scholarly journals A Multi-Scale Virtual Terrain for Hierarchically Structured Non-Location Data

2021 ◽  
Vol 10 (6) ◽  
pp. 379
Author(s):  
Rui Xin ◽  
Tinghua Ai ◽  
Ruoxin Zhu ◽  
Bo Ai ◽  
Min Yang ◽  
...  
Keyword(s):  
Kernel Density ◽  
Hierarchical Data ◽  
Terrain Model ◽  
Location Data ◽  
Multi Scale ◽  
Spatial Metaphors ◽  
The Hierarchical Structure ◽  
Abstract Data ◽  
Fractal Noise ◽  
Terrain Features

Metaphor are commonly used rhetorical devices in linguistics. Among the various types, spatial metaphors are relatively common because of their intuitive and sensible nature. There are also many studies that use spatial metaphors to express non-location data in the field of visualization. For instance, some virtual terrains can be built based on computer technologies and visualization methods. In virtual terrains, the original abstract data can obtain specific positions, shapes, colors, etc. and people’s visual and image thinking can play a role. In addition, the theories and methods used in the space field could be applied to help people observe and analyze abstract data. However, current research has limited the use of these space theories and methods. For instance, many existing map theories and methods are not well combined. In addition, it is difficult to fully display data in virtual terrains, such as showing the structure and relationship at the same time. Facing the above problems, this study takes hierarchical data as the research object and expresses both the data structure and relationship from a spatial perspective. First, the conversion from high-dimensional non-location data to two-dimensional discrete points is achieved by a dimensionality reduction algorithm to reflect the data relationship. Based on this, kernel density estimation interpolation and fractal noise algorithms are used to construct terrain features in the virtual terrains. Under the control of the kernel density search radius and noise proportion, a multi-scale terrain model is built with the help of level of detail (LOD) technology to express the hierarchical structure and support the multi-scale analysis of data. Finally, experiments with actual data are carried out to verify the proposed method.

Beamtrees: Compact Visualization of Large Hierarchies

2003 ◽  
Vol 2 (1) ◽  
pp. 31-39 ◽  
Author(s):  
Frank van Ham ◽  
Jarke J. van Wijk
Keyword(s):  
Hierarchical Structure ◽  
User Study ◽  
Three Dimensional ◽  
New Method ◽  
Data Sets ◽  
Two Dimensional ◽  
Hierarchical Data ◽  
Organization Structures ◽  
The Hierarchical Structure

Beamtrees are a new method for the visualization of large hierarchical data sets, such as directory structures and organization structures. Nodes are shown as stacked circular beams such that both the hierarchical structure as well as the size of nodes are depicted. The dimensions of beams are calculated using a variation of the treemap algorithm. Both a two-dimensional and a three-dimensional variant are presented. A small user study indicated that beamtrees are significantly more effective than nested treemaps and cushion treemaps for the extraction of global hierarchical information.

The Impact Mechanism of Fractal Noise on PN Code Detection System

2014 ◽  
Vol 13 (02) ◽  
pp. 1450017 ◽  
Author(s):  
Gang Xiong ◽  
Shu-Ning Zhang ◽  
Hui-Chang Zhao
Keyword(s):  
White Noise ◽  
Detection System ◽  
Random Phase ◽  
Average Power ◽  
Fractal Model ◽  
Sea Clutter ◽  
Impact Mechanism ◽  
Multi Scale ◽  
Fractal Noise ◽  
The Impact

Taking the pseudo-random phase modulated CW radar for example, this paper studies the impact mechanism of a class of non-stationary fractal noise on PN code detection system, especially signal mixing and matching filter. The cross correlation function, power spectrum function and average power of pseudo-random signal and fractal noise are deduced, compared with the impact of white noise on the pseudo code detection system. We analyze the impact mechanism of three kinds of sea clutter model, namely fractal Brownian model (FBM), the multifractal (MF) model and the non-stationary random fractal model (e.g., infinitely divisible cascades, IDC), on the pseudo-random code detection system, and demonstrate the reason why the multi-scale filtering method in wavelet domain and the MF methods fail to eliminate the effect of sea clutter. Based on the natural sea clutter data, we simulate and analyze the influence of white noise and fractal noise comparatively on detection system, which indicates that the effect of fractal noise cannot be inhibited effectively by the traditional correlation detection and MF analysis, and finally we put forward possible solutions.

