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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Yujia Xiang Hao Shen Le Xie Hesheng Wang School of Biomedical Engineering Shanghai Jiao Tong University Shanghai CHN Institute of Forming Technology & Equipment Shanghai Jiao Tong University Shanghai CHN Institute of Forming Technology & Equipment School of Biomedical Engineering Shanghai Jiao Tong University Shanghai CHN Department of Automation Shanghai Jiao Tong University Shanghai CHN

ISBN: (纸本)9781728126234

Cardiovascular disease remains a primary cause of morbidity globally. Percutaneous coronary intervention plays a crucial role in the treatment. The radiation exposure of surgeons during the cardiovascular intervention can be avoided by master-slave surgical robots. This paper introduces a master- slave guidewire and catheter robotic system to protect the surgeons from X ray radiation to the most extent. And the jitters of master manipulators are mitigated by Kalman filtering algorithm. With two master manipulators, it helps to retain the surgeon's traditional operating habits. Also, a vascular model trial was conducted to validate that this interventional robotic system could complete the alternate progress and rotation of interventional guidewire and catheter.

关键词： Surgeons Master-slave Catheters Guidewire Jitter Radiation Exposure Cardiovascular Diseases Manipulators filtering algorithms X-rays image filtering algorithm Particle filtering algorithm

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Continuous strain prediction for fatigue assessment of an offshore wind turbine using Kalman filtering techniques 7

Continuous strain prediction for fatigue assessment of an of...

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IEEE Workshop on Environment Energy Structural Monitoring Systems

作者： Maes, K. De Roeck, G. Lombaert, G. Iliopoulos, A. Van Hemelrijck, D. Devriendt, C. Guillaume, P. Katholieke Univ Leuven Dept Civil Engn B-3001 Leuven Belgium VUB Dept Mech Mat & Construct B-1050 Brussels Belgium VUB Acoust & Vibrat Res Grp B-1050 Brussels Belgium

ISBN: (纸本)9781479982158

Offshore wind turbines are exposed to continuous wind and wave excitation. The continuous monitoring of high periodic strains at critical locations is important to assess the remaining lifetime of the structure. Some of the critical locations are not accessible for direct strain measurements, e.g. at the mud-line, 30 meter below the water level. Response estimation techniques can then be used to estimate the response at unmeasured locations from a limited set of response measurements and a system model. This paper shows the application of a Kalman filtering algorithm for the estimation of strains in the tower of an offshore monopile wind turbine in the Belgian North Sea. The algorithm makes use of a model of the structure and a limited number of response measurements for the prediction of the strain responses. It is shown that the Kalman filter algorithm is able to account for the different types of excitation acting on the structure in operational conditions, in this way yielding accurate strain estimates that can be used for continuous fatigue assessment of the wind turbine.

关键词： Fatigue assessment offshore Wind Energy Conversion Systems Kalman filtering turbines WAVE INTERFERENCE filtering algorithms image filtering algorithm Particle filtering algorithm Operating conditions

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Fast Mean Shift Based Traffic image filtering algorithm

Fast Mean Shift Based Traffic Image Filtering Algorithm

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IEEE Intelligent Vehicles Symposium

作者： Zhang Yu Shi Zhong-ke Wang Run-quan Northwestern Polytech Univ Air Traff Management Syst Inst Xian 710072 Peoples R China

ISBN: (纸本)9781424435036

This paper describes a novel fast mean shift algorithm based on an accelerated iteration strategy. This new method focuses on solving the problem of high calculation complexity when high data dimension or large data sets are involved in mean shift. By predicting the mean shift vector, improved method reduces the number of iteration and speed up the calculation. The application of traffic image filtering is provided also. Experiment results of traffic image filtering demonstrate the efficiency of the fast mean shift algorithm.

