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4th International Conference of Science and Information technology in Smart Administration, ICSINTESA 2024

作者： Poyai, Mana Maneetham, Dechrit Sutyasadi, Petrus Rajamangala University of Technology Thanyaburi Faculty of Technical Education Department of Mechatronics Engineering Pathumthani Thailand Vocational Faculty Sanata Dharma University Department of Mechatronics Yogyakarta Indonesia

ISBN: (纸本)9798350376111

This study presents a new technique that integrates LabVIEW and Python to enhance the control of DC motor drives through the utilization of machine learning methods. The objective of our research is to leverage LabVIEW's graphical programming interface in combination with the Scikit-learn Python package to train a Polynomial Regression model. The goal is to forecast Pulse Width Modulation (PWM) values at desired Rotations per Minute (RPM) of a DC motor. The accuracy and reliability of the trained model are evaluated using performance assessment metrics such as the R-squared (R2) coefficient of determination and the Root Mean Square Error (RMSE). The R2 score assesses the precision of the model's fit, whereas the RMSE quantifies the model's predictive capability by comparing observed and predicted data. This research also includes a comparison between the machine learning technique for DC motor drive and conventional PID control. The findings illustrate the successful integration of LabVIEW and Python in managing DC motor drives, achieving low overshooting and faster attainment of the set RPM point compared to classical PID control. This emphasizes machine learning's capacity to enhance control strategies;the application of Polynomial Regression yields promising results, demonstrating its effectiveness in predicting PWM and RPM values with an R2 value ranging from 96% to 98% and RMSE within 19. © 2024 IEEE.

关键词： Python

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Clustering and Outlier Analysis for Key Performance Indicators in Battery Energy Storage Systems applications 17

Clustering and Outlier Analysis for Key Performance Indicato...

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17th IEEE International Conference on Compatibility, Power Electronics and Power engineering, CPE-POWERENG 2023

作者： Zequera, Rolando Antonio Gilbert Rassolkin, Anton Vaimann, Toomas Kallaste, Ants Tallinn University of Technology Department of Electrical Power Engineering and Mechatronics Tallinn Estonia

ISBN: (纸本)9798350300048

This research work focuses on implementing outlier analysis and clustering to provide an assessment of the charging and discharging processes of Battery Energy Storage Systems (BESSs). K-Means, Density-based spatial clustering of applications with noise (DBSCAN), and Local Outlier Factor (LOF) are the main algorithms executed to illustrate Key Performance Indicators (KPIs) and their corresponding critical values during battery operation. Additional Data Mining methods are implemented to provide feature selection, correlation analysis, parameter estimation, and validation. Implemented algorithms show that there is a strong correlation between specific variables at certain operation stages, which is complemented by the lifetime period of BESSs. © 2023 IEEE.

关键词： Digital storage

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Data Science-based Techniques for Modelling and Diagnostics of Battery Cells Based on End-of-Life criteria 36

Data Science-based Techniques for Modelling and Diagnostics ...

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36th International Conference on Electrical Drives and Power Electronics, EDPE 2023

作者： Zequera, Rolando Antonio Gilbert Rassolkin, Anton Vaimann, Toomas Kallaste, Ants Tallinn University of Technology Department of Electrical Power Engineering and Mechatronics Tallinn Estonia

ISBN: (纸本)9798350322750

This research work focuses on implementing Data Science techniques for Battery Energy Storage Systems (BESSs) according to Health and Charge indicators based on End-of-Life (EOL) criteria. A simple Equivalent Circuit Model (ECM) is implemented to illustrate the behavior of a battery cell in parallel with numerical methods. Density-based spatial clustering of Applications with Noise (DBSCAN), Ordering Points to identify the Clustering Structure (OPTICS), and Local Outlier Factor (LOF) are the Unsupervised techniques implemented for anomaly detection, while Multi-Layer Perceptron (MLP), Long-Short Term Memory (LSTM), and Gated Recurrent Unit (GRU) are the Supervised algorithms executed for diagnostics. Similar results are obtained in the outlier analysis, additionally, Supervised algorithms show a high level of performance and provide a basis for determining the capacity of the battery based on the End-of-Life criteria (EOL) of a Battery Energy Storage System (BESS). © 2023 IEEE.

