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A revised time domain force identification method based on bayesian formulation

INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING 2019年第7期118卷 411-431页

作者： Li, Qiaofeng Lu, Qiuhai Tsinghua Univ Sch Aerosp Engn Beijing Peoples R China

Time domain force identification problems are generally ill-posed. Regularization techniques are widely adopted to well-condition the problem. Traditional regularization such as Tikhonov regularization is mathematically equivalent to assuming a zero-mean prior distribution on the unknown force. This assumption could be unreasonable for problems where notable trend components exist in force histories, such as vehicle-bridge moving force identification and cutting tool force identification. In this paper, a revised method is proposed to address this issue. The proposed method formulates the force identification problem within the bayesian framework. The trend components are considered as low-order polynomials and as the mean term in the prior distribution of the force history. The joint maximum a posterior estimate of unknown variables is derived. The solution algorithm is given based on conditional maximization. A mass-spring system and a cutting tool under forces containing various types of trend components and vehicle-bridge systems under moving interaction forces are simulated to validate the proposed method.

关键词： bayesian formulation conditional maximization force identification inverse problem time domain

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Probabilistic prediction of intact rock strength using point load tests using a bayesian formulation

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GEORISK-ASSESSMENT AND MANAGEMENT OF RISK FOR ENGINEERED SYSTEMS AND GEOHAZARDS 2020年第3期14卷 206-215页

作者： Guevara-Lopez, F. Jimenez, R. Gardoni, P. Asem, P. Tech Univ Madrid Dept Ground Engn Madrid Spain Univ Illinois Dept Civil & Environm Engn 205 N Mathews Ave Urbana IL 61801 USA

This paper develops probabilistic predictive models for the unconfined compressive strength of intact rock based on the point load strength tests. A database of point load strength and unconfined compressive strength measurements for sedimentary, igneous and metamorphic rocks is first developed. The measurements are corrected to their standard equivalent values, namely unconfined compressive strength and point load strength for specimens with 50 mm diameter. A bayesian framework is used to assess different candidate model forms and to estimate the corresponding model parameters. This bayesian approach uses the prior information for the values of the unconfined compressive strength and point load strength measurements to develop an unbiased method for prediction of unconfined compressive strength based on point load strength tests. The bayesian estimation of the model parameters accounts for the relevant sources of uncertainty including aleatory and epistemic uncertainties and for possible measurement errors in the point load strength test data. Probabilistic models are developed for sedimentary, igneous and metamorphic rocks. It is found that (i) a model of q(u(50)) = A(I-s(50))(B) form provides the best fit to the laboratory data, and (ii) accounting for measurement errors and separating the database into subgroups based on different rock types decrease the model uncertainty.

关键词： Point load index unconfined compressive strength bayesian formulation

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Pedestrians Crowd Segmentation Based on bayesian formulation

Pedestrians Crowd Segmentation Based on Bayesian Formulation

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2015 International Conference on Advances in Mechanical Engineering and Industrial Informatics(AMEII 2015)

作者： Yun Liu Guangpeng Ma School of Information Science & Technology Qingdao University of Science & Technology

ISBN: (纸本)9781510806481

Crowd people segmentation algorithm in complex environments is proposed based on bayesian formula. In this method solved the case of the occlusion of crowd pedestrian which are difficult to detect one pedestrian from each other. This algorithm uses the bayesian formula to convert the problem that how to segmentation the crowd people into seeking maximum a posterior probability. Using the assumption segmentation model of the pedestrian to match the foreground area, and then modify segmentation model for converging to the maximum posterior probability through multiple iterations. Experiments result that using the PETS 2009 video database show that this algorithm can effectively and accurately detect the pedestrian whose occlusion is less half and the part of severely blocked pedestrians can also be identification.

关键词： bayesian formulation head detection Pedestrian Crowd Segmentation

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Visual and textual fusion for semantically supervised region-based retrieval

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MULTIMEDIA SYSTEMS 2009年第4期15卷 201-219页

作者： Ji, Rongrong Yao, Hongxun Xu, Pengfei Sun, Xiaoshuai Harbin Inst Technol Dept Comp Sci Harbin 150001 Peoples R China

This paper presents a unified annotation and retrieval framework, which integrates region annotation with image retrieval for performance reinforcement. To integrate semantic annotation with region-based image retrieval, visual and textual fusion is proposed for both soft matching and bayesian probabilistic formulations. To address sample insufficiency and sample asymmetry in the annotation classifier training phase, we present a region-level multi-label image annotation scheme based on pair-wise coupling support vector machine (SVM) learning. In the retrieval phase, to achieve semantic-level region matching we present a novel retrieval scheme which differs from former work: the query example uploaded by users is automatically annotated online, and the user can judge its annotation quality. Based on the user's judgment, two novel schemes are deployed for semantic retrieval: (1) if the user judges the photo to be well annotated, Semantically supervised Integrated Region Matching is adopted, which is a keyword-integrated soft region matching method;(2) If the user judges the photo to be poorly annotated, Keyword Integrated bayesian Reasoning is adopted, which is a natural integration of a Visual Dictionary in online content-based search. In the relevance feedback phase, we conduct both visual and textual learning to capture the user's retrieval target. Better annotation and retrieval performance than current methods were reported on both COREL 10,000 and Flickr web image database (25,000 images), which demonstrated the effectiveness of our proposed framework.