关键词： image filtering algorithm Shift iterative methods image filtering Traffic Fasting Dataset

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Islanding Detection for Microgrid Based on Frequency Tracking Using Extended Kalman Filter algorithm

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JOURNAL OF APPLIED MATHEMATICS 2014年第unknown期000卷 186360-1-186360-11页

作者： Li, Bin Wang, Jingpeng Bao, Hailong Zhang, Huiying Tianjin Univ Minist Educ Key Lab Smart Grid Tianjin 300072 Peoples R China

Islanding detection is essential for secure and reliable operation of microgrids. Considering the relationship between the power generation and the load in microgrids, frequency may vary with time when islanding occurs. As a common approach, frequency measurement is widely used to detect islanding condition. In this paper, a novel frequency calculation algorithm based on extended Kalman filter was proposed to track dynamic frequency of the microgrid. Taylor series expansion was introduced to solve nonlinear state equations. In addition, a typical microgrid model was built using MATLAB/SIMULINK. Simulation results demonstrated that the proposed algorithm achieved great stability and strong robustness in of tracking dynamic frequency.

关键词： Islanding Detection algorithm dynamic frequency MICRO-GRID equations of state frequency tracking Islanding Particle filtering algorithm algorithms filtering algorithms Power production Extended Kalman filters image filtering algorithm

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Nonlinear Optimal Tracking With Incomplete State Information Using Finite-Horizon State Dependent Riccati Equation (SDRE)

Nonlinear Optimal Tracking With Incomplete State Information...

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American Control Conference

作者： Ahmed Khamis D. Subbaram Naidu Department of Electrical Engineering Idaho State University Pocatello Idaho USA

ISBN: (纸本)9781479932757

In this paper, an online technique for finite-horizon nonlinear stochastic tracking problems is presented. The idea of the proposed technique is to integrate the Kalman filter algorithm and the State Dependent Riccati Equation (SDRE) technique. Unlike the ordinary methods which deal with the linearized system, this technique will estimate the unmeasured states of the nonlinear system directly, and this will make the proposed technique effective for wide range of operating points. Numerical example is given to illustrate the effectiveness of the proposed technique.

关键词： Riccati equations filtering algorithms image filtering algorithm Particle filtering algorithm tracking nonlinearity

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A Non-inertial Acceleration Suppressor for Low Cost Inertial Measurement Unit Attitude Estimation

A Non-inertial Acceleration Suppressor for Low Cost Inertial...

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IEEE International Conference on Robotics and Biomimetics (ROBIO)

作者： Valenti, Roberto G. Dryanovski, Ivan Xiao, Jizhong CUNY Dept Elect Engn New York NY 10031 USA CUNY Grad Ctr Dept Comp Sci New York NY 10016 USA

ISBN: (纸本)9781479927456

This paper presents a method to evaluate the attitude of a rigid body under condition of high non-gravitational acceleration. Most of the attitude estimation algorithms based on data from low cost Inertial Measurement Units (IMU), assume that the total acceleration perceived by the accelerometer be gravity or at most small variations of it. When the actual conditions are far away from such assumption, the attitude estimation results in wrong evaluations. We propose a method that uses an external RGB-D camera to measure The noninertial linear acceleration. Such acceleration is subtracted to the total acceleration reading of the accelerometer in order to obtain a truthful gravity direction that will be fed into the fusion algorithm. Performance of our attitude estimation has been evaluated empirically under non-gravitational acceleration. We compare our results against the output of a commercially avalaible IMU sensor based on a Kalman Filter algorithm as well as the estimation of a recently developed fusion algorithm based on a gradient descent algorithm, showing significant improvement.

关键词： acceleration accelerometers Gravity Attitude filtering algorithms image filtering algorithm Particle filtering algorithm INERTIAL MEASUREMENT UNITS algorithms

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Asynchronous Networked Estimation System for Continuous Time Stochastic Processes

Asynchronous Networked Estimation System for Continuous Time...

