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Detecting blindspots in colonoscopy by modelling curvature

Detecting blindspots in colonoscopy by modelling curvature

引用

IEEE international conference on robotics and automation (ICRA)

作者： Abrahams, George Herve, Anthony Bernth, Julius E. Yvon, Marc Hayee, Bu Liu, Hongbin Kings Coll London HaMMeR Lab London England IBM Europe Human Centr Innovat Ctr Zurich Switzerland Kings Coll Hosp London Gastroenterol London England

ISBN: (纸本)9781728190778

Optical colonoscopy is the gold standard for colorectal cancer screening, however even under optimal conditions only 81% of the internal tissue is inspected, in part causing up to 22% of early adenomas to be missed. Blindspots commonly occur at acute bends where the camera's view is blocked. 3D reconstruction alone is insufficient to assess screening completeness as predictions of both the seen and unseen mucosa are required. Existing works in blindspot detection nevertheless use a highly detailed 3D reconstruction as the first step, the complexity of which degrades processing speed and reliability. We demonstrate that this complexity is not needed to predict whether acute bends have been adequately inspected. We propose a parametric model of the colon with only 2 variables: radius and curvature. By incorporating curvature, our method can predict the occlusion which acute bends cause. We use CT scans from 12 patients which on average contain 20 beards. We use a custom colonoscopy simulator and, assuming known geometry, show that a curved model reliably predicts these blindspots while a non-curved model always misses them. From our frame-by-frame predictions we build a panoramic map of the tissue inspected over the procedure. We show that this correctly identifies all 10 blindspots during 3 minutes of colonoscopy. We envisage that by alerting clinicians to mucosa which they have missed in real time, they can revisit these areas, improving the detection of polyps and cancers.

关键词： Integrated optics Geometry Three-dimensional displays Colonoscopy Predictive models Real-time systems Complexity theory

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Mesh Stiffness models for Modified Straight Bevel Gears without and with Slice Coupling 4

Mesh Stiffness Models for Modified Straight Bevel Gears with...

引用

2022 4th international conference on Mechanical Engineering and automation, MEA 2022

作者： Shu, Ruizhi Chen, Siyu Ge, Shuaishuai Tan, Rulong Yin, Qin School of Mechanical Engineering Chongqing University of Technology Chongqing400054 China College of Vehicle Engineering Chongqing University of Technology Chongqing400054 China The State key Laboratory of Mechanical Transmissions Chongqing University Chongqing400044 China

The analysis of the dynamics of gear systems is based on the determination of the time-varying mesh stiffness (TVMS). The infinitesimal approach was used to slice the straight bevel gear's teeth. After that, the beam deformation theory was combined to determine the stiffness components of each tooth segment. On this basis, the meshing stiffness models with and without slice coupling were constructed using the methods of accumulative integral potential energy and slice coupling, respectively. Lastly, the results of the suggested methodology were compared to those of the finite element analysis to ensure accuracy. The result shows that both analytical methods can solve the TVMS of straight bevel gears accurately and quickly. © 2023 Institute of Physics Publishing. All rights reserved.

关键词： Gear teeth

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Mobile MoCap: Retroreflector Localization On-The-Go

Mobile MoCap: Retroreflector Localization On-The-Go

引用

IEEE international conference on automation Science and Engineering (CASE)

作者： Gary Lvov Mark Zolotas Nathaniel Hanson Austin Allison Xavier Hubbard Michael Carvajal Taşkin Padir Institute for Experiential Robotics Northeastern University Boston MA USA