scholarly journals Multi-scale Information Retrieval for BIM using Hierarchical Structure Modelling and Natural Language Processing

2021 ◽  
Vol 26 ◽  
pp. 409-426
Author(s):  
Jie Wang ◽  
Xinao Gao ◽  
Xiaoping Zhou ◽  
Qingshen Xie
Keyword(s):  
Information Retrieval ◽  
Natural Language Processing ◽  
Natural Language ◽  
Hierarchical Structure ◽  
Language Processing ◽  
Tree Model ◽  
Multi Scale ◽  
Building Information ◽  
The Hierarchical Structure ◽  
Retrieval Scheme

Building Information Modelling (BIM) captures numerous information the life cycle of buildings. Information retrieval is one of fundamental tasks for BIM decision support systems. Currently, most of the BIM retrieval systems focused on querying existing BIM models from a BIM database, seldom studies explore the multi-scale information retrieval from a BIM model. This study proposes a multi-scale information retrieval scheme for BIM jointly using the hierarchical structure of BIM and Natural Language Processing (NLP). Firstly, a BIM Hierarchy Tree (BIH-Tree) model is constructed to interpret the hierarchical structure relations among BIM data according to Industry Foundation Class (IFC) specification. Secondly, technologies of NLP and International Framework for Dictionaries (IFD) are employed to parse and unify the queries. Thirdly, a novel information retrieval scheme is developed to find the multi-scale information associated with the unified queries. Finally, the retrieval method proposed in this study is applied to an engineering case, and the practical results show that the proposed method is effective.

Exploring the Effects of Vehicular and Operational Characteristics on Bus Roadworthiness: An Application of the Multilevel Modeling Approach

2019 ◽  
Vol 2673 (4) ◽  
pp. 811-822 ◽  
Author(s):  
Jianrong Qiu ◽  
David B. Logan ◽  
Jennifer Oxley ◽  
Christopher Lowe
Keyword(s):  
Data Structure ◽  
Multilevel Modeling ◽  
Multilevel Analysis ◽  
Hierarchical Structure ◽  
Research Evidence ◽  
Hierarchical Data ◽  
Modeling Approach ◽  
Operational Characteristics ◽  
The Hierarchical Structure ◽  
Inspection Data

This paper examines the effects of vehicular and operational characteristics on bus roadworthiness. The analysis was based on annual bus inspection data in Victoria, Australia, between 2014 and 2017, consisting of 17,630 inspections of 6,447 vehicles run by 252 operators. A multilevel modeling approach was employed to account for the hierarchical data structure where inspections are nested within vehicles and vehicles within operators. The results offered insights into the effects on bus roadworthiness of characteristics attributable to inspections, vehicles, and operators. The probability of failing an inspection was found to be positively associated with vehicle age and odometer reading. Vehicle make played an important role in roadworthiness outcome, with the performance of different makes varying significantly. Small operators carried the highest risk of failure and large operators the lowest, irrespective of the location of operation. The multilevel analysis revealed that 28.9% of the variation in inspection outcomes occurred across operators and 5.2% across vehicles, which verified the presence of the hierarchical structure. The findings from this study provide safety regulators with solid research evidence to formulate policies aimed at enhancing bus roadworthiness.

scholarly journals BHi-Cect: a top-down algorithm for identifying the multi-scale hierarchical structure of chromosomes

2020 ◽  
Vol 48 (5) ◽  
pp. e26-e26
Author(s):  
Vipin Kumar ◽  
Simon Leclerc ◽  
Yuichi Taniguchi
Keyword(s):  
Hierarchical Structure ◽  
Detection Methods ◽  
Interaction Patterns ◽  
Top Down ◽  
Chromosome Conformation ◽  
Multi Scale ◽  
Topologically Associating Domains ◽  
Genome Wide ◽  
The Hierarchical Structure ◽  
Respective Function

