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Energy Efficient Speed Planning of Electric Vehicles for Vehicle-Following Scenarios Using Data-driven Koopman Model

Energy Efficient Speed Planning of Electric Vehicles for Veh...

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IEEE international conference on Big Data and Cloud computing (BdCloud)

作者： Bin Chen Miaoben Wang Feng Zhou Haoyang Yan Ronghua Du College of Automotive and Mechanical Engineering Changsha University of Science and Technology Changsha China School of Electrical and Information Engineering Changsha University of Science and Technology Changsha China

In the context of urban road following scenarios, ensuring the precise speed prediction and control of electric vehicle while rigorously maintaining the safe driving conditions has persistently presented a formidable challenge. To address such issues, a novel data-driven approach for the energy efficient speed planning of electric vehicles in vehicle-following scenarios is proposed. Incorporating Koopman operator theory, the developed control-oriented vehicle dynamics prediction model leverages real-world vehicle-following data in various vehicle-following scenarios. Subsequently, a data-driven Koopman model predictive control (MPC) method is developed based on the established prediction model, which focuses on reducing the energy consumption through speed planning while ensuring safe following distances. The comparative simulation results ultimately demonstrate the effectiveness of the proposed method under various driving scenarios.

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Classification of Car Parts Using Deep Neural Network 14th

Classification of Car Parts Using Deep Neural Network

引用

14th APCA international conference on automatic control and Soft computing (controlO)

作者： Khanal, Salik Ram Amorim, Eurico Vasco Filipe, Vitor Univ Tras Os Montes & Alto Douro Sch Sci & Technol P-5000801 Vila Real Portugal INESC TEC Inst Syst & Comp Engn Technol P-4200465 Porto Portugal

ISBN: (纸本)9783030586553;9783030586539;9783030586522

Quality automobile inspection is one of the critical application areas to achieve better quality at low cost and can be obtained with the advance computer vision technology. Whether for the quality inspection or the automatic assembly of automobile parts, automatic recognition of automobile parts plays an important role. In this article, vehicle parts are classified using deep neural network architecture designed based on ConvNet. The public dataset available in CompCars [1] were used to train and test a VGG16 deep learning architecture with a fully connected output layer of 8 neurons. The dataset has 20,439 RGB images of eight interior and exterior car parts taken from the front view. The dataset was first separated for training and testing purpose, and again training dataset was divided into training and validation purpose. The average accuracy of 93.75% and highest accuracy of 97.2% of individual parts recognition were obtained. The classification of car parts contributes to various applications, including car manufacturing, model verification, car inspection system, among others.

关键词： Machine vision Quality control inspection Automobile inspection Deep learning Convolutional neural network VGG16

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A SPWM Strategy Based Dual-Inverter control for Electric Vehicle with Open End Winding Induction Motor

A SPWM Strategy Based Dual-Inverter Control for Electric Veh...

引用

Power, control and computing Technologies (ICPC2T), conference on

作者： Duriya Murali Hritik Kumar Ranjan Kumar Behera Electrical Engg. Dept Indian Institute of Technology Patna Patna Bihar India

ISBN: (数字)9798331530402

ISBN: (纸本)9798331530419

This paper presents a comprehensive study on the implementation of a dual 2 -level (2L) voltage source inverter (VSI) to drive an open-end winding induction motor (OEWIM) for electric vehicle (EV) applications. This paper presents a mathematical model of an OEWIM drive, which is powered by dual 2L VSI with separate DC supplies. A sinusoidal pulse width modulation (SPWM) strategy is employed, incorporating a phase shift of the carrier wave. By comparing a sinusoidal wave with a triangular signal, this strategy enables the generation of swing states for the two inverters in a spontaneous and symmetrical manner. The proposed configuration offers numerous advantages, including improved efficiency, reduced harmonic distortion, enhanced control capabilities, and fault-tolerant operation. The proposed system is designed to meet the demanding requirements of EVs, such as high torque density, fault tolerant operation, and improved power quality. In this study, through the suggested model utilizing the MATLAB/Simulink platform for analysis and simulation. The performance is validated thought experiments. The proposed model benefits from a robust and versatile environment that enables accurate representation and evaluation of the system’s behavior, performance, and dynamics.

