Plant data visualisation using network graphs

PeerJ ◽

10.7717/peerj.5579 ◽

2018 ◽

Vol 6 ◽

pp. e5579

Author(s):

Afrina Adlyna Mohamad-Matrol ◽

Siow-Wee Chang ◽

Arpah Abu

Keyword(s):

Morphological Characteristics ◽

Graph Model ◽

Graphical Form ◽

Computer Assisted ◽

Data Visualisation ◽

Model Data ◽

Network Graph ◽

Novice Users ◽

Plant Data ◽

Meaningful Relationships

BackgroundThe amount of plant data such as taxonomical classification, morphological characteristics, ecological attributes and geological distribution in textual and image forms has increased rapidly due to emerging research and technologies. Therefore, it is crucial for experts as well as the public to discern meaningful relationships from this vast amount of data using appropriate methods. The data are often presented in lengthy texts and tables, which make gaining new insights difficult. The study proposes a visual-based representation to display data to users in a meaningful way. This method emphasises the relationships between different data sets.MethodThis study involves four main steps which translate text-based results from Extensible Markup Language (XML) serialisation format into graphs. The four steps include: (1) conversion of ontological dataset as graph model data; (2) query from graph model data; (3) transformation of text-based results in XML serialisation format into a graphical form; and (4) display of results to the user via a graphical user interface (GUI). Ontological data for plants and samples of trees and shrubs were used as the dataset to demonstrate how plant-based data could be integrated into the proposed data visualisation.ResultsA visualisation system named plant visualisation system was developed. This system provides a GUI that enables users to perform the query process, as well as a graphical viewer to display the results of the query in the form of a network graph. The efficiency of the developed visualisation system was measured by performing two types of user evaluations: a usability heuristics evaluation, and a query and visualisation evaluation.DiscussionThe relationships between the data were visualised, enabling the users to easily infer the knowledge and correlations between data. The results from the user evaluation show that the proposed visualisation system is suitable for both expert and novice users, with or without computer skills. This technique demonstrates the practicability of using a computer assisted-tool by providing cognitive analysis for understanding relationships between data. Therefore, the results benefit not only botanists, but also novice users, especially those that are interested to know more about plants.

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WIZUALIZACJA JAKO NARZĘDZIE WSPOMAGAJĄCE ANALIZĘ DANYCH W PROCESIE DYDAKTYCZNYM

Metody Ilościowe w Badaniach Ekonomicznych ◽

10.22630/mibe.2020.21.4.20 ◽

2021 ◽

Vol 21 (4) ◽

pp. 224-234

Author(s):

Monika Zielińska-Sitkiewicz ◽

Mariola Chrzanowska

Keyword(s):

Teaching Experience ◽

Graphical Form ◽

Data Visualisation ◽

Data Presentation ◽

Graphical Data ◽

Great Support ◽

Incorrect Data

Presentation of information in a graphical form is one of the basic forms of data presentation. It is a great support during both the preliminary and further analysis. However, an incorrect graphical form can lead to misinterpretation and, in consequence, to erroneous conclusions. This paper presents some examples of graphical data visualisation that come from authors’ teaching experience. The article includes cases of both correct and incorrect data presentation.

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Peer Review #1 of "Plant data visualisation using network graphs (v0.1)"

10.7287/peerj.5579v0.1/reviews/1 ◽

2018 ◽

Keyword(s):

Peer Review ◽

Data Visualisation ◽

Plant Data

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Automatic Detection of Directions of Dimensional Control in Mechanical Parts

Volume 1: Materials; Micro and Nano Technologies; Properties, Applications and Systems; Sustainable Manufacturing ◽

10.1115/msec2014-4143 ◽

2014 ◽

Cited By ~ 2

Author(s):

Prashant Mohan ◽

Payam Haghighi ◽

Jami J. Shah ◽

Joseph K. Davidson

Keyword(s):

Linear Chain ◽

Tolerance Analysis ◽

Relative Orientation ◽

Graphical Form ◽

Computer Assisted ◽

Dimensional Control ◽

Mechanical Parts ◽

Geometric Tolerancing ◽

Feature List ◽

Cad Models

This research is part of a larger project which aims at developing a tool to help designers create effective GD&T schemas. The first step towards this goal is to determine the particular directions in which dimensions and tolerances need to be controlled. These directions we label here as “Directions of (Dimensional) Control” or DoC for short. Regardless of whether one uses chain dimensioning, reference dimensioning or geometric tolerancing, all size and basic dimensions of position line up in a finite number of directions or Directions of Control. This paper presents an approach for automatically identifying the directions of control from CAD models of mechanical parts. The only input to the system is the geometry of parts or assemblies in STEP file format. The analysis is done part by part for an assembly. First, planar and cylindrical features are recognized and their normal/axes extracted. The extracted features are then organized into groups of parallel normal or axes directions. Cylindrical features can belong to two or more Directions of Control, while planar features belong can only belong to one. Features in each DoC are then ordered based on perpendicular relative distances. Each ordered feature list forms a linear chain in which nodes represent features and links are attributed with relative distance to the nearest neighbors on each side. DoC chains are related to each other by relative orientation. Therefore, the chains are combined into a unified graph, using the junction nodes to contain the relative orientation between the chains. The extracted Directions of Control can be output in both textual and graphical form. Although the primary motivation for automatic DoC graph generation is computer assisted tolerancing and automatic tolerance analysis, the paper also discusses other applications in manufacturing.

