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structure detection of Wiener-Hammerstein Systems With Process Noise

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2017年第3期66卷 569-576页

作者： Zhang, Erliang Schoukens, Maarten Schoukens, Johan Zhengzhou Univ Dept Mech Engn Zhengzhou 450001 Peoples R China Vrije Univ Brussel Dept Fundamental Elect & Instrumentat B-1050 Brussels Belgium

Identification of nonlinear block-oriented models has been extensively studied. The presence of the process noise and more precisely its location in the block-oriented model essentially influence the development of a consistent identification algorithm. This paper is proposed with the aim to localize the process noise in the block-oriented model for accurate nonlinear modeling. To this end, the response of a Wiener-Hammerstein system is theoretically analyzed, and the disturbance component in the output, caused by the process noise preceding the static nonlinearity, is shown to be dependent on the input signal. Inspired by such a theoretical observation, a simple and new protocol is developed to determine the location of the process noise with respect to the static nonlinearity using an input signal that is periodic but nonstationary within one period. In addition, the proposed technique is promising to detect the type of certain static nonlinearity (e.g., dead zone and saturation). Finally, it is validated on a simulated example and a real-life benchmark.

关键词： Block-oriented model nonstationary input process noise structure detection system identification

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structure detection and parameter estimation for NARX models in a unified EM framework

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AUTOMATICA 2012年第5期48卷 857-865页

作者： Baldacchino, Tara Anderson, Sean R. Kadirkamanathan, Visakan Univ Sheffield Dept Automat Control & Syst Engn Sheffield S1 3JD S Yorkshire England

In this paper, we consider structure detection and parameter estimation of the nonlinear autoregressive with exogenous inputs (NARX) model, using the EM (expectation-maximisation) algorithm. The parameter estimation step uses particle smoothing to obtain the necessary expectations in the E-step and the parameters are then estimated in closed form in the M-step. The model structure detection is performed using an F-test, which makes use of the parameter information matrix (inverse of the covariance matrix), obtained from an augmentation of the EM algorithm. The steps for obtaining the information matrix are robust, guaranteeing a positive semi-definite information matrix to use in the structure detection step. For the case of unknown model orders, a method is proposed using the stochastic complexity (SC) information criterion for selecting between candidate models. The SC is composed of the information matrix (representing model complexity) and a likelihood estimate (representing model accuracy), which are both generated as byproducts of the augmented EM algorithm. Numerical results demonstrate that the EM approach performs well in comparison to a standard alternative based on orthogonal least squares, and also avoids the need to estimate a noise model for the case of measurement noise corrupted output signals. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Nonlinear system identification NARX Expectation-maximisation structure detection Particle filter

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structure detection in Mixed-Integer Programs

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INFORMS JOURNAL ON COMPUTING 2018年第3期30卷 570-587页

作者： Khaniyev, Taghi Elhedhli, Samir Erenay, Fatih Safa Univ Waterloo Dept Management Sci Waterloo ON N2H 3G1 Canada

Despite vast improvements in computational power, many large-scale optimization problems involving integer variables remain difficult to solve. Certain classes, however, can be efficiently solved by exploiting special structure. One such structure is the singly bordered block-diagonal (BBD) structure that lends itself to Dantzig-Wolfe decomposition, Lagrangian relaxation, and branch and price. We start by introducing a new measure of goodness to capture desired features in BBD structures such as granularity of the structure, homogeneity of the block sizes, and isomorphism of the blocks. We then use it to propose a new approach to identify the best BBD structure inherent in the constraint matrix. The main building block of the proposed approach is the use of a community detection methodology in lieu of graph/hypergraph partitioning methods to alleviate one major drawback of the existing approaches in the literature: predefining the number of blocks. When tested on MIPLIB2003/2010 instances and compared against the state-of-the-art technique, the proposed algorithm is found to identify very good structures and require shorter CPU time to reach comparable bounds, in most cases.

关键词： structure detection bordered block diagonal community detection goodness measure Dantzig-Wolfe reformulation Lagrangian relaxation

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structure detection FROM A 3D SET OF POINTS WITH ANISOTROPIC ALPHA-SHAPES

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INTERNATIONAL JOURNAL OF IMAGE AND GRAPHICS 2007年第4期7卷 689-708页

作者： Bougleux, Sebastien Melkemi, Mahmoud Elmoataz, Abderrahim CNRS GREYC UMR 6072 ENSICAEN 6 Bd Marechal Juin F-14050 Caen France LMIA Equipe MAGE F-68093 Mulhouse France Univ Caen Basse Normandie Equipe VAI F-50000 St Lo France

We present a method to extract polyhedral structures from a three-dimensional set of points, even if these structures are embedded in a perturbed background. The method is based on a family of affine diagrams which is an extension of the Voronoi diagram. These diagrams, namely anisotropic diagrams, are defined by using a parameterized distance whose unit ball is an ellipsoidal one. The parameters, upon which this distance depends, control the elongation and the orientation of the associated ellipsoidal ball. The triangulations, dual to the anisotropic diagrams, have the property to connect points that are not neighbors in the Voronoi diagram. Based on these triangulations, we define a family of three-dimensional anisotropic a-shapes. Unlike Euclidean a-shapes, anisotropic ones allow us to detect linear and planar structures in a given direction. The detection of more general polyhedral structures is obtained by merging several anisotropic a-shapes computed for different orientations.

