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Adaptive neural network control for a soft robotic manipulator

Adaptive neural network control for a soft robotic manipulat...

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international conference on Informative and Cybernetics for Computational Social Systems (ICCSS)

作者： Jiangtao Han Zhijie Liu Wei He School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Shunde Graduate School of University of Science and Technology Beijing Foshan China School of Automation and Electrical Engineering and Institute of Artificial Intelligence University of Science and Technology Beijing Beijing China

ISBN: (数字)9781728162461

ISBN: (纸本)9781665419352

this paper mainly introduces the modeling based on Cosserat rod theory and focuses on the adaptive neural network controller design based on model. In dealing with the external interference with the environment and the unmodeled dynamics of the system, a neural network (NN) is introduced to compensate it, and using Backstepping method to design the adaptive controller, finally the stability of the closed-loop system and the convergence of the signal in the system is proved with Lyapunov function theory, ensuring the end of the manipulator can track the given signal. In addition, the simulation experiment of soft manipulator swing and constant angle tracking control are carried out, and the simulation shows the rationality of the proposed controller.

关键词： Adaptation models Adaptive systems Neural networks Soft robotics Stability analysis Manipulator dynamics Lyapunov methods

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Exploring Innovations in Autonomous robotics and Mechatronics: Application in Manufacturing and Healthcare

Exploring Innovations in Autonomous Robotics and Mechatronic...

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Smart Electronics and Communication (ICOSEC), international conference on

作者： T.Grace Shalini L. Ganeshabu Priyanka Chemudugunta Nagendar Yamsani Ch.Ashok Kumar A. Athiraja Department of Computational Intelligence School of Computing SRM Institute of Science and Technology (SRMIST)Kattankulathur Chengalpattu Tamil Nadu India Department of Robotics and Automation Rajalakshmi Engineering College Rajalakshmi Nagar Thandalam Mevalurkuppam Chennai Tamil Nadu India Department of Robotics and Automation Karpaga Vinayaga College of Engineering and Technology Chengalpattu Tamil Nadu India Department of School of Computer Science and Artificial Engineering SR University Warangal India Department of Mechanical Engineering Malla Reddy Engineering College Main Campus Maisammaguda Hyderabad Telangana India Department of CSE (AIML) Malla Reddy College of Engineering Secunderabad Telangana India

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

Autonomous robotics and mechatronics have drastically changed the manufacturing and healthcare sectors by increasing productivity, precision, and flexibility. this work addresses the pressing need for new methods to advance these sectors by merging intelligent algorithms with autonomous systems. A hybrid strategy was employed, combining Deep Reinforcement Learning (DRL) and Adaptive Control Systems (ACS), to develop and optimize robotic operations. Extensive studies were conducted to assess the success of this strategy, which demonstrated a 25% reduction in operating expenses and a 30% increase in production efficiency in manufacturing settings. the method produced a precision rate of about 99% in the healthcare industry, which is significantly better than the 93% average of current systems. It also reduced patient recovery times by an estimated 20%. these results highlight the improved performance and promise of ACS with DRL combination in autonomous robotics, which may have revolutionary implications for the manufacturing and healthcare industries. the success, highlighted by economic and medical benefits, demonstrates the efficacy of our methodology and provides a strong platform for future developments in autonomous robotics and mechatronics.

关键词： Industries Technological innovation Mechatronics Service robots Scalability Medical services Manufacturing Adaptive control Tuning Testing

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An Error-In-Variables (EIV) based Bayesian probabilistic approach to estimating cell mechanical properties using Atomic Force Microscopy

An Error-In-Variables (EIV) based Bayesian probabilistic app...