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Conference on Control and Fault-Tolerant Systems

作者： Zdzislaw Kowalczuk Mariusz Domzalski Faculty of Electronics Telecommunications and Informatics Gdansk University of Technology

ISBN: (纸本)9781479928569

In this paper we examine an asynchronous networked estimation system for state estimation of continuous time stochastic processes. Such a system is comprised of several estimation nodes connected using a possibly incomplete communication graph. Each of the nodes uses a Kalman filter algorithm and data from a local sensor to compute local state estimates of the process under observation. It also performs data fusion of local estimates and data received from other directly connected nodes. Asynchronism means that each node preforms measurement, estimation, and data fusion in moments of time that are independent of the work cycles of the other nodes.

关键词： Data fusion Asynchronous Ganglia Markov chain Stochastic Processes State estimation filtering algorithms image filtering algorithm Particle filtering algorithm data integration Moment of force

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A Simplified Adaptive Kalman Filter algorithm for Carrier Recovery of M-QAM Signals

A Simplified Adaptive Kalman Filter Algorithm for Carrier Re...

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17th International Conference on Methods and Models in Automation and Robotics (MMAR)

作者： Gal, Janos Campeanu, Andrei Politehn Univ Dept Commun Timisoara 300223 Romania

ISBN: (纸本)9781467321242

The paper proposes a carrier synchronization procedure for high-order QAM modulated signals that uses a decision-directed extended Kalman filtering (EKF) technique. The proposed method combines the Kalman filter with a lock detector to establish the status of the synchronization process, modifying adaptively the Kalman filter parameters. As compared with the complete EKF filter, the proposed synchronization algorithm simplifies the Kalman measurement model to a single scalar equation reducing significantly the computational complexity of the algorithm. Simulations show that the simplified Kalman algorithm has equivalent synchronization performances with the complete EKF filter.

关键词： synchronous process Kalman filters adaptive Kalman filters signals carrier synchronization complexity classes higher-order filtering algorithms image filtering algorithm Particle filtering algorithm extended Kalman filtering algorithms Filters modulated signal

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Harvesting Large-Scale Weakly-Tagged image Databases from the Web

Harvesting Large-Scale Weakly-Tagged Image Databases from th...

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IEEE Conference on Computer Vision and Pattern Recognition

作者： Jianping Fan Yi Shen Ning Zhou Yuli Gao Department of Computer Science UNC-Charlotte Multimedia Interaction and Understanding HP Labs

ISBN: (纸本)9781424469840;9781424469857

To leverage large-scale weakly-tagged images for computer vision tasks (such as object detection and scene recognition), a novel cross-modal tag cleansing and junk image filtering algorithm is developed for cleansing the weakly-tagged images and their social tags (i.e., removing irrelevant images and rinding the most relevant tags for each image) by integrating both the visual similarity contexts between the images and the semantic similarity contexts between their tags. Our algorithm can address the issues of spams, polysemes and synonyms more effectively and determine the relevance between the images and their social tags more precisely, thus it can allow us to create large amounts of training images with more reliable labels by harvesting from large-scale weakly-tagged images, which can further be used to achieve more effective classifier training for many computer vision tasks.

关键词： images computer vision World Wide Web congenital web webs TAGGING harvesting Object detection visual databases image filtering algorithm

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Spatial ARMA models and its applications to image filtering

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BRAZILIAN JOURNAL OF PROBABILITY AND STATISTICS 2009年第2期23卷 141-165页

作者： Bustos, Oscar Ojeda, Silvia Vallejos, Ronny Univ Nacl Cordoba Fac Matemat Astron & Fis RA-5000 Cordoba Argentina Univ Tecn Federico Santa Maria Dept Matemat Valparaiso Chile Univ Valparaiso Valparaiso Chile

The objective of this review paper is to summarize the main properties of the spatial ARMA models and describe some of the well-known methods used in image filtering based on estimation of spatial autoregressive models. A new proposal based on robust RA estimation is also presented. Previous studies have shown that under additive outliers the RA estimator is resistant to a small percentage of contamination and behaves better than the LS, M, and GM estimators. A discussion about how well these models fit to a digital image is presented. Some applications using real images are presented to illustrate how an image is filtered in practice.

关键词： Spatial autoregressive model robust estimators image filtering algorithm

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