scholarly journals On the sensitivity of the location, size and shape of the GPS ambiguity search space to certain changes in the stochastic model

1997 ◽  
Vol 71 (9) ◽  
pp. 541-551 ◽  
Author(s):  
P. J. G. Teunissen
Keyword(s):  
Stochastic Model ◽  
Search Space ◽  
Size And Shape

Counterexample Generation in Stochastic Model Checking Based on PSO Algorithm with Heuristic

2016 ◽  
Vol 26 (07) ◽  
pp. 1117-1143 ◽  
Author(s):  
Yan Ma ◽  
Zining Cao ◽  
Yang Liu
Keyword(s):  
Model Checking ◽  
Stochastic Model ◽  
Essential Feature ◽  
Pso Algorithm ◽  
Search Space ◽  
Prototype Tool ◽  
Indirect Path ◽  
Np Complete ◽  
Effective Path ◽  
Stochastic Model Checking

Providing counterexample for the refutation of a property is an essential feature of model checking, if it is not the most important. However, generating counterexample in stochastic model checking needs a dedicated algorithm. It usually costs too much time and memory, and sometimes it cannot find the counterexample. What is worse, generating smallest counterexample in stochastic model checking has been proved to be NP-complete, and it is unlikely to be efficiently approximable. Although there are a few heuristic methods that are applied to the construction of the counterexample, it is usually difficult to determine the heuristic function which is critical for counterexample generating. In this paper, we present a particle swarm optimization (PSO)-based approach to generating counterexample for stochastic model checking. We define the diagnostic sub-graph as counterexample, and extend PSO algorithm with heuristic (HPSO) to generate counterexample. It adopts indirect path coding scheme to expand the scope of the search space, and employs heuristic operator to generate more effective path. The validity of our approach is illustrated by some case studies in a prototype tool. The experiments show that HPSO algorithm can significantly outperform the present algorithm for counterexample generation in stochastic model checking.

Unbiased estimation of particle number and sizes using the new stereological tools: the disector, the fractionator, the selector, and the nucleator — or just point-sampled intercepts

1988 ◽  
Vol 46 ◽  
pp. 428-429
Author(s):  
H.J.G. Gundersen
Keyword(s):  
Particle Number ◽  
Small Distance ◽  
Parallel Plane ◽  
Unbiased Estimation ◽  
Severe Problem ◽  
New Era ◽  
Size And Shape ◽  
Distinct Property ◽  
Sectioned Tissue

Previously, all stereological estimation of particle number and sizes were based on models and notoriously gave biased results, were very inefficient to use and difficult to justify. For all references to old methods and a direct comparison with unbiased methods see recent reviews.The publication in 1984 of the DISECTOR, the first unbiased stereological probe for sampling and counting 3—D objects irrespective of their size and shape, signalled the new era in stereology — and give rise to a number of remarkably simple and efficient techniques based on its distinct property: It is the only known way to obtain an unbiased sample of 3-D objects (cells, organelles, etc). The principle is simple: within a 2-D unbiased frame count or sample only cells which are not hit by a parallel plane at a known, small distance h.The area of the frame and h must be known, which might sometimes in itself be a problem, albeit usually a small one. A more severe problem may arise because these constants are known at the scale of the fixed, embedded and sectioned tissue which is often shrunken considerably.

The influence of confocal microscope design on imaging performance

1993 ◽  
Vol 51 ◽  
pp. 144-145
Author(s):  
C J R Sheppard
Keyword(s):  
Image Quality ◽  
Fluorescence Imaging ◽  
Confocal Microscope ◽  
Industrial Applications ◽  
Three Dimensions ◽  
Design Parameters ◽  
Weak Signals ◽  
Size And Shape ◽  
Imaging Performance ◽  
Fundamental Limitation

The confocal microscope is now widely used in both biomedical and industrial applications for imaging, in three dimensions, objects with appreciable depth. There are now a range of different microscopes on the market, which have adopted a variety of different designs. The aim of this paper is to explore the effects on imaging performance of design parameters including the method of scanning, the type of detector, and the size and shape of the confocal aperture.It is becoming apparent that there is no such thing as an ideal confocal microscope: all systems have limitations and the best compromise depends on what the microscope is used for and how it is used. The most important compromise at present is between image quality and speed of scanning, which is particularly apparent when imaging with very weak signals. If great speed is not of importance, then the fundamental limitation for fluorescence imaging is the detection of sufficient numbers of photons before the fluorochrome bleaches.

A Fruitful Stochastic Model

1964 ◽  
Vol 9 (7) ◽  
pp. 273-276
Author(s):  
ANATOL RAPOPORT
Keyword(s):  
Stochastic Model

A Stochastic Model for Evolution of Altruistic Genes

1996 ◽  
Vol 6 (4) ◽  
pp. 445-453 ◽  
Author(s):  
Roberta Donato
Keyword(s):  
Stochastic Model

SIZE AND SHAPE OF CRYSTAL FACES ON HEATED METAL TIPS

1984 ◽  
Vol 45 (C9) ◽  
pp. C9-29-C9-37
Author(s):  
Vu Thien Binh ◽  
M. Drechsler
Keyword(s):  
Size And Shape ◽  
Heated Metal

A Stochastic Model for the Inheritance of the Cancer Proneness Phenotype

1987 ◽  
Vol 26 (03) ◽  
pp. 117-123
Author(s):  
P. Tautu ◽  
G. Wagner
Keyword(s):  
Stochastic Model ◽  
Gaussian Process ◽  
Covariance Function ◽  
Neoplastic Disease ◽  
Disease Phenotype ◽  
Probabilistic Representation ◽  
Temporal Behaviour ◽  
Continuous Trait ◽  
Continuous Parameter ◽  
Level Zero

SummaryA continuous parameter, stationary Gaussian process is introduced as a first approach to the probabilistic representation of the phenotype inheritance process. With some specific assumptions about the components of the covariance function, it may describe the temporal behaviour of the “cancer-proneness phenotype” (CPF) as a quantitative continuous trait. Upcrossing a fixed level (“threshold”) u and reaching level zero are the extremes of the Gaussian process considered; it is assumed that they might be interpreted as the transformation of CPF into a “neoplastic disease phenotype” or as the non-proneness to cancer, respectively.

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