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Robust Model Fitting Using Higher Than Minimal Subset Sampling

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2016年第2期38卷 350-362页

作者： Tennakoon, Ruwan B. Bab-Hadiashar, Alireza Cao, Zhenwei Hoseinnezhad, Reza Suter, David RMIT Univ Sch Aerosp Mech & Mfg Engn Melbourne Vic 3001 Australia Swinburne Univ Technol Fac Sci Engn & Technol Hawthorn Vic 3122 Australia Univ Adelaide Sch Comp Sci Adelaide SA 5005 Australia

Identifying the underlying model in a set of data contaminated by noise and outliers is a fundamental task in computer vision. The cost function associated with such tasks is often highly complex, hence in most cases only an approximate solution is obtained by evaluating the cost function on discrete locations in the parameter (hypothesis) space. To be successful at least one hypothesis has to be in the vicinity of the solution. Due to noise hypotheses generated by minimal subsets can be far from the underlying model, even when the samples are from the said structure. In this paper we investigate the feasibility of using higher than minimal subset sampling for hypothesis generation. Our empirical studies showed that increasing the sample size beyond minimal size (p), in particular up to p + 2, will significantly increase the probability of generating a hypothesis closer to the true model when subsets are selected from inliers. On the other hand, the probability of selecting an all inlier sample rapidly decreases with the sample size, making direct extension of existing methods unfeasible. Hence, we propose a new computationally tractable method for robust model fitting that uses higher than minimal subsets. Here, one starts from an arbitrary hypothesis (which does not need to be in the vicinity of the solution) and moves until either a structure in data is found or the process is re-initialized. The method also has the ability to identify when the algorithm has reached a hypothesis with adequate accuracy and stops appropriately, thereby saving computational time. The experimental analysis carried out using synthetic and real data shows that the proposed method is both accurate and efficient compared to the state-of-the-art robust model fitting techniques.

关键词： Model fitting robust statistics hypothesis generation data segmentation higher than minimal subset sampling

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Elements of a Computational Model for Multi-Party Discourse: The Turn-Taking Behavior of Supreme Court Justices

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JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY 2009年第8期60卷 1607-1615页

作者： Hawes, Timothy Lin, Jimmy Resnik, Philip Univ Maryland Dept Linguist College Pk MD 20742 USA Univ Maryland Inst Adv Comp Studies College Pk MD 20742 USA Univ Maryland iSch College Pk MD 20742 USA

This work explores computational models of multi-party discourse, using transcripts from U.S. Supreme Court oral arguments. The turn-taking behavior of participants is treated as a supervised sequence-labeling problem and modeled using first- and second-order conditional random fields (CRFs). We specifically explore the hypothesis that discourse markers and personal references provide important features in such models. Results from a sequence prediction experiment demonstrate that incorporating these two types of features yields significant improvements in accuracy. Our experiments are couched in the broader context of developing tools to support legal scholarship, although we see other natural language processing applications as well.

关键词： discourse analysis communicaton patterns data segmentation computational linguistics law

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CNC internal data based incremental cost-sensitive support vector machine method for tool breakage monitoring in end milling

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2018年 74卷 90-103页

作者： Xu, Guangda Zhou, Huicheng Chen, Jihong Huazhong Univ Sci & Technol Natl Numer Control Syst Engn Res Ctr Wuhan 430074 Hubei Peoples R China

A tool breakage monitoring (TBM) system needs to detect tool breakage promptly in an unattended automation workshop. Traditional TBM systems that employ external sensors to acquire diagnostic signals such as spindle power for making judgments are inconvenient since extra sensors should be installed. Moreover, the signals from the external sensors are independent of the computer numerical control (CNC) system, and it is difficult to label them with the corresponding machining task information so that the target data can be segmented automatically. This paper proposes an incremental cost-sensitive support vector machine (ICSSVM) tool breakage monitoring method based on CNC internal data, which is inherently machining-task labeled and can be accessed directly from the CNC system without extra sensors. To satisfy the dataset's integrity at the initial stage of model training, a simulation method that is based on the actual tool breakage characteristics is applied to generate simulated tool breakage data. The ICSSVM method combines cost-sensitive SVM (CSSVM) and modified incremental SVM (ISVM) to solve the imbalanced classification problem, which increases the misclassification probability of the minority class, train the model incrementally from the absence of samples, and guarantee high algorithm efficiency as the size of the dataset increases. It is proved that the ICSSVM algorithm has better algorithmic efficiency compared to the batch cost-sensitive SVM (BCSSVM). It is also proved that the ICSSVM algorithm has better imbalanced classification performance than batch SVM (BSVM), as assessed by the receiver operator characteristic (ROC) curves. The industrial practicability of the proposed method is verified by actual machining with a CNC system integrated with the TBM module.

