scholarly journals Validity of the diffused aerial image model: an assessment based on multiple test cases

2003 ◽  
Author(s):  
David Fuard ◽  
Maxime Besacier ◽  
Patrick Schiavone
Keyword(s):  
Aerial Image ◽  
Test Cases ◽  
Image Model ◽  
Multiple Test

Multitool and Multi-Axis Computer Numerically Controlled Accumulation for Fabricating Conformal Features on Curved Surfaces

2014 ◽  
Vol 136 (3) ◽  
Author(s):  
Yayue Pan ◽  
Chi Zhou ◽  
Yong Chen ◽  
Jouni Partanen
Keyword(s):  
Tool Path ◽  
Prototype System ◽  
Test Cases ◽  
Curved Surfaces ◽  
Engineering Systems ◽  
Multiple Test ◽  
Optical Cable ◽  
Spherical Surfaces ◽  
Computer Numerically Controlled ◽  
Planning Methods

In engineering systems, features such as textures or patterns on curved surfaces are common. In addition, such features, in many cases, are required to have shapes that are conformal to the underlying surfaces. To address the fabrication challenge in building such conformal features on curved surfaces, a newly developed additive manufacturing (AM) process named computer numerically controlled (CNC) accumulation is investigated by integrating multiple tools and multiple axis motions. Based on a fiber optical cable and a light source, a CNC accumulation tool can have multi-axis motion, which is beneficial in building conformal features on curved surfaces. To address high resolution requirement, the use of multiple accumulation tools with different curing sizes, powers, and shapes is explored. The tool path planning methods for given cylindrical and spherical surfaces are discussed. Multiple test cases have been performed based on a developed prototype system. The experimental results illustrate the capability of the newly developed AM process and its potential use in fabricating conformal features on given curved surfaces.

scholarly journals Integration with an adaptive harmonic mean algorithm

2020 ◽  
Vol 35 (24) ◽  
pp. 1950142
Author(s):  
Allen Caldwell ◽  
Philipp Eller ◽  
Vasyl Hafych ◽  
Rafael Schick ◽  
Oliver Schulz ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Probability Distribution ◽  
Marginal Likelihood ◽  
Harmonic Mean ◽  
High Dimensional ◽  
Test Cases ◽  
Multiple Test ◽  
Mathematical Properties

Numerically estimating the integral of functions in high dimensional spaces is a nontrivial task. A oft-encountered example is the calculation of the marginal likelihood in Bayesian inference, in a context where a sampling algorithm such as a Markov Chain Monte Carlo provides samples of the function. We present an Adaptive Harmonic Mean Integration (AHMI) algorithm. Given samples drawn according to a probability distribution proportional to the function, the algorithm will estimate the integral of the function and the uncertainty of the estimate by applying a harmonic mean estimator to adaptively chosen regions of the parameter space. We describe the algorithm and its mathematical properties, and report the results using it on multiple test cases.

Accuracy of diffused aerial image model for full-chip-level optical proximity correction

2000 ◽  
Author(s):  
Jee-Suk Hong ◽  
Hee-Bom Kim ◽  
Hyoung-Soon Yune ◽  
Chang-Nam Ahn ◽  
Young-Mo Koo ◽  
...  
Keyword(s):  
Aerial Image ◽  
Image Model ◽  
Optical Proximity Correction

Aerial image model and application to aberration measurement

2010 ◽  
Author(s):  
Anatoly Y. Bourov ◽  
Liang Li ◽  
Zhiyong Yang ◽  
Fan Wang ◽  
Lifeng Duan
Keyword(s):  
Aerial Image ◽  
Image Model

Aerial image of 3D phase-shifted reticle: 3D fast aerial image model

1994 ◽  
Author(s):  
Eytan Barouch ◽  
Uwe Hollerbach ◽  
Steven A. Orszag
Keyword(s):  
Aerial Image ◽  
Image Model

scholarly journals A Hybrid Approach for Test Case Prioritization using PSO Based on Software Quality Metrics

2018 ◽  
Vol 7 (3.12) ◽  
pp. 300
Author(s):  
K Senthil Kumar ◽  
A Muthukumaravel
Keyword(s):  
Software Quality ◽  
Search Algorithm ◽  
Hybrid Approach ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Test Case ◽  
Test Cases ◽  
Software Application ◽  
Multiple Test ◽  
Software Quality Metrics

