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1st International Conference on computational intelligence and Sustainable Engineering Solution, CISES 2022

作者： Gupta, Atul Srivastava, Vivek Choudekar, Pallavi Ruchira, Ruchira Amity University Uttar Pradesh India

ISBN: (数字)9781665480048

ISBN: (纸本)9781665480048

This paper describes the design and development of a home automation system that links its component parts using Wi-Fi technology as the network architecture. Essentially, the proposed system comprises of just two parts. The server (web server), which regulates, coordinates, and keeps an eye on users' homes, is the crucial component because it houses the system's brain. Users and system administrators have local (LAN) and remote (Internet) access to manage and control system code (internet). The system supports a variety of home automation systems, including power management and security components. The suggested technique is more flexible and scalable than current home automation systems. Goal of the work is to utilize easily available electronics modules to implement a cost effective, Compact and efficient home automation project. We made a system which gives service at low cost compared to the cost of the on the market, there are a variety of home automation systems. and it is also very compact. © 2022 ieee.

关键词： automation

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MINER-RRT*: A Hierarchical and Fast Trajectory Planning Framework in 3D Cluttered Environments

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ieee TRANSACTIONS ON automation SCIENCE AND ENGINEERING 2025年 22卷 10973-10985页

作者： Wang, Pengyu Tang, Jiawei Lin, Hin Wang Zhang, Fan Wang, Chaoqun Wang, Jiankun Shi, Ling Meng, Max Q. -H. Southern Univ Sci & Technol Shenzhen Key Lab Robot Percept & Intelligence Shenzhen 518055 Peoples R China Southern Univ Sci & Technol Dept Elect & Elect Engn Shenzhen 518055 Peoples R China Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Peoples R China Shandong Univ Sch Control Sci & Engn Jinan 250100 Shandong Peoples R China Southern Univ Sci & Technol Jiaxing Res Inst Jiaxing 314001 Peoples R China Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G 2R3 Canada Chinese Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China

Trajectory planning for quadrotors in cluttered environments has been challenging in recent years. While many trajectory planning frameworks have been successful, there still exists potential for improvements, particularly in enhancing the speed of generating efficient trajectories. In this paper, we present a novel hierarchical trajectory planning framework to reduce computational time and memory usage called MINER-RRT*, which consists of two main components. First, we propose a sampling-based path planning method boosted by neural networks, where the predicted heuristic region accelerates the convergence of rapidly-exploring random trees. Second, we utilize the optimal conditions derived from the quadrotor's differential flatness properties to construct polynomial trajectories that minimize control effort in multiple stages. Extensive simulation and real-world experimental results demonstrate that, compared to several state-of-the-art (SOTA) approaches, our method can generate high-quality trajectories with better performance in 3D cluttered environments (https://***/fXuuMRX19q0). Note to Practitioners-The motivation is the problem of planning trajectories for quadrotor autonomous flight in 3D cluttered and complex scenarios such as wild forest exploration and subterranean environment search-and-rescue. Sampling-based path planning methods are suitable for dealing with the complexity of the physical environment but are not convenient for computing dynamics and their differentials. Optimization-based trajectory generation methods are appropriate for handling various high-order constraints but rely on high-quality initial path solutions. Therefore, this paper combines the advantages of the two methods to propose a novel trajectory planning framework that can generate high-quality trajectories for quadrotors faster than many previous algorithms. We conduct numerous simulations and real-world experiments to verify that our method can be effectively deployed in re

关键词： Trajectory Trajectory planning Planning Quadrotors Three-dimensional displays Polynomials Trajectory optimization Splines (mathematics) Resource management Heuristic algorithms Robot trajectory planning sampling-based algorithm deep neural network

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Artificial intelligence-based Fault Detection and Diagnosis: Towards Application in a Chemical Process

Artificial Intelligence-based Fault Detection and Diagnosis:...

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2023 CAA symposium on Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS 2023

作者： Liu, Yiqi Li, Zhi Chen, Hongtian The School of Automation Science & Engineering Key Laboratory of Autonomous Systems and Networked Control Ministry of Education South China University of Technology Guangzhou510640 China Shanghai Jiao Tong University Department of Automation Shanghai200240 China

ISBN: (纸本)9798350337754

As the chemical processes become more and more complicated, it is crucial but also difficult to detect and diagnose faults in the system than ever. With the development of machine learning algorithms and cloud computing, to monitor chemical processes completely with the help of big data become true. However, most machine learning methods suffer from unexplainable problem. By employing Explainable Artificial intelligence(XAI) techniques, this paper applies two kinds of unsupervised machine learning methods, One-Class Support Vector Machine and Depth-based Isolation Forest Feature Importance, to detect and diagnosis faults in a chemical process. By combining two different fault explanation methodologies, accuracy and reliability of the fault diagnosis results can be achieved by the validation in a simulated continuous stirred tank reactor. © 2023 ieee.