关键词： Tool breakage monitoring data segmentation Incremental learning Cost-sensitive learning Support vector machine

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Moving Sum Procedure for Change Point Detection under Piecewise Linearity

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TECHNOMETRICS 2024年第3期66卷 358-367页

作者： Kim, Joonpyo Oh, Hee-Seok Cho, Haeran Sejong Univ Dept Math & Stat Seoul South Korea Seoul Natl Univ Dept Stat Seoul South Korea Univ Bristol Sch Math Bristol England

We propose a computationally and statistically efficient procedure for segmenting univariate data under piecewise linearity. The proposed moving sum (MOSUM) methodology detects multiple change points where the underlying signal undergoes discontinuous jumps and/or slope changes. It controls the family-wise error rate at a given significance level and achieves consistency in multiple change point detection, with a minimax optimal estimation rate when the signal is piecewise linear and continuous, all under weak assumptions permitting serial dependence and heavy-tailedness. Computationally, the complexity of the MOSUM procedure is O(n), which, combined with its good performance on simulated datasets, makes it highly attractive compared to the existing methods. We further demonstrate its good performance on a real data example on rolling element-bearing prognostics.

关键词： Change point analysis data segmentation MOSUM Piecewise linear model

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Parallel-Dynamic Interpolation Algorithm of Sea Surface Height for Future 2D Altimetry Mapping of Sea Surface Height

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Journal of Ocean University of China 2021年第5期20卷 1121-1135页

作者： DI Jiankai MA Chunyong CHEN Ge College of Information Science and Engineering Ocean University of ChinaQingdao 266100China Laboratory for Regional Oceanography and Numerical Modeling Laboratory for Ocean Dynamics and ClimateQingdao National Laboratory for Marine Science and TechnologyQingdao 266237China

The sea surface height data volume of the future wide-swath two-dimensional(2D)altimetric satellite is thousands of times greater than that of nadir altimetric *** time complexity of the 2D altimetry mapping reaches O(n^(3)).It is challenging to map the global grid products of future 2D altimetric *** this study,to improve the efficiency of global data mapping,a new algorithm called parallel-dynamic interpolation(PA-DI)was *** the use of 2D data segmentation and fine-grained data mosaic methods,the parallel along-track DI processes were accelerated,and a fast and efficient spatial-temporal high-resolution and low-error enhanced mapping method was *** determined from a comparison of the single-threaded DI with the PA-DI,the new algorithm optimized the time complexity from O(n^(3))to O(n^(3)/KL),which improved the mapping efficiency and achieved the expected *** to the test results of the observing system simulation experiments,the PA-DI algorithm may provide an efficient and reliable method for future wide-swath 2D altimetric satellite mapping.

关键词： parallel computation data segmentation data mosaic dynamic interpolation wide-swath 2D altimetric satellite

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Evidence fusion-based alarm system design considering coarse and fine changes of process variable

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JOURNAL OF PROCESS CONTROL 2022年 113卷 68-79页

作者： Weng, Xu Xu, Xiaobin Chang, Leilei Hou, Pingzhi Wang, Guodong Dustdar, Schahram Hangzhou Dianzi Univ Sch Automation Hangzhou 310018 Peoples R China Hangzhou Dianzi Univ China Austria Belt & Rd Joint Lab Artificial Intel Hangzhou 310018 Peoples R China Hangzhou YanShi S&T Co Ltd Hangzhou 310018 Peoples R China Shanghai Inst Comp Technol Shanghai 200000 Peoples R China TU Wien Distributed Syst Grp A-1040 Vienna Austria

