An optimized step-size random search (OSSRS)

Sheela, B. V.

doi:10.1016/0045-7825(79)90035-5

Cited by 15 publications

(5 citation statements)

References 7 publications

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“…In each test case, total 100,000 to 200,000 trajectories were generated to provide a sufficiently large database for determining a reliable probability curve. After finding appropriate values of coefficients a 1 , a 2 , a 3 , and a 4 by means of random-search curve fitting [22] of Equation ( 1), the simulated values of SF 6 concentration were calculated by Equation (4).…”

Section: Numerical Simulationmentioning

confidence: 99%

Reducing Risk of Airborne Transmitted Infection in Hospitals by Use of Hospital Curtains

Ching

Leung

et al. 2008

Indoor and Built Environment

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An in-patient suffering from an airborne infectious disease should be properly isolated in a negative-pressure isolation room to prevent hospital-acquired infection. However, before the infectious status is identified by clinical diagnosis, the patient may be assigned to stay with others in a hospital ward with multiple beds. Under these circumstances, there may be a risk of infection of the neighboring patients. Therefore, reasonable infection control measures should be implemented in multi-bed hospital wards, intensive care units, among others. The use of hospital curtains as physical barriers against transmission of disease is potentially a simple but effective method to reduce the risk of infection. In this investigation, the effectiveness of hospital curtains in prevention against airborne transmission of diseases in hospital rooms was evaluated by numerical modeling analysis. A computational fluid dynamics (CFD)-statistical hybrid modeling method, which was validated by tracer gas measurements, was employed to determine the transient dispersion of airborne contaminants. It was found that among all the case studies, the use of curtains between two beds could reduce the peak concentration for each individual neighboring patient in a bioaerosol dispersion process. A curtain fully extended to the length of a bed yielded significantly higher protection than a partially extended curtain and the reduction in peak concentration could be as much as 65%.

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Section: Numerical Simulationmentioning

confidence: 99%

Reducing Risk of Airborne Transmitted Infection in Hospitals by Use of Hospital Curtains

Ching

Leung

et al. 2008

Indoor and Built Environment

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“…The composite strategy comprises of a random search method [8] to bring the ensuing search into the neighborhood ofthe optimum solution and a deterministic search [9] to carry out a fmer search within this neighborhood of the optimum solution. This approach was selected based on prior successful experience in various applications [10], [1 1] sans a good initial guess.…”

Section: Solution Methodologymentioning

confidence: 99%

<title>Optimal features-in feature-out (FEI-FEO) fusion for decisions in multisensor environments</title>

Belur¹,

Dasarathy

1998

SPIE Proceedings

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The study presents a formal methodology to the problem offeature level fusion, that had been previously addressed in the literature mostly in an ad hoc manner on a case by case basis only. The input set of features extracted from multiple sensors (data sources) are optimally fused to derive a synthetic feature so as to enhance the effective discrimination potential among the defmed set of decision classes. This 'features in -feature out (FEI-FEO)' fusion process, unlike most other fusion schemes reported in the literature, is designed through a formal learning phase in which an optimal mapping from the multisensor derived feature space to a single unified feature is developed. This learning, accomplished through a new composite random and deterministic search based optimization tool, defmes the transformation for the FEI-FEO process. This transformation is applied to the multi-sensor generated feature sets in the operational phase to derive the fused feature values corresponding to the objects under observation. The corresponding classification decisions are made on the basis of relative closeness ofthese feature values to the different class mean values in the transformed single dimensional feature space. The new methodology has been implemented in MATLAB which, being a vector/matrix oriented language, is an ideal candidate for solving problems in patter recognition and learning. The method is applied to well-known data sets available on the web for testing pattern recognition algorithms to assess its effectiveness relative to the traditional classification methods from both conceptual as well as computational view points.

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“…Based on the initial guess values in Table 6, estimates for these parameters were improved by (1) an optimized step‐size random search (OSSRS) method38 and (2) a sensitivity study. In addition to RMS TC , the parameter fits are compared regarding their: Mean error MN TC which gives an indication of relative location of the predicted and experimental temperature profiles.…”

Section: Model Parametersmentioning

confidence: 99%