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检索条件"任意字段=8th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2019"

共 336 条记录，以下是251-260 订阅

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A Hybrid MTS Anomaly Detection Method Based on Reconstruction and Adaptive Spatial-Temporal Graph Network 13

A Hybrid MTS Anomaly Detection Method Based on Reconstructio...

引用

13th ieee data driven control and learning systems conference, ddcls 2024

作者： Yuan, X. Ning, Ke Wei, K. He, Yan-Lin Zhu, Qun-Xiong Zhang, Ming-Qing Luo, Yi Zhang, Yang College of Information Science and Technology Beijing University of Chemical Technology Beijing100020 China Ministry of Education of China Engineering Research Center of Intelligent Pse Beijing100020 China Macao Polytechnic University Faculty of Applied Sciences 999078 China Research Institute of Mine Big Data Chinese Institute of Coal Science Beijing100013 China

ISBN: (纸本)9798350361674

Effective anomaly detection of multivariate time series data is crucial for modern industrial applications. However, existing works usually ignore spatial and temporal dependencies of series. the data with complicated spatial-temporal feature put forward higher requirements for accurate detection of anomalies. In this paper, we propose an anomaly detection method for multivariate time series based on spatial-temporal network (STN). Firstly, the time series are reconstructed by Generative Adversarial Networks (GAN). the reconstruction block is used to amplify the influence of the abnormal state and prompt the anomalies more detectable, as poorly reconstructed data of abnormal state further worsen the quality of the final predicted value. Secondly, an adaptive learning graph structure layer is proposed. the graph structure is learned by adaptive method without any prior knowledge and is used for reconstructed data prediction. thirdly, we use STN to extract spatial and temporal dependencies of series. Especially, attention mechanism is introduced into graph convolution network to enhance the prediction performance by focusing on more important neighbor node message. Finally, our model is tested on 3 public datasets to evaluate performance. the results demonstrate that our method performs better than baselines. © 2024 ieee.

关键词： Generative adversarial networks

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$H_{\infty}$ control for Singular Markovian Jump Delay systems with Mode-Dependent Derivative-Term Coefficient

$H_{\infty}$ Control for Singular Markovian Jump Delay Syste...

引用

data driven control and learning systems (ddcls)

作者： Yufeng Tian Zhanshan Wang College of Information Science and Engineering Northeastern University Shenyang China

ISBN: (纸本)9781728114552;9781728114545

this paper focuses on the $H_{\infty}$ control problem of singular Markovian jump delay systems with mode-dependent derivative-term coefficient through an extended decomposition system. By computing a proper Lyapunov functional, a generally stochastic stability condition of singular Markovian jump systems is achieved. On this basis, a delay-dependent stabilization condition of considered system is derived in terms of tractable linear matrix inequalities (LMIs), and the $H_{\infty}$ controller gains are directly designed. Two numerical examples are introduced to illustrate the effectiveness of the proposed results.

关键词： Numerical stability Symmetric matrices Delay systems Stability criteria Indexes Linear matrix inequalities

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A Comparison of data Imputation Methods Utilizing Machine learning for a New IoT System Platform 8

A Comparison of Data Imputation Methods Utilizing Machine Le...

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8th International conference on control, Decision and Information Technologies (CoDIT)

作者： Kalay, Sena Cinar, Eyup Saricicek, Inci Eskisehir Osmangazi Univ Dept Comp Engn Eskisehir Turkey Ctr Intelligent Syst Applicat Res CISAR Eskisehir Turkey Eskisehir Osmangazi Univ Dept Ind Engn Eskisehir Turkey

ISBN: (纸本)9781665496070

IoT systems are being used widely place in manufacturing. the volume of the sensor data in these systems is significant. In real-life scenarios, missing sensor data can cause problems, especially for data-driven machine learning (ML) models. the gaps due to missing sensor data should be handled before employing machine learning models. the common practices are to remove the missing data completely or apply simple arithmetic operations. However, there are more sophisticated approaches in the literature that can be applied to these real-time IoT systems considering the native data characteristics. this study compares the performance of regression-based ML algorithms missing data imputation methods such as Support Vector Regression (SVR), Decision Tree Regression (DTR), Ridge Regression, K-Nearest Neighbors Regression (KNN), MissForest (MF), and XGBoost Regression (XGB). Missing data in different positions and proportions are created utilizing experimentally collected timeseries sensor data from a newly developed IoT system platform. the initial work based on the ML models is presented on these datasets together with an overview of the IoT system architecture. the average RMSE and R-2 values of the six ML models showed that the Ridge Regression outperforms the other ML models for the missing data imputation.

关键词： Performance evaluation Support vector machines System performance systems architecture Machine learning data models Real-time systems

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HybridRTS: A Hybrid Congestion control Framework with Rule and Reinforcement learning for Low-Latency WebRTC Live Video Streaming 24

HybridRTS: A Hybrid Congestion Control Framework with Rule a...

