Iterative Design: Design Guidelines

The VR Book ◽  
2015 ◽  
pp. 453
Keyword(s):  
Design Guidelines ◽  
Iterative Design

scholarly journals GeoAnnotator: A Collaborative Semi-Automatic Platform for Constructing Geo-Annotated Text Corpora

2019 ◽  
Vol 8 (4) ◽  
pp. 161 ◽  
Author(s):  
Morteza Karimzadeh ◽  
Alan MacEachren
Keyword(s):  
Language Processing ◽  
Visual Analytics ◽  
Ground Truth ◽  
Design Guidelines ◽  
Design Decisions ◽  
Web Based ◽  
Iterative Design ◽  
Automated Algorithm ◽  
Text Corpora ◽  
Human Annotator

Ground-truth datasets are essential for the training and evaluation of any automated algorithm. As such, gold-standard annotated corpora underlie most advances in natural language processing (NLP). However, only a few relatively small (geo-)annotated datasets are available for geoparsing, i.e., the automatic recognition and geolocation of place references in unstructured text. The creation of geoparsing corpora that include both the recognition of place names in text and matching of those names to toponyms in a geographic gazetteer (a process we call geo-annotation), is a laborious, time-consuming and expensive task. The field lacks efficient geo-annotation tools to support corpus building and lacks design guidelines for the development of such tools. Here, we present the iterative design of GeoAnnotator, a web-based, semi-automatic and collaborative visual analytics platform for geo-annotation. GeoAnnotator facilitates collaborative, multi-annotator creation of large corpora of geo-annotated text by generating computationally-generated pre-annotations that can be improved by human-annotator users. The resulting corpora can be used in improving and benchmarking geoparsing algorithms as well as various other spatial language-related methods. Further, the iterative design process and the resulting design decisions can be used in annotation platforms tailored for other application domains of NLP.

scholarly journals A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems

2021 ◽  
Vol 11 (3-4) ◽  
pp. 1-45
Author(s):  
Sina Mohseni ◽  
Niloofar Zarei ◽  
Eric D. Ragan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Everyday Life ◽  
Intelligent Systems ◽  
Evaluation Methods ◽  
Design Guidelines ◽  
Evaluation Methodology ◽  
Iterative Design ◽  
Explainable Ai ◽  
User Groups

The need for interpretable and accountable intelligent systems grows along with the prevalence of artificial intelligence ( AI ) applications used in everyday life. Explainable AI ( XAI ) systems are intended to self-explain the reasoning behind system decisions and predictions. Researchers from different disciplines work together to define, design, and evaluate explainable systems. However, scholars from different disciplines focus on different objectives and fairly independent topics of XAI research, which poses challenges for identifying appropriate design and evaluation methodology and consolidating knowledge across efforts. To this end, this article presents a survey and framework intended to share knowledge and experiences of XAI design and evaluation methods across multiple disciplines. Aiming to support diverse design goals and evaluation methods in XAI research, after a thorough review of XAI related papers in the fields of machine learning, visualization, and human-computer interaction, we present a categorization of XAI design goals and evaluation methods. Our categorization presents the mapping between design goals for different XAI user groups and their evaluation methods. From our findings, we develop a framework with step-by-step design guidelines paired with evaluation methods to close the iterative design and evaluation cycles in multidisciplinary XAI teams. Further, we provide summarized ready-to-use tables of evaluation methods and recommendations for different goals in XAI research.

Flexural design of precast, prestressed ultra-high-performance concrete members

PCI Journal ◽  
2020 ◽  
Vol 65 (6) ◽  
pp. 35-61
Author(s):  
Chungwook Sim ◽  
Maher Tadros ◽  
David Gee ◽  
Micheal Asaad
Keyword(s):  
Prestressed Concrete ◽  
High Performance ◽  
High Performance Concrete ◽  
Limit State ◽  
Design Guidelines ◽  
Design Tool ◽  
Supplementary Cementitious Materials ◽  
Ultra High Performance Concrete ◽  
Concrete Members ◽  
Cylinder Strength

Ultra-high-performance concrete (UHPC) is a special concrete mixture with outstanding mechanical and durability characteristics. It is a mixture of portland cement, supplementary cementitious materials, sand, and high-strength, high-aspect-ratio microfibers. In this paper, the authors propose flexural design guidelines for precast, prestressed concrete members made with concrete mixtures developed by precasters to meet minimum specific characteristics qualifying it to be called PCI-UHPC. Minimum specified cylinder strength is 10 ksi (69 MPa) at prestress release and 18 ksi (124 MPa) at the time the member is placed in service, typically 28 days. Minimum flexural cracking and tensile strengths of 1.5 and 2 ksi (10 and 14 MPa), respectively, according to ASTM C1609 testing specifications are required. In addition, strain-hardening and ductility requirements are specified. Tensile properties are shown to be more important for structural optimization than cylinder strength. Both building and bridge products are considered because the paper is focused on capacity rather than demand. Both service limit state and strength limit state are covered. When the contribution of fibers to capacity should be included and when they may be ignored is shown. It is further shown that the traditional equivalent rectangular stress block in compression can still be used to produce satisfactory results in prestressed concrete members. A spreadsheet workbook is offered online as a design tool. It is valid for multilayers of concrete of different strengths, rows of reinforcing bars of different grades, and prestressing strands. It produces moment-curvature diagrams and flexural capacity at ultimate strain. A fully worked-out example of a 250 ft (76.2 m) span decked I-beam of optimized shape is given.

Rim Slip and Bead Fitment of Tires: Analysis and Design2

2006 ◽  
Vol 34 (1) ◽  
pp. 38-63 ◽  
Author(s):  
C. Lee
Keyword(s):  
Pressure Distribution ◽  
Contact Pressure ◽  
Frictional Torque ◽  
Finite Element Analyses ◽  
Inflation Pressure ◽  
Contact Pressure Distribution ◽  
Design Modification ◽  
Iterative Design ◽  
Slip Resistance ◽  
Braking Torque

Abstract A tire slips circumferentially on the rim when subjected to a driving or braking torque greater than the maximum tire-rim frictional torque. The balance of the tire-rim assembly achieved with weight attachment at certain circumferential locations in tire mounting is then lost, and vibration or adverse effects on handling may result when the tire is rolled. Bead fitment refers to the fit between a tire and its rim, and in particular, to whether a gap exists between the two. Rim slip resistance, or the maximum tire-rim frictional torque, is the integral of the product of contact pressure, friction coefficient, and the distance to the wheel center over the entire tire-rim interface. Analytical solutions and finite element analyses were used to study the dependence of the contact pressure distribution on tire design and operating attributes such as mold ring profile, bead bundle construction and diameter, and inflation pressure, etc. The tire-rim contact pressure distribution consists of two parts. The pressure on the ledge and the flange, respectively, comes primarily from tire-rim interference and inflation. Relative contributions of the two to the total rim slip resistance vary with tire types, depending on the magnitudes of ledge interference and inflation pressure. Based on the analyses, general guidelines are established for bead design modification to improve rim slip resistance and mountability, and to reduce the sensitivity to manufacturing variability. An iterative design and analysis procedure is also developed to improve bead fitment.

Multimodal and Adaptive Learning Management: An Iterative Design

2015 ◽  
Vol 12 (3) ◽  
pp. 13-18
Author(s):  
David R. Squires ◽  
◽  
Michael A. Orey ◽  
Keyword(s):  
Adaptive Learning ◽  
Iterative Design ◽  
Learning Management
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