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共 359 条记录，以下是331-340 订阅

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Machine learning-driven Traffic Flow Prediction Using Cloud control Big data Analysis

Machine Learning-Driven Traffic Flow Prediction Using Cloud ...

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Virtual Reality (ICVR), ieee International conference on

作者： Khurrum Jalil Qian Chen Muhammad Noaman Zahid Fazila Jalil Department of Traffic Engineering University of Shanghai for Science and Technology Shanghai China School of Information Hunan University of Humanities Science and Technology Loudi China School of Economics and Management University of Science and Technology Beijing Beijing China

ISBN: (数字)9798350364231

ISBN: (纸本)9798350364248

Due to the increasing number of intelligent vehicles (IVs) on existing roads, IVs present several challenges, such as congestion, CO 2 emissions, idling, and travel delays. Transportation departments rely on forecasting methods using traffic flow data to address issues before implementing new plans for smooth traffic flow. To achieve this, managing the vast amounts of intelligent transportation system (ITS) data necessitates the adoption of cloud computing for data storage capabilities and machine learning methods to facilitate efficient traffic prediction analysis. In addition, real-time data analysis is crucial for evaluating the effectiveness of the on-going implementation of the ITS plans and identifying gaps for improvement. In this paper, the proposed method integrates a machine learning-based traffic flow prediction model that uses cloud computing for traffic data analytics. then, the efficiency ratio of the proposed method is evaluated. through experimentation, we demonstrate the effectiveness of our method, achieving a prediction accuracy of up to 90%. this result suggests that the proposed approach is highly competitive compared to existing solutions.

关键词： Cloud computing Solid modeling data analysis Roads Transportation Memory Virtual reality Machine learning Predictive models Real-time systems

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Closed-Loop Pitch Attitude control of Biomimetic Robotic Fish 9

Closed-Loop Pitch Attitude Control of Biomimetic Robotic Fis...

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9th ieee data driven control and learning systems conference (ddcls)

作者： Zhang, Yujie Li, Zonggang Du, Yajiang Lanzhou Jiaotong Univ Sch Mech Engn Lanzhou Peoples R China Lanzhou Jiaotong Univ Robot Inst Lanzhou Peoples R China

ISBN: (纸本)9781728159225

this paper considers the pitch control of a robotic fish with three degree-of-freedom(DOF) pectoral fins, wire-driven flexible body and a passive caudal fin. the linear model with undetermined parameters is first suggested to describe the dynamics of the pitch angles, which are the control parameters to adjust the posture of robotic fish. then, a control network based on Central Pattern Generators(CPGs) is proposed to separately adjust the state of each DOF. Finally, a compound controller is designed to adjust the amplitudes of pectoral Fins such that the desired pitch angle is tracked, in which an adaptive law combined with RBF neural network is proposed to estimate the undetermined parameters. the stability of the proposed algorithm is proved and then the validity is investigated by the simulation results.

关键词： Biomimetic robotic fish CPG pitch attitude control adaptive control RBF neural network

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Rapid Knowledge Transfer Based on Gradient Response and Cross-Domain Consistency 19

Rapid Knowledge Transfer Based on Gradient Response and Cros...

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19th ieee International conference on Networking, Sensing and control, ICNSC 2022

作者： Chen, Jinshu Xu, Qihui Jiang, Yanhui Kang, Qi Tongji University Department of Control Science and Engineering Shanghai201804 China

ISBN: (纸本)9781665472432

With the rapid development of deep learning in the field of computer vision, data scarcity is a common and serious problem. data generation is viable to solve this problem. GAN (Generative Adversarial Networks) uses internal discriminator (D) and generator (G) against each other to improve performance. MOGAN (Morphologic-structure-aware Generative Adversarial Network) is an unsupervised structure-aware controllable generative model for a single training sample. It can maintain the rationality of semantic information while ensuring the diversity of generated results. this paper proposes a knowledge transfer method driven by gradient response and cross-domain consistency. the method guides the training of new tasks by using the generative model completed by the original task training. We complete the rapid transfer from the existing model to the new model. Our method can achieve better results at the same time cost. We have demonstrated the effectiveness of this method through experiments. © 2022 ieee.

关键词： Generative adversarial networks

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Estimating the Probability Density Function of New Fabrics for Fabric Anomaly Detection 10th

Estimating the Probability Density Function of New Fabrics f...

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10th International conference on Pattern Recognition Applications and Methods (ICPRAM)

作者： Rippel, Oliver Mueller, Maximilian Muenkel, Andreas Gries, thomas Merhof, Dorit Rhein Westfal TH Aachen Inst Imaging & Comp Vis Aachen Germany Rhein Westfal TH Aachen Inst Text Tech Aachen Germany

ISBN: (纸本)9789897584862

Image-based quality control aims at detecting anomalies (i.e. defects) in products. Supervised, data driven approaches have greatly improved Anomaly Detection (AD) performance, but suffer from a major drawback: they require large amounts of annotated training data, limiting their economic viability. In this work, we challenge and overcome this limitation for complex patterned fabrics. Investigating the structure of deep feature representations learned on a large-scale fabric dataset, we find that fabrics form clusters according to their fabric type, whereas anomalies form a cluster on their own. We leverage this clustering behavior to estimate the Probability Density Function (PDF) of new, previously unseen fabrics, in the deep feature representations directly. Using this approach, we outperform supervised and semi-supervised AD approaches trained on new fabrics, requiring only defect-free data for PDF-estimation.

关键词： Anomaly Detection Quality control Fabric Inspection Transfer learning Probability Density Estimation

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A wind turbine fault identification using machine learning approach based on pigeon inspired optimizer 10

A wind turbine fault identification using machine learning a...

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10th ieee International conference on Intelligent Computing and Information systems, ICICIS 2021

作者： Gad, Ibrahim Hassanien, Aboul Ella Tanta University Faculty of Science Tanta Egypt Egypt Faculty of Computers and Ai Cairo University Cairo Egypt

ISBN: (纸本)9781665440769

Wind turbines (WT) need regular maintenance, which should be carried out as soon as possible after installation. Additionally, due to unscheduled maintenance, wind farms may lose valuable time, resulting in a loss of revenue. Since the health of the turbine is constantly monitored, it is an easy task to discover faults early and schedule regular maintenance as soon as it becomes necessary. the supervisory control and data acquisition (SCADA) data used in this study was generated by an Irish wind farm. It is a well-known dataset that contains information on the performance of the turbines. the data is categorized into three main types: operational data, status data, and warning data. Feature selection is essential in developing machine learning methods. Irrelevant features in datasets have a negative impact on the algorithm's performance, and they also increase the durationof time required to train the machine learning model before it can be used. this work presents a feature selection technique based on pigeon inspired optimizer for the widely used SCADA data. the experimental findings demonstrate that the LSTM algorithmproduces the best results, with accuracy, precision, recall and F-score values of 98%, 95%, 98%, and 97%, respectively. As a result, the proposed model can be utilized to detect defects in wind turbines in the field. © 2021 ieee.

关键词： Wind turbines

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Machine learning for Fatigue Detection using Fitbit Fitness Trackers 10

Machine Learning for Fatigue Detection using Fitbit Fitness ...

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10th International conference on Sport Sciences Research and Technology Support, icSPORTS 2022

作者： Husom, Erik Johannes Dautov, Rustem Nedisan, Adela-Aniela Gonidis, Fotis Papatzelos, Spyridon Malamas, Nikolaos SINTEF Digital Forskningsveien 1 Oslo0373 Norway University of Oslo Department of Informatics Gaustadalléen 23B Oslo0373 Norway Gnomon Informatics SA Antoni Tritsi 21 Thessaloniki57001 Greece

ISBN: (纸本)9789897586101

Fatigue can be a pre-cursor to many illnesses and injuries, and cause fatal work-related incidents. Fatigue detection has been traditionally performed in lab conditions with stationary medical-grade diagnostics equipment for electroencephalography making it impractical for many in-field scenarios. More recently, the ubiquitous use of wearable sensor-enabled technologies in sports, everyday life or fieldwork has enabled collecting large amounts of physiological information. According to recent studies, the collected biomarkers related to sleep, physical activity or heart rate have proven to be in correlation with fatigue, making it a natural fit for applying automated data analysis using Machine learning. Accordingly, this paper presents our novel Machine learning-driven approach to fatigue detection using biomarkers collected by general-purpose wearable fitness trackers. the developed method can successfully predict fatigue symptoms among target users, and the overall methodology can be further extended to other diagnostics scenarios which rely on collected wearable data. Copyright © 2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.

