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检索条件"任意字段=International Conference on Advances in Science, Engineering and Robotics Technology"

共 47866 条记录，以下是1231-1240 订阅

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Modeling and Simulation of Compensation Control Strategy for System with Performance Degradation 6

Modeling and Simulation of Compensation Control Strategy for...

引用

6th international conference on robotics, Control and Automation engineering, RCAE 2023

作者： Gao, Wenke Zhang, Lei Chen, Biao Wang, Xiaohui Wang, Wenlin SINO-German College of Intelligent Manufacturing Shenzhen Technology University Shenzhen518118 China School of Mechanical and Electrical Engineering Xuzhou University of Technology Xuzhou221018 China School of Energy and Power Engineering Lanzhou University of Technology Lanzhou730050 China

ISBN: (纸本)9798350315301

How to keep a performance degraded system to running in high-quality before failure is a difficult issue. This study focuses on the performance degraded system and designs a compensation control strategy to compensate for performance changes caused by the degradation of the system components. The strategy enables the system to maintain output close to that of the new system before failure. The theoretical analysis results indicate that when H(0)>>1, the compensated system output approximately equals to the output of the new system. The second-order mechanical system composed of a mass spring damping is taken as an example to illustrate the proposed strategy, assuming that the mass and damping coefficients remain constant while the spring rigidity deteriorates, the steady-state values and overshoot changes of the system were simulated and analyzed. The simulation results show that the gap between the steady-state values and overshoot of the free degradation system and using compensation control strategy is significant, and increases with the severity of the system degradation. The research results can provide theoretical reference in maintaining high-quality running for degraded systems before failure. © 2023 IEEE.

关键词： Damping

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Pushing and Rotating a Heavy Mass Rigid Body Using an Omnidirectional Aerial Manipulator 6

Pushing and Rotating a Heavy Mass Rigid Body Using an Omnidi...

引用

6th international conference on robotics and Automation in Industry, ICRAI 2024

作者： Ullah, Hameed Soto, Santos Miguel Orozco Mazhar, Naveed Ahmad, Irfan Lippiello, Vincenzo Ruggiero, Fabio University of Naples Federico II Department of Electrical Engineering and Information Technology Naples Italy Science and Technology College Autonomous University of Mexico City Mexico City Mexico National University of Sciences and Technology Unmanned Vehicles Research Lab DEE CEME Islamabad Pakistan Engineering School of Bilbao University of the Basque Country Department of Automatic Control and Systems Bilbao Spain

ISBN: (纸本)9798350355703

This paper presents the pushing and rotation of a heavy rigid body using an omnidirectional actively tilted aerial manipulator, controlled via a hybrid force/position controller. Unlike conventional unmanned aerial vehicles, the proposed system enables precise horizontal force application, making it ideal for aerial physical interaction tasks. The hybrid controller is implemented in the Robot Operating System and validated through extensive simulations in a Gazebo physics-engine-based environment. The manipulator's performance was evaluated in two scenarios: circular trajectory tracking and the rotation of a heavy rigid mass. Results demonstrated its capability to follow complex trajectories with high accuracy and exert sustained forces for object manipulation. The system exhibited excellent stability and precision, underscoring its potential for real-world applications in challenging environments. © 2024 IEEE.

关键词： Omnidirectional antennas

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ML Based Light-Weight Security Protocol for IoFT Device 6

ML Based Light-Weight Security Protocol for IoFT Device

引用

6th international conference on robotics and Automation in Industry, ICRAI 2024

作者： Khan, Awais Aziz, Ayesha Tariq, Muhamad Kamal, Mohsin National Univeristy of Computer and Emerging Sciences Department of Electrical Engineering Islamabad Pakistan School of Computer Science National Univeristy of Computer and Emerging Sciences Islamabad Pakistan School of Electrical Engineering and Computer Science National Univeristy Science and Technology Islamabad Pakistan