Motion capture through tracking retroreflectors obtains highly accurate pose estimation, which is frequently used in robotics. Unlike commercial motion capture systems, fiducial marker-based tracking methods, such as AprilTags, can perform relative localization without requiring a static camera setup. However, popular pose estimation methods based on fiducial markers have lower localization accuracy than commercial motion capture systems. We propose Mobile Mo-Cap, a system that utilizes inexpensive near-infrared cameras for accurate relative localization even while in motion. We present a retroreflector feature detector that performs 6- DoF (six degrees-of-freedom) tracking and operates with minimal camera exposure times to reduce motion blur. To evaluate the proposed localization technique while in motion, we mount our Mobile MoCap system, as well as an RGB camera to benchmark against fiducial markers, onto a precision-controlled linear rail and servo. The fiducial marker approach employs AprilTags, which are pervasively used for localization in robotics. We evaluate the two systems at varying distances, marker viewing angles, and relative velocities. Across all experimental conditions, our stereo-based Mobile MoCap system obtains higher position and orientation accuracy than the fiducial approach. The code for Mobile MoCap is implemented in ROS 2 and made publicly available at https//***/RIVeR-Lab/mobile_mocap

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Validation of a Novel Parallel-Actuated Shoulder Exoskeleton Robot for the Characterization of Human Shoulder Impedance

Validation of a Novel Parallel-Actuated Shoulder Exoskeleton...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Chang, Dongjune Hunt, Justin Atkins, John Lee, Hyunglae Arizona State Univ Sch Engn Matter Transport & Energy Tempe AZ 85287 USA

ISBN: (纸本)9781728190778

This study validates the effectiveness of a recently developed parallel-actuated shoulder exoskeleton robot for the purpose of characterizing the neuromuscular properties of the human shoulder joint. In particular, shoulder mechanical impedance was quantified, which can be represented by a 2nd order system consisting of spring, damper and inertia. The shoulder exoskeleton robot, which utilizes a new type of 4-bar spherical parallel manipulator (4B-SPM), has inherently low inertia and as a result can provide fast perturbations that are often essential for characterizing neuromuscular properties. The robot was first evaluated by using a physical shoulder mockup with adjustable and known spring and mass properties. The results of the mockup test confirmed the reliability of the robot for the characterization of the mockup properties. Stiffness of the tested springs was accurately quantified with an error of less than 1.6 Nm/rad in any of the tested conditions. A pilot study with 5 human subjects further confirmed that the robot could be successfully used to quantify multi-dimensional human shoulder impedance in both pitch and yaw directions with high reliability (R-2 > 0.97). The average human shoulder stiffness and damping at around the neutral arm posture under low muscle activation (< 5% maximum voluntary contraction) were 30.9 Nm/rad and 3.0 Nms/rad, respectively.

关键词： Neuromuscular Perturbation methods Exoskeletons Shoulder Manipulators Shock absorbers Impedance

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Learning Sufficient Correlations Among Points for 3D Non-rigid Point Cloud Registration

Learning Sufficient Correlations Among Points for 3D Non-rig...

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international conference on Control, automation and robotics (ICCAR)

作者： Chen Li Anshun Xue Chongkun Xia Houde Liu Bin Liang Shenzhen International Graduate School Tsinghua University Shenzhen China Department of Automation Tsinghua University Beijing China

Unsupervised end-to-end deep learning-based framework for non-rigid point cloud correspondence has been proposed to conveniently and effectively find the alignment relationship between two unordered point sets. However, due to the lacking of datasets in this field, researchers are still facing the challenge in designing novel neural networks which could sufficiently learn the correct correspondence and displacements of every points. In this paper, we propose a novel end-to-end unsupervised network, which consists of three modules from front to back: the feature embedding module, the correspondence prediction module and the shared deformer module. Specifically, We first use a hierarchical dilated graph convolution network(HDGCN) to boost the feature extraction as it has the potential in finding more correlations among local points. Then those features are fed into the correspondence prediction module to get the correspondence matrix, and we make it learnable in high-dimensional space. Finally, to acquire more precise point-wise displacements, we make the permuted point cloud into the one where each point carrys higher-dimensional information, and we input it to the shared deformer module which outputs the final non-rigid registration results. We name it HCDNet3D(HDGCN+Corr+Deform) and we experimentally prove our HCDNet3D makes better performance on correspondence rate and visual non-rigid registration results, comparing with several existing methods. According to the extensive experiment results, HCDNet3D outperforms state-of-the-art methods on both synthetic and real-world datasets of non-rigid 3D point clouds, including those directly computing point drifts and those taking meshes as input.

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Robust ESO Based Control for Interval Plant: Application to Piezoelectric Positioning Stage Working at Changing Temperatures

Robust ESO Based Control for Interval Plant: Application to ...

引用

Electrical Engineering, Control and robotics (EECR), international conference on

作者： Mingchen Chen Peng Yan School of Automation Science and Electrical Engineering Beihang University Beijing China School of Mechanical Engineering Shandong University Jinan China

ISBN: (数字)9798350370003

ISBN: (纸本)9798350370010

There are a number of challenges in precision control of piezoelectric actuated micro/nano-motion devices, including uncertainty in modeling and environmental disturbances. Especially, the parameters of piezoelectric actuated systems may wander noticeably at varying temperatures due to the intrinsic properties of piezoelectric materials. In this work the issue is addressed by importing an interval model to cover all kinds of uncertainties and developing a robust extended state observer(ESO) based control which can limit the closed-loop zeros and poles to acceptable areas although the interval plant exists. With the aid of an equivalency between linear ESO-based control and a type of two-degree-of-freedom controller and root locus approach, all of the controller parameters are adjusted collaboratively in the proposed method. Lastly numerical simulations are performed to validate the effectiveness of the proposed method and to compare it with a conventional ESO based control.

关键词： Robust control Uncertainty Temperature Piezoelectric materials Numerical simulation Temperature control Numerical models

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An Omnidirectional Guidance Robot System for Visually Impaired Individuals with Nonlinear MPC

An Omnidirectional Guidance Robot System for Visually Impair...

引用

IEEE international conference on robotics and Biomimetics

作者： Hailei Yuan Qingquan Na Hui Zhou Li Yang School of Automation Nanjing University of Science and Technology Nanjing Jiangsu China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

In this article, we develop an intelligent guiding robot system based on omnidirectional kinesthetic feedback. This system provides a safer and more efficient solution for the independent travel of blind and visually impaired people (BVIP). The system includes a novel omnidirectional kinesthetic feedback guidance device and a platform equipped with various sensors (LiDAR, depth camera, IMU, etc.) and a computing unit. Meanwhile, we construct a perception-planning-control framework. To ensure the safety of the person during the guidance process, we develop a human-robot coupling model to describe the real-time state of the human-robot system. Tracking controllers execute these commands to drive the guiding device. Our system is deployed on the Smart Cane platform and has been validated through simulations and real-world scenarios, and demonstrate excellent trajectory tracking capability.

关键词： Couplings Laser radar Trajectory tracking Robot vision systems Predictive models Sensor systems Real-time systems Safety Root mean square Predictive control

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Diagnosis of Localized Bearing Defects using Texture Analysis of Vibration Envelope Signals and Machine Learning

Diagnosis of Localized Bearing Defects using Texture Analysi...

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international conference on robotics and Artificial Intelligence (ICRAI)

作者： Cong Dai Nguyen Fazal E Nasir Shahbaz Khan Sheraz Ali Khan Jong-Myon Kim Faculty of Radio-Electronic Engineering Le Quy Don Technical University Hanoi Vietnam Advanced Robotics and Automation Lab National Center for Robotics and Automation Peshawar Pakistan Institute of Manufacturing University of Engineering and Applied Sciences Swat Pakistan Department of Mechatronics Engineering University of Engineering and Technology Peshawar Pakistan Department of IT Convergence University of Ulsan Ulsan South Korea PD Technology Ulsan South Korea