关键词： Battery management systems

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Operation of the Step-Up/Down Bidirectional Partial Power Converter Near Zero Series Voltage 17

Operation of the Step-Up/Down Bidirectional Partial Power Co...

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17th IEEE International Conference on Compatibility, Power Electronics and Power engineering, CPE-POWERENG 2023

作者： Hassanpour, Naser Chub, Andrii Blinov, Andrei Vinnikov, Dmitri Tallinn University of Technology Departments of Electrical Power Engineering and Mechatronics Tallinn Estonia

ISBN: (纸本)9798350300048

The advantages of step-up/down series partial power converters (S-PPCs) are comprehensively addressed in the literature. However, their functionality at the critical point of equal port voltages is neglected. This paper analyzes a bi-directional step-up/down S-PPC to confirm its correct operation at this critical point. This S-PPC contains a four-quadrant DC-DC stage. Two modulation strategies are applied to the converter depending on the power flow direction. The phase shift modulation (PSM) regulates the current and voltage in the buck mode of the DC-DC stage. The reverse power flow control can extend the boost function of the DC-DC stage in the S-PPC when it works in the second and fourth quadrants. The series port voltage is decreased to almost zero to investigate the functionality of the S-PPC in all modes. The experimental prototype is tested under 5A current and 350V port voltage and the results confirm the correctness of the theoretical analysis. © 2023 IEEE.

关键词： Electric load flow

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Robust Localization Approach Using Hybrid Correspondence NDT and Real-Time Uncertainty Estimation

Robust Localization Approach Using Hybrid Correspondence NDT...

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2024 IEEE/SICE International Symposium on System Integration, SII 2024

作者： Aoki, Koki Takahashi, Yuta Sato, Tomoya Ninomiya, Yoshiki Meguro, Junichi Graduate School of Science and Technology Meijo University Division of Mechatronics Engineering Japan Map IV Inc. Japan Institute of Innovation for Future Society Nagoya University Japan Meijo University Faculty of Science and Technology Department of Mechatronics Engineering Japan

ISBN: (纸本)9798350312072

This study proposes an approach for robust localization with low computational cost in autonomous vehicles. We introduce EKF localization that combines NDT and DR. To estimate the uncertainty due to the real environment of NDT, we utilize multiple searches from many initial positions. Because multiple searches increase processing time, we limit the arrangement of initial positions based on the Hessian matrix of the NDT score function. The initial position arrangement according to the Hessian matrix is based on the direction in which error is likely to occur. This allows us to achieve real-time uncertainty estimation within a LiDAR period of 10Hz. Moreover, we propose hybrid correspondence NDT (HC-NDT) to reduce the computational cost of each search. HC-NDT reduces unnecessary correspondences between query points and ND voxel in neighbor search. In the convergence evaluation, HC-NDT reduced the processing time and maintained the accuracy compared to NDT with multiple correspondences. In the localization evaluation, our EKF localization with HC-NDT and our real-time uncertainty estimation suppressed errors within 0.3 m in challenging environments for NDT. The execution time of our uncertainty estimation with HC-NDT was reduced by up to 50 % compared to conventional NDT. © 2024 IEEE.