ISBN: (纸本)9798350355703

Forest plays a vital role in environmental change and the protection of forests is the utmost important thing. The study attempts to suggest a solution for a smart forest (Internet of Forest Things). As nowadays, the Internet of Things is emerging very vastly and a large number of researchers are working towards the field of Internet of Forest Things (loFT). The protection of smart forests is of utmost importance because the infrastructure is physically unprotected and they generate huge data about the forest. Due to scalability, smart forest controllers are small-sized and low-cost devices with less computational capabilities. Keeping it in mind, the designed algorithms should be lightweight so that they can meet the requirements. To address this issue, this paper proposes a machine learning approach to develop a lightweight security solution to address data altering and man-in-the-middle attacks. The algorithm generates a link fingerprint of the received signal strength indicator (RSSI) value of the communicating IoFT node. The packet is sent to the server unit and it is decoded and matched with the table to identify the path. A trained machine learning model at the server gets the RSSI value from the packet and successfully classify about the presence of an attack. © 2024 IEEE.

关键词： k-nearest neighbors

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A Method for Enhancing Robustness of Viewpoint Planning Based on Spatial Supervoxels 4

A Method for Enhancing Robustness of Viewpoint Planning Base...

引用

4th international conference on Artificial Intelligence, robotics, and Communication, ICAIRC 2024

作者： Chang, Lianjie Qu, Fuheng Liu, Chenghao Wang, Enguo Ding, Shiqi Su, Chengzhi Changchun University of Science and Technology School of Computer Science and Technology Changchun China Harbin Aircraft Industry Group Co. Ltd. Workshop 45 Harbin China Changchun University of Science and Technology School of Mechanical and Electrical Engineering Changchun China Changchun University of Science and Technology School of Artificial Intelligence Changchun China

ISBN: (纸本)9798331531225

In the viewpoint planning task for unknown objects, the accuracy of the perception system and calibration lead to the collected point cloud data containing numerous noise and stratification, which interferes with the viewpoint planning *** address the above problems, this paper proposes a method to enhance the robustness of viewpoint planning based on spatial ***, the spatial voxel environment is constructed to store the triangular meshed point cloud containing noise, which is used to record the positional distribution information of the point cloud in space;then, the voxels containing the point cloud are neighborhood-grown to form a Supervoxel unit, which expands the receptive field;And finally, the point cloud boundaries are detected and categorized using spatial Supervoxel, which are used to guide viewpoint *** verify the effectiveness of the proposed method, this paper develops a robot perception system, uses an aircraft radome as the experimental object and introduces varying levels of system errors for the viewpoint planning *** experimental results indicate that the proposed method can accurately detect and classify point cloud boundaries to guide viewpoint planning, achieving complete surface reconstruction of unknown objects, even in the presence of a certain degree of noise in the perception system. The overall modeling time is approximately 7 minutes, demonstrating strong robustness and real-time performance. © 2024 IEEE.

关键词： Robot programming

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Pedestrian Target Detection Model Based on Improved Yolov8 5

Pedestrian Target Detection Model Based on Improved Yolov8

引用

5th international conference on Artificial Intelligence and Electromechanical Automation, AIEA 2024

作者： Huang, Jinglun Liang, Jianan Cao, Yongjun School of Electronics and Information Engineering Wuyi University Guangdong Jiangmen China Guangdong Key Laboratory of Modern Control Technology Institute of Intelligent Manufacturing Gdas Guangdong Guangzhou China South China Robotics Innovation Research Institute Guangdong Foshan China School of Mechanical & Automotive Engineering South China University of Technology Guangdong Guangzhou China

ISBN: (纸本)9798350366174

Dense pedestrian detection is a key technology in public intelligent surveillance. It identifies the location and number of pedestrians in videos through object detection methods, thereby achieving intelligent surveillance of crowds. However, in crowded scenes, due to occlusion and small pedestrian targets, it is easy to miss pedestrians. To solve this problem, this paper proposes an improved YOLOv8 detection algorithm, YOLOv-8FastTCD. Firstly, the FasterNet module is used to replace the backbone network. Secondly, a task collaborative dynamic detection head (TCDDH) is introduced after the neck network. The performance of this model on the CrowdHuman dense pedestrian detection dataset has been improved, with mAP50 and mAP50-95 reaching 78.4% and 51.5% respectively, which are 2.1% and 2.0% higher than the original YOLOv8n model. © 2024 IEEE.