Bearings are important because they play a critical role in the operation of many machines and devices by reducing friction and enabling smooth motion between two moving parts. They are commonly used in rotating machinery, such as electric motors, turbines, pumps, and gearboxes, where they support and guide the rotating shafts. The primary cause of failure in wind turbines and induction motors is bearing faults, resulting in significant downtime and economic losses. Detecting these faults accurately and promptly is crucial to preventing unexpected shutdowns. Most of the existing methods for bearing fault detection rely on envelope analysis, which involves filtering and demodulating the raw signal, and Fourier analysis of the envelope signal to identify defect frequencies. Recent research has focused on improving the visibility of these frequencies by selecting an optimal band for filtering, using sub-band analysis and spectral kurtosis, which is a complex process and can be time-consuming. This paper presents a new approach to envelope signal analysis using texture analysis of the envelope signals that are projected as 2D grayscale images where each fault generates a unique texture. These textures are encoded using the local binary pattern operator and then used for fault classification. The method is tested on a publicly available seeded fault dataset.

关键词： Vibrations Fault diagnosis Shafts Time-frequency analysis Filtering Pumps Machine learning

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Research on Image Denoising Algorithm Based on Self-Supervised Learning

Research on Image Denoising Algorithm Based on Self-Supervis...

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robotics and Computer Vision (ICRCV), international conference on

作者： Weijie Lv Xiangyang Chen Mingyu Yang School of Computer Science and Engineering Wuhan Institute of Technolog Wuhan China

ISBN: (数字)9798331527426

ISBN: (纸本)9798331527433

In recent years, neural network models based on self-supervised learning have shown excellent performance in the field of image denoising. However, current denoising methods either rely on specific noise models or can only perform denoising tasks under specified noise distributions. Moreover, these network models often have a large number of parameters, leading to inefficient parameter usage. To address these issues, inspired by the traditional Partial Differential Equation (PDE) methods in image denoising, we attempted to combine Neural Ordinary Differential Equations (NODEs) with Convolutional Neural Networks (CNNs) to produce a neural network model with NODE-like properties. This was aimed at improving the parameter efficiency of the model. Additionally, we introduced a new architecture within the model, namely normalization equivariance, which enables existing neural network models to possess the property of normalization equivariance. Compared to models lacking this property, models with normalization equivariance often exhibit stronger generalization capabilities and robustness. Through comparative experiments conducted on artificially synthesized noise and noise from real-world colored images, the results indicate that our proposed method demonstrates remarkable denoising effects across different scenarios. It exhibits stronger generalization capabilities and restores most of the texture details within the original images.

关键词： Partial differential equations Noise Noise reduction Neural networks Self-supervised learning Ordinary differential equations Mathematical models Robustness Image restoration Image denoising

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Universally Strict Black-Box Attacks Against Deep Neural Networks 8

Universally Strict Black-Box Attacks Against Deep Neural Net...

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8th international conference on Image, Vision and Computing, ICIVC 2023

作者： Lu, Zhengzhi Yang, Guoan School of Automation Science and Engineering Xi'An Jiaotong University Xi'an China

ISBN: (纸本)9798350335231

Adversarial attacks have demonstrated the vulnerability of deep neural networks (DNNs), which raises considerable security concerns. The existing attack methods require either prior knowledge of the victim DNNs and labels or frequent model querying. However, these requirements are usually infeasible or time-consuming, leading to suspicions about whether the attacks can be launched in real-world scenarios. To this end, we propose a universally strict black-box attack, which generates adversarial samples only using unlabeled data. It reduces the reliance on external information such as victim models, training processes, and ground-truth labels. Specifically, we first learn a latent manifold using contrastive learning. Then a novel universally adversarial loss is proposed to obtain adversaries directly in the latent space. It utilizes the dissimilarity between samples to craft perturbations without accessing labels and decision boundaries. Moreover, we propose a cluster selection for negative samples to improve the effectiveness of our attack. By evaluating the universally strict black-box attack against baseline models, we find it reaches an average fooling rate of 57.93%, which is on par with transfer-based black-box attacks. Our method shows the threats of adversarial attacks under more practical conditions and could serve as a new benchmark for assessing the robustness of DNNs. © 2023 IEEE.

关键词： Image classification

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