In view of the coarse and fine changes of process variable in industrial systems, this paper introduces a univariate alarm design method based on dynamic evidence fusion. Firstly, in order to describe the coarse statistical characteristics of historical sample data, the multi-transition data segmentation based on memory and forgetting strategies and the referential evidential matrix (REM) construction are presented. Secondly, the real-time sample of process variable is transformed into alarm evidence by matching with REM, and then such multiple pieces of alarm evidence continuously acquired in time are fused by evidence reasoning (ER) rule with the interval-valued fusion weights and reliabilities of alarm evidence, so as to accurately adapt the fine change of process variable. Finally, numerical experiment and motor rotor alarm experiment are implemented to validate that the proposed method has better performances than traditional alarm design methods.

关键词： Industrial alarm system Evidence fusion data segmentation Dempster-Shafer theory of evidence

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Fast and Exact Fiber Surfaces for Tetrahedral Meshes

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IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2017年第7期23卷 1782-1795页

作者： Klacansky, Pavol Tierny, Julien Carr, Hamish Geng, Zhao Univ Utah SCI Inst Salt Lake City UT 84112 USA Univ Leeds Leeds LS2 9JT W Yorkshire England UPMC Univ Paris 06 Sorbonne Univ CNRS LIP6UMR 7606 Paris France

Isosurfaces are fundamental geometrical objects for the analysis and visualization of volumetric scalar fields. Recent work has generalized them to bivariate volumetric fields with fiber surfaces, the pre-image of polygons in range space. However, the existing algorithm for their computation is approximate, and is limited to closed polygons. Moreover, its runtime performance does not allow instantaneous updates of the fiber surfaces upon user edits of the polygons. Overall, these limitations prevent a reliable and interactive exploration of the space of fiber surfaces. This paper introduces the first algorithm for the exact computation of fiber surfaces in tetrahedral meshes. It assumes no restriction on the topology of the input polygon, handles degenerate cases and better captures sharp features induced by polygon bends. The algorithm also allows visualization of individual fibers on the output surface, better illustrating their relationship with data features in range space. To enable truly interactive exploration sessions, we further improve the runtime performance of this algorithm. In particular, we show that it is trivially parallelizable and that it scales nearly linearly with the number of cores. Further, we study acceleration data-structures both in geometrical domain and range space and we show how to generalize interval trees used in isosurface extraction to fiber surface extraction. Experiments demonstrate the superiority of our algorithm over previous work, both in terms of accuracy and running time, with up to two orders of magnitude speedups. This improvement enables interactive edits of range polygons with instantaneous updates of the fiber surface for exploration purpose. A VTK-based reference implementation is provided as additional material to reproduce our results.

关键词： Bivariate data data segmentation data analysis isosurfaces continuous scatterplot

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Automatic augmentation and segmentation system for three-dimensional point cloud of pavement potholes by fusion convolution and transformer

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ADVANCED ENGINEERING INFORMATICS 2024年 60卷

作者： Dong, Jiaxiu Wang, Niannian Fang, Hongyuan Lu, Hongfang Ma, Duo Hu, Haobang Zhengzhou Univ Yellow River Lab Zhengzhou 450001 Henan Peoples R China Zhengzhou Univ Sch Water Conservancy Engn Zhengzhou 450001 Peoples R China Natl Local Joint Engn Lab Major Infrastructure Tes Zhengzhou 450001 Peoples R China Collaborat Innovat Ctr Water Conservancy & Transpo Zhengzhou 450001 Henan Peoples R China Southeast Univ Sch Civil Engn Nanjing 211100 Jiangsu Peoples R China

