A Comparison of Virtual Condition Cylinder Evaluation Methods in Coordinate Metrology

2008 ◽  
Author(s):  
Steven Turek ◽  
Sam Anand
Keyword(s):  
Sample Size ◽  
Geometric Analysis ◽  
Sampled Data ◽  
Material Condition ◽  
Novel Approach ◽  
Real World Applications ◽  
Coordinate Metrology ◽  
Normal Method ◽  
Geometric Relationship

When a cylindrical datum feature is specified at maximum material condition (MMC) or least material condition (LMC) a unique circumstance arises: a virtual condition (VC) cylindrical boundary must be defined [1]. The geometric relationship between a cylindrical point cloud obtained from inspection equipment and a VC cylinder has not been specifically addressed in previous research. In this research, novel approaches to this geometric analysis are presented, analyzed, and validated. Two of the proposed methods are new interpretations of established methods applied to this unique geometric circumstance: least squares and the maximum inscribing cylinder (MIC) or minimum circumscribing cylinder (MCC). The third method, the Hull Normal method, is a novel approach specifically developed to address the VC cylinder problem. Each of the proposed methods utilizes a different amount of sampled data, leading to various levels of sensitivity to sample size and error. The three methods were applied to different cylindrical forms, utilizing various sampling techniques and sample sizes. Trends across sample size were analyzed to assess the variation in axial orientation when compared to the true geometric form, and a relevant case study explores the applicability of these methods in real world applications.

A Novel Approach to the Automatic Designation of Predefined Census Enumeration Areas and Population Sampling Frames: A Case Study in Somalia

2019 ◽  
Author(s):  
Sarchil Qader ◽  
Veronique Lefebvre ◽  
Amy Ninneman ◽  
Kristen Himelein ◽  
Utz Pape ◽  
...  
Keyword(s):  
Population Sampling ◽  
Census Enumeration ◽  
Novel Approach

Using hidden Markov models to find discrete targets in continuous sociophonetic data

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Daniel Duncan
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Abstract Representation ◽  
Language Variation And Change ◽  
Novel Approach ◽  
Single Target ◽  
The Individual ◽  
Individual Speaker

Abstract Advances in sociophonetic research resulted in features once sorted into discrete bins now being measured continuously. This has implied a shift in what sociolinguists view as the abstract representation of the sociolinguistic variable. When measured discretely, variation is variation in selection: one variant is selected for production, and factors influencing language variation and change are influencing the frequency at which variants are selected. Measured continuously, variation is variation in execution: speakers have a single target for production, which they approximate with varying success. This paper suggests that both approaches can and should be considered in sociophonetic analysis. To that end, I offer the use of hidden Markov models (HMMs) as a novel approach to find speakers’ multiple targets within continuous data. Using the lot vowel among whites in Greater St. Louis as a case study, I compare 2-state and 1-state HMMs constructed at the individual speaker level. Ten of fifty-two speakers’ production is shown to involve the regular use of distinct fronted and backed variants of the vowel. This finding illustrates HMMs’ capacity to allow us to consider variation as both variant selection and execution, making them a useful tool in the analysis of sociophonetic data.

scholarly journals Using Fuzzy Logic in Test Case Prioritization for Regression Testing Programs with Assertions

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Ali M. Alakeel
Keyword(s):  
Fuzzy Logic ◽  
Regression Testing ◽  
Test Case ◽  
Test Cases ◽  
Test Case Prioritization ◽  
Software Developers ◽  
Novel Approach ◽  
History Of ◽  
Supporting Tool

Program assertions have been recognized as a supporting tool during software development, testing, and maintenance. Therefore, software developers place assertions within their code in positions that are considered to be error prone or that have the potential to lead to a software crash or failure. Similar to any other software, programs with assertions must be maintained. Depending on the type of modification applied to the modified program, assertions also might have to undergo some modifications. New assertions may also be introduced in the new version of the program, while some assertions can be kept the same. This paper presents a novel approach for test case prioritization during regression testing of programs that have assertions using fuzzy logic. The main objective of this approach is to prioritize the test cases according to their estimated potential in violating a given program assertion. To develop the proposed approach, we utilize fuzzy logic techniques to estimate the effectiveness of a given test case in violating an assertion based on the history of the test cases in previous testing operations. We have conducted a case study in which the proposed approach is applied to various programs, and the results are promising compared to untreated and randomly ordered test cases.

Sample size and informetric model goodness-of-fit outcomes: a search engine log case study

2006 ◽  
Vol 32 (3) ◽  
pp. 212-222 ◽  
Author(s):  
Isola Ajiferuke ◽  
Dietmar Wolfram ◽  
Felix Famoye
Keyword(s):  
Sample Size ◽  
Search Engine ◽  
Goodness Of Fit
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