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automatic detection of vehicles in outdoor parking lots from zenith perspective using neural networks

Automatic detection of vehicles in outdoor parking lots from...

2020 Summer computer Simulation Conference, SCSC 2020, Held at the 2020 Summer Simulation Multi-Conference, SummerSim 2020

作者： García, Juan Manuel Carrera Piorno, Joaquín Recas Mata-Garcia, María Guijarro Department of Computer Architecture and Automatic Control Faculty of Computer Science Universidad Complutense de Madrid Spain

ISBN: (纸本)9781713819462

Nowadays there are a variety of methods to assist parking users in finding free sites in parking lots. However, there is no automatic system that takes into account the size of the car looking for a space or whether the cars adjacent to the free spaces are correctly parked. This paper presents a new method for detecting and calculating the area of vehicles in images taken from a zenith plane using computer vision and machine learning techniques that will help to create a vehicle-oriented search algorithm dedicated to finding the optimal spaces for vehicles entering an outdoor parking lot based on its characteristics. Results with scaled-down and real vehicles show that this new method can detect the area of the vehicles in an image with an average accuracy of 97.98%. ©2020 Society for Modeling & Simulation International (SCS)

关键词： Vehicles

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Intelligent fault diagnosis based on multi-resolution and one-dimension convolutional neural networks

Intelligent fault diagnosis based on multi-resolution and on...

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2021 International Conference on System science and engineering, ICSSE 2021

作者： Liu, Po-Yi Chen, Chih-Cheng Liong, Sze-Teng Tsai, Ming-Han Hsieh, Ping-Cheng Wang, Kun-Ching Feng Chia University Department of Mechanical and Computer-Aided Engineering Taichung Taiwan Feng Chia University Department of Automatic Control Engineering Taichung Taiwan Feng Chia University Department of Electronic Engineering Taichung Taiwan Feng Chia University Department of Information Engineering and Computer Science Taichung Taiwan

ISBN: (纸本)9781665448482

Intelligent fault diagnosis (IFD) plays an important role to increase the safety and reliability of rotating machinery. In recent years, there is a large number of deep-learning-based algorithms applied to IFD. Many studies have shown that the one-dimension convolutional neural network (1D-CNN) performs well in fault diagnosis on original vibration signals. However, the original vibration signals are very complicated and difficult to analyze directly. Some critical information is often hidden in different frequency subbands, such as some fault state is only related to specific subbands. In order to enhance the learning progression well organized for the 1D-CNN model, the proposed approach is based on wavelet packet decomposition (WPD) and 1D-CNN. First, decompose the original vibration signal into different frequency subbands with WPD to make the signals more concise. Next, to be compatible with the 1D-CNN model, we proposed a novel 1D-CNN with multi-resolution (CNN-MR) and the experiment result according to Case Western Reserve University (CWRU) Bearing Data Center. Through CNN-MR, it achieves high accuracy for fault diagnosis. © 2021 IEEE.

关键词： Failure analysis

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A Power Split Strategy for a Vehicular Hybrid Energy Storage System Using Adaptive Neuro Fuzzy Inference System 8

A Power Split Strategy for a Vehicular Hybrid Energy Storage...

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8th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2023

作者： Li, Jiao Zhang, Lijun Yang, Longzhi Wang, Bo School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Huazhong University of Science and Technology Belt and Road Joint Laboratory on Measurement and Control Technology Wuhan430074 China Northumbria University Faculty of Engineering and Environment Department of Computer Science and Digital Technologies Newcastle upon TyneNE1 8ST United Kingdom

ISBN: (纸本)9798350300178

This paper presents a real-time power split strategy for a battery-supercapacitor hybrid energy storage system. The objective of the proposed strategy is to alleviate battery degradation through effective supercapacitor utilization. The proposed strategy employs an adaptive neural fuzzy inference system, which is capable of learning a power split strategy from offline optimization results and distributing power in real-time. The performance of the strategy proposed and a low pass filtering technique is compared through simulations. The results demonstrate that the proposed method can effectively reduce battery capacity loss and perform well under unknown driving cycles in real time. © 2023 IEEE.