Abstract High-throughput chromosome conformation capture (Hi-C) technology enables the investigation of genome-wide interactions among chromosome loci. Current algorithms focus on topologically associating domains (TADs), that are contiguous clusters along the genome coordinate, to describe the hierarchical structure of chromosomes. However, high resolution Hi-C displays a variety of interaction patterns beyond what current TAD detection methods can capture. Here, we present BHi-Cect, a novel top-down algorithm that finds clusters by considering every locus with no assumption of genomic contiguity using spectral clustering. Our results reveal that the hierarchical structure of chromosome is organized as ‘enclaves’, which are complex interwoven clusters at both local and global scales. We show that the nesting of local clusters within global clusters characterizing enclaves, is associated with the epigenomic activity found on the underlying DNA. Furthermore, we show that the hierarchical nesting that links different enclaves integrates their respective function. BHi-Cect provides means to uncover the general principles guiding chromatin architecture.

EncCon: An Approach to Constructing Interactive Visualization of Large Hierarchical Data

2005 ◽  
Vol 4 (1) ◽  
pp. 1-21 ◽  
Author(s):  
Quang Vinh Nguyen ◽  
Mao Lin Huang
Keyword(s):  
Hierarchical Structure ◽  
Experimental Evaluation ◽  
Dimensional Space ◽  
Two Dimensional ◽  
Hierarchical Data ◽  
Division Algorithm ◽  
Layout Algorithm ◽  
The Hierarchical Structure ◽  
A New Technique ◽  
Screen Space

This paper describes a new technique called EncCon for visualizing and navigating large hierarchical information. This technique consists of two components: visualization and navigation. Visualization uses a fast enclosure + connection method to calculate the geometrical layout for the display of large hierarchies in a two-dimensional space. Our technique uses a rectangular division algorithm for recursively positioning the graph. This visualization aims to maximize the utilization of display space while retaining a good geometrical layout as well as a clear (explicit) presentation of the hierarchical structure of graphs. This paper also presents an experimental evaluation of EncCon's layout algorithm. Besides the layout algorithm, EncCon uses a new focus + context viewing technique for the navigation of large hierarchies. We use the zooming + layering concept to achieve the focus + context viewing, rather than the traditional enlarge + embedded concept, which is used by most of the available focus + context techniques. Technically, it employs semi-transparency to achieve the display of two layers of information in z-coordination at the same visualization. Both context view and detail view are drawn at two separate layers. These layers are then displayed in an overlapped manner at the same physical screen space.

scholarly journals Analysis of Landslide Occurrence using DTM-Based Weighted Overlay: A Case Study in Tropical Mountainous Forest of Cameron Highlands, Malaysia

2021 ◽  
Vol 19 (5) ◽  
pp. 1-13
Author(s):  
Paul Lau Hua Ming ◽  
◽  
Azita Ahmad Zawawi ◽  
Keyword(s):  
Human Life ◽  
Digital Terrain Model ◽  
High Sensitivity ◽  
Weighted Overlay ◽  
Landslide Occurrence ◽  
Forest Management Planning ◽  
Terrain Model ◽  
Cameron Highlands ◽  
Low Sensitivity ◽  
Terrain Features

Landslides are massive natural disasters all around the world. In general, our society is only concerned with the landslides that can cause economic distress and impact human life. Landslides in remote areas such as mountainous forests have often been neglected. Referring to the historical disaster event, forest landslides have vast potential to cause unexpected ecological and social damage. This study reveals the terrain characteristics of the complex mountainous forest area of Cameron Highlands (CH), Malaysia, and demonstrates an approach to evaluate the terrain sensitivity of CH. Terrain assessment can be a powerful tool to prevent or reduce the risk of landslides. In this study, terrain features; elevation, slope gradient, aspect, topography wetness index (TWI), and length-slope factor (LS Factor) were extracted using a Digital Terrain Model (DTM) at 10 m resolution. The selected terrain features were incorporated using weighted overlay analysis to derive a terrain sensitivity map (TSM) using SAGA GIS software. The map identified five types of terrain sensitivity classified as very high sensitivity, high sensitivity, moderate sensitivity, low sensitivity, and very low sensitivity; these areas have a coverage of 0.78 km2, 114.31 km2, 107.50 km2, 102.99 km2, and 0.65 km2, respectively. The findings suggest that the sensitive areas are scattered throughout all of the mountainous forests of CH; thus, this enhanced the risk of landslide. Results showed 79.25% accuracy, which is satisfactory to be a guideline for forest management planning and assist decision making in the respective region.