关键词： Support vector machines

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Incorporating Visual intelligence in Line Following Robots 9

Incorporating Visual Intelligence in Line Following Robots

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9th ieee International symposium on Smart Electronic Systems, iSES 2023

作者： Pujara, Dhyanik Naik, Palak Gautam, Riya Mecwan, Akash Institute of Technology Nirma University Electronics and Comminication Department Ahmedabad India

ISBN: (纸本)9798350383249

Line follower robot is a mobile robot that is programmed to detect the path and trace the same. A colored line follower is an improved version of the line follower robot which is able to select the desired line among different colored lines as per the instructions given by the user. Line-following robots have gained significant attention due to their applications in various fields, including industrial automation, transportation, and entertainment. Traditional line-followers use basic infrared sensor-based systems, which restrict their ability to handle complex and dynamic surroundings and also work for either black or white path. In order to improve the robot's ability to navigate and track a colored path, this paper suggests two methods that make use of sophisticated image processing and computer vision algorithms. Open CV and python are used to design the path detection system and a feedback-based control loop mechanism called PID controller is also implemented for robot movement. Out of the two proposed methods, the experimental results demonstrate the effectiveness of IR Analogy Algorithm over Image Warping based algorithm. © 2023 ieee.

关键词： Python

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A Continuous Learning Method for Fault Location in Spacecraft Attitude control System

A Continuous Learning Method for Fault Location in Spacecraf...

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2023 China automation Congress, CAC 2023

作者： Ren, Junjian Cheng, Yuehua Wang, Tian Liu, Deyuan Zeng, XinHua Snoussi, Hichem School of Automation Science and Electrical Engineering Beihang University Beijing China College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing China Institute of Artificial Intelligence Sklsde Beihang University Beijing China Academy for Engineering and Technology Fudan University Shanghai China University of Technology of Troyes Troyes France

ISBN: (纸本)9798350303759

In today's information age, mankind is faced with a huge speed of knowledge and technology update. The swift progress renders the conventional learning model inadequate for addressing human needs anymore. In the field of machine learning, since data in the real world is always changing, traditional machine learning systems need to be constantly retrained to adapt to new data, which is very demanding in terms of computational resources and time. Aiming at the problem that spacecraft attitude control system faults change with the change of on-orbit time leading to the decrease of localization accuracy, this paper proposes a continuous learning method for spacecraft control system fault localization. Aiming at the spacecraft attitude control system fault localization problem, a spacecraft attitude control system fault localization model is constructed, first the spacecraft attitude control system is simulated, then the fault localization system is simulated by neural network, the different fault data images are generated by residual generation, and the classification is performed by using the neural network. In response to the change of fault types and amplitudes over time in spacecraft operation, the optimized continual learning method is used to solve the situation that the model forgets the knowledge of old faults, which improves the model's ability to cope with and memorize a variety of different faults. © 2023 ieee.

关键词： Orbits

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Deep Reinforcement Learning for AoI-aware Transmission control in Energy-Harvesting Grant-Free NOMA IoT Networks 11

Deep Reinforcement Learning for AoI-aware Transmission Contr...

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11th International symposium on Telecommunication, IST 2024

作者： Sheikhi, Marzieh Hakami, Vesal Zarrabi, Houman Iran University of Science and Technology School of Computer Engineering Tehran Iran Iran Telecommunication Research Center Department of Communication Technologies Tehran Iran

ISBN: (纸本)9798350356250

The advent of Internet of Things (IoT) networks has revolutionized various real-time applications, such as healthcare, smart cities, and remote automation, by enabling seamless data exchange and real-time monitoring and control. Real-time applications require fresh data, which is quantified by a novel metric, Age of Information (AoI), defined as the time elapsed since the generation time of last successfully received data. Additionally, due to energy limitations in most IoT scenarios, the trade-off between age and energy becomes an important challenge in these networks. Motivated by the need to optimize both data freshness and energy efficiency, this paper considers a real-time scenario in energy-harvesting (EH) IoT networks that operate based on grant-free (GF) Non-Orthogonal Multiple Access (NOMA) mechanism. Given the foresighted nature of the transmission control problem due to the stochastic dynamics of the AoI and energy charging processes, we introduce a deep reinforcement learning (DRL) framework that dynamically manages transmission decisions and resource block allocation, minimizing the average sum AoI across IoT devices, while considering energy feasibility. Through extensive simulations, we demonstrate that our proposed DRL-based approach significantly outperforms existing methods in terms of expected cumulative cost, showcasing its potential for enhancing the performance of future IoT networks. © 2024 ieee.