The regular three-dimensional (3D) detection of potholes is essential for the assessment of pavement conditions. However, some problems associated with the segmentation of pavement potholes, such as the scarcity of 3D point cloud data of pavement potholes and the segmentation interference of asphalt aggregate particle gaps ('False potholes'), need to be solved. Therefore, a 3D point cloud augmentation and segmentation system for pavement potholes was proposed in this paper. The system comprises a fusion 3D convolution with transformer for 3DGAN (Conv-Trans-3DGAN) and a fusion 3D convolution and transformer 3D segmentation network called Conv-Trans-3DSeg. First, a Conv-Trans-3DGAN was developed for pavement potholes, based on the improved GAN by fusion convolution and transformer, to generate 3D point cloud data. Conv-Trans-3DGAN was used to generate high-quality 3D point cloud data for pavement potholes. Second, an innovative network of Conv-Trans3DSeg was developed to segment 3D point cloud data for pavement potholes. The global and local features of the pothole region were extracted by Conv-Trans-3DSeg to solve the problem of asphalt aggregate gaps leading to false potholes. The segmentation accuracy and F1-score of a trained Conv-Trans-3DSeg model can reach 92.075% and 91.688% respectively. Ablation comparison experiments were conducted, and the accuracy and F1-score were improved by 4.733% and 3.03%, respectively. Compared with the better known PointNet, PointNet++, Point Transformer (PT) and PointNeXt, the accuracy of the proposed model was improved by 7.694%, 2.819%, 3.34% and 3.743% respectively;the F1 score was improved by 5.211%, 2.721%, 2.102% and 3.894% respectively. In addition, the proposed Conv-Trans-3DSeg has a segmentation efficiency of 21.3FPS. The results show that the proposed segmentation network achieves accurate and efficient recognition and segmentation of threedimensional point cloud data of potholes.

关键词： Convolution and Transformer Conv-Trans-3DGAN Conv-Trans-3DSeg data augmentation data segmentation

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Adaptive regression model-based real-time optimal control of central air-conditioning systems

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APPLIED ENERGY 2020年 276卷 115427-115427页

作者： Hussain, Syed Asad Huang, Gongsheng Yuen, Richard Kwok Kit Wang, Wei City Univ Hong Kong Dept Architecture & Civil Engn Kowloon Hong Kong Peoples R China Southeast Univ Sch Architecture Nanjing Peoples R China

Model-based, real-time optimal control is an effective tool to improve the energy efficiency of central air-conditioning systems. However, its performance relies heavily on the accuracy of the system models, whereas the development of accurate models for central air-conditioning systems is not easy due to their complex dynamics and non-linearities. This study presents an adaptive regression model-based real-time optimal control strategy for central air-conditioning systems. In the proposed strategy, regression models are adopted to describe the relationship between the power consumption of the system and the variables that are optimised. Their simple structures enable a low computation load for model updating and real-time optimisation. The length of the training data (for model updating) is investigated, and a suitable length is found using a similarity check-based method. Case studies were carried out to assess the performance of the proposed strategy, and they demonstrated that (1) a week was the optimal length of the training data for the case system, (2) the proposed strategy saved energy use by 3.48-10.59% when compared with a benchmark system with no optimisation, and (3) the proposed method reduced the computational load by 85% when compared with a simplified physical model-based optimal control without adaptive modelling.

关键词： Optimal control data segmentation Adaptive model Energy saving Central air-conditioning system

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Process Partitions from Time-Ordered Clusters

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JOURNAL OF QUALITY TECHNOLOGY 2009年第1期41卷 3-17页

作者： Harnish, Peter Nelson, Ben Runger, George Weyerhaeuser Co Longview WA USA Arizona State Univ Tempe AZ USA

Statistical analysis of massive multivariate industrial data sets might not be effective unless the data is first partitioned into stable operating regions. Standard statistical methods that are based on global models, like principal component and regression analyses, can be ineffective if the process is not under statistical control. Here, constrained clustering is proposed as a practical, robust, general solution to detect change points and partition historical process data. The constraint is that only observations (or clusters) that are contiguous in time can be joined. This paper describes a method for partitioning data sets into stable regions by modifying agglomerative clustering algorithms to take into account the time order within the data set. A stopping criterion is proposed to evaluate the number of change points generated, and several empirical studies with simulated and actual data are presented, The result is a partitioned data set that is better suited for analysis by standard statistical methods.

关键词： Change Point Cluster Analysis data Mining data segmentation Partitioning

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