关键词： Fault tolerance Torque Induction motors Accuracy Windings Fault tolerant systems Electric vehicles Mathematical models Regulation Torque measurement

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A State-Space Model Inversion control Method for Shake Table Systems 14th

A State-Space Model Inversion Control Method for Shake Table...

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14th APCA international conference on automatic control and Soft computing (controlO)

作者： Ramirez-Senent, Jose Garcia-Palacios, Jaime H. Diaz, Ivan M. Univ Politecn Madrid Escuela Tecn Super Ingenieros Caminos Canales & Puertos Madrid 28040 Spain

ISBN: (纸本)9783030586553;9783030586539;9783030586522

Shake tables used to perform laboratory dynamic testing of structures are commonly powered by hydraulic servoactuators. Due to the inherent non-linearity of servohydraulic systems, advanced control algorithms are required to achieve accurate reference tracking. Traditionally, these algorithms have been based on estimating, at a first stage, the Frequency Response Function of the system and inverting it to yield the Impedance Function. The Impedance is then operated with the desired system output and the result transformed into time domain to obtain an initial estimate of the drive to be fed to the system. This estimate is improved during the test in an iterative fashion. One of the main drawbacks of this approach is the fact that references which are not known beforehand and change in real time, cannot be directly addressed. In this work, a control method based on the inversion of a state-space model of the servo-actuator is presented. First off, a non-linear model of the testing system is developed. Next, a Feedback Linearization procedure to minimize servovalve non linearities is explained and the state-space identification procedure is described. Then, the model inversion method is addressed, together with the state variables estimator required to perform real time control. Finally, the simulation results obtained are discussed. The suggested method shows an excellent performance in numerical experiments;nevertheless, further studies must be undertaken to ensure its successful implementation in actual shake table systems.

关键词： Shake table Feedback linearization System identification Dynamics inversion State-space modeling

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Classification of Sleep Stages Using Single-Channel ECG Signals: A Comparative Analysis of Machine Learning and Deep Learning Methods

Classification of Sleep Stages Using Single-Channel ECG Sign...

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IEEE international conference on Signal Processing, Communications and computing (ICSPCC)

作者： Ahmad Ullah Muhammad Salman Khan Department of Computer Science and Engineering College of Engineering Qatar University Doha Qatar Department of Electrical Engineering College of Engineering Qatar University Doha Qatar

ISBN: (数字)9798350366556

ISBN: (纸本)9798350366563

In this paper, we provide an extensive evaluation of machine learning (ML) and deep learning (DL) methods for automatic sleep stage classification using a single-channel electrocardiogram (ECG) signal. To explore ML methods, we extracted 21 heart rate variability (HRV) features using the R-peak detection. These features were used to train and compare the performance of various models such as support vector machine (SVM), decision tree (DT), random forest (RF), k-nearest neighbor (K-NN), and Naïve Bayes (NB). Furthermore, we analyzed the DL methods to overcome the manual feature extraction. This method involved feeding raw time-series ECG signal to a 1-dimensional convolutional neural network (CNN) and short-time Fourier transform (STFT) based spectrogram to a 2D CNN. The overall evaluation was performed using 15-subject data each from two publicly available datasets. To process the data for analysis, resampling, bandpass filtering, baseline correction, 30-second interval extraction from whole-night ECG recordings, and z-score normalization techniques were applied. We conducted experiments on one dataset and then replicated the experiments on the other dataset to assess the robustness and generalizability of the methods. Through a comparative analysis, the spectrogram-based 2D CNN emerged as the effective method with the highest test accuracy and k-score of 87.65% and 0.82 respectively. This study contributes to ECG-based sleep classification research, highlighting the effectiveness of deep learning for effective classification.

关键词： Deep learning Support vector machines Electrocardiography Feature extraction Robustness Recording Convolutional neural networks Heart rate variability Spectrogram Random forests

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A Model Predictive control-based Flexible Power Point Tracker for Grid-tied Photovoltaic Inverters

A Model Predictive Control-based Flexible Power Point Tracke...