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IEEE/RAS-EMBS international conference on Biomedical robotics and Biomechatronics (BioRob)

作者： Rajarshi Roy Lei Li Carol Keefer Jaydev P. Desai Robotics Automation and Medical Systems (RAMS) Laboratory University of Maryland College Park MD USA Department of Animal and Avian Sciences University of Maryland College Park MD USA

Contact mode Atomic Force Microscopy (AFM) is popularly used by the biophysics community to study mechanical properties of cells and tissue due to its ability to quantify biological processes like disease proliferation and stem cell differentiation. While AFM indentation experiments are relatively straightforward, interpretation of the AFM data is subject to some debate. three factors make the AFM force data prone to incorrect interpretation, namely, the appropriate contact model to use to fit the post-contact AFM data, identifying the point when the AFM probe contacts the cell, and variability in the stiffness of the probe used to estimate material properties of cells. A lot of research has been directed towards developing constitutive models based on sample material property assumptions, however, variability in the AFM probe stiffness and contact point uncertainty has received limited attention. this suggests the need to have an integrated probabilistic model to estimate mechanical properties from the AFM force data which can account for errors contributed from these two sources. In this work, we have developed an Error-In-Variables (EIV) based Bayesian Changepoint model to estimate the contact point and the Young's Modulus from AFM force curves on fixed mouse Embryonic Stem cells (mESC) in the indentation range of 1-1.5 μm. Our results indicate that the EIV-based Bayesian Approach can be used to obtain robust estimates of material properties of biomaterials.

关键词： Probes Young's modulus Force Bayesian methods Uncertainty Calibration

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Research Progress in Cell Classification Detection Based on Deep Learning

Research Progress in Cell Classification Detection Based on ...

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IEEE international conference on Cyber Technology in automation, Control, and Intelligent Systems

作者： Dongze Gai Chuang Zhang Yiwei Zhang Jiaduo Guo Qi Zhang Hengshen Qin Dan Dang School of Computer Science and Engineering Shenyang Jianzhu University Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China School of Mechanical Engineering Shenyang Jianzhu University Shenyang China School of Science Shenyang Jianzhu University Shenyang China

ISBN: (数字)9798331506056

ISBN: (纸本)9798331506063

this paper reviews the application of deep learning in cell classification detection, highlights the importance of convolutional neural networks in image analysis, and discusses the application of image preprocessing, feature extraction and deep learning models. Despite the progress made by deep learning techniques in improving cell classification accuracy, challenges such as data quality, model generalization and interpretability remain. this article suggests future research directions, including combining medical knowledge with deep learning and developing multimodal learning to improve the accuracy and efficiency of cell detection. Meanwhile, this article also explores the potential of label-free cell classification methods combining optically induced optically-induced dielectrophoresis (ODEP) techniques with deep learning to achieve cell classification without labeling, providing a new perspective on cell analysis. optically induced.

关键词： Deep learning YOLO Biomedical optical imaging Accuracy Image analysis Optical imaging Feature extraction Labeling Image preprocessing Medical diagnostic imaging

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Temporal-Spectral Generative Adversarial Network based Multi-Level Consistency Learning for Epileptic Seizure Prediction

Temporal-Spectral Generative Adversarial Network based Multi...

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Industrial automation, robotics and Control Engineering (IARCE), international conference on

作者： Zhaoliang Xu Weigang Cui Yang Li Department of Automation Science and Electrical Engineering Beihang University Beijing China School of Engineering Medicine Beihang University Beijing China

ISBN: (数字)9798350380323

ISBN: (纸本)9798350380330

Recently, deep learning techniques have been extensively explored and offer an efficient means for predicting epileptic seizures. However, due to the variability between different domains in the features of electroencephalogram (EEG) data, most of the existing methods only use the limited training data of the target subject, which is difficult to fully cover the data distribution and thus degrades the model performance. To solve this problem, a novel temporal-spectral generative adversarial network based multi-level consistency learning (TSGCN-MLCL) strategy is introduced in this paper. Specifically, the multi-level consistency learning (MLCL) strategy optimizes the seizure prediction model by training from three levels, i.e., inter-domain, intra-domain and inter-sample, which realizes the data distribution alignment of the source and target domain from multiple views. Second, considering the problem of limited labeled data in the target domain, we introduce the temporal-spectral generative adversarial network (TSGCN) to augment new training data, which generates high-quality simulated EEG samples according to the temporal-spectral distribution of real signals, and improves the finetuning process on the target domain. Our proposed approach attains a sensitivity of 96.7%, accompanied by a false prediction rate of 0.072 per hour, as demonstrated on the publicly available CHB-MIT epileptic seizure dataset. this performance surpasses that of current state-of-the-art studies, affirming the effectiveness of our methodology.

