Optimization of optical lens-controlled scanning electron microscopic resolution using generalized regression neural network and genetic algorithm

Kim, Byungwhan; Kwon, Sanghee; Kim, Dong Hwan

doi:10.1016/j.eswa.2009.05.007

Cited by 16 publications

(8 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Because of the concept of GA, it can be considered as a more efficient "tweaking" of settings to find an optimal configuration, ideal for optimization of the settings of electron microscopy. For example, Kim et al [198] suggested that the combination of a generalized regression neural network (GRNN) and GA can be used to search for the best resolution of SEM. A prediction model of the SEM's resolution was built by using a GRNN and the GA was employed to enhance the prediction model.…”

Section: Applications Of Genetic Algorithmsmentioning

confidence: 99%

Advancing electron microscopy using deep learning

Chen,

Barnard

2024

J. Phys. Mater.

View full text Add to dashboard Cite

Electron microscopy, a sub-field of microanalysis, is a victim of its own success. The widespread use of electron microscopy for imaging molecules and materials has had an enormous impact on our understanding of countless systems and has accelerated impacts in drug discovery and materials design, for electronic, energy, environment and health applications. With this success a bottleneck has emerged, as the rate at which we can collect data has significantly exceeded the rate at which we can analyse it. Fortunately, this has coincided with the rise of advanced computational methods, including data science and machine learning. Deep learning, a sub-field of machine learning capable of learning from large quantities of data such as images, is ideally suited to overcome some of the challenges of electron microscopy at scale. There are a variety of different deep learning approaches relevant to the field, with unique advantages and disadvantages. In this review, we describe some well-established methods, with some recent examples, and introduce some new methods currently emerging in computer science. Our summary of deep learning is designed to guide electron microscopists to choose the right deep learning algorithm for their research and prepare for their digital future.

show abstract

Section: Applications Of Genetic Algorithmsmentioning

confidence: 99%

Advancing electron microscopy using deep learning

Chen,

Barnard

2024

J. Phys. Mater.

View full text Add to dashboard Cite

show abstract

“…The method is suitable for regression problems where an assumption of linearity is not justified. A GRNN configuration consists of four layers, which include the input layer, pattern layer, summation layer, and output layer (Kim et al 2010). Each input unit in the input layer corresponds to individual observed parameters.…”

Section: Generalized Regression Neural Networkmentioning

confidence: 99%

“…The output layer divides the output of each Ssummation neuron by that of each D-summation neuron. Therefore, a predicted value ŷ(x) to an unknown input vector x can be expressed as (Kim et al 2010)…”

Section: Generalized Regression Neural Networkmentioning

confidence: 99%

Prediction of mechanical stress in roller leveler based on vibration measurements and steel strip properties

et al. 2017

View full text Add to dashboard Cite

The continuous development of steel products generates new challenges for the maintenance of manufacturing machines in steel mills. Substantial mechanical stress is inflicted on the machines during the processing of modern high-strength steels. This increases the risks of damage and flaws in the processed material may appear if the capability of a machine is exceeded. Therefore, new approaches are needed to prevent the machine condition from deteriorating. This study introduces an approach to the prediction of mechanical stress inflicted on a roller leveler during the processing of cold steel strips. The relative stress level is indicated by features extracted from an acceleration signal. These features are based on the calculation of generalized norms. Steel strip properties are used as explanatory variables in regression models to predict values for the extracted vibration features. The models tested in this study include multiple linear regression, partial least squares regression and generalized regression neural network. The models were tested using an extensive data set from a roller leveler that is in continuous operation in a steel mill. The prediction accuracy of the best models identified indicates that the relative stress level inflicted by each steel strip could be predicted based on its properties.

show abstract

“…Each unit in the pattern layer is connected to two units (S and D summation neurons) in the summation layer. The S neuron measures the sum of weighted outputs of the pattern layer while the D neuron computes the unweighted outputs of the units in the pattern layer (Kim et al, 2010;Haidar et al, 2011). Using GRNN, the spread (smoothing factor, σ) which can affect the degree of generalization of the network must be determined first.…”

Section: Generalized Regression Neural Network (Grnn)mentioning

confidence: 99%

A neural network-based methodology of quantifying the association between the design variables and the users’ performances

Wong

Chan

2014

International Journal of Production Research

View full text Add to dashboard Cite

User performance is highly correlated with design variables of a system. Such association can be described as display-control relationship. In this study, a neural network-based methodology is proposed to identify and quantify the association among design variables (inputs), and to compute their relative influences on the two performance measures (outputs) of user response time and response accuracy, using artificial neural network (ANN), generalized regression neural network (GRNN), support vector regression (SVR), multiple linear regression (MLR), and response surface model (RSM). Based on the results of the comparison, it is found that neural network-based methods are more reliable than support vector regression-based methods in computing the relative influence of design variables. As a result of our analysis, the best option for optimizing each of the measures is suggested. Some useful observations about the design of man-machine systems are also presented, discussed and visualized. In the study of man-machine systems, quantitative methods are seldom adopted for examining the mappings between various displays and controls under a variety of operating conditions. The major contribution of this study is to provide some insights into the usefulness of quantitative methods in evaluating man-machine design in terms of display-control compatibility, and to extract explanatory information from renowned black-box systems such as neural networks.

show abstract

Optimization of optical lens-controlled scanning electron microscopic resolution using generalized regression neural network and genetic algorithm

Cited by 16 publications

References 10 publications

Advancing electron microscopy using deep learning

Advancing electron microscopy using deep learning

Prediction of mechanical stress in roller leveler based on vibration measurements and steel strip properties

A neural network-based methodology of quantifying the association between the design variables and the users’ performances

Contact Info

Product

Resources

About