关键词： Machine learning

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Combining Model-Based and data-Based Approaches for Online Predictions of Human Trajectories

Combining Model-Based and Data-Based Approaches for Online P...

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ieee/RAS-EMBS International conference on Biomedical Robotics and Biomechatronics (BioRob)

作者： Aymeric Orhan Dorian Verdel Olivier Bruneau Franck Geffard Bastien Berret CEA List Université Paris-Saclay Palaiseau France Department of Bioengineering Human Robotics Group Imperial College of Science Technology and Medicine London United-Kingdom LURPA Mechanical Engineering Department ENS Paris-Saclay Université Paris-Saclay Gif-sur-Yvette France CIAMS Sport Sciences Department Univcrsité Paris-Saclay Orsay France CIAMS Université d'Orléans Orléans France

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

Accurately predicting human movement trajectories is of critical interest in multiple fields, including human-exoskeleton interaction. In general, such predictions can be obtained from model-based approaches (e.g., optimal control theory) or from data-driven approaches (e.g., learning from human demonstrations). data-driven methods generally require numerous demonstrations but avoid the computational burden of model-based methods. In this paper, we introduce a hybrid method mixing the strengths of these two approaches to enable the prediction of human trajectories in a receding-horizon fashion when the number of actual demonstrations is very limited. First, we propose to use the stochastic optimal feedforward-feedback control framework to generate a large set of humanlike trajectories, including their variability. Second, we used the probabilistic movement primitives (ProMPs) frame-work to learn the distribution of these synthetic trajectories and make online predictions about the upcoming human trajectories from the observation of past movement data. Here, we evaluated our hybrid method on an existing data set composed of arm reaching movements in a parasagittal plane. Overall, our method proves to be advantageous when generalization is needed and demonstrations are lacking, such as in novel targets scenarios. the introduced method shows promise to efficiently predict realistic human trajectories on a given time horizon, even when limited or no human demonstration is available for the task at hand.

关键词： Computational modeling Optimal control Stochastic processes Training data Predictive models Motors Cost function Real-time systems Trajectory Robots

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A Spatial-temporal Graph based Hybrid Infectious Disease Model with Application to COVID-19 10th

A Spatial-temporal Graph based Hybrid Infectious Disease Mod...

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10th International conference on Pattern Recognition Applications and Methods (ICPRAM)

作者： Zheng, Yunling Li, Zhijian Xin, Jack Zhou, Guofa UC Irvine Dept Math Irvine CA 92697 USA UC Irvine Dept Hlth Sci Irvine CA USA

ISBN: (纸本)9789897584862

As the COVID-19 pandemic evolves, reliable prediction plays an important role in policymaking. the classical infectious disease model SEIR (susceptible-exposed-infectious-recovered) is a compact yet simplistic temporal model. the data-driven machine learning models such as RNN (recurrent neural networks) can suffer in case of limited time series data such as COVID-19. In this paper, we combine SEIR and RNN on a graph structure to develop a hybrid spatio-temporal model to achieve both accuracy and efficiency in training and forecasting. We introduce two features on the graph structure: node feature (local temporal infection trend) and edge feature (geographic neighbor effect). For node feature, we derive a discrete recursion (called I-equation) from SEIR so that gradient descend method applies readily to its optimization. For edge feature, we design an RNN model to capture the neighboring effect and regularize the landscape of loss function so that local minima are effective and robust for prediction. the resulting hybrid model (called IeRNN) improves the prediction accuracy on state-level COVID-19 new case data from the US, out-performing standard temporal models (RNN, SEIR, and ARIMA) in 1-day and 7-day ahead forecasting. Our model accommodates various degrees of reopening and provides potential outcomes for policymakers.