关键词： Training Sensitivity Service robots Training data Predictive models Generative adversarial networks Brain modeling Electroencephalography Data models Overfitting

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Comparative Analysis of Uncertainty Quantification models in Active Learning for Efficient System Identification of Dynamical Systems

Comparative Analysis of Uncertainty Quantification Models in...

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IEEE international conference on automation Science and Engineering (CASE)

作者： Hans Mertens Frances Zhu Mechanical Engineering Robotics Space Exploration Lab University of Hawai‘i at Mānoa Hawai‘i Institute of Geophysics and Planetology University of Hawai‘i at Mānoa

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Active learning algorithms strategically select informative input samples, aiming to enhance understanding and optimize sample efficiency for dynamical systems. the efficacy of machine learning (ML) models with uncertainty quantification in identifying dynamical systems with different characteristics has not yet been explored. this paper investigates four ML models: Gaussian processes (GPs) with radial basis function (RBF), GPs with Matérn kernels, Bayesian neural networks (NNs) with Monte Carlo dropout(MCD), and ensemble neural networks (ENNs). Both continuous and non-continuous learning strategies are employed for all models. Five well known dynamical systems with different characteristics, state dimensionalities, and dynamical complexities are simulated to test the performance of the models along the metrics of test RMSE as well as samples and run time until convergence. through systematic experimentation, this paper provides guidance for researchers selecting models and algorithms in active learning for system identification, with insights into their strengths and limitations. Gaussian Processes with RBF kernels are shown to be the best overall choice while ENNs with Bayesian inference emerge as a scalable alternative, showing promise for high-dimensional tasks. Employing continuous training has proven to enhance the performance of Gaussian processes with RBF kernels, as well as ENNs, resulting in significant improvements in both sample and run-time efficiency.

关键词： Training Measurement Analytical models Uncertainty Monte Carlo methods Gaussian processes Artificial neural networks System identification Kernel Dynamical systems

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international conference on Control, automation and Systems ( ICCAS)

作者： Jinhoon Wang Woonghee Lee I Made Putra Arya Winata Junghyun Oh Department of Robotics Kwangwoon University Seoul Korea

ISBN: (数字)9788993215380

ISBN: (纸本)9798331517939

Active 3D reconstruction is a research field that focuses on planning paths to actively acquire data efficiently for 3D reconstruction. Among these, Next-Best-View (NBV) based path planning studies methods to select best viewpoints for 3D reconstruction. Recently, realistic modeling techniques such as Neural Radiance Fields and Gaussian Splatting have emerged, leading to active research on applying NBV to new 3D modeling methods. However, recent NBV techniques are designed to consider only uncertainty when selecting the best image from candidate images, which limits their efficiency in data acquisition. To overcome this limitation, we propose a similar image avoidance policy using SSIM, which avoids selecting images similar to those used in training. this approach prevents overfitting and effectively reduces the number of data used in training while acquiring the overall shape and surface information of the model with a small amount of data. We then verify the performance of the proposed method by comparing it to 3D models generated with random data and FisherRF using image quality metrics.

关键词： Training Image quality Solid modeling Surface reconstruction three-dimensional displays Uncertainty Shape Data models Image reconstruction Overfitting

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Stability Analysis for T-S Fuzzy Semi-Markovian Switching CVNs with Mixed Delays and General Uncertain Transition Rates

Stability Analysis for T-S Fuzzy Semi-Markovian Switching CV...

