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Robust H∞Output Feedback Control for Vehicle Lateral Motion System with Quantization and Data Dropouts 9

Robust H∞Output Feedback Control for Vehicle Lateral Motion...

引用

9th international conference on Electrical engineering, Control and robotics, EECR 2023

作者： Ye, Zhuangwu Wuhan University of Science and Technology The School of Information Science and Engineering China

ISBN: (纸本)9781665491204

This study examines H∞ static output feedback control problem for discrete-time vehicle lateral dynamic system with quantization error and data packet dropout. The quadrilateral polytope model representes vehicle lateral dynamic system. The number of linear matrix inequalities is reduced by the polytope method based on the longitudinal velocity time varying parameters set. Additionally, this paper models data packet dropout by the Bernoulli random distribution approach, and quantizes the measured output signal by a dynamic quantizer. Such that the quantization error and data packet dropout are described in vehicle lateral dynamic system. The goal of this study is to construct a static output feedback controller that will ensure the system is stochastically stable and H∞ performance. Finally, the suggested controller design method is validated by using the co-simulation of Matlab/Simulink and Carsim. © 2023 IEEE.

关键词： Linear matrix inequalities

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Image segmentation algorithm based on multi-granularity clustering 7

Image segmentation algorithm based on multi-granularity clus...

引用

7th international conference on Mechatronics and Intelligent robotics, ICMIR 2023

作者： Xia, Shuyin Xia, Cenjun Dai, Dawei Ni, Jinyuan School of Computer Science and Technology Chongqing University of Posts and Telecommunication ChongQing400065 China School of Computer Science and Engineering Chongqing University of Technology Chongqing400054 China

ISBN: (数字)9781510668089

ISBN: (纸本)9781510668072

This article proposes an image processing algorithm based on multi-scale clustering, which can improve the probability of a classifier correctly classifying an image even after it has been subjected to noise attacks. The algorithm mainly involves performing multi-scale clustering on the pixels of the image based on features such as color space, distance, texture, etc., and then incorporating the clustering results into the training of the classifier. By directly extracting the main feature information from the image for training, the algorithm prevents large classification errors due to changes in certain pixel values, thus reducing the impact of attacks on other pixels in the image and greatly improving the robustness of the image. Therefore, this algorithm is a robust and efficient denoising method. © 2023 SPIE. All rights reserved.

关键词： Pixels

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Optimized Flame Detection Using a YOLOv5 Neural Network

Optimized Flame Detection Using a YOLOv5 Neural Network

引用

2024 international conference on Computing, robotics and System sciences, ICRSS 2024

作者： Karim, Shahid Ibrar, Muhammad Bilal, Anas Qadir, Akeel Bibi, Irfana School of Information Engineering Xi'an Eurasia University Xi'an710065 China Software College Shenyang Normal University Shenyang China College of Information Science and Technology Hainan Normal University Haikou571158 China Faculty of Computing and Information Technology University of Punjab Department of Computer Science Punjab Lahore Pakistan

ISBN: (纸本)9798331511296

With the rising prevalence of smart homes, there's an increasing demand for comprehensive automation solutions to mitigate fire risks, especially when homeowners are absent or in homes with elderly residents. This study focuses on enhancing flame detection techniques, particularly for smaller, hard-to-detect fire sources. A customized Yolov5 neural network was developed to improve detection precision. This enhancement was facilitated using a dataset of over 2,000 images from lab camera observations and online platforms. These images were then manually labeled and clustered for anchor dimensions using the K-means++ algorithm. The CIOU and diou - nms techniques were integrated to optimize the network's performance. Upon training, the refined model was tested on the Jet Nano, revealing its high efficacy in flame detection. © 2024 IEEE.

关键词： Smart homes

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The Effectiveness of CNN-Based Models in Macroscopic Motion Direction Detection 6

The Effectiveness of CNN-Based Models in Macroscopic Motion ...

引用

6th international conference on robotics and Computer Vision, ICRCV 2024

作者： Qiu, Zhiyu Chen, Tianqi Hua, Yuxiao Todo, Yuki Graduate School of Natural Science & Technology Kanazawa University Division of Electrical Engineering and Computer Science Kanazawa Japan Kanazawa University Faculty of Electrical Information and Communication Engineering Kanazawa Japan

ISBN: (纸本)9798331527426

Convolutional Neural Networks (CNNs) have revolutionized the field of computer vision, offering significant advancements in image and video recognition, classification, and segmentation tasks. Leveraging hierarchical pattern recognition capabilities, CNNs have demonstrated superior performance over traditional machine learning methods. One particularly critical application of CNNs is in motion direction detection, which is essential for enhancing security in surveillance systems, ensuring safe navigation in autonomous vehicles, and providing valuable insights in sports analytics. In this study, our primary goal was to investigate how different types of input data impact the accuracy of motion direction detection. We applied five distinct preprocessing methods to test the effect of input data on accuracy. Furthermore, we conducted experiments in various object and background scenarios to evaluate adaptability of simple 4-layer CNN, EfficientNetBO and ResNet50 in detecting motion direction. Finally, we tested the noise resistance of these three models. The results indicate that CNNs are capable of extracting the macroscopic motion direction of objects in images. Our study highlights the potential of CNN-based models for efficient and effective motion direction detection. © 2024 IEEE.

