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Game-Theoretic Stochastic Model Predictive Decision-Making Considering Surrounding Vehicle Intentions for intelligent Vehicles 8

Game-Theoretic Stochastic Model Predictive Decision-Making C...

引用

8th CAA international conference on Vehicular control and Intelligence, CVCI 2024

作者： Dai, Qikun Shao, Xiaolong Chen, Hong Liu, Jun Guo, Hongyan Meng, Qingvu College of Communication Engineering Jilin University National Key Laboratory of Automotive Chassis Integration and Bionics Changchun China College of Electronic and Information Engineering Tongji University Shanghai China Jilin University National Key Laboratory of Automotive Chassis Integration and Bionics Changchun China

ISBN: (纸本)9798331504892

When a Robotaxi encounters congestion ahead in its lane, it often faces significant delays in completing a lane change due to its limited ability to acquire space for lane-changing from adjacent lane obstacles. To optimize lane-changing opportunities while avoiding collisions, this paper proposes a Game-Theoretic Stochastic Model Predictive Decision-Making (GSMPD) method. This method accounts for the stochastic trajectories of obstacles and their yielding strategies. First, a lateral and longitudinal decision-making model was established, covering planning, control, and dynamics, where lane competition behaviors are considered as integer decision variables. Second, a collision risk assessment method was developed that considers the stochastic future trajectories of obstacles. Third, an obstacle-dominant and host-vehicle-following game optimization problem was established, which considers the obstacle's yielding intention and collision risk;the optimal game decision was obtained by solving the optimization problem using a genetic algorithm. Finally, the effectiveness of this method was validated through Hardware-in-the-Loop (HiL) experiments using a Nuvo-8108GC industrial computer, Simulink, and SCANeR. The results show that the vehicle demonstrates its lane-changing intentions by laterally deviating within the lane and safely completes overtaking maneuvers. © 2024 IEEE.

关键词： Risk assessment

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Smart Crop Recommendation System with Plant Disease Identification

Smart Crop Recommendation System with Plant Disease Identifi...

引用

2024 international conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Diwakar, Ravikant Kumar, Amit Kumar, Gaurav Choudhary, Aditya Vashistha, Jaya Sahoo, Abhaya Kumar Kalinga Institute of Industrial Technology School of Computer Science Engineering Bhubaneswar India

ISBN: (纸本)9798350378092

Agriculture is the backbone of many countries, including India, and provides livelihoods to millions of people facing challenges such as climate change and plant disease outbreaks. Through research, a web application has been developed that provides real-time recommendations for crop selection based on various factors such as soil nutrients, temperature, humidity, pH levels, and rainfall. Recent advances in machine learning and artificial intelligence offer promising solutions to these problems, enabling accurate, data-driven decision-making in agriculture. These technologies have the potential to transform how we predict crop yields and detect plant diseases, thus improving agricultural practices. To achieve this, we trained and examined seven machine learning models, Decision tree, Naive Bayes, SVM, Logistic Regression, Random Forest, XGBoost, and KNN. Among these, Random forest gives the highest accuracy, making it the best choice for crop forecasting. In addition to crop recommendation, the web application also integrates a Plant Disease Identification system using Convolutional Neural Network (CNN). By analyzing leaf images, CNN detects and accurately classifies plant diseases, allowing farmers to intervene early to prevent crop losses. This study aims to empower farmers with accessible technology to make informed decisions, improve crop selection, and effectively cure plant diseases. Combining crop recommendation with disease detection, intelligent crop recommendation systems with plant disease detection contribute to sustainable agriculture, economic stability, and food security in India and beyond. © 2024 IEEE.

关键词： Smart agriculture

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Synthesis of Dynamic Virtual-Inertia for Microgrid System Applications

Synthesis of Dynamic Virtual-Inertia for Microgrid System Ap...

引用

2025 international conference on intelligent control, Computing and Communications, IC3 2025

作者： Justo, Jackson J. Demidova, Galina Lukichev, Dmitry Poliakov, Nikolai Makolo, Peter Bansal, Ramesh C. Itmo University Faculty of Control System and Robotics Saint-Petersburg Russia University of Dar es Salaam Department of Electrical Engineering P.O. Box 33335 Dar es Salaam Tanzania United Republic of University of Sharjah Depatment of Electrical Engineering Sharjah United Arab Emirates University of Pretoria Department of Electric Electronic and Computer Engineering Pretoria South Africa

ISBN: (纸本)9798331527495

During the last two decades, interest in grid integrated renewable energy sources (RERs) into the electrical power grids has significantly grown. The motive being a promising strategy to reduce greenhouse gas emissions generated by conventional power plants (CPPs) and hence environmental consequences, decrease losses in distribution lines to improve power system supply efficiency and reliability to consumers. As a result, several CPPs are being replaced by RERs such as solar PV system, wind energy, micro hydro-turbine, fuel cell, and small gas power plants. These are installed close to customers on low-voltage and medium-voltage sites to reduce losses in the network and improve power quality, overall efficiency and reliability. However, grid-connected RERs via inverter decreases system inertia due to decrease in CPPs with rotating mass serving as an immediate natural response to frequency deviations following network disturbances. Therefore, this paper presents the design of dynamic virtual-inertia control (DVIC) scheme. The developed DVIC incorporates the mechanism to adjust active and reactive demand in response to any frequency and voltage variations to ensure system stability. To validate the suitability of the presented technique, MATLAB/Simulink model is developed and simulated under various operating scenarios. Using the results obtained simulation study, it is evidently the proposed DVIC shows improved performance is achieved than when no inertia control system applied. © 2025 IEEE.