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Energy Technologies for Future Grids (ETFG), IEEE international conference on

作者： Mostefa Kermadi Sajib Ahmed Saad Mekhilef Marizan Mubin Hazlie Mokhlis Department of Electrical Engineering Power Electronics and Renewable Energy Research Laboratory (PEARL) Universiti Malaya Kuala Lumpur Malaysia School of Science Computing and Engineering Technologies Swinburne University of Technology Hawthorn VIC Australia Department of Electrical Engineering Universiti Malaya Kuala Lumpur Malaysia

In distributed photovoltaic (PV) systems, the grid operator may impose the limit of power injected by the PV system at the point of common coupling. This power limit might be lower than the power generated by the PV system during the peak generation hour. In this work, we propose a flexible power point tracking (FPPT) method based on model predictive control for grid-connected photovoltaic inverters. A finite control set-model predictive controller (FCS-MPC) for the control of the inverter DC bus voltage and the grid current is employed to operate at the desired reference voltage set by the FPPT algorithm and inject the desired PV-generated power into the utility grid. The grid-side controller injects the power with a unity power factor using the voltage-oriented control principle in a rotational d-q reference frame. The rotation of the d-q reference frame is synchronized by the phase-locked loop algorithm. The validity of the proposed control system is confirmed through experimental tests conducted with the aid of solar array and grid simulators, a microlabbox controller, and a two-level inverter paired with an R-L filter.

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Development of an automatic Sweet Pepper Harvesting Robot and Experimental Evaluation

Development of an Automatic Sweet Pepper Harvesting Robot an...

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IEEE international conference on Robotics and Automation (ICRA)

作者： Qinghui Pan Dong Wang Jie Lian Yongxiang Dong Chaochao Qiu Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education and the School of Control Science and Engineering Dalian University of Technology Dalian China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

The aging population and diminishing working population in agriculture motivate the development of autonomous harvesting robots. Although autonomous harvesting is expanding rapidly, the commercial application of sweet pepper harvesting robots still faces challenges. This paper presents the development of a sweet pepper harvesting robot and reports its experimental verification, which mainly includes end-effector design, visual perception, and grasping pose control. The end-effector adopts electrical control, mainly composed of a servo-electric two-finger parallel clamping module, a swing-cutting module, and a fruit recovery device. Equipped with a tactile sensor array, it can accurately sense the sweet pepper peduncle position and the end-effector state (harvesting failure) to complete the precise cutting. An end-effector grasping pose control algorithm of the manipulator is proposed, which can control the end-effector to grasp along the direction of the fruit peduncle and perpendicular to the tangent direction of the picking point by estimating the pose of the sweet pepper peduncle. Finally, the robot and proposed method were verified in a plant factory. The experimental findings demonstrate that the developed harvesting robot can complete robust detection of fruit peduncles and non-destructive picking of sweet pepper, with an average picking time of about 15 seconds.

关键词： Sociology Tactile sensors Grasping End effectors Production facilities Sensors Statistics

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A Neuro-Wave controlled Wheelchair Combined with Gesture and Voice control

A Neuro-Wave Controlled Wheelchair Combined with Gesture and...

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international conference on computing and Networking Technology (ICCNT)

作者： Md. Saidur Rahman Md. Shakabul Islam Sourav Md. Asaduzzaman Sarker K. M. Rumman Md Saiful Islam Sajol Md. Shoriful Islam Shovon Dept. of EEE American International University - Bangladesh (AIUB) Dhaka Bangladesh Dept. of EEE Islamic University of Technology (IUT) Dhaka Bangladesh Dept. of Biomedical Physics & Technology University of Dhaka Dhaka Bangladesh Dept. of EEE Bangladesh University of Engineering and Technology (BUET) Dhaka Bangladesh Dept. of GEB Shahjalal University of Science and Technology Bangladesh (SUST) Sylhet Bangladesh