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Peer Review #2 of "Plant data visualisation using network graphs (v0.2)"

10.7287/peerj.5579v0.2/reviews/2 ◽

2018 ◽

Author(s):

OL Pescott

Keyword(s):

Peer Review ◽

Data Visualisation ◽

Plant Data

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A New Disassembly Network Graph Model and its Automatic Generation

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.195-196.1083 ◽

2012 ◽

Vol 195-196 ◽

pp. 1083-1088

Author(s):

Xiao Hong Jin ◽

Guang Dong Tian

Keyword(s):

Adjacency Matrix ◽

Process Model ◽

Graph Model ◽

Automatic Generation ◽

Generation Model ◽

Network Graph ◽

Logical Relationship ◽

Disassembly Process ◽

Storage Type

Disassembly process model plays an important role in the generation of disassembly sequences and evaluation of disassembly process.Based on the concept of adjacency matrix and disassembly precedence matrix, combined with the related definitions of constraint nodes, such as, actual constraint node etc. A new disassembly network graph model is introduced, which can express the constrain information between parts, the priority of the constraints and and/or logical relationship between parts. In addition, according to the storage type and VC tool, its automatic generation model is obtained.

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Computer-Assisted Analysis of Human Semen Concentration and Motility

International Journal of E-Health and Medical Communications ◽

10.4018/ijehmc.2020100102 ◽

2020 ◽

Vol 11 (4) ◽

pp. 17-33

Author(s):

Karima Boumaza ◽

Abdelhamid Loukil

Keyword(s):

Semen Analysis ◽

Hybrid Approach ◽

Sperm Concentration ◽

Morphological Characteristics ◽

Computer Assisted ◽

Human Semen ◽

Low Contrast ◽

Traditional Assessment ◽

Sperm Detection ◽

Speed Accuracy

Computer-assisted semen analysis systems insist on evaluating sperm characteristics. These systems afford capacity to study and evaluate sperm statistical and morphological characteristics such as concentration, morphology, and motility, which have an important role in diagnosis and treatment of male infertility. In this paper, the proposed algorithm allows the assessment of concentration and motility rate of sperms in microscopic videos. First, enhancement process is required because of microscopic images limitations such as low contrast and noises. Then, for true sperm recognition among noise and debris, a hybrid approach is proposed using a combination between segmentation techniques. After, the use of geometric features of the bounding ellipse of the sperm head led to define sperm concentration. Finally, inter-frame difference is applied for motile sperm detection. The proposed method was tested on microscopic videos of human semen; the performance of this method is analyzed in terms of speed, accuracy, and complexity. Obtained results during the experiments are very promising compared with those obtained by the traditional assessment, which is the most widely used and approved in the laboratories.

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Localization in Wireless Sensor Networks: Solvability Improvement Technique Using Priori Information from Sensing Data and Network Properties in Unit Disk Graph Model

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.931-932.999 ◽

2014 ◽

Vol 931-932 ◽

pp. 999-1003

Author(s):

Phisan Kaewprapha

Keyword(s):

Unique Solution ◽

Graph Model ◽

Search Space ◽

Point Of View ◽

Theoretical Point ◽

Network Graph ◽

Free Position ◽

Sparse Networks ◽

Priori Information ◽

Priori Knowledge

From the theoretical point of view, network localization can be viewed as finding a unique solution from distances constraint among points. The one of the difficulties is that even if the network is uniquely localizable, it is proven to be an NP-Hard [1]. It is also true that the network graph has to be sufficiently dense [2]. This poses even more challenges to the original problem as we often work on sparse networks. To cope with this, in [3], we introduce priori knowledge to assist the process of finding the unique localization solution. It helps to speed up the searching algorithm; however, the ambiguity still exists among sparse networks. In this paper we try to bring as much priori knowledge as possible to assist or to be used as constraints. Hopefully this will reduce search space and reach the unique solution quickly. In clean environment, this extra info will, by some magnitude, bring the graph closer to the unique answer. We start from integer-coordinate noise-free position and then add sources of priori knowledge. Then we examine the case where assisted data can be noisy. A search is used within the noisy but useful constraint. The justification of using the assisted knowledge is from the practical uses of some networks, e.g. sensor network, where other measurements are available and they are often correlated and can be helpful in determining the positions.

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