关键词： Supercapacitor

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RTLAB-Based Performance Validation of PV- Powered Single-Phase Asynchronous Motor Pumps

RTLAB-Based Performance Validation of PV- Powered Single-Pha...

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International Conference on Advanced Electrical engineering (ICAEE)

作者： Fezazi Omar Abdelkader Semmak Hasan Köten Mohammed Abd El Djalil Djehaf Ayad Ahmed Nour El Islam Biswadip Basu Mallik ICEPS Laboratory (Intelligent Control & Electrical Power Systems) Djillali Liabes University Sidi Bel-Abbes Algeria Department of Automatic Control Faculty of Electrical Engineering Djillali Liabes University Sidi Bel-Abbes Algeria Mechanical Engineering Department Mechanical Engineering Department Engineering Faculty Istanbul Medeniyet University North Campus Uskudar Istanbul TURKEY Kasdi merbah university Ouargla Algeria Department of Basic Science and Humanities Institute of Engineering & Management University of Engineering & Management Kolkata West Bengal India

ISBN: (数字)9798350349351

ISBN: (纸本)9798350349368

In this paper, a comprehensive investigation of the performance evaluation and validation of a single-phase asynchronous motor pump system coupled with a photovoltaic (PV) power source is presented. The purpose of this study was to evaluate the practicality and effectiveness of this configuration for pumping applications. The RTLAB platform, which enables real-time simulation and emulation of dynamic systems, includes important components, such as the use of a single-phase asynchronous motor as the pump's power source, the incorporation of a PV system to supply renewable energy, and the use of RTLAB for real-time validation. Through rigorous simulation and experimentation, the motor response performance, such as speed, torque, and current response, was analyzed under varying solar irradiance and load conditions. In addition, the PV power and DC bus voltage were supervised after motor driving. This research fills the knowledge gap between the theoretical analysis and real-world applications of single-phase asynchronous motor pump systems combined with PV sources. This approach, which validates the results using RTLAB, provides a validation method to evaluate the performance and guide design choices for effective and environmentally friendly pumping solutions. Keywords- Single-phase asynchronous motors, PV, RTLAB validation, SimPower System MATLAB.

关键词： Maximum power point trackers Solar irradiance Renewable energy sources Radiation effects Torque Systems operation Motors Real-time systems Stability analysis Robustness

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Sample Complexity for Evaluating the Robust Linear Observers Performance under Coprime Factors Uncertainty

arXiv

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arXiv 2022年

作者： Zhang, Yifei Ukil, Sourav Kumar Sperilă, Andrei Sabău, Şerban Department of Electrical and Computer Engineering Stevens Institute of Technology NJ07030 United States Department of Automatic Control and Computer Science "Politehnica" University of Bucharest Bucharest Romania

This paper addresses the end-to-end sample complexity bound for learning in closed loop the state estimator-based robust H2 controller for an unknown (possibly unstable) Linear Time Invariant (LTI) system, when given a fixed state-feedback gain. We build on the results from Ding et al. (1994) to bridge the gap between the parameterization of all state-estimators and the celebrated Youla parameterization. Refitting the expression of the relevant closed loop allows for the optimal linear observer problem given a fixed state feedback gain to be recast as a convex problem in the Youla parameter. The robust synthesis procedure is performed by considering bounded additive model uncertainty on the coprime factors of the plant, such that a min-max optimization problem is formulated for the robust H2 controller via an observer approach. The closed-loop identification scheme follows Zhang et al. (2021), where the nominal model of the true plant is identified by constructing a Hankel-like matrix from a single time-series of noisy, finite length input-output data by using the ordinary least squares algorithm from Sarkar et al. (2020). Finally, a H∞ bound on the estimated model error is provided, as the robust synthesis procedure requires bounded additive uncertainty on the coprime factors of the model. Copyright © 2022, The Authors. All rights reserved.

