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检索条件"任意字段=International Conference on Methods and Models in Automation and Robotics"

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Finite Time Adaptive Algorithm for Indentification Stationary Signal Generators Parameters that are Product of Sinuses

Finite Time Adaptive Algorithm for Indentification Stationar...

引用

international Russian automation conference (RusAutoCon)

作者： S. M. Vlasov V. A. Zhdanov A. A. Pyrkin Department of Control Systems and Robotics ITMO University St. Petersburg Russia

In this paper, we address the challenge of identifying parameters of stationary signal generators in finite time, where these generators are products of sinusoidal signals. It's posited that all parameters of the original signal remain unknown. We introduce a new method for parameterizing the signal under consideration. The measured signal is represented as the output of a linear generator possessing an unknown state matrix. In the initial step, the measured signal is transformed using the Jordan form of the matrix, supplemented with delay units. Subsequently, based on the parameters of the original signal, a linear regression model is derived. In the final step, unknown parameters are calculated using the derived linear regression models. The efficacy of the proposed methodology is validated through numerical simulation.

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ARAS-Farabi Experimental Framework for Skill Assessment in Capsulorhexis Surgery 9

ARAS-Farabi Experimental Framework for Skill Assessment in C...

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9th RSI international conference on robotics and Mechatronics (ICRoM)

作者： Ahmadi, Mohammad Javad Allahkaram, Mohammad Sina Rashvand, Ashkan Lotfi, Faraz Abdi, Parisa Motaharifar, Mohammad Mohammadi, S. Farzad Taghirad, Hamid D. K N Toosi Univ Technol Adv Robot & Automated Syst Tehran Iran Univ Isfahan Dept Elect Engn Esfahan Iran Univ Tehran Med Sci Tehran Iran

ISBN: (纸本)9781665420945

Automatic surgical instruments detection in recorded videos is a key component of surgical skill assessment and content-based video analysis. Such analysis may be used to develop training techniques, especially in ophthalmology. This research focuses on capsulorhexis, the most fateful process in cataract surgery, which is a very delicate procedure and requires very high surgical skill. Assessment of the surgeon's skill in handling surgical instruments is one of the main parameters of surgical quality assessment, and requires the proper detection of important instruments and tissues during a surgical procedure. The traditional methods to accomplish this task are very time-consuming and effortful, and therefore, automating this process by using computer vision approaches is a stringent requirement. In order to accomplish this requirement, a proper dataset is prepared. By consulting the expert surgeons, the pupil, and the surgical tool, namely the capsulorhexis cystotome, are annotated in this dataset. Then, we created a general framework for implementing and examining different approaches, models, and techniques on the developed dataset, and reporting the comparative analysis. This study shows that the object detection task can be accurately performed for various scenarios using this dataset. The developed dataset, the developed deep learning general framework, and other developments are made public for further research.

关键词： Object detection Surgical instrument detection Dataset preparation automation Cataract surgery Capsulorhexis cystotome

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Research on Driver’s License OCR Based on CRNN Deep Network

Research on Driver’s License OCR Based on CRNN Deep Network

引用

robotics, Artificial Intelligence and Intelligent Control (RAIIC), international conference on

作者： Jun Wang Hongke Chen Chengyi College Jimei University Xiamen Fujian China School of Mechanical and Electrical Engineering & Automation Xiamen University Tan Kah Kee College Zhangzhou Fujian China

ISBN: (数字)9798350352627

ISBN: (纸本)9798350352634

A driver’s license OCR method based on deep learning was designed, in response to the poor performance of traditional methods in driver’s license recognition. Disturbed by equipment issues and external environment, the collected driver’s license images suffer from uneven lighting and tilting, which seriously affect the recognition effect. Carry out driver’s license pre-processing through image enhancement, tilt correction, red stamp localization, text ROI extraction, and character region segmentation. Then, recognize the extracted text regions based on the CRNN deep learning network model. The experimental results show that this method can achieve significantly better recognition performance than traditional methods, with character and field recognition accuracy respectively reaching 92.89% and 8.87%, proving its effectiveness.

关键词： Deep learning Image segmentation Image recognition Text recognition Optical character recognition Licenses Character recognition

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Adaptive Stiffness Control of Series Elastic Actuator Manipulators Based on Dynamic System

Adaptive Stiffness Control of Series Elastic Actuator Manipu...