关键词： Solar fuels

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Development of a Drilling Cycle Program with Decreasing Depth of Pecking Drilling 2

Development of a Drilling Cycle Program with Decreasing Dept...

引用

2nd international conference on Mechatronics and Automation Technology, ICMAT 2023

作者： Lin, Xinze Liu, Ping Wu, Min Ningbo Hanvos Kent School The Barstow School Ningbo Campus Zhejiang Ningbo315202 China Intelligent Equipment Research Institute Ningbo Polytechnic Zhejiang Ningbo315800 China

ISBN: (纸本)9781643684826

Deep hole machining accounts for a large proportion in machining. Due to the special depth of the hole being processed, the conventional deep hole drilling fixed cycle can lead to overheating of the tool after several times of drilling. Using the macro functions of the FANUC system, a new G code is developed to gradually reduce the depth of the drilling hole when processing deep holes. After being verified by the GLU28x40 CNC, the newly defined G code is input and programmed according to the prescribed syntax, Equivalent to a fixed loop implanted in the control system. When machining deep holes, it can effectively alleviate the problem of excessive tool temperature during the pecking drilling process and improve processing accuracy. © 2024 The Authors.

关键词： Infill drilling

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Static Precision Sensitivity Analysis of Five-Axis Aviation Turbine Disc Tenon Groove Broaching Equipment 3

Static Precision Sensitivity Analysis of Five-Axis Aviation ...

引用

3rd international conference on computer, control and Robotics, ICCCR 2023

作者： Song, Ping Song, Bei Lin, Chenghui Liu, Zhonghua Zhang, Shunqi School of Mechatronic Engineering and Automation Shanghai University Shanghai China Zhejiang CHR Intelligent Equipment Co. Ltd. Technology Department Zhejiang China

ISBN: (纸本)9781665492126

Broaching is the most important processing technology of mortise and tenon of aviation turbine disk. The precision of broaching equipment determines the processing quality of mortise and tenon. According to the principle of multi-body kinematics and the homogeneous coordinate transformation, the mathematical model of static accuracy analysis of the five-Axis aircraft turbine disk tenon broaching machine is established, and the tool setting error matrix is derived. Monte Carlo method was used to obtain the statistical distribution of errors in line with the assembly requirements and motion accuracy, and the simulation model of the tool process was built to complete the prediction of the static accuracy of the broaching machine. The influence of various errors in motion on the sensitivity of the tool accuracy was analyzed systematically. © 2023 IEEE.

关键词： Sensitivity analysis

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An Adaptive Impedance control Method for Human-Robot Interaction 1

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16th international conference on intelligent Robotics and Applications, ICIRA 2023

作者： Huang, Yanjiang Chen, Haoyang Cui, Zhaoyu Zhang, Xianmin School of Mechanical and Automotive Engineering South China University of Technology Guangzhou510640 China Guangdong Province Key Laboratory of Precision Equipment and Manufacturing Technology South China University of Technology Guangdong Guangzhou510640 China Key Laboratory of Safety of Intelligent Robots for State Market Regulation Guangdong Testing Institute of Product Quality Supervision Guangzhou510670 China

ISBN: (数字)9789819964987

ISBN: (纸本)9789819964970

In human-robot collaborative grinding tasks, the compliance of the robot to the operator is required, while the robot end effector is limited to a certain space to avoid danger. Therefore, an adaptive impedance control method, in which the stiffness coefficient changes with the position of the robot end effector, is proposed in this paper. Based on the Lyapunov method, the stability of the adaptive impedance controller is demonstrated. The results of simulation and experiments show that, with the method proposed in this paper, the operating space of the robot is limited to a certain range, and its compliance to the operator is good. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Human robot interaction

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Grid Impedance Shaping for Grid-Forming Inverters: A Soft Actor-Critic Deep Reinforcement Learning Algorithm 10

Grid Impedance Shaping for Grid-Forming Inverters: A Soft Ac...

引用

10th IEEE international Power Electronics and Motion control conference, IPEMC 2024 ECCE Asia

作者： Oshnoei, Arman Sorouri, Hoda Oshnoei, Soroush Teodorescu, Remus Blaabjerg, Frede Aalborg University Department of Energy Aalborg Denmark Aarhus University Department of Electrical and Computer Engineering Aarhus Denmark

ISBN: (纸本)9798350351330

This paper proposed an advanced method for adjusting grid impedance in grid-forming inverters, utilizing the Soft Actor-Critic Deep Reinforcement Learning (SAC-DRL) algorithm. The approach contains a flexible strategy for controlling virtual impedance, supported by an equivalent grid impedance estimator. This facilitates accurate modifications of virtual impedance based on the grid's X/R ratio and the converter's power capacity, aiming to optimize power flow and maintain grid stability. A unique feature of this methodology is the division of virtual reactance into two segments: one adhering to standard control protocols and the other designated for precision enhancement via the SAC-DRL method. This strategy introduces a layer of intelligence to the system, strengthening its resilience against fluctuations in grid impedance. Experimental validations, executed on a laboratory setup, verify the robustness of this approach, highlighting its potential to significantly improve intelligent power grid management practices. © 2024 IEEE.