Effective functionality checking of any software application is the crucial event that determines the quality of outcome obtained.  Generally, checking scenarios that involves multiple test cases in mixture with multiple components is time consuming and also increases the quality assurance cost. Selection of suitable method/approach for optimization and prioritization of test cases as well as appropriate evaluation of the application would result in reduction of fault detection effort without appreciable information loss and further would also significantly decrease the clearing up cost. In the proposed method, test cases are optimized and then prioritized by Particle Swarm Optimization algorithm (PSO) and Improved Cuckoo Search algorithm (ICSA), respectively. Finally, the result will be evaluated for software quality measures. 

scholarly journals Artificial Intelligence in Smart Farms: Plant Phenotyping for Species Recognition and Health Condition Identification Using Deep Learning

AI ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 274-289
Author(s):  
Anirban Jyoti Hati ◽  
Rajiv Ranjan Singh
Keyword(s):  
Data Augmentation ◽  
Species Recognition ◽  
Health Condition ◽  
Plant Phenotyping ◽  
Test Cases ◽  
Training Time ◽  
Multiple Test ◽  
Unequal Number ◽  
Multiclass Classifier ◽  
Evaluation Parameters

This paper analyses the contribution of residual network (ResNet) based convolutional neural network (CNN) architecture employed in two tasks related to plant phenotyping. Among the contemporary works for species recognition (SR) and infection detection of plants, the majority of them have performed experiments on balanced datasets and used accuracy as the evaluation parameter. However, this work used an imbalanced dataset having an unequal number of images, applied data augmentation to increase accuracy, organised data as multiple test cases and classes, and, most importantly, employed multiclass classifier evaluation parameters useful for asymmetric class distribution. Additionally, the work addresses typical issues faced such as selecting the size of the dataset, depth of classifiers, training time needed, and analysing the classifier’s performance if various test cases are deployed. In this work, ResNet 20 (V2) architecture has performed significantly well in the tasks of Species Recognition (SR) and Identification of Healthy and Infected Leaves (IHIL) with a Precision of 91.84% and 84.00%, Recall of 91.67% and 83.14% and F1 Score of 91.49% and 83.19%, respectively.

Linear Visco-Resistive Computations of Magnetohydrodynamic Waves: I. The Code and Test Cases

1994 ◽  
Vol 144 ◽  
pp. 503-505
Author(s):  
R. Erdélyi ◽  
M. Goossens ◽  
S. Poedts
Keyword(s):  
Resonant Absorption ◽  
Numerical Code ◽  
Mhd Waves ◽  
Test Cases ◽  
Numerical Accuracy ◽  
Element Discretization ◽  
Flux Tubes ◽  
Magnetohydrodynamic Waves ◽  
Viscosity Coefficients ◽  
Magnetic Flux Tubes

AbstractThe stationary state of resonant absorption of linear, MHD waves in cylindrical magnetic flux tubes is studied in viscous, compressible MHD with a numerical code using finite element discretization. The full viscosity tensor with the five viscosity coefficients as given by Braginskii is included in the analysis. Our computations reproduce the absorption rates obtained by Lou in scalar viscous MHD and Goossens and Poedts in resistive MHD, which guarantee the numerical accuracy of the tensorial viscous MHD code.

Computational Micrograph Registration with Sieve Processes

1996 ◽  
Vol 54 ◽  
pp. 440-441
Author(s):  
P.J. Phillips ◽  
J. Huang ◽  
S. M. Dunn
Keyword(s):  
Planar Graph ◽  
Efficient Algorithm ◽  
Spatial Organization ◽  
Spatial Arrangement ◽  
Stereo Image ◽  
Image Model ◽  
Geometric Invariants ◽  
Point Set

In this paper we present an efficient algorithm for automatically finding the correspondence between pairs of stereo micrographs, the key step in forming a stereo image. The computation burden in this problem is solving for the optimal mapping and transformation between the two micrographs. In this paper, we present a sieve algorithm for efficiently estimating the transformation and correspondence.In a sieve algorithm, a sequence of stages gradually reduce the number of transformations and correspondences that need to be examined, i.e., the analogy of sieving through the set of mappings with gradually finer meshes until the answer is found. The set of sieves is derived from an image model, here a planar graph that encodes the spatial organization of the features. In the sieve algorithm, the graph represents the spatial arrangement of objects in the image. The algorithm for finding the correspondence restricts its attention to the graph, with the correspondence being found by a combination of graph matchings, point set matching and geometric invariants.

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