关键词： Additives

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Carbon-Aware Computing in a Network of Data Centers: A Hierarchical Game-Theoretic Approach

arXiv

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arXiv 2024年

作者： Breukelman, Enno Hall, Sophie Belgioioso, Giuseppe Dörfler, Florian The KTH Royal Institute of Technology School of Electrical Engineering and Computer Science Division of Decision and Control Systems Malvinas väg 10 StockholmSE-100 44 Sweden The Automatic Control Laboratory Department of Electrical Engineering and Information Technology ETH Zurich Physikstrasse 3 Zürich8092 Switzerland

Over the past decade, the continuous surge in cloud computing demand has intensified data center workloads, leading to significant carbon emissions and driving the need for improving their efficiency and sustainability. This paper focuses on the optimal allocation problem of batch compute loads with temporal and spatial flexibility across a global network of data centers. We propose a bilevel game-theoretic solution approach that captures the inherent hierarchical relationship between supervisory control objectives, such as carbon reduction and peak shaving, and operational objectives, such as priority-aware scheduling. Numerical simulations with real carbon intensity data demonstrate that the proposed approach successfully reduces carbon emissions while simultaneously ensuring operational reliability and priority-aware scheduling. © 2024, CC BY-NC-ND.

关键词： Carbon

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GETAE: Graph Information Enhanced Deep Neural Network Ensemble ArchitecturE for Fake News Detection

arXiv

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arXiv 2024年

作者： Truica, Ciprian-Octavian Apostol, Elena-Simona Marogel, Marius Paschke, Adrian Computer Science and Engineering Department Faculty of Automatic Control and Computers National University of Science and Technology Politehnica Bucharest Splaiul Independentei 313 Bucharest060042 Romania Academy of Romanian Scientists Ilfov 3 Bucharest050044 Romania Department of Mathematics and Computer Science Freie Universität Berlin Arnimallee 14 Berlin14195 Germany Fraunhofer Institute for Open Communication Systems Kaiserin-Augusta-Allee 31 Berlin10589 Germany

In today’s digital age, fake news has become a major problem that has serious consequences, ranging from social unrest to political upheaval. To address this issue, new methods for detecting and mitigating fake news are required. In this work, we propose to incorporate contextual and network-aware features into the detection process. This involves analyzing not only the content of a news article but also the context in which it was shared and the network of users who shared it, i.e., the information diffusion. Thus, we propose GETAE, Graph Information Enhanced Deep Neural Network Ensemble ArchitecturE for Fake News Detection, a novel ensemble architecture that uses textual content together with the social interactions to improve fake news detection. GETAE contains two Branches: the Text Branch and the Propagation Branch. The Text Branch uses Word and Transformer Embeddings and a Deep Neural Network based on feed-forward and bidirectional Recurrent Neural Networks ([Bi]RNN) for learning novel contextual features and creating a novel Text Content Embedding. The Propagation Branch considers the information propagation within the graph network and proposes a Deep Learning architecture that employs Node Embeddings to create novel Propagation Embedding. GETAE Ensemble combines the two novel embeddings, i.e., Text Content Embedding and Propagation Embedding, to create a novel Propagation-Enhanced Content Embedding which is afterward used for classification. The experimental results obtained on two real-world publicly available datasets, i.e., Twitter15 and Twitter16, prove that using this approach improves fake news detection and outperforms state-of-the-art models. © 2024, CC BY-NC-ND.

关键词： Graph embeddings

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MEST: Multi-plane Embedding and Spatial-temporal Transformer for Parkinson's disease diagnosis

MEST: Multi-plane Embedding and Spatial-temporal Transformer...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Liu, Jin Du, Hao Bi, Qian Liao, Haiyan Pan, Yi Central South University School of Computer Science and Engineering Changsha China Hunan Frist Normal University School of Mathematics and Statistics Changsha China Central South University Department of Radiology The Second Xiangya Hospital Changsha China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Faculty of Computer Science and Control Engineering Shenzhen China

