over time, various algorithms have been developed to make such machines and computer systems that can copy human brains, and multiple programming languages has been utilized to implement such algorithms. To give the unparalleled data and calculation availability power, interested in leveraging data-driven machine learning technologies to solve challenges that traditional engineering solutions hindered by algorithm or modeling flaws has revived. that study seeks to investigate the fundamental concepts of machine learning and to attempt to implement a few of its applications in Python. © 2021 ieee.

关键词： Python

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Proceedings of the 2019 10th ieee International conference on Intelligent data Acquisition and Advanced Computing systems: Technology and Applications, IDAACS 2019

Proceedings of the 2019 10th IEEE International Conference o...

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10th ieee International conference on Intelligent data Acquisition and Advanced Computing systems: Technology and Applications, IDAACS 2019

ISBN: (纸本)9781728140698

the proceedings contain 211 papers. the topics discussed include: data-driven fault detection and identification in wind turbines through performance assessment;simulation of gas flow for welding process control in the arctic environment;approach to managing data from diverse sources;latency reduction techniques for NB-IoT networks;development of architectural realizations of phototherapy computer’s systems for prevention and treatment;3D virtual biomimetic network: a topology for resilient intelligent wireless sensor networks;machine learning based medicine distribution system;and fuzzy method with Z-numbers for choosing target group of users for UX applications.

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learning Model Predictive control with Error Dynamics Regression for Autonomous Racing

Learning Model Predictive Control with Error Dynamics Regres...

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ieee International conference on Robotics and Automation (ICRA)

作者： Xue, Haoru Zhu, Edward L. Dolan, John M. Borrelli, Francesco Carnegie Mellon Univ Robot Inst Pittsburgh PA 15213 USA Univ Calif Berkeley Dept Mech Engn Berkeley CA 94720 USA

ISBN: (纸本)9798350384581;9798350384574

this work presents a novel learning Model Predictive control (LMPC) strategy for autonomous racing at the handling limit that can iteratively explore and learn unknown dynamics in high-speed operational domains. We start from existing LMPC formulations and modify the system dynamics learning method. In particular, our approach uses a nominal, global, nonlinear, physics-based model with a local, linear, data-driven learning of the error dynamics. We conducted experiments in simulation and on 1/10th scale hardware, and deployed the proposed LMPC on a full-scale autonomous race car used in the Indy Autonomous Challenge (IAC) with closed loop experiments at the Putnam Park Road Course in Indiana, USA. the results show that the proposed control policy exhibits improved robustness to parameter tuning and data scarcity. Incremental and safety-aware exploration toward the limit of handling and iterative learning of the vehicle dynamics in high-speed domains is observed both in simulations and experiments.

关键词： Predictive control systems

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Proceedings of the 2019 10th ieee International conference on Intelligent data Acquisition and Advanced Computing systems: Technology and Applications, IDAACS 2019

Proceedings of the 2019 10th IEEE International Conference o...

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10th ieee International conference on Intelligent data Acquisition and Advanced Computing systems: Technology and Applications, IDAACS 2019

ISBN: (纸本)9781728140681

the proceedings contain 211 papers. the topics discussed include: data-driven fault detection and identification in wind turbines through performance assessment;simulation of gas flow for welding process control in the arctic environment;approach to managing data from diverse sources;latency reduction techniques for NB-IoT networks;development of architectural realizations of phototherapy computer's systems for prevention and treatment;3D virtual biomimetic network: a topology for resilient intelligent wireless sensor networks;machine learning based medicine distribution system;and fuzzy method with Z-numbers for choosing target group of users for UX applications.

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Design of teaching system for computer network application learning

Design of teaching system for computer network application l...

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10th ieee International conference on Computer and Information Technology, CIT-2010, 7th ieee International conference on Embedded Software and systems, ICESS-2010, 10th ieee Int. Conf. Scalable Computing and Communications, ScalCom-2010

作者： Li, Liu Chen, Tianzhou Shi, Qingsong Hu, Wei Instructional Division of Computer Technology Zhejiang Sci-Tech Universcity HangZhou China College of Computer Science Zhejiang University Hangzhou Zhejiang 310027 China

ISBN: (纸本)9780769541082

driven by continuing scaling of Moore's law, computer system becomes more and more complicated. the Network application technology of the computer is a basic course in computer architecture. Computer assisted teaching system is used to make the students understand the Network application technology of the computer more easily. A multimedia teaching system is introduced in this paper. the system consists of netmeeting subsystem, streaming media subsystem, TCP/IP control subsystem, FTP subsystem, remote management subsystem, process management subsystem, and user management interface. Some further optimizations, such as load balance and data compression, are employed in the system to improve the user experience. © 2010 ieee.

关键词： Teaching

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Embedded On-line System for Electrical Energy Measurement and Forecasting in Buildings 10

Embedded On-line System for Electrical Energy Measurement an...

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10th ieee International conference on Intelligent data Acquisition and Advanced Computing systems - Technology and Applications (IDAACS)

作者： Nichiforov, Cristina Stamatescu, Grigore Stamatescu, Iulia Arghira, Nicoleta Fagarasan, Ioana Iliescu, Sergiu Stelian Univ Politehn Bucuresti Dept Automat Control & Ind Informat Bucharest Romania Graz Univ Technol Inst Tech Informat Graz Austria

ISBN: (纸本)9781728140681

Fine grained measurement of electrical energy consumption in commercial buildings is essential for improved fault diagnosis and control with impact on the overall operation as well as user comfort. An open system architecture is presented for data collection, processing and communication of measured energy patterns at the local and aggregated level. the implementation is based on an embedded development board with current and voltage sensors, supported by open-source software and packages. Suitable user and programmatic interfaces allow reliable bidirectional connection to external automation equipment and information systems. such as the Building Management systems (BMS). Recent advances in advanced algorithms for time series pre-processing and data-driven modelling allow good quality in situ predictions for the collected measurements. A relevant example consists of neural network based learning systems which are able to provide accurate hour-ahead and day-ahead predictions that contribute to reduction of peak demand with economic and environmental impact. Integration of such platforms in higher-level CyberPhysical Energy systems (CPES) is further discussed.

关键词： energy monitoring internet of things load forecasting neural networks smart buildings

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Enabling Efficient and Flexible Interpretability of data-driven Anomaly Detection in Industrial Processes with AcME-AD 10

Enabling Efficient and Flexible Interpretability of Data-dri...

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10th International conference on control, Decision and Information Technologies (CoDIT)

作者： Zaccaria, Valentina Masiero, Chiara Dandolo, David Susto, Gian Antonio Univ Padua Dept Informat Engn Padua Italy Statwolf Data Sci Srl Dublin Ireland

ISBN: (纸本)9798350373981;9798350373974

While Machine learning has become crucial for Industry 4.0, its opaque nature hinders trust and impedes the transformation of valuable insights into actionable decision, a challenge exacerbated in the evolving Industry 5.0 with its human-centric focus. this paper addresses this need by testing the applicability of AcME-AD in industrial settings. this recently developed framework facilitates fast and user-friendly explanations for anomaly detection. AcME-AD is model-agnostic, offering flexibility, and prioritizes real-time efficiency. thus, it seems suitable for seamless integration with industrial Decision Support systems. We present the first industrial application of AcME-AD, showcasing its effectiveness through experiments. these tests demonstrate AcME-AD's potential as a valuable tool for explainable AD and feature-based root cause analysis within industrial environments, paving the way for trustworthy and actionable insights in the age of Industry 5.0.

关键词： Anomaly Detection Explainable Artificial Intelligence Industrial Internet of things Industry 5.0 Outlier Detection Unsupervised learning

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