关键词： FasterNet Pedestrian Detection TCDDH YOLOv8

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PLS Based Fault Detection and Diagnosis For HVAC Systems 7

PLS Based Fault Detection and Diagnosis For HVAC Systems

引用

7th international conference on robotics, Control and Automation engineering, RCAE 2024

作者： Wang, Yintao Huang, Siyu Hu, Yu Zhu, Jinpeng Qiu, Aibing School of Electrical Engineering and Automatioon Nantong University Nantong China School of Imformation Science and Technology Nantong University Nantong China

ISBN: (纸本)9798350355642

Heating, Ventilation and Air Conditioning (HVAC) systems are increasingly popular for improving indoor environments, which results in growing energy consumption of buildings. Once the malfunction of one component in such a system cannot be detected and handled in time, the faults may lead to an increase in energy consumption of the whole system. Even worse, huge economic losses and serious accidents will be caused. Data-based techniques can take full advantage of the vast amounts of available process data generated during HVAC operation. Partial least squares (PLS) is more suitable for handling nonlinear data in HVAC systems compared with traditional methods such as principal component analysis (PCA). However, traditional PLS cannot detect faults timely. Therefore, this paper proposes a method based on rapid iterations of the PLS algorithm to achieve fault detection and diagnosis. This method accelerates the algorithm's iteration by adjusting convergence bound dynamically and uses the residual contribution rates for fault location. This method is applied to a HVAC model with thirty rooms, established using TRNSYS, to validate the effectiveness of the proposed method. © 2024 IEEE.

关键词： HVAC

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Environment Based Multi-Object Grasp Planning in Bin Picking Process 4

Environment Based Multi-Object Grasp Planning in Bin Picking...

引用

4th international conference on Artificial Intelligence, robotics, and Communication, ICAIRC 2024

作者： Xiong, Heng Xie, Fawu Bai, Long Guo, Changrong Xu, Jianfeng Huazhong University of Science and Technology The State Key Laboratory of Intelligent Manufacturing Equipment and Technology School of Mechanical Science and Engineering Wuhan430074 China HUST-Wuxi Research Institute Wuxi214174 China

ISBN: (纸本)9798331531225

Given the potential of bin picking to eliminate manual handing practices in manufacturing and logistics industries, this paper presents an environment-based grasp planning pipeline to address the challenges posed by complex object arrangements in cluttered environments. The pipeline integrates offline grasp planning system with online grasp filter, designed specifically for multi-object cluttered bin picking tasks. In the offline phase, the pipeline utilizes the improved Particle Swarm Optimization (PSO) algorithm to generate the grasp solutions by incorporating gripper and object models. In the online phase, the system considers grasp and robot kinematics to prioritize objects and determine the best grasp based on the grasp task scene model and the precomputed solutions from the offline phase. The proposed pipeline is validated through both simulated and experimental trials using the Franka Emika Panda robotic arm. The results demonstrate the effectiveness of the pipeline in handling multi-object cluttered bin picking tasks. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Deep Learning Based Digital Image Encryption 1

Deep Learning Based Digital Image Encryption

引用

1st IEEE international conference on advances in Computing, Communication and Networking, ICAC2N 2024

作者： Faisal, Shah Agrawal, Amrit Kumar Verma, Abhishek Singh Sharda School of Engineering and Technology Sharda University Department of Computer Science and Engineering Utter Pradesh Greater Noida India Sharda School of Engineering and Technology Sharda University Department of Computer Science and Engineering Utter Pradesh Greater Noida India

ISBN: (纸本)9798350356816

As technology has evolved, digital images have become increasingly popular. However, these images often exhibit a high degree of correlation and redundancy among pixels, which creates specific requirements for applications like real-time communication and assessment. Traditional encryption approaches for digital images are inefficient, and the algorithms are difficult. A thorough analysis can highlight existing patterns and provide light on unstudied issues in deep learning and chaotic image encryption. This paper, provides a schematic review of digital image encryption using deep learning architecture. We also proposed a framework that merges the strengths of deep learning and chaotic systems, tackling both computational efficiency and the need for secure encryption. The combination is crucial for pulling intricate patterns from large sets of data and using the chaotic, unpredictable nature of the map to create highly secure encryption keys. The complex behavior of the multi-dimensional chaotic map, along with deep learning model's ability to extract features from vast data, ensures our generated keys are both random and unpredictable which makes them highly resistant to attempts at pattern finding and reverse engineering, safeguarding the integrity of the encryption. © 2024 IEEE.