关键词： COVID-19 Machine learning Spatial-Temporal Graph RNN

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10th International conference on Model and data Engineering, MEDI 2021

10th International Conference on Model and Data Engineering,...

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10th International conference on Model and data Engineering, MEDI 2021

ISBN: (纸本)9783030784270

the proceedings contain 24 papers. the special focus in this conference is on Model and data Engineering. the topics include: Social Neural Hybrid Recommendation with Deep Representation learning;cloud Query Processing with Reinforcement learning-Based Multi-objective Re-optimization;top-K Formal Concepts for Identifying Positively and Negatively Correlated Biclusters;anonymization Methods of Structured Health Care data: A Literature Review;categorical Modeling of Multi-model data: One Model to Rule them All;guaranteeing Information Integrity through Blockchains for Smart Cities;a Blockchain-Based Platform for the e-Procurement Management in the Public Sector;COVIDonto: An Ontology Model for Acquisition and Sharing of COVID-19 data;k-Nearest Neighbors Classification of Semantic Web Ontologies;model-Based Approach for Co-optimization of Safety and Security Objectives in Design of Critical Architectures;automated Generation of datasets from Fishbone Diagrams;GPU-Based Algorithms for Processing the k Nearest-Neighbor Query on Disk-Resident data;revisiting data Compression in Column-Stores;using Multidimensional Skylines for Regret Minimization;enhancing Sedona (formerly GeoSpark) with Efficient k Nearest Neighbor Join Processing;towards a Model-Based Approach to Support Physical Test Process of Aircraft Hydraulic systems;multi-facets Contract for Modeling and Verifying Heterogeneous systems;an Approach to Detect Cyberbullying on Social Media;an Ontology Engineering Case Study for Advanced Digital Forensic Analysis;more Automation in Model driven Development;realisability of control-State Choreographies;A Low-Cost Authentication Protocol Using Arbiter-PUF.

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Attention M-net for Automatic Pixel-Level Micro-crack Detection of Photovoltaic Module Cells in Electroluminescence Images 9

Attention M-net for Automatic Pixel-Level Micro-crack Detect...

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9th ieee data driven control and learning systems conference (ddcls)

作者： Jiang, Yu Zhao, Chunhui Ding, Waner Hong, Ling Shen, Qu Zhejiang Univ State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China Zhejiang Univ Coll Control Sci & Engn Hangzhou 310027 Peoples R China Zhejiang Energy Grp R&D Zhejiang Prov Key Lab Solar Energy Utilizat & Ene Hangzhou 310027 Peoples R China

ISBN: (纸本)9781728159225

In the solar power industry, quality inspection of solar cells is a very important part of the production and application process. Micro-crack is a type of common defect that may be present in photovoltaic (PV) module cells which can reduce the power generation efficiency and service lifetime. However, it is difficult to identify micro-crack by the naked eye because it is readily confused with non-uniform backgrounds and complex textures in Electroluminescence (EL) images. In this paper, we propose Attention M-net which combines efficient segmentation model structure and attention mechanism. It is a novel micro-crack detection model for automated pixel-level micro-crack detection of PV module cells. the M-shaped structure solves "All Black" issue that is easy to occur due to the severe imbalance of the micro-crack segmentation dataset. And integration of attention mechanism into the network significantly improves the accuracy of segmentation. Because of the above two advantages, the proposed model can be accurately learned from a small annotated dataset, thereby saving the time of micro-crack EL images collection and pixel-level annotations. We extensively trained the proposed model on a small dataset of 20 annotated EL images which include 10 monocrystalline and 10 polycrystalline. the evaluated results on the test dataset demonstrate the high efficiency and accuracy of Attention M-net for pixel-level micro-crack detection of PV module cells in EL images.

关键词： Photovoltaic modules Micro-crack detection M-net "All Black" issue Attention mechanism

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