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IEEE international conference on automation Science and Engineering (CASE)

作者： Kunlin Guo Chao Zeng Weiyong Si Ning Wang Chenguang Yang Key Laboratory of Autonomous Systems and Networked Control School of Automation Science and Engineering South China University of Technology Guangzhou China Department of Computer Science University of Liverpool Liverpool UK School of Computer Science and Electronic Engineering University of Essex Colchester Bristol Robotics Laboratory University of the West of England Bristol UK

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

In traditional control methods, Series Elastic Actuator (SEA) joint manipulators are limited by their hardware and can only perform simple tasks with low stiffness. To address this issue, we propose a stiffness adjustment control strategy for SEA manipulators based on Dynamic Systems (DS) with good generalization performance. By enhancing the generalization capability of DS in complex tasks and combining posture control, the SEA manipulators can autonomously adjust control gains and postures according to task requirements to achieve higher stiffness at the end-effector. Experimental results involving the activation of air switches with different stiffness levels and installation angles have demonstrated the effectiveness of our proposed method. The results indicate that compared to traditional methods of adjusting the control gain and retraining DS for similar tasks, our approach exhibits superior generalization performance while maintaining end-effector stiffness during interactions.

关键词： Actuators Computer aided software engineering automation Adaptive systems Control systems End effectors Hardware Dynamical systems Manipulator dynamics

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Language Model Optimization Based on Quantum Machine Learning for Long Chinese Text Processing

Language Model Optimization Based on Quantum Machine Learnin...

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Computing, robotics and System Sciences (ICRSS), international conference on

作者： Tianze Wang International School Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798331511296

ISBN: (纸本)9798331511302

Chinese language presents unique challenges in text processing due to its complex structure, including high inter-lexical dependence, flexible word order, and rich contextual expressions. These characteristics make it particularly difficult for large models to effectively handle long Chinese texts using traditional machine learning (ML) algorithms. This paper explores the application of Quantum Machine Learning (QML) as a solution to enhance the performance of large language models (LLMs) in processing long Chinese texts. By leveraging the inherent advantages of QML, such as higher processing efficiency and deeper semantic understanding, this research demonstrates through experimental comparisons that QML models significantly outperform traditional ML models in both speed and comprehension accuracy. The paper systematically introduces text processing methods using both traditional ML and QML, and presents a simplified QML model designed to enhance text processing capabilities. The findings underscore the potential of QML in optimizing the future performance of LLMs, particularly for languages with complex linguistic structures like Chinese. This research holds significant implications for advancing LLM technologies in text-heavy applications.

关键词： Accuracy Quantum computing Machine learning algorithms Large language models Semantics Machine learning Linguistics Robots Optimization Text processing

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A Flexible Structured Solver for Continuous-time Algebraic Riccati Equations 18

A Flexible Structured Solver for Continuous-time Algebraic R...

引用

18th international conference on Informatics in Control, automation and robotics (ICINCO)

作者： Sima, Vasile Natl Inst Res & Dev Informat Modelling Simulat Optimizat Dept Bd Maresal Averescu 8-10 Bucharest Romania Tech Sci Acad Romania Bucharest Romania

ISBN: (纸本)9789897585227

The solution of algebraic Riccati equations (AREs) is a fundamental computation in optimal control and other domains. The available solvers lack the flexibility in choosing a solution technique, or specifying options and parameters. The quality of a computed solution depends not only on the problem conditioning, but also on the various decisions made by the solver designer. This paper proposes a flexible solver for continuous-time AREs that allows the user to choose among several structured solution approaches, orthogonalization methods, and balancing options and parameters. No selection ensures the best results for all problems. Therefore, it is sometimes useful to try alternative pathways and find the best solution. The new solver has been used to solve the examples from the SLICOT CAREX benchmark collection. The numerical results in extensive tests illustrate the good performance of the proposed flexible solver.

关键词： Balancing Hamiltonian Matrix Numerical methods Optimal Control Riccati Equation

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Developing of A Rigid-Compliant Finger Joint Exoskeleton Using Topology Optimization Method

Developing of A Rigid-Compliant Finger Joint Exoskeleton Usi...

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IEEE international conference on robotics and automation (ICRA)

作者： Liang, Renghao Xu, Guanghua He, Bo Li, Min Teng, Zhicheng Zhang, Sicong State Key Lab Mfg Syst Engn Xian 710049 Peoples R China Xi An Jiao Tong Univ Sch Mech Engn Xian 710049 Peoples R China

ISBN: (纸本)9781728190778

Robotic hand exoskeletons can provide assistance to people who suffer from hand functional disability or spinal cord injury (SCI). However, the current hand exoskeletons remain challenging with respect to having a user-friendly design that satisfies human motion with a lightweight structure. Here we propose a method of using topology optimization in the design of finger exoskeletons, which is a lightweight and integrate manufactured exoskeleton. The exoskeleton is designed by generating the topology configuration according to the bending state of the finger. The objective function in the optimization is set to be the maximization of output displacement of the design domain. After obtaining the topology configuration, a conjugate surface flexure hinge is developed as an elastic joint to replace the compliant part of the topologies. Finally, a rigid-compliant parallel exoskeleton is fabricated by a 3D printer and the performance of the mechanism is verified.