关键词： Alpha-shapes computational morphology Delaunay triangulation interpolation structure detection

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TabCellNet: Deep learning-based tabular cell structure detection q

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NEUROCOMPUTING 2021年 440卷 12-23页

作者： Jiang, JiChu Simsek, Murat Kantarci, Burak Khan, Shahzad Univ Ottawa Sch Elect Engn & Comp Sci Ottawa ON K1N 6N5 Canada Lytica Inc 308 Legget Dr Kanata ON K2K 1Y6 Canada

There is an increasing demand for automated document processing techniques as the volume of electronic component documents increase. This is most prevalent in the supply chain optimization sector where vast amount of documents need to be processed and is time consuming and prone to error. detection of tables and table structures serves as a crucial step to automate document processing. While table detection is a well investigated problem, tabular structure detection is more complex, and requires further improvements. To address this, this study proposes a deep learning model that focuses on high precision tabular cell structure detection. The proposed model creates a benchmark for the ICDAR2013 dataset cell structure with comparison to the previous state of the art table detection models as well as proposing alternative models. Our methodology approaches improving table structure detection through the detection of cells instead of row and columns for better generalization capabilities for heterogeneous table structures. Our proposed model advances prior models by improving major parts of the detection pipeline, mainly the two-stage detector, backbone, backbone architecture, and non maximum-suppression (NMS). TabCellNet consists of Hybrid Task Cascade (HTC) with Combinational Backbone Network (CBNet), dual ResNeXt101 and Soft-NMS to achieve a precision of 89.2% and recall of 98.7% on the hand annotated ICDAR2013 cell structure dataset. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Deep learning Convolutional neural networks Image processing Document processing Table detection Tabular data extraction Page object detection structure detection

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On cropped versus uncropped training sets in tabular structure detection

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NEUROCOMPUTING 2022年 513卷 114-126页

作者： Akkaya, Yakup Simsek, Murat Kantarci, Burak Khan, Shahzad Univ Ottawa Sch Elect Engn & Comp Sci Ottawa ON K1N 6N5 Canada Gnowit Inc 383 Celtic Ridge Crescent Kanata ON K2W 0B5 Canada Lytica Inc 308 Legget Dr Kanata ON K2K 1Y6 Canada

Automated document processing for tabular information extraction is highly desired in many organiza-tions, from industry to government. Prior works addressed this problem under table detection and table structure detection tasks. Proposed solutions that leverage deep learning approaches have been giving promising results;however, the impact of dataset structures on table structure detection has not been investigated. In this study, we provide a comparison of table structure detection performance with cropped and uncropped datasets. The cropped set consists of only table images that are cropped from documents assuming tables are detected perfectly. The uncropped set consists of regular document images. Experiments show that deep learning models can improve the detection performance by up to 9% in average precision and average recall on the cropped versions. Furthermore, the impact of cropped images is negligible under the Intersection over Union (IoU) values of 50%-70% when compared to the uncropped versions in small-scale datasets. However, beyond 70% IoU thresholds, cropped datasets pro-vide significantly higher detection performance. In a large-scale dataset, cropped version outperforms the uncropped set under all IoU thresholds. (c) 2022 Elsevier B.V. All rights reserved.

关键词： Deep learning Convolutional neural networks Image processing Document processing Table detection Tabular data extraction Page object detection structure detection

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Unsupervised structure detection in Biomedical Data

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2015年第4期12卷 753-760页

作者： Vogt, Julia E. Mem Sloan Kettering Canc Ctr Computat Biol Ctr New York NY 10065 USA

A major challenge in computational biology is to find simple representations of high-dimensional data that best reveal the underlying structure. In this work, we present an intuitive and easy-to-implement method based on ranked neighborhood comparisons that detects structure in unsupervised data. The method is based on ordering objects in terms of similarity and on the mutual overlap of nearest neighbors. This basic framework was originally introduced in the field of social network analysis to detect actor communities. We demonstrate that the same ideas can successfully be applied to biomedical data sets in order to reveal complex underlying structure. The algorithm is very efficient and works on distance data directly without requiring a vectorial embedding of data. Comprehensive experiments demonstrate the validity of this approach. Comparisons with state-of-the-art clustering methods show that the presented method outperforms hierarchical methods as well as density based clustering methods and model-based clustering. A further advantage of the method is that it simultaneously provides a visualization of the data. Especially in biomedical applications, the visualization of data can be used as a first pre-processing step when analyzing real world data sets to get an intuition of the underlying data structure. We apply this model to synthetic data as well as to various biomedical data sets which demonstrate the high quality and usefulness of the inferred structure.