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This paper presents the design and implementation of a neuro-wave-controlled wheelchair with a gesture and voice control system. This research includes the Gesture controlled Wheelchair (GCW), Voice controlled Wheelchair (VCW), and Brain controlled Wheelchair (BCW). The GCW uses a pseudo glove carrying an accelerometer as input, while the VCW uses a microphone for voice input. Arduino Integrated Development Environment (IDE) develops the necessary software. BCW scheme utilizes a Bluetooth-connected Brainwave sensor module to send neuro-wave data wirelessly to a laptop for processing. The data is being processed using MATLAB Simulink Digital Signal Processing (DSP) toolbox to generate commands for the movement of the wheelchair. Implementing the self-controlled automatic wheelchair provides an essential solution for paralyzed patients to control their movements independently, thereby increasing their mobility and independence. The proposed design shows promising results and provides a solid foundation for integrating more advanced artificial intelligence technologies in disability assistance. The combination of neuro-wave, gesture, and voice control offers a holistic approach to disability assistance, increasing the wheelchair’s usability and ease of use for physically disabled individuals.

关键词： Headphones Training Portable computers Software packages Wheelchairs Digital signal processing Transforms Speech recognition Solids Usability

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Research on the Key Technology of Intelligent Road Detection and Maintenance Special Vehicle Based on Deep Learning

Research on the Key Technology of Intelligent Road Detection...

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Energy and electrical engineering (EEE), international conference on

作者： Wanhua Che Bo Wu Zhaohui Liu Wenxin Liu Jilin Communications Polytechnic Changchun China Shandong University of Science and Technology Qingdao China State Key Laboratory of Automotive Simulation and Control (ASCL) Jilin University Changchun China

ISBN: (数字)9798350341768

ISBN: (纸本)9798350341775

With the continuous improvement of road traffic safety standards, the impact of pavement damage on the safety and operational efficiency of vehicle travelling has received increasing attention from researchers. In this paper, a YOLOv8-based automatic pavement damage detection and damage condition assessment method is proposed to automatically detect the pavement damage condition using YOLOv8-seg. In addition, intelligent road maintenance solutions are discussed. The YOLOv8-seg model was chosen because it strikes a good balance between speed and accuracy for real-time pavement breakage pothole segmentation. Migration learning was implemented to tune the pre-trained YOLOv8-seg model for effective pothole detection and segmentation. In addition, lane line detection is accomplished using SCNN and MATLAB to improve the efficiency and accuracy of road maintenance and ensure the safe operation of vehicles. The method not only has the potential to improve the current situation of road maintenance, but also provides strong technical support for the sustainable development of automated driving technology in practical applications.

关键词： Training Accuracy Roads Predictive models Mathematical models Road traffic Maintenance Safety Sustainable development Standards

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Components for COVID19 Outbreak control Model: A System Dynamics Perspective 7

Components for COVID19 Outbreak Control Model: A System Dyna...

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7th international conference on Software engineering and Computer Systems and 4th international conference on Computational science and Information Management, ICSECS-ICOCSIM 2021

作者： Ibrahim, Aisyah Abdul Kadir, Tuty Asmawaty Daniyal, Hamdan Kamaludin, Adzhar Faculty of Computing College of Computing Applied Sciences Universiti Malaysia Pahang Pahang Pekan Malaysia Faculty of Electrical and Electronic Engineering Technology Universiti Malaysia Pahang Pahang Pekan Malaysia

ISBN: (纸本)9781665414074

The world is facing a massive challenge as the COVID-19 outbreak strikes across the globe. Many efforts have been made to detect, control and contain the coronavirus proactively and aggressively before a further catastrophe occurs. Indeed, ending the global COVID-19 pandemic is not a simple task. It requires adequate planning and implementation of sustainable strategies and interventions to control COVID-19 from keep spreading globally. One way to address this issue is using System Dynamics (SD). With this aim in mind, this paper presents an initial COVID-19 modelling work in the formulation stage of SD methodology. A literature review was carried out on published and unpublished papers to understand the essential outbreak model design structure. Within this process, a total of 15 COVID-19 models in SD were gathered and analysed. As the outcome, this paper highlights the components of the conceptual representation model for the COVID-19 outbreak, which later can serve as the core basis for modelling complex COVID19 outbreak dynamics and interventions for future development. As an implication, a comprehensive model can be developed to support decision making. © 2021 IEEE.

关键词： COVID-19

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