关键词： Uncertainty analysis

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RESEARCH ON POWER INTERNET OF THINGS MODEL AND RESOURCE ALLOCATION BASED ON EDGE COMPUTING

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical engineering and Computer Science 2023年第1期85卷 105-116页

作者： Li, Jing Lu, Xutao Liu, Feng Huang, Xiangquan Lin, He Ren, Yifeng School of Electrical and Control Engineering North University of China China College of Mechatronics Engineering North University of China China Chongqing Optoelectronics Technology Institute China Sichuan Institute of Piezoelectric & Acoustooptic Technology China

In order to improve the efficiency of resource allocation in the power Internet of Things, a micro-service model for power Internet of Things was proposed by edge computing Business data was divided into 4 different types, and resource matching calculation for aggregated services was completed by edge computing. Computing resource allocation model was used to obtain the computing resource allocation and resource supply of the edge computing terminals. In the test experiment, the CPU resource occupancy curve was obtained in the condition of different resource scaling ratios. It verified the feasibility of the algorithm. The resource utilization ratio of simultaneous trigger service and non-simultaneous trigger service was compared and analyzed. The results show that it verifies the optimization effect of this algorithm, which can reduce the total cost. © 2023, Politechnica University of Bucharest. All rights reserved.

关键词： Internet of things

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Data Logging Model for Metropolitan Vehicle Movement Monitoring and Control System

Data Logging Model for Metropolitan Vehicle Movement Monitor...

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2023 International Conference on Science, engineering and Business for Sustainable Development Goals, SEB-SDG 2023

作者： Adeniyi, Samuel Jack, Kufre Esenowo Salami, Taiye Hassan Ojekunle, Paul Olamide Olatomiwa, Lanre Joseph Oyelami, Abdulhafeez Oluwatobi Federal University of Technology Department of Mechatronics Engineering Niger State Minna Nigeria

ISBN: (纸本)9798350324785

The automotive industry has experienced a spectacular expansion in the last decade, with an increase in number of cars in metropolitan cities. Maintaining track of automobiles based on their plates number in order to manage vehicular traffic properly in the city has posed difficulty. This research presents an artificial intelligence-based technology technique (YOLO) for tracking vehicle movement based on the vehicle's plate number with data logging model and a centralized database structure for vehicles identification and monitoring, using other techniques such as image processing and IoT mechanism for detection and recognition accuracy. In order to create an Intelligent Plate Number Recognition (IPNR) System, this study employs artificial intelligence, computer vision (image processing), laser scanning technologies, and convolutional neural networks (CNN). This model concepts and computations underpin potential solutions to this issue, guaranteeing a range of approaches to achieving the desired outcome. This work focuses on plate number identification using the contours tracing approach, as well as edge identification and sharpening using optical character recognition algorithm based on OpenCV libraries. The vehicle is monitored on real time using the GPS technology where the vehicle plate image was captured with the Pi camera to produce high-quality images. This research employs a wide range of techniques across the board (from license plate detection to character recognition) to boost the system's speed as much as feasible with negligible overhead. The studies demonstrated how useful image processing tools were used for data logging, and character recognition when combined with vehicles in urban environments. To carry out monitoring and management of the system, a responsive web application was designed for data logging. © 2023 IEEE.

关键词： Automotive industry

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A framework for robotic grasping of 3D objects in a tabletop environment

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Multimedia Tools and Applications 2024年 1-30页

作者： Ansary, Sainul Islam Mishra, Atul Deb, Sankha Deb, Alok Kanti Advanced Technology Development Centre IIT Kharagpur721302 India Mechanical Mechatronics Engineering Department LNMIIT Jaipur302031 India Mechanical Engineering Department IIT Kharagpur721302 India Electrical Engineering Department IIT Kharagpur721302 India

Automatic grasping of unknown 3D objects is still a very challenging problem in robotics. Such challenges mainly originate from the limitations of perception systems and implementations of the grasp planning methods for handling arbitrary 3D objects on real robot platforms. This paper presents a complete framework for robotic grasping of unknown 3D objects in a tabletop environment. The framework comprises of a 3D perception system for obtaining the complete point cloud of the objects, followed by a module for finding the best grasp by an object-slicing based grasp planner, a module for trajectory generation for pick and place operations, and finally performing the planned grasps on a real robot platform. The proposed 3D object perception captures the complete geometry information of the target object using two depth cameras placed at different locations. A hole-filling algorithm is also proposed to quickly fill the missing data points in the captured point cloud of target object. The object-slicing based grasp planner is extended to handle the obstacles posed by the neighbouring objects on a tabletop environment. Then, the proposed framework is tested on common household objects by performing pick and place operations on a real robot fitted with an adaptive gripper. Moreover, finding the best feasible grasp in the presence of neighbouring objects is also demonstrated such as avoiding the table-top and surrounding objects. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Robot programming