关键词： Convolutional neural networks

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Hybrid Approach for Accurate Vocal Anomaly Detection Using State-of-the-Art

Hybrid Approach for Accurate Vocal Anomaly Detection Using S...

引用

2023 international conference on Recent advances in science and engineering technology, ICRASET 2023

作者： Manikandan, M. Rengarajan, A. Naval, Preeti Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Department of Computer Science and Information Technology Bangalore India Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India

ISBN: (纸本)9798350306927

Accurate voice anomaly detection is achieved by the suggested method's combination of signal processing for feature extraction and improvement with deep learning. Preprocessing, feature extraction, and anomaly detection are the three pillars upon which this approach rests. The raw vocal signal is improved during preprocessing by denoising and filtering. From the preprocessed signal, the feature extraction stage derives Mel-Frequency Cepstral Coefficients (MFCCs) and their derivatives. Finally, an ensemble of three algorithms, including Support Vector Machines (SVM), Long Short-Term Memory (LSTM) Networks, and Convolutional Neural Networks (CNN), is used in the anomaly identification stage to conduct an in-depth evaluation of the detection of voice anomalies. The suggested hybrid method is meant to be more flexible and efficient in practical settings. © 2023 IEEE.

关键词： Long short-term memory

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A Magnetic Micro Robotic Trajectory Controller Design for Cell Trapping 8

A Magnetic Micro Robotic Trajectory Controller Design for Ce...

引用

8th IEEE international conference on Advanced robotics and Mechatronics, ICARM 2023

作者： Wang, Fanghao Jing, Daoyuan Zhang, Youchao Guo, Xiangyu Liu, Chuanjie Zhou, Mingchuan College of Biosystems Engineering and Food Science Zhejiang University Robotic Micro-nano Manipulation Lab Hangzhou China

ISBN: (纸本)9798350300178

Cell trapping is considered a complicated and challenging task due to the small scale of manipulation involved. Image guided robotic micro manipulation is among the novel and promising solutions that may enhance human capabilities. In this paper we demonstrate 3-degree-of-freedom (3-DOF) wireless magnetic control of an untethered microrobot (2-DOF position and 1-DOF pointing orientation). An accurate orientation control model has been employed to determine the microrobot's orientation in 2D space. In addition, multiple orientation experiments were conducted to verify the proposed model. Finally, a feedback control algorithm including a Proportional-Integral-Derivative controller (PID) is conducted to eliminate external disturbance during the trajectory tracking task. This system was primarily designed to control magnetic microrobots for cell trapping in vitro. © 2023 IEEE.

关键词： robotics

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Dynamic Behavior of Levitated Flexible Steel Plate in Bending Electromagnetic Levitation System During Disturbance Input 27

Dynamic Behavior of Levitated Flexible Steel Plate in Bendin...

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27th international conference on Electrical Machines and Systems, ICEMS 2024

作者： Uchida, Yamato Ogawa, Kazuki Kobayashi, Ikkei Kuroda, Jumpei Uchino, Daigo Ikeda, Keigo Kato, Taro Endo, Ayato Narita, Takayoshi Kato, Hideaki Tokai University Course of Mechanical Engineering Kanagawa Hiratsuka Japan Aichi University of Technology Department of Electronics and Robotics Aichi Gamagori Japan National Institute of Standards and Technology Tokai University Kanagawa Hiratsuka Japan Research of Institute of Science and Technology Tokai University Kanagawa Hiratsuka Japan Numazu National College of Technology Department of Mechanical Engineering Shizuoka Numazu Japan Hokkaido University of Science Department of Mechanical Engineering Hokkaido Sapporo Japan Tokyo University of Technology Department of Mechanical Engineering Hachioji Tokyo Japan Fukuoka Institute of Technology Department of Electrical Engineering Fukuoka Fukuoka Japan Tokai University Department of Mechanical Systems Engineering Kanagawa Hiratsuka Japan

ISBN: (纸本)9784886864406

In thin steel plate production lines, contact conveying by rollers is used, which sometimes results in scratches and plating defects on the steel plate surface. To solve this problem, non-contact conveyance by magnetic levitation is expected. When thin steel plates are magnetically levitated, magnetic levitation control becomes difficult due to deflection caused by the flexibility of the plate. Therefore, we have proposed a curved magnetic levitation system that improves the levitation stability by curving the steel plate for magnetic levitation. However, the dynamic behavior of thin steel plates during bending magnetic levitation have not been clarified. In this report, we performed a levitation experiments of bending steel plate, and input disturbance to the bending levitation device. From the measured response of levitated and oscillated steel plate, it was confirmed that the bending thin steel plate was in rigid body motion due to the disturbance. © 2024 The Institute of Electrical Engineers of Japan.

关键词： Magnetic levitation

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Lightweight Underwater Target Detection Algorithm Based on Improved YOLOv7 4

Lightweight Underwater Target Detection Algorithm Based on I...