关键词： Three-dimensional displays Exoskeletons Optimization methods Lightweight structures Fasteners Linear programming Topology

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Unbiased Scene Graph Generation via Visual Language models

Unbiased Scene Graph Generation via Visual Language Models

引用

international conference on Control, automation and Systems ( ICCAS)

作者： Eunseo Kim Han-Mu Park Artificial Intelligence Research Center Korea Electronics Technology Institute Seoul Korea

ISBN: (数字)9788993215380

ISBN: (纸本)9798331517939

Scene graph generation becomes significantly important as it bridges the gap between linguistic and visual information of scenes, facilitating a high-dimensional understanding of scenes. In this paper, we analyze the limitations of current scene graph generation methods induced by the inherent semantic relationship biases embedded in existing datasets. Furthermore, we propose a method to enhance scene graph by leveraging visual language models (VLMs). This approach leverages the strengths of VLMs in understanding and generating triplets with semantic predicates, ensuring unbiased and fine-grained scene graphs.

关键词： Visualization automation Filtering Semantics Linguistics Control systems Real-time systems Question answering (information retrieval) Prompt engineering Videos

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De-noising of Low Dose CT Liver Images Using Improved Discrete Wavelet Transform 7th

De-noising of Low Dose CT Liver Images Using Improved Discre...

引用

7th IFIP TC 12 international conference on Computational Intelligence in Data Science, ICCIDS 2024

作者： Maria, H. Heartlin Kayalvizhi, R. Evangeline, I. Keren Princess, T. George Mangalya, T. Rashmika Prakash, J. Shakthi Department of Electronics and Communication Engineering SRM Institute of Science and Technology Kattankulathur Chennai603203 India Department of Robotics and Automation SRM Easwari Engineering College Chennai India Department of Medical Electronics SRM Valliammai Engineering College Kattankulathur India Department of Artificial Intelligence and Datascience St. Joseph’s Institute of Technology Chennai India

ISBN: (纸本)9783031699818

CT scans have become increasingly common in recent years and are essential for diagnosing various medical conditions. But the most significant element influencing a CT image’s quality is noise, which frequently taints low-dose scans. The radiologists’ choice may then be impacted by this. Therefore, in order to enhance visibility and increase the clarity of the image, LDCT images must be removed of noise before being utilized for diagnosis. This work is one such attempt to remove noise from low dose Computed Tomography (CT) liver images. In the present work, the noise from the CT liver images is eliminated using a modified discrete wavelet transform (DWT). The scheme system is used in conjunction with the DWT. The lifting approach improves the wavelet transform’s accuracy, simplicity, efficiency, and flexibility. Furthermore, the noisy wavelet coefficients are denoised via Bayesian shrinkage. The de-noised image PSNR, MSE, SSIM, and SNR values are computed, and a comparison with the traditional de-noising methods is carried out. The quantitative study reveals that the suggested approach has produced noteworthy outcomes when compared to the efficacy of conventional procedures. Furthermore, as the results and discussion section demonstrate, the suggested lifting technique in conjunction with the DWT minimizes the memory needs while simultaneously reducing complexity. © IFIP international Federation for Information Processing 2024.

关键词： Discrete wavelet transforms

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LESS-Map: Lightweight and Evolving Semantic Map in Parking Lots for Long-term Self-Localization

LESS-Map: Lightweight and Evolving Semantic Map in Parking L...

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IEEE international conference on robotics and automation (ICRA)

作者： Mingrui Liu Xinyang Tang Yeqiang Qian Jiming Chen Liang Li College of Control Science and Engineering Zhejiang University Hangzhou P. R. China Department of Automation Shanghai Jiao Tong University Shanghai P. R. China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Precise and long-term stable localization is essential in parking lots for tasks like autonomous driving or autonomous valet parking, etc. Existing methods rely on a fixed and memory-inefficient map, which lacks robust data association approaches. And it is not suitable for precise localization or long-term map maintenance. In this paper, we propose a novel mapping, localization, and map update system based on ground semantic features, utilizing low-cost cameras. We present a precise and lightweight parameterization method to establish improved data association and achieve accurate localization at centimeter-level. Furthermore, we propose a novel map update approach by implementing high-quality data association for parameterized semantic features, allowing continuous map update and refinement during re-localization, while maintaining centimeter-level accuracy. We validate the performance of the proposed method in real-world experiments and compare it against state-of-the-art algorithms. The proposed method achieves an average accuracy improvement of 5cm during the registration process. The generated maps consume only a compact size of 450 KB/km and remain adaptable to evolving environments through continuous update.

关键词： Location awareness Accuracy Scalability Semantics Cameras Robustness Maintenance

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