关键词： Image annotation Image retrieval Relevance feedback Visual and textual fusion Multi-label annotation Pairwise coupling Bagging bayesian formulation Soft region matching Visual dictionary Visual and textual learning

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Time domain force identification based on adaptive l_q regularization

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JOURNAL OF VIBRATION AND CONTROL 2018年第23期24卷 5610-5626页

作者： Li, Qiaofeng Lu, Qiuhai Tsinghua Univ Sch Aerosp Engn Beijing Peoples R China

Traditional time domain force identification methods require prior knowledge about the force profile to apply the appropriate regularization term. Generally speaking, l(1) and l(2) regularization are applied for sparse-type and continuous-type forces respectively. However, prior knowledge about the force type may be unavailable in engineering practice. It is then necessary to incorporate the determination of q (as in l(q) regularization) into the identification process. In this paper, we propose two methods to address the problem: the joint and marginal posterior modes of the force history. The identification problem is formulated within the bayesian framework. The force history, precision parameters, and q are all treated as unknown random parameters, and estimated based on vibration measurements only. The proposed methods are numerically validated on a mass-spring system, an engineering-scale support structure and experimentally validated on a cantilever beam. It is shown that the proposed methods by considering the data-driven determination of q could adapt to the force profile and consistently provide satisfactory results.

关键词： Force identification time domain adaptive l(q) regularization inverse problem bayesian formulation

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Stochastic DT-MRI connectivity mapping on the GPU

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IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2007年第6期13卷 1504-1511页

作者： McGraw, Tim Nadar, Mariappan W Virginia Univ Morgantown WV 26506 USA Siemens Corp Res New York NY 10022 USA

We present a method for stochastic fiber tract mapping from diffusion tensor MRI (DT-MRI) implemented on graphics hardware. From the simulated fibers we compute a connectivity map that gives an indication of the probability that two points in the dataset are connected by a neuronal fiber path. A bayesian formulation of the fiber model is given and it is shown that the inversion method can be used to construct plausible connectivity. An implementation of this fiber model on the graphics processing unit (GPU) is presented. Since the fiber paths can be stochastically generated independently of one another, the algorithm is highly parallelizable. This allows us to exploit the data-parallel nature of the GPU fragment processors. We also present a framework for the connectivity computation on the GPU. Our implementation allows the user to interactively select regions of interest and observe the evolving connectivity results during computation. Results are presented from the stochastic generation of over 250,000 fiber steps per iteration at interactive frame rates on consumer-grade graphics hardware.

关键词： Bayes Methods Biodiffusion Biomedical MRI Brain Computer Graphic Equipment Data Visualisation Medical Image Processing Neurophysiology Probability Stochastic Processes Tensors Brain Data Visualization Magnetic Resonance Imaging Parallel Algorithm Graphics Processing Unit Inversion Method bayesian formulation Neuronal Fiber Path Probability Graphics Hardware Diffusion Tensor Stochastic Fiber Tract Mapping GPU Stochastic DT MRI Connectivity Mapping Stochastic Processes Diffusion Tensor Imaging Tensile Stress Visualization Graphics Anisotropic Magnetoresistance Injuries Ellipsoids Magnetic Resonance Imaging Hardware Diffusion Tensor Magnetic Resonance Imaging Stochastic Tractography

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Boundary finding with prior shape and smoothness models

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2000年第7期22卷 738-743页

作者： Wang, YM Staib, LH Yale Univ Dept Elect Engn & Diagnost Radiol New Haven CT 06520 USA

We propose a unified framework for boundary finding, where a bayesian formulation, based on prior knowledge and the edge information of the input image (likelihood), is employed. The prior knowledge in our framework is based on principal component analysis of four different covariance matrices corresponding to independence, smoothness, statistical shape. and combined models, respectively. indeed, snakes. modal analysis, Fourier descriptors, and point distribution models can be derived from or linked to our approaches of different prior models. When the true training set does not contain enough variability to express the full range of deformations, a mixed covariance matrix uses a combined prior of the smoothness and statistical variation modes. It adapts gradually to use more statistical modes of variation as larger data sets are available.