关键词： Data mining knowledge discovery unsupervised learning structure detection bioinformatics network analysis clustering

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Improved structure detection For Polynomial NARX Models Using a Multiobjective Error Reduction Ratio

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JOURNAL OF CONTROL AUTOMATION AND ELECTRICAL SYSTEMS 2013年第6期24卷 764-772页

作者： Milani Martins, Samir Angelo Nepomuceno, Erivelton Geraldo Santos Barroso, Marcio Falcao Univ Fed Sao Joao del Rei Dept Elect Engn Frei Orlando Sq170 Ctr BR-36307352 Sao Joao Del Rei MG Brazil

This paper addresses the problem of structure detection for polynomial NARX models. It develops MERR, a multiobjective extension of a methodology well-known as the error reduction ratio (ERR). It is shown that it is possible to choose terms which take into account dynamics of prediction error and other types of affine information, such as fixed points or static curve. Two examples are included to illustrate the proposed methodology. A numerical example shows that the technique is able to reconstruct the structure of a system, known a priori. The identification of a pilot DC-DC buck converter shows that the proposed approach is capable to find models valid over a wide range of operation points. In this latter example, MERR is compared with ERR in two forms: (i) affine information is applied only in the structure selection for MERR and (ii) affine information is applied for structure selection for MERR and for parameter estimation for both MERR and ERR. In both comparisons, MERR presented nondominated solutions of Pareto set.

关键词： Multiobjective system identification NARX models structure detection

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Research and improving on speckle MMSE filter based on adaptive windowing and structure detection

Research and improving on speckle MMSE filter based on adapt...

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IEEE International Conference on Vehicular Electronics and Safety (ICVES 2005)

作者： Sun, ZG Han, CZ Kang, X Xian Jiaotong Univ Sch Elect & Informat Engn Xian 710049 Peoples R China

ISBN: (纸本)0780394356

The appearance of speckle makes it difficult for image segmentation, classification and target detection. By analyzing MMSE filter and the enhanced one, it can be seen that although these filters are better trade-off between noise reduction and fine detail preserving, a basic premise is required for all statistic filters that the sample mean and variance of a pixel is equal to its local mean and variance based on pixels within a neighborhood surrounding it. It is validated only if the filtering area is large enough to assure the sample calculation robust and doesn't contain edge features, linear features or point targets for stationary assumption. An improved filter based on adaptive windowing and structure detection is proposed in this paper. This filter outputs the mean intensity of filtering area in homogeneous region. Maximum homogeneous filtering area decided by adaptive windowing technique is required to keep sample calculation robust. structure feature such as line, edge and point target may appear in heterogeneous region and this invalidates the basic premise of stationary for all local statistic filters. Point target is preserved owing to special distribution and maximum window subset of linear and edge feature is decided by using different ratio detectors. Filtering process is accomplished calculating local statistics in window subset determined. Experiments on simulated image and real SAR one demonstrate that the improved MMSE filter proposed for speckle noise is superior both in noise reduction and in fine detail preserving.

关键词： speckle MMSE adaptive windowing structure detection

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RF-Detector: 3D structure detection of Tiny Objects via RFID Systems 29

RF-Detector: 3D Structure Detection of Tiny Objects via RFID...

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29th International Conference on Computer Communications and Networks (ICCCN)

作者： Ning, Jingyi Xie, Lei Wang, Chuyu Bu, Yanling Ye, Baoliu Lu, Sanglu Nanjing Univ State Key Lab Novel Software Technol Nanjing 210023 Peoples R China

ISBN: (数字)9781728166070

ISBN: (纸本)9781728166070

Nowadays, detecting and evaluating the internal structure of packages becomes a crucial task for logistics systems to guarantee the reliability and security. However, prior solutions such as X-ray diffraction and WiFi-based detection are not suitable for this purpose. X-ray-based methods usually require manual analysis or image processing algorithms with high complexity, whileWiFi-based solutions may fail to detect complex structures due to the significant error of the RF-signal features. In this paper, we propose RF-Detector, a low-cost RFID solution for performing 3D structure detection of items contained in the packages, including the item orientations and relative locations. We thoroughly investigate a brand-new sensing model for RFID-based 3D structure detection, i.e., revolving scanning. We propose not only the fundamental revolving model but also a novel calibration method towards the undesired deployment. We have implemented a prototype system to evaluate the performance of RF-Detector. Extensive evaluations in real settings show the effectiveness of RF-Detector, achieving very high accuracy of the internal 3D structure detection.

关键词： RFID structure detection Internet of Things

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