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Enhanced Prediction of Metamaterial Antenna Parameters Using Advanced Machine Learning Regression Models

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Progress In Electromagnetics Research C 2024年 146卷 1-12页

作者： Jain, Prince Sahoo, Prabodh K. Khaleel, Aymen D. Al-Gburi, Ahmed Jamal Abdullah Department of Mechatronics Engineering Parul Institute of Technology Parul University Gujarat Vadodara391760 India Computer Engineering Department College of Engineering Al-Iraqia University Iraq Jalan Hang Tuah Jaya Durian Tunggal Melaka76100 Malaysia

The integration of machine learning (ML) regression models in predicting the parameters of metamaterial antennas significantly reduces the design time required for optimizing antenna performance compared to traditional simulation tools. Metamaterial antennas, known for overcoming the bandwidth constraints of small antennas, benefit greatly from these advanced predictive models. This study applies and evaluates four ML regression models — Extra Trees, Random Forest, XGBoost, and CatBoost — to predict key antenna parameters such as S11, gain, and bandwidth. Each model’s performance is assessed using metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), R-squared (R2), Mean Absolute Percentage Error (MAPE), and Root Mean Squared Error (RMSE) across different training and testing set configurations (30%, 50%, and 70%). The Extra Trees model achieves the best performance for predicting gain, with an R2 of 0.9990, MAE of 0.0069, MSE of 0.0002, RMSE of 0.0145, and MAPE of 0.3106. Feature importance analysis reveals that specific features, such as pr and p0 for gain and Y a and Xa for bandwidth, are critical in the predictive models. These findings highlight the potential of ML methods to improve the efficiency and accuracy of metamaterial antenna design. © 2024, Electromagnetics Academy. All rights reserved.

关键词： Mean square error

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Soft-template synthesis of hierarchically porous structured polydimethylsiloxane toward flexible capacitive pressure sensor

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Science China(Technological Sciences) 2023年第9期66卷 2696-2706页

作者： HE ShiXue WU Jian DUAN YanHao SU BenLong LIU ShouYao TENG Fei WANG YouShan School of Mechatronics Engineering Harbin Institute of TechnologyHarbin 150001China National Key Laboratory of Science and Technology on Advanced Composites in Special Environments Harbin Institute of TechnologyHarbin 150090China Center for Rubber Composite Materials and Structures Harbin Institute of TechnologyWeihai 264209China

Flexible pressure sensors play an important role in the field of monitoring, owing to their inherent safety and the fact that they are embedded at the material level. Capacitive pressure sensors have been proven to be quite versatile, with the ability to change the sensitivity and monitoring range by modifying the pore structure of the dielectric layer(elastic modulus). In this paper, capacitive pressure sensors are devised, comprising hierarchical porous polydimethylsiloxane. Due to the inherent hollow and hierarchical micropore structure, the capacitive pressure sensor allows operation at a wider pressure range(~1000 kPa) while maintaining sensitivity(6.33 MPa-1) in the range of 0–300 k Pa. Subsequently, the capacitance output model of the sensor is optimized, which provides an overall approximation of the experimental values for the sensor performance. Additionally, the signal response of the“break up the whole into parts”(by analysis of the whole sensor in parts) is simulated and outputted by the finite element analysis. The simplified analysis model provides a good understanding of the relationship between the local pressure and the signal response of the pressure sensor. For practical applications, seal monitoring and rubber wheel pressure array system are tested, and the proposed sensor shows sufficient potential for application in large deformation elastomer products.

关键词： hierarchically porous structured flexible pressure sensor capacitive response mechanism hydrogel/silicone rubber emulsion

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