关键词： boundary finding correspondence statistical shape models smoothness models deformable models prior knowledge bayesian formulation

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GNSS data demodulation over fading environments: Antipodal and M-ary CSK modulations

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IET RADAR SONAR AND NAVIGATION 2021年第2期15卷 113-127页

作者： Ortega, Lorenzo Vila-Valls, Jordi Poulliat, Charly Closas, Pau Telecommun Space & Aeronaut Lab TeSA F-31500 Toulouse France Univ Toulouse ISAE SUPAERO Toulouse France Univ Toulouse INP ENSEEIHT Toulouse France Northeastern Univ Dept Elect & Comp Engn Boston MA 02115 USA

This article investigates new strategies to compute accurate low-complexity log-likelihood ratio (LLR) values based on the bayesian formulation under uncorrelated fading channels for both antipodal and code shift keying modulations when no channel state information (CSI) is available at the receiver. These LLR values are then used as input to modern error-correcting schemes used in the data decoding process of last-generation Global Navigation Satellite System signals. Theoretical analysis based on the maximum achievable rate is presented for the different methods in order to evaluate the performance degradation with respect to the optimal CSI channel. Finally, the frame error rate simulation results are shown, validating the appropriate performance of the proposed LLR approximation methods.

关键词： frame error rate simulation results Bayes methods LLR approximation methods antipodal modulations bayesian formulation decoding error statistics satellite navigation low-complexity log-likelihood ratio approximation theory maximum achievable rate demodulation GNSS data demodulation last-generation Global Navigation Satellite System signals error-correcting schemes code shift fading channels channel state information channel coding uncorrelated fading channels optimal CSI channel error correction codes M-ary CSK modulations radio receivers modulation coding

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Communication structure planning for multisensor detection systems

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IEE PROCEEDINGS-RADAR SONAR AND NAVIGATION 2001年第1期148卷 2-8页

作者： Kasetkasem, T Varshney, PK Syracuse Univ Dept Elect Engn & Comp Sci Syracuse NY 13244 USA

The authors address the problem of communication structure planning in multisensor detection systems under communication constraints and also under a generalised cost formulation. The communication constraint is specified in terms of the amount of communication allowed in the system, while the generalised cost functions measure the efficiency of communication used. System optimisation algorithms are del eloped for both criteria. Examples for both bayesian and Neyman-Pearson formulations are presented for illustration. A variety of scenarios are considered to study their effect on system performance.

关键词： efficiency optimisation communication structure planning Signal detection bayesian formulation system optimisation algorithms signal detection Neyman-Pearson formulation Bayes methods generalised cost formulation Communication network design, planning and routing Sensor fusion multisensor detection systems Optimisation techniques Other topics in statistics telecommunication network planning sensor fusion

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Inverse Problem in Resonant Ultrasound Spectroscopy With Sampling and Optimization: A Comparative Study on Human Cortical Bone

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IEEE TRANSACTIONS ON ULTRASONICS FERROELECTRICS AND FREQUENCY CONTROL 2022年第2期69卷 650-661页

作者： Shen, Fei Fan, Fan Wang, Rui Wang, Yue Yang, Songyan Wu, Qiong Laugier, Pascal Cai, Xiran Niu, Haijun Beihang Univ Sch Biol Sci & Med Engn Beijing Adv Innovat Ctr Biomed Engn Beijing 100191 Peoples R China Sorbonne Univ CNRS INSERM Lab Imagerie Biomed LIB F-75006 Paris France

The bayesian inference with prior knowledge has been proposed recently to solve the inverse problem in resonant ultrasound spectroscopy. It allows inferring the elastic properties of high damping materials, such as cortical bone with less dependence on the initial guessed values. In this method, the estimation of the stiffness coefficients is expressed as a probabilistic solution to the inverse problem, which can be achieved by sampling or optimization methods. However, the detailed performance comparison of these two strategies applied to high damping materials has not been fully studied. In this work, the full stiffness tensor of 52 transversely isotropic cortical bone specimens was obtained using Markov chain Monte Carlo (MCMC) sampling and particle swarm optimization (PSO), respectively. Results showed that the local probability distributions of stiffness coefficients estimated by the two methods are consistent. Compared with MCMC, the average calculation speed of PSO is ten times faster [614 s +/- 59 s (MCMC) versus 53 s +/- 22 s (PSO)]. The mean standard error between theoretical and experimental resonant frequencies was slightly smaller for PSO compared with MCMC. In conclusion, PSO, a global optimization strategy, is suitable to solve the inverse problem for high damping materials.

关键词： Resonant frequency Frequency measurement Cortical bone Inverse problems Bayes methods Damping Ultrasonic variables measurement bayesian formulation particle swarm optimization (PSO) resonant ultrasound spectroscopy (RUS) stiffness coefficients

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