关键词： Reinforcement learning

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Gesture-Enhanced Presentation control for Education 8

Gesture-Enhanced Presentation Control for Education

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8th IEEE international conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2024

作者： Saumya, Y.M. Dcruz, Colin Christon Rodrigues, Shane Priyanka Dsouza, Austin Braggs, Daniel Loy Pereira, Elwin Jason St Joseph Engineering College Dept. of CSE Vamanjoor India

ISBN: (纸本)9798350350593

Presentation skills are vital in many areas of life. Giving presentations is probably a common experience for anyone, whether they are a worker, student, business owner, or employee of an organisation. The requirement to manage and manipulate the slides with a keyboard or other specialised device might make presentations seem tedious at times. Enabling users to control the slideshow with hand gestures is the aim of this work. Gestures have become increasingly common in human-computer interaction in recent years. Several PowerPoint functionalities have been attempted to be controlled by hand movements by the system. This system maps motions using multiple Python modules and uses machine learning to identify motions with minute variances. Creating the perfect presentation is becoming increasingly difficult due to a number of aspects, including the slides, the keys to switching the slides, and the audience's composure. An intelligent presentation system that is based on hand gestures makes it simple to update or modify the slides. Allowing viewers to explore and manipulate the slideshow with hand movements is the technology's main objective. The technique recognises various hand motions for a variety of tasks using machine learning. A means of recognition opens up a line of communication between people and machines. © 2024 IEEE.

关键词： Gesture recognition

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A Novel Machine Learning-Based Framework for Predicting Diabetes in Healthcare

A Novel Machine Learning-Based Framework for Predicting Diab...

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2025 international conference on intelligent control, Computing and Communications, IC3 2025

作者： Oliver, A. Sheryl Gupta, Kimmi Singh, Jagendra Agnihotri, Atul Kumar Vasavi, M. Kaur, Gaganjot Srm Institute of Science and Technology Department of Computational Intelligence Chennai India School of Computer Science Engineering & Technology Bennett University Greater Noida India School of Engineering and Technology Chhatrapati Shahu Ji Maharaj University Electronics and Communication Engineering Kanpur India Cvr College of Engineering Department of Et -Cyber Security Hyderabad India Raj Kumar Goel Institute of Technology Department of Computer Science Ghaziabad India

ISBN: (纸本)9798331527495

The rise in diabetes is considered the greatest global health issue;thus, the call for early and accurate predictions of the disease is a dire need in the health sector. The detection of the disease in its initial stages would allow patients to undergo timely interventions and appropriate treatments. A novel approach for the prediction of stages and severity of diabetes by utilizing various models within a machine learning framework, such as Support Vector Machine (SVM), Artificial Neural Network (ANN), Decision tree learning (DT), and Random Forest (RF) is proposed in this paper. The training data would involve the history of medical conditions, age, BMI, insulin, skin thickness, glucose level, the number of pregnancies, and existence or non-existence of diabetes. The models were trained on 70% of the data and tested on the remaining 30%. The results show that SVM outperforms all other models, achieving an accuracy of 96.5%, followed by ANN with 94.5%, DT with 90.2 %, and RF with 86.7%. These results indicate that SVM, followed by ANN, is the most effective model for predicting diabetes stages. The accuracy and reduction of data loss have been constant improvements with epochs 30 to 360;therefore, models can be used in real-time applications in the clinical area. Thus, this research concludes that machine learning models, such as SVM, may be helpful in predicting and diagnosing early cases of diabetes;thus, these models could be helpful in making good decisions on healthcare and improving the patient's health. © 2025 IEEE.

关键词： Diseases

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2022 international conference on Recent advances in Electrical engineering and computer Sciences, RAEE and CS 2022

2022 International Conference on Recent Advances in Electric...

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2022 international conference on Recent advances in Electrical engineering and computer Sciences, RAEE and CS 2022

ISBN: (纸本)9781665491020

The proceedings contain 16 papers. The topics discussed include: multi-model ensemble based approach for heart disease diagnosis;enhancement of rotor angle stability with superconducting fault current limiter;PID controller for tracking of autonomous mobile robot combined with artificial potential field and iterative learning control;development of a CNN-based time-varying reactive power forecast model for power systems;review of software reliability through prediction models;deep auto encoder based chatbot for discrete math course;support estimation of analytic eigenvectors of Para Hermitian matrices;performance comparison of single and dual loop control scheme for grid connected inverter with LCL filter;electronic deception jamming: false target generation in radars;in silico study to find a potent inhibitor, vilazodone, to cure COVID-19 using molecular docking;and design and analysis of on-campus hybrid charging station for electric vehicles.

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