ISBN: (纸本)9781665468190

Parkinson's disease (PD) is a common neurodegenerative disorder that impairs the patient's quality of life. Medical imaging technology has provided a variety of neuroimages for PD diagnosis studies. However, how to effectively integrate the rich representations from multi-modality data is still a challenging task. To address this challenging task, we propose a multiplane embedding and spatial-temporal Transformer (MEST) framework for PD diagnosis. Firstly, we project structural magnetic resonance imaging (sMRI) into 2D images from coronal, sagittal and axial directions, respectively. Then, the multi-plane dynamic images are learned by pre-trained VGG11 and attention mechanism for representation learning. Afterwards, time series information of functional magnetic resonance imaging (fMRI) is used to construct dynamic functional connection images. To capture information changes in brain, spatial-temporal connectivity Transformer (SCTransformer) is utilized to address spatial-temporal redundancy and dependencies. To integrate multimodality data, ensemble learning is designed based on majority voting strategy to perform PD diagnosis. We evaluate the proposed method on 279 subjects from an in-house and Parkinsons Progression Markers Initiative (PPMI) dataset. Experimental results show that the MEST achieves promising performance with accuracies of 0.856 and 0.885, and outperforms some state-of-the-art methods. © 2022 IEEE.

关键词： Embeddings

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Adaptive Identification of Linear Systems with a Mix of Static and Time-Varying Parameters

SSRN

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SSRN 2022年

作者： Niedźwiecki, Maciej Gańcza, Artur Shen, Lu Zakharov, Yuriy the Faculty of Electronics Telecommunications and Computer Science Department of Automatic Control Gdá nsk University of Technology Gdańsk80-233 Poland Department of Electronic Engineering University of York United Kingdom

We consider identification of sparse linear systems with a mix of static and time-varying parameters. Such systems are typical in underwater acoustics, for instance, in applications requiring identification of the acoustic channel, such as underwater acoustic communications, navigation and continuous-wave sonar. The recently proposed fast local basis function (fLBF) algorithm provides high performance when identifying time-varying systems. In this paper, we further improve the performance of the fLBF algorithm by exploiting properties of the system. Specifically, we propose an adaptive time-invariance test to identify whether a particular system tap is static or time-varying and exploit this knowledge for choosing the number of basis functions. We also propose a regularization scheme that exploits the system sparsity and an adaptive technique for estimating the regularization parameter. Finally, a debiasing technique is proposed to reduce an inherent bias of fLBF estimates. The high performance of the fLBF algorithm with the proposed techniques is demonstrated in scenarios of underwater acoustic communications, using numerical and real experiments. © 2022, The Authors. All rights reserved.

关键词： Continuous time systems

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Roblets: Robotic Tablets That Self-Assemble and Self-Fold into a Robot

Roblets: Robotic Tablets That Self-Assemble and Self-Fold in...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Junyi Han Daniela Rus Shuhei Miyashita Automatic Control and Systems Engineering Department University of Sheffield Sheffield UK Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge MA USA Insigneo Institute for in silico Medicine

Inspired by human proteins that are synthesized from only 20 types of amino acids, the development of self-assembly methods that allow robots to be built simply by randomly stirring the parts has been explored for many years. The key challenges include how to synthesize parts in pieces into a three-dimensional functional structure in a practical time, and subsequently, achieve a controlled robotic motion, all with minimal human intervention. This study proposes a method of self-assembling a 3D robot by first self-assembling random parts into a 2D structure and then self-folding it into a 3D shape. Once self-folded, the robot, whose compositional parts contain magnets, becomes capable of performing basic tasks such as block-pushing upon an application of an external magnetic field. Self-assembly from parts into a two-dimensional structure was performed by repeatedly colliding the parts with each other, and combining them with complementary-shaped parts, like matching jigsaw puzzle pieces. Self-folding was performed by shrinking a heat-responsive film attached across the hinge of each assembly part in hot water, causing the entire 2D structure to self-fold. The experiment demonstrated a series of 13 parts self-assembling into the shape of a 3D beetle, then walking and pushing an object in 13 minutes. The self-assembly process is programmed (mechanically) to generate the same geometry even if the number of parts is greater than the necessary number for the structure, thus is capable of generating multiple structures simultaneously.

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