scholarly journals Comparative Multi-scale Hierarchical Structure of the Tail, Plantaris, and Achilles Tendons in the Rat

2018 ◽  
Author(s):  
Andrea H. Lee ◽  
Dawn M. Elliott
Keyword(s):  
Hierarchical Structure ◽  
Single Species ◽  
Confocal Imaging ◽  
Multi Scale ◽  
Multiple Length Scales ◽  
Human Pathology ◽  
The Hierarchical Structure ◽  
Characteristic Features ◽  
Definition Of ◽  
Species Specific

AbstractRodent tendons are widely used to study human pathology, such as tendinopathy and repair, and to address fundamental physiological questions about development, growth, and remodeling. However, how the gross morphology and the multi-scale hierarchical structure of rat tendons, such as the tail, plantaris, and Achillles tendons, compare to that of human tendons are unknown. In addition, there remains disagreement about terminology and definitions. Specifically, the definition of fascicle and fiber are often dependent on the diameter size and not their characteristic features, which impairs the ability to compare across species where the size of the fiber and fascicle might change with animal size and tendon function. Thus, the objective of the study was to select a single species that is widely used for tendon research (rat) and tendons with varying mechanical functions (tail, plantaris, Achilles) to evaluate the hierarchical structure at multiple length scales. This study was designed including, histology, SEM, and confocal imaging. We confirmed that rat tendons do not contain fascicles, and thus the fiber is the largest tendon subunit in the rat. In addition, we provided a structurally-based definition of a fiber as a bundle of collagen fibrils that is surrounded by elongated cells, and this definition was supported by both histologically processed and unprocessed tendons. In all rat tendons studied, the fiber diameters were consistently 10-50 µm, and this diameter appears to be conserved across larger species. Specific recommendations were made for the strengths and limitations of each rat tendon as tendon research models. Understanding the hierarchical structure of tendon can advance the design and interpretation of experiments and development of tissue engineered constructs.

scholarly journals Adaptive Multi-scale Prognostics and Health Management for Smart Manufacturing Systems

2020 ◽  
Vol 7 (3) ◽  
Author(s):  
Benjamin Y. Choo ◽  
Stephen C. Adams ◽  
Brian A. Weiss ◽  
Jeremy A. Marvel ◽  
Peter A. Beling
Keyword(s):  
Manufacturing Systems ◽  
Manufacturing System ◽  
Health Management ◽  
Smart Manufacturing ◽  
Health State ◽  
Prognostics And Health Management ◽  
Multi Scale ◽  
Decision Points ◽  
The Hierarchical Structure ◽  
Smart Manufacturing Systems

The Adaptive Multi-scale Prognostics and Health Management (AM-PHM) is a methodology designed to enable PHM in smart manufacturing systems. In application, PHM information is not yet fully utilized in higher-level decisionmaking in manufacturing systems. AM-PHM leverages and integrates lower-level PHM information such as from a machine or component with hierarchical relationships across the component, machine, work cell and assembly line levels in a manufacturing system. The AM-PHM methodology enables the creation of actionable prognostic and diagnostic intelligence up and down the manufacturing process hierarchy. Decisions are then made with the knowledge of the current and projected health state of the system at decision points along the nodes of the hierarchical structure. To overcome the issue of exponential explosion of complexity associated with describing a large manufacturing system, the AM-PHM methodology takes a hierarchical Markov Decision Process (MDP) approach into describing the system and solving for an optimized policy. A description of the AM-PHM methodology is followed by a simulated industry-inspired example to demonstrate the effectiveness of AM-PHM.

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