关键词： Resource allocation

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Integrated Design of Automated Cage Chicken control Based on Electromechanical control System

Integrated Design of Automated Cage Chicken Control Based on...

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2023 International Conference on Electronics and Devices, computational Science, ICEDCS 2023

作者： Yan, Senquan Luoding Polytechnic Guangdong Yunfu China

ISBN: (纸本)9798350343038

In modern agriculture, how to improve the efficiency and quality of chicken house breeding has become an important issue. This research aims to realize the intelligence and automation of chicken house breeding through the integrated design of automated cage chicken breeding control of electromechanical control system, so as to deal with the problems existing in traditional chicken house breeding methods. Through experimental verification, it is found that the design application can significantly improve the feeding environment, reduce feed waste and improve the growth of chickens. The experimental data shows that in the fourth week of the automated cage chicken control system, the temperature of the chicken house dropped to 23°C, the humidity dropped to 59%, and the weekly feed consumption decreased from 120kg to 115kg, while the growth of the chicken gradually increased, and finally reached 560 grams. To sum up, the integrated design of the automated cage chicken control of the electromechanical control system provides a beneficial plan and idea for realizing the high efficiency and high quality of chicken house farming. © 2023 ieee.

关键词： Houses

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A Runtime-Efficient Multi-Object Tracking Approach for Automotive Perception Systems

A Runtime-Efficient Multi-Object Tracking Approach for Autom...

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International symposium on Applied computational intelligence and Informatics ( SACI)

作者： László Lindenmaier Balázs Czibere Szilárd Aradi Tamás Bécsi Dept. of Control for Transportation and Vehicle Systems Budapest University of Technology and Economics Budapest Hungary

There is an increasing demand for fully autonomous driving with the spread of advanced driver assistance systems. However, a higher level of automation requires an enhanced environment perception system. The automotive smart sensors detecting the surrounding objects are usually subject to various uncertainties. Objects are sometimes misdetected, false detections may also occur, and the sensor measurements are noisy. Multi-object tracking algorithms aim to compensate for these uncertainties, estimating precisely and continuously the state of surrounding objects. The real-time execution of these algorithms is crucial in automotive applications. This paper presents an improved runtime-efficient local nearest neighbor-based approach. The performance metrics of the proposed method get close to the state-of-the-art algorithms, besides having significantly lower computational complexity.

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MULTI-ROBOT PATH PLANNING AND COOPERATIVE OBSTACLE AVOIDANCE BASED ON MONTE-CARLO TREE SEARCH 10

MULTI-ROBOT PATH PLANNING AND COOPERATIVE OBSTACLE AVOIDANCE...

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10th International symposium on Test automation and Instrumentation, ISTAI 2024

作者： Dai, Juan Jing, Yanzhang Su, Zhong Zhu, Cui Beijing Key Laboratory of High Dynamic Navigation Technology Beijing Information Science & Technology University Beijing China Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing China School of Automation Beijing Information Science & Technology University Beijing China School of Information and Communication Engineering Beijing Information Science & Technology University Beijing China

ISBN: (纸本)9781837242672

With the rapid development of artificial intelligence technologies, multi-robot cooperative processing tasks are becoming more and more common in various applications. However, traditional single-robot planning algorithms show obvious limitations in dealing with complex social interactions and dynamic environments in the real world. To address this challenge, this paper applies Monte Carlo Tree Search (MCTS) to multi-robot path planning and proposes a collaborative obstacle avoidance motion planning algorithm based on MCTS. Specifically, the algorithm prunes the Monte Carlo tree by excluding cases that do not meet the robot speed constraints and potential collisions, which significantly improves the computational speed and success rate of the algorithm, making it applicable to the multi-robot path conflict problem. In addition, we design a policy space that conforms to the robot's operation and a cost function that meets the requirements of robot path planning to plan an efficient cooperative path for each robot. Through a series of simulation experiments, we demonstrate the algorithm's path planning and obstacle avoidance performance in a variety of conflict scenarios. The results show that the method is highly cooperative and effective in complex environments. © The Institution of Engineering & Technology 2024.

关键词： Cost functions

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Quantification of Competencies-based Curricula for Artificial intelligence

Quantification of Competencies-based Curricula for Artificia...

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2023 ASEE Annual Conference and Exposition - The Harbor of Engineering: Education for 130 Years, ASEE 2023

作者： Jin, Yufang Applonie, Robert Morton, Paul E. Conkel, Mason Cole Nguyen, Thuy Khanh Qian, Chunjiang The University of Texas San Antonio United States Electrical and Computer Engineering Klesse College of Engineering and Integrated Design University of Texas San Antonio United States University of Texas San Antonio United States

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