关键词： Convolutional neural networks

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Promoting STEM Education through the Preparation of Multicultural National robotics Teams in Qatar (Evaluation)

Promoting STEM Education through the Preparation of Multicul...

引用

2023 ASEE Annual conference and Exposition - The Harbor of engineering: Education for 130 Years, ASEE 2023

作者： Katbeh, Tala Cieslinski, G. Benjamin Bazzi, Hassan Texas A&M University Qatar

In recent years, university outreach programs have increased as an effective method of imparting a comprehensive perspective into science, technology, engineering, and mathematics (STEM) careers and disciplines to school students. Different models of STEM education exist and the implementation of educational robotics to pre-college students has been deemed impactful in influencing the students' interest in STEM fields. This is due to the accessibility of educational robotics as well providing an engaging, hands-on method of combining technology, engineering, and design. Therefore, with the increase in educational robotics programs, having an end goal for the students is necessary for learning motivation. That is why involvement in international robotics competitions would meet this component. Moreover, combining the multicultural aspect that has been increasingly predominant in STEM helps raise the educational structure to a melting pot pedagogy. This provides a solid foundation for the students' development as they experience diversity consciousness even in a technical setting. This paper highlights the importance and the techniques behind joining groups of multicultural backgrounds in Qatar to represent national teams in international competitions - notably the well-renowned FIRST Global Challenge, an Olympics-style robotics competition that brings teams from different countries to solve the challenges facing our world. Preparing for the FIRST Global Challenge includes gaining effective communication and problem-solving skills as the students work as a team to devise creative solutions to real-world engineering problems. The approach with which the students representing two different nations - Afghanistan and Qatar - were trained in a shared workspace will be presented. Qatar is known for its cultural diversity, and the schools across Qatar include students from over 100 nationalities. Therefore, determining a technique to be delivered to a multicultural environme

关键词： Students

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MiniBert24: A Lightweight Transformer-Based Model for Stock Market Movement Prediction 3

MiniBert24: A Lightweight Transformer-Based Model for Stock ...

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3rd IEEE international conference on robotics, Automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2024

作者： Rodrigues, Gourab Nicholas Nazmul Hossain Mir, Md Mahmud Bhuiyan, Md Shahriar Al Rafi, Md Fuad, K.M. Nafiur Rahman Shahedul Islam, Md. Nur, Kamruddin Washington University of Science and Technology 2900 Eisenhower ave AlexandriaVA22314 United States Bangladesh University of Business and Technology Dhaka Bangladesh Stamford University Bangladesh Department of Computer Science and Engineering Dhaka Bangladesh American International University-Bangladesh Department of Computer Science Dhaka Bangladesh

ISBN: (纸本)9798331534400

The rapid growth of financial markets has necessitated developing more precise and efficient frameworks to predict stock price movements. This paper introduces MiniBert24, a lightweight, transformer-based architecture specifically designed for stock market movement prediction by leveraging news related to finance and past stock price information. The model is tailored for high computational efficiency, combining a reduced number of transformer layers with fewer attention heads while retaining strong predictive power. We utilize a unique dataset incorporating historical headlines from the Reddit WorldNews channel and Dow Jones Industrial Average (DJIA) data, creating a binary classification task to forecast daily stock price movement. MiniBert24 obtains an excellent AUC of 96.7% and an F1-score of 96.43%, outperforming standard transformer models while reducing computational overhead. These results demonstrate the potential of MiniBert24 to serve as a robust tool for market prediction in real-time trading environments. The model's ability to fuse textual data with stock market indicators provides a compelling solution for financial analysts and algorithmic traders seeking efficient, high-performing predictive models. © 2024 IEEE.

关键词： Prediction models

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