引用

24th ieee International conference on High Performance Computing and Communications, 8th ieee International conference on data Science and systems, 20th ieee International conference on Smart City and 8th ieee International conference on Dependability in Sensor, Cloud and Big data systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Zhang, Kaizhe Wang, Zhiwen Ma, Hansen Du, Haipeng Zhang, Weizhan School of Computer Science and Technology Bdke Lab China School of Continuing Education Xi'an Jiaotong University Xi'an710049 China

ISBN: (纸本)9798350319934

Web Real-Time Communication (WebRTC) technology meets real-time communication standards and is currently supported by mainstream browsers to meet low-latency live streaming requirements. the WebRTC Google Congestion control (GCC) algorithm utilizes a one-way delay gradient signal for conservative bandwidth estimation. However, this makes it sensitive to jitter, resulting in a decrease in the estimated bandwidth and low video quality. In this paper, we propose HybridRTS, a framework for congestion control aiming to strategically switch between proposed rule-based and reinforcement learning (RL) based methods. HybridRTS is evaluated via trace-driven simulations and real-world experiments, outperforming GCC with improvements in average Quality of Service (QoS) scores of 11.57% - 18.57%. Additionally, HybridRTS outperforms standalone rule-based and RL-based methods with decreasing in average latency by 11.46% - 29.66%. According to extensive experiments, HybridRTS yields solid results on various network conditions. © 2022 ieee.

关键词： Reinforcement learning

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A Quality Prediction Hybrid Model of Manufacturing Process Based on Genetic Programming 11

A Quality Prediction Hybrid Model of Manufacturing Process B...

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11th ieee data driven control and learning systems conference, ddcls 2022

作者： Peng, Chong Cheng, Zhijian Ren, Hongru Lu, Renquan Guangdong University of Technology School of Computer Science and Technology and Guangdong Province Key Laboratory of Intelligent Decision and Cooperative Control Guangzhou510006 China Guangdong University of Technology School of Automation and Guangdong Province Key Laboratory of Intelligent Decision and Cooperative Control Guangzhou510006 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

the design of manufacturing parameters in the initial stage is backed by quality prediction to realise intelligent manufacturing. Accurate prediction translates to better quality, lower costs and more flexibility. However, the real production is a complicated and variable process, most of which involved multiple parameters simultaneously. the data on the basis of feature construction can filter the impurities of data, accuracy of predictive model can be satisfied. Existing approaches to provide results are useless when the insufficient mining of the relationship between the data or the some case without adequate manufacturing data and expertise. In this paper, a two-stage hybrid approach with genetic programming is proposed for quality prediction. the feature construction is realized by genetic programming in the first stage, and the new features are utilized as additives to subsequent stage of the extreme gradient boosting. the comparison experiments indicate that the two-stage hybrid model outperforms the existing methods in overall performance. © 2022 ieee.

关键词： Genetic programming

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Fault Classification using Deep learning in a Grid-Connected Photovoltaic systems* 8

Fault Classification using Deep Learning in a Grid-Connected...

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8th International conference on control, Decision and Information Technologies (CoDIT)

作者： Hichri, Amal Mansouri, Majdi Hajji, Mansour Bouzrara, Kais Nounou, Hazem Nounou, Mohamed Natl Engn Sch Monastir Res Lab Automat Signal Proc & Image Monastir 5019 Tunisia Texas A&M Univ Qatar Dept Elect & Comp Engn Program Doha 23874 Qatar Prince Sultan Univ Dept Math & Sci Riyadh 11586 Saudi Arabia Texas A&M Univ Qatar Dept Chem Engn Program Doha 23874 Qatar

ISBN: (纸本)9781665496070

PV systems are prone to failure owing to aging and external/environmental factors. these failures can affect a range of system components, such as PV modules, connecting lines, and converters/inverters, resulting in decreased efficiency, performance, and even system failure. As a result, problem detection and diagnosis (FDD) is an important issue in high-efficiency gridconnected PV systems. Deep learning techniques are the most well-known data-driven methodologies. the biggest advantage of deep learning algorithms, in diagnosis, are learning effectiveness, intelligent FDD becomes more effective. this paper therefore presents a comparative study of FDD based deep learning. the techniques include the Convolutional Neural Network (CNN) and Long Short Time Memory (LSTM). Finally, the FDD based frameworks are implemented using simulated PV data. the diagnosis results show that the CNN and LSTM-based fault diagnosis methods are able to detect and diagnose faults under different operating modes.

关键词： Deep learning Fault diagnosis Photovoltaic systems Voltage measurement Fault detection Robustness Hardware

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A Fusion Optimization Method Based on Observation Tracking and Extremum Seeking for Photovoltaic System

A Fusion Optimization Method Based on Observation Tracking a...

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data driven control and learning systems (ddcls)

作者： Yutao Cheng Tianzhen Wang Tianhao Tang François Auger Shanghai Maritime University Logistics Engineering College Shanghai China Nantes university Saint Nazaire France

ISBN: (纸本)9781728114552

Maximum power tracking control is indispensable for Photovoltaic system to gain higher output power. For nonlinear system as photovoltaic system, extremum seeking control algorithm can reach good control effect. In view of the contradiction between tracking performance and algorithm complexity, and the problem of misjudgment when environment changes, a fusion optimization method based on observation tracking and extremum seeking is proposed in this paper, the fuses step based on the output power difference. the experimental results show that the optimized algorithm can further improve the conversion efficiency and performance compared with other algorithms while maintaining low complexity and implementation cost.

