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Heterogeneous network modeling and segmentation of building information modeling data for parallel triangulation and visualization

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AUTOMATION IN CONSTRUCTION 2021年 131卷 103897-103897页

作者： Zhou, Xiaoping Wang, Mengmeng Liu, Yu-Shen Wang, Qian Guo, Maozu Zhao, Jichao Beijing Univ Civil Engn & Architecture Beijing Key Lab Intelligent Proc Bldg Big Data Beijing 100044 Peoples R China Tsinghua Univ Sch Software Beijing Peoples R China Natl Univ Singapore Dept Bldg Singapore Singapore CNPC Managers Training Inst Beijing 100096 Peoples R China

Building Information Modeling (BIM) is a promising technology for the construction industry, and BIM models have become widely accepted as 3D construction models. Currently, BIM data are often organized by product level to support cross-platform parallel and real-time visualization. However, cross-platform visualization of BIM products with large volumes of geometric data still poses a challenge to both triangulation and rendering processes. Existing efforts have mainly used geometric simplification and geometric data streaming technologies. This study addresses this issue from a different perspective by partitioning original large-scale BIM products into small BIM sub-products. First, a novel heterogeneous geometric relationship model (HeGeo) is proposed to categorize BIM relationships into reference, decomposition, and association relationships according to Industry Foundation Classes. On top of the HeGeo, a strategy for partitioning the geometric data of the original BIM products is proposed. Empirical studies were conducted on nine BIM models with a BIM product embedding all the geometric data. The experimental results showed that the proposed scheme improved triangulation efficiency 3.05 +/- 0.57 times with the same hardware resources and the same triangulation tool through a parallel computing framework and raised the triangular mesh loading efficiency 1.53 +/- 0.29 times by processing requests concurrently. In addition, the proposed scheme improved the user experience of online BIM visualization tools through incremental rendering. The segmentation scheme was generalized to product-level and floor-level schemes by constraining the segmentation nodes to BIM products and floors respectively. Thus, the segmentation scheme could be applied to any BIM model and facilitate BIM adoption by all stakeholders during a building's life cycle.

关键词： data segmentation Building information modeling (BIM) Industry foundation classes (IFC) Parallel computing Triangulation Visualization

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Fast segmentation and Dynamic Monitoring of Time-Lapse 3D GPR data Based on U-Net

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REMOTE SENSING 2022年第17期14卷 4190-4190页

作者： Shang, Ke Zhang, Feizhou Song, Ao Ling, Jianyu Xiao, Jiwen Zhang, Zihan Qian, Rongyi China Univ Geosci Sch Geophys & Informat Technol Beijing 100083 Peoples R China Peking Univ Sch Earth & Space Sci Beijing 100871 Peoples R China Minist Emergency Management China MEMC Natl Inst Nat Hazards Beijing 100085 Peoples R China Chinese Acad Sci Inst Software Beijing 100190 Peoples R China

As the amount of ground-penetrating radar (GPR) data increases significantly with the high demands of nondestructive detection methods under urban roads, a method suitable for time-lapse data dynamic monitoring should be developed to quickly identify targets on GPR profiles and compare time-lapse datasets. This study conducted a field experiment aiming to monitor one backfill pit using three-dimensional GPR (3D GPR), and the time-lapse data collected over four months were used to train U-Net, a fast neural network based on convolutional neural networks (CNNs). Consequently, a trained network model that could effectively segment the backfill pit from inline profiles was obtained, whose Intersection over Union (IoU) was 0.83 on the test dataset. Moreover, segmentation masks were compared, demonstrating that a change in the southwest side of the backfill pit may exist. The results demonstrate the potential of machine learning algorithms in time-lapse 3D GPR data segmentation and dynamic monitoring.

关键词： 3D ground-penetrating radar time-lapse monitoring U-Net data segmentation

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Joint analysis and segmentation of time-varying data with outliers

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DIGITAL SIGNAL PROCESSING 2024年 145卷

作者： Colonnese, Stefania Scarano, Gaetano Marra, Marcello Markopoulos, Panos P. Pados, Dimitris A. Sapienza Univ Rome Rome Italy Univ Texas San Antonio San Antonio TX 78249 USA Florida Atlantic Univ Boca Raton FL 33431 USA

Principal-Component Analysis (PCA) is a fundamental tool in data science and machine learning, used for compressing, analyzing, visualizing, and processing large datasets. At the same time, temporal segmentation is important for coherent component analysis of big data collections generated by time-varying distributions. However, both segmentation and PCA can be critically affected and misled by corrupted points that often exist in big data collections. To address these issues, we propose a novel and robust method for joint segmentation and principal-component analysis of time-varying data, based on L1-norm formulations. Our proposed method estimates robust L1-norm principal components (L1-PCs) over different temporal horizons and combines them to perform outlier detection, data segmentation, and subspace estimation. Numerical studies on real-world data, including videos and smartphone-sensed human body motion measurements, corroborate the merits of the proposed method in terms of segmentation, PCA, and outlier detection/removal.

关键词： data segmentation L1-norm principal-component analysis Time series Outliers Subspace clustering

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segmentation of Geophysical data: A Big data Friendly Approach

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Procedia Computer Science 2015年 53卷 39-47页

作者： David R.B. Stockwell Ligang Zhang Brijesh Verma Centre for Intelligent and Networked Systems at Central Queensland University Australia

A new scalable segmentation algorithm is proposed in this paper for the forensic determination of level shifts in geophysical time series. While a number of segmentation algorithms exist, they are generally not ‘big data friendly’ due either to quadratic scaling of computation time in the length of the series N or subjective penalty parameters. The proposed algorithm is called SumSeg as it collects a table of potential break points via iterative ternary splits on the extreme values of the scaled partial sums of the data. It then filters the break points on their statistical significance and peak shape. Our algorithm is linear in N and logarithmic in the number of breaks B , while returning a flexible nested segmentation model that can be objectively evaluated using the area under the receiver operator curve (AUC). We demonstrate the comparative performance of SumSeg against three other algorithms. SumSeg is available as an R package from the development site at http://***/davids99us/anomaly.

关键词： data segmentation data size geophysical change points

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Hybrid-based segmentation of Massive Three-dimensional Point Cloud data

Hybrid-based Segmentation of Massive Three-dimensional Point...

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2011 International Conference on Electronic & Mechanical Engineering and Information Technology(EMEIT 2011)(2011年机电工程与信息技术国际会议)

作者： Fengxuan Jing Xiaoyao Xie Liming Liu Xiaojie Liu Key Laboratory of Information and Computing Science of Guizhou Province.Guizhou Normal UniversityGui Department of Mathematics and Computer Science Guizhou Normal University.Guiyang China

data segmentation is an important part of the reverse engineering. Point cloud data segmentation is a technique of dividing different regions of the stitched scattered point cloud into a single geometric property. The technique is a critical part of the former reverse modeling. This article draws ona segmentation method based on hybrid, and proposes a grouping method of improving three-dimensional point cloud data which based on backtracking. During the measurement process,the method avoids the sparse phenomenon of point cloud cause by object occlusion, the change of reflecting rate or unsatisfactory reduction algorithm processing and some other ***, it resolves the "isolated island "question of point cloud which generated in the split process.

关键词： data segmentation reverse modeling hybrid massive point cloud

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A novel isosurface segmentation method using common boundary tests

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COMPUTERS & GRAPHICS-UK 2023年 110卷 86-97页

作者： Wang, Cuilan Georgia Gwinnett Coll Lawrenceville GA 30043 USA

Visualizing the isosurfaces that represent material boundaries is an important technique for understanding the features of interest in a scalar volumetric dataset. However, one isosurface may contain multiple types of boundaries, i.e., boundaries between different pairs of materials. In this paper, we present a novel isosurface segmentation method that aids in learning structural information of a dataset by separating different types of boundaries in one isosurface. This method uses common boundary tests to classify a point on the isosurface. The test determines whether a point on the isosurface that is at a boundary shared by both the isosurface and a reference isosurface. It uses a gradient-guided sampling approach and is based on material boundary properties. A new region growing algorithm is developed to improve the segmentation results. Our new method can also be used to segment an isosurface that passes through both material boundaries and the interior of a material. Two applications of the new method are also demonstrated in the paper. One is to render and segment section planes to enhance visualization. The other one is to obtain more accurate and meaningful isosurface statistics.(c) 2022 Elsevier Ltd. All rights reserved.