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international conference on Autonomous Robots and Agents, ICARA

作者： Qiang Li Jinling Liang School of Mathematics Southeast University Nanjing China

ISBN: (数字)9781665404693

ISBN: (纸本)9781665446457

this paper concerns with the stochastic stability problem for Takagi-Sugeno (T-S) fuzzy semi-Markovian switching complex-valued networks (CVNs) with mixed delays, where the transition rates of the semi-Markovian process are in the general uncertain form which contain two cases: completely unknown or unknown but with known upper/lower bounds. Based on the Lyapunov stability theory and the stochastic analysis technique, several mode-dependent stability criteria are established to guarantee the considered T-S fuzzy CVN to be asymptotically stable in the mean-square sense. Finally, one numerical example is provided to demonstrate feasibility of the obtained theoretical results.

关键词： Asymptotic stability Stability criteria Hidden Markov models Switches Delays Numerical stability Lyapunov methods

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Flexible Robot Dynamics and Vibration: From Analytical Modeling to Operational Modal Analysis

Flexible Robot Dynamics and Vibration: From Analytical Model...

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international conference on Autonomous Robots and Agents, ICARA

作者： Viet-Hung Vu Zhaoheng Liu Marc thomas Dept. of Mechanical and Aerospace Engineering Royal Military College of Canada Kingston ON Canada Dept. of Mechanical Engineering École de technologie supérieure Montreal QC Canada

ISBN: (数字)9798350394245

ISBN: (纸本)9798350394252

this paper presents recent studies on the dynamics and vibration of a flexible robot developed for industrial use that features six degrees of freedom. the flexibility of the joints and links is considered. We investigate the nonlinear dynamics and non-stationary vibration behavior of the robot's structure during grinding applications using both analytical and experimental methods. A linearization technique is introduced for the analytical modeling of the robot's dynamics across its entire workspace. Experimental investigations include modal testing and operational modal analysis, utilizing autoregressive models (ARX) and the Kalman filter to accurately characterize the robot's dynamic properties, dynamic contact forces, and the adaptation of these parameters in non-stationary vibrations. the effective performance of both approaches and the congruence between analytical and experimental findings suggest the potential for a hybrid modal analysis technique, tailored for flexible structures and robots operating under complex, real-world conditions.

关键词： Vibrations Analytical models Adaptation models Service robots Modal analysis Dynamics Mathematical models

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Sleep Stage Classification Using EEG Signals: A Empirical Wavelet Transform-Based Decomposition Approach

Sleep Stage Classification Using EEG Signals: A Empirical Wa...

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international conference on Control, automation, robotics and Vision (ICARCV)

作者： Rajveer Singh Lalawat Varun Bajaj Prabin Kumar Padhy Department of Electronics and Communication Engineering Indian Institute of Information Technology Design and Manufacturing Jabalpur Jabalpur India Department of Electronics and Communication Maulana Azad National Institute of Technology Bhopal Bhopal India

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

the objective of signal processing using transformation techniques, is to accurately detect the frequency decomposition and extract small variations within the signals. Previous studies have explored various algorithms aiming to decompose signals and classify sleep stages based on EEG signals, offering valuable insights. However, there is still scope for improvement in handling highly non-stationary signals across all sleep stages. the article proposes employing spectrum partitioning from six sleep phases and the Empirical Wavelet Transform (EWT) to properly decompose extremely non-stationary EEG data. this approach uses an adaptive filter bank to control dynamic data in the frequency domain to accurately distinguish sleep stages. Our experiments demonstrate that, the Support Vector Machines (SVM) classifier outperforms Random Forest (RF) and k-Nearest Neighbors (KNN) classifiers, and it achieves notable classification accuracy for all sleep stages. this method enhances the performance of medical devices and benefits for their use in healthcare.

关键词： Support vector machines Wavelet transforms Sleep Signal processing algorithms Nearest neighbor methods Feature extraction Brain modeling Electroencephalography Data models Classification algorithms

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