关键词： Photovoltaic system Maximum power point tracking Output power difference Algorithm fusion Photovoltaic systems maximum power point trackers extremum values Output power control effect optimization method Optimization algorithms Algorithmic complexity Conversion efficiency

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Blynk-Powered IoT System with Machine learning for Personalized Plant Care 8

Blynk-Powered IoT System with Machine Learning for Personali...

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8th International conference on Inventive systems and control, ICISC 202

作者： Pappula, Madhavi Myneni, Jaswanth Chowdary Gandra, Manasa Tadigadapa, Raghavendra Akella, Bala Krishna Murthy Lakireddy Balireddy College of Engineering AI&DS AP Mylavaram India

ISBN: (纸本)9798350386578

this research explores the development of a prototype Internet of things (IoT)-enabled smart irrigation system and investigates its potential integration with machine learning (ML). the primary mechanism, constructed using Blynk, facilitates real-time sensor data monitoring (temperature, humidity, and moisture) and enables manual irrigation control via a mobile application. the secondary mechanism includes the integration of Machine learning (ML) model, facilitating data-driven predictions for optimal watering schedules. this ML integrated IoT system has the potential to revolutionize irrigation practices, promoting water conservation and potentially enhancing plant health through precise watering based on real-time sensor data and historical trends. © 2024 ieee.

关键词： Smart agriculture

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A data-driven Fuzzy Modelling Framework for the Classification of Imbalanced data 8

A Data-driven Fuzzy Modelling Framework for the Classificati...

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8th ieee International conference on Intelligent systems (IS)

作者： Rubio-Solis, Adrian Panoutsos, George thornton, Steve Univ Sheffield Automat Control & Syst Engn Sheffield S10 2TN S Yorkshire England

ISBN: (纸本)9781509013531

the design and implementation of data-driven Fuzzy Models (DDFMs) to learn balanced industrial/manufacturing data has demonstrated to be a popular machine learning methodology. However, DDFMs have also proven to perform poorly when it comes to learn from heavily imbalanced data. In this study we propose a DDFM to tackle the challenge of a two-class imbalanced case study for rail quality. We integrate a number of machine learning methods, namely: Granular Computing (GrC), RBF Neural Networks (RBF-NN), Feature Selection (FS) to create a DDFM framework which is sensitive to imbalanced data. the rationale behind the DDFM framework can be described into three main stages: in the first stage, a Fast Correlation-Based Filter (FCBF) is employed to select the most representative features. Subsequently, the concept of iterative granulation is applied to group (cluster) the rail data set. Granulation provides a number of information granules which can be viewed as fuzzy constraints. In the second stage, an RBF-NN is used as a Neural Fuzzy Model (NFM) whose initial parameters are the parameters of the fuzzy sets created during the granulation process. Finally, a twofold bootstrapping strategy is performed. On the one hand, bootstrapping is used to balance the rate between the majority and minority class. On the other hand, bootstrapping estimates the appropriate number of fuzzy linguistic rules in the NFM. the proposed modelling framework is tested against a manufacturing case study provided by TATA Steel, UK.

关键词： Granular Computing (GrC) Fuzzy Logic systems (FLSs) Neural Fuzzy System (NFM) RBF Neural Networks Feature Selection (FS) Imbalanced data

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BBR-FIT: An Intelligent BBR based on the Reinforcement learning to Boost the Network Efficiency over Time-Varying Networks 24

BBR-FIT: An Intelligent BBR based on the Reinforcement Learn...

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作者： Xie, Yi Jiang, Xianliang Jin, Guang Chen, Haiming Ningbo University Ningbo China

ISBN: (纸本)9798350319934

Recently, the emerging high-speed and low-latency communication techniques (e.g., 5G and WiFi6) reactivated the popularity of some Internet applications. Most of them simultaneously need the support of high-throughput and low-latency transmission provided by underlying networks. As the core component to ensure reliable data delivery, TCP congestion control algorithms have attracted the attention of Linux kernel developers over the past two decades. BBR is a representative congestion control algorithm developed by Google in 2016. Compared with traditional algorithms, BBR probes the maximum available bottleneck bandwidth and minimum Round-Trip Time. this mechanism makes BBR perform better than previous schemes over high bandwidth delay and lossy networks. However, according to the extensive measurement of BBR over China Mobile 5G networks, we found that the performance of BBR is not good when the network state changes frequently. It is mainly caused by the fixed gain parameter. To solve this issue, we adopt the wisdom of reinforcement learning techniques to fine-tune the gain parameter and provide an intelligent BBR (named BBR-FIT). Extensive experiments with the mahimahi tool and real testbed indicate that, compared with BBR, BBR-FIT can promote the link utilization (2x) without inducing an increase in packet delay. © 2022 ieee.

关键词： Reinforcement learning

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