关键词： Volume Visualization Isosurfaces Volume rendering Classification data segmentation Boundary detection Region growing

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Determining the number of segments for piece-wise linear representation of discrete-time signals

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COMPUTERS & CHEMICAL ENGINEERING 2019年 120卷 46-53页

作者： Wang, Jiandong Yu, Yan Chen, Kuang Shandong Univ Sci & Technol Coll Elect Engn & Automat Qingdao Shandong Peoples R China Peking Univ Coll Engn Beijing Peoples R China

Piece-wise linear representation (PLR) separates a discrete-time continuous-valued signal into a few short data segments, each of which is represented by a straight line. One prerequisite parameter in the PLR is the number of data segments. This paper proposes a new method to determine the number of data segments. The method is based on a key observation on two balancing effects of the number of data segments on the confidence intervals of line approximations. The confidence intervals define parallelogram-shaped spaces, and an index is formulated as the weighted percentage of these spaces occupied by data points in data segments. The number of data segments is determined as the one achieving the maximum value of the formulated index. Numerical and industrial examples are provided to illustrate the effectiveness of the proposed method. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： data segmentation Piecewise linear representation Linear regression Confidence intervals

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Dynamic detection of window starting positions and its implementation within an activity recognition framework

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JOURNAL OF BIOMEDICAL INFORMATICS 2016年第0期62卷 171-180页

作者： Ni, Qin Patterson, Timothy Cleland, Ian Nugent, Chris Univ Politecn Madrid Dept Telemat & Elect Engn E-28040 Madrid Spain Univ Ulster Sch Comp & Math Coleraine BT52 1SA Londonderry North Ireland

Activity recognition is an intrinsic component of many pervasive computing and ambient intelligent solutions. This has been facilitated by an explosion of technological developments in the area of wireless sensor network, wearable and mobile computing. Yet, delivering robust activity recognition, which could be deployed at scale in a real world environment, still remains an active research challenge. Much of the existing literature to date has focused on applying machine learning techniques to pre-segmented data collected in controlled laboratory environments. Whilst this approach can provide valuable ground truth information from which to build recognition models, these techniques often do not function well when implemented in near real time applications. This paper presents the application of a multivariate online change detection algorithm to dynamically detect the starting position of windows for the purposes of activity recognition. (C) 2016 Elsevier Inc. All rights reserved.

关键词： data segmentation Activities of Daily Living (ADLs) Change detection Feature selection Activity recognition

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WoS Query Partitioner: A Tool to Retrieve Very Large Numbers of Items From the Web of Science Using Different Source-Based Partitioning Approaches

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JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY 2010年第8期61卷 1582-1597页

作者： Alonso, Sergio Javier Cabrerizo, Francisco Herrera-Viedma, Enrique Herrera, Francisco Univ Granada Software Engn Dept Granada Spain Univ Granada Comp Sci & Artificial Intelligence Dept Granada Spain

Thomson Reuters' Web of Science (WoS) is undoubtedly a great tool for scientiometrics purposes. It allows one to retrieve and compute different measures such as the total number of papers that satisfy a particular condition;however, it also is well known that this tool imposes several different restrictions that make obtaining certain results difficult. One of those constraints is that the tool does not offer the total count of documents in a dataset if it is larger than 100,000 items. In this article, we propose and analyze different approaches that involve partitioning the search space (using the Source field) to retrieve item counts for very large datasets from the WoS. The proposed techniques improve previous approaches: They do not need any extra information about the retrieved dataset (thus allowing completely automatic procedures to retrieve the results), they are designed to avoid many of the restrictions imposed by the WoS, and they can be easily applied to almost any query. Finally, a description of WoS Query Partitioner, a freely available and online interactive tool that implements those techniques, is presented.

关键词： data segmentation information retrieval retrieval effectiveness scientometrics very large databases

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Supervised Manifold Distance segmentation

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IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2011年第11期17卷 1637-1649页

作者： Kniss, Joe Wang, Guanyu 1 Univ New Mexico Dept Comp Sci Adv Graph Lab Albuquerque NM 87131 USA

We present a simple and robust method for image and volume data segmentation based on manifold distance metrics. This is done by treating the image as a function that maps the 2D (image) or 3D (volume) to a 2D or 3D manifold in a higher dimensional feature space. We explore a range of possible feature spaces, including value, gradient, and probabilistic measures, and examine the consequences of including these measures in the feature space. The time and space computational complexity of our segmentation algorithm is O(N), which allows interactive, user-centric segmentation even for large data sets. We show that this method, given appropriate choice of feature vector, produces results both qualitatively and quantitatively similar to Level Sets, Random Walkers, and others. We validate the robustness of this segmentation scheme with comparisons to standard ground-truth models and sensitivity analysis of the algorithm.

关键词： Hypothesis testing visual evidence data segmentation extraction of surfaces (isosurfaces, material boundaries) multifield multimodal and multivariate data uncertainty visualization

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