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4th international conference on Industrial Automation, robotics and Control engineering, IARCE 2024

作者： Guo, Huihui Chen, Liang Yang, Junwei Zhou, Shaowu Yin, Tao School of Information and Electrical Engineering Hunan University of Science and Technology Xiangtan China Southwest Institute of Electronic Technology Chengdu China

ISBN: (纸本)9798350380323

Underwater robots frequently grapple with challenges such as false detection and missed detection due to factors like turbid water and low contrast of detected objects. These issues significantly hinder the efficiency and accuracy of underwater exploration tasks. To address these challenges, this study introduces a novel lightweight underwater target detection method based on an improved version of the YOLOv7 model. The core innovation lies in the integration of the GhostNet network as the backbone of the proposed method. By leveraging GhostNet's efficient architecture, we manage to drastically reduce the overall parameter quantity and eliminate excessive redundant feature information within the network. This not only results in a more compact network model but also enhances the overall detection accuracy. Furthermore, to broaden the network's receptive field and capture more intricate details, we introduce the MP-D module. This module cleverly employs dilated convolutions to replace the conventional downsampling convolutions, thereby enabling the extraction of more precise positional information of small targets without incurring additional computational overhead. The experimental results underscore the effectiveness of our proposed algorithm. Compared to the original YOLOv7 algorithm, our method achieves a significant reduction in model size by 24.6MB and a notable decrease in parameter quantity by 35.3%. Moreover, it boosts the recall rate by 5.5% and the mean Average Precision by 1.2%, demonstrating its robustness and superiority in underwater target detection tasks. © 2024 IEEE.

关键词： Interpolation

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Optimizing Robotic Arm Efficiency: Synergizing Aerodynamics with Arduino Control for Enhanced Six Degrees of Freedom 2nd

Optimizing Robotic Arm Efficiency: Synergizing Aerodynamics ...

引用

2nd international conference on Modern Research in Aerospace engineering, MRAE 2023

作者： Mohan, Akshay Akurathi, Mohan Kumar Vashisht, Priyanka Jatain, Aman Department of Computer Science & Engineering Amity University Haryana Gurgaon India Manav Rachna University Faridabad India

ISBN: (纸本)9789819713059

The combination of robotics and aerodynamics has proven to be an interesting field of study, leading to advances in many fields. This brief presents a new approach to the design and control of a robotic arm that focuses on aerodynamics. The robotic arm uses an Arduino microcontroller for simplicity and ease of use. The aerodynamic design of the robot arm is designed to increase efficiency and effectiveness by reducing weight and improving lift. By using the aerodynamic structure, the structure of the arm is streamlined to reduce air resistance, resulting in more fluid and clear power. The choice of heavy and durable materials further contributes to the aerodynamic performance. The control of the robotic arm is used by the Arduino microcontroller, which provides various platforms to work and interact with various sensors and actuators. Control algorithms include real-time feedback from position and force sensors to make precise and responsive movements. Aerodynamic design principles are incorporated into the controller, which can adjust the configuration of the robotic arm according to atmospheric conditions. This research contributes to the field of robotics by demonstrating the benefits of integrated aerodynamics and Arduino-based control in the creation of human arms. These findings lead to the development of more efficient robotic systems and provide valuable insights for future research at the intersection of robotics and aerodynamics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Robotic arms

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Stacked Bi-LSTM for Advanced Toxicity Detection in Comment Classification 6

Stacked Bi-LSTM for Advanced Toxicity Detection in Comment C...

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6th IEEE international conference on Artificial Intelligence in engineering and technology, IICAIET 2024

作者： Kumar, N. Naveen Abraham, Prabhakaran Kaliba, Syed Ibrahim Diniesh, V.C. Maruthamuthu, R. Balamurugan Seshu Kumar, P. Vekkudu, Naveen Madanapalle Institute of Technology & Science Department of Computer Applications Andhra Pradesh Madanapalle India Mai-nefhi College of Engineering and Technology Department of Computer Science and Engineering Mainefhi Eritrea Kongu Engineering College Department of Electronics & Communication Engineering Tamil Nadu Erode India Jain University Department of Information Science and Engineering Karnataka Bangalore India

ISBN: (纸本)9798350389692

The surge in Internet usage has revolutionized online forums, providing dynamic forums for active participation and meaningful debates. However, this has also exposed users to the risk of harassment. Efforts to address toxic comments in online forums have faced challenges, with current solutions unveiling unreliability. advances in hardware, cloud computing, and natural language processing (NLP) enable the development of more robust approaches, predominantly NLP-based deep learning models. Pre-trained transformer models are gradually utilized for accurate toxic comment classification, bigger traditional methods. Stacked LSTM models, emphasizing hierarchical feature learning and improved contextual understanding, outperform single layer counterparts, offering an effective solution for online content moderation and sentiment analysis. This research indicates a significant stride towards more dependable and sophisticated toxic comment detection, addressing the challenges of social media effectively. © 2024 IEEE.

关键词： Sentiment analysis

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