Wing Optimization Using Design of Experiment, Response Surface, and Data Fusion Methods

2003 ◽  
Vol 40 (4) ◽  
pp. 741-750 ◽  
Author(s):  
A. J. Keane
Keyword(s):  
Data Fusion ◽  
Response Surface ◽  
Design Of Experiment ◽  
Wing Optimization ◽  
Fusion Methods

scholarly journals Sustainable Synthesis Processes for Carbon Dots through Response Surface Methodology and Artificial Neural Network.

Processes ◽  
2019 ◽  
Vol 7 (10) ◽  
pp. 704 ◽  
Author(s):  
Yahaya Pudza ◽  
Zainal Abidin ◽  
Abdul Rashid ◽  
Md Yasin ◽  
Noor ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Design Of Experiment ◽  
Smart Materials ◽  
Carbon Dots ◽  
Synthesis Process ◽  
Artificial Neural ◽  
Hidden Layer

Nowadays, to ensure sustainability of smart materials, it is imperative to eliminate or reduce carbon footprint related to nano material production. The concept of design of experiment to provide an optimal synthesis process, with a desired yield, is indispensable. It is the researcher’s goal to get optimum value for experiments that requires multiple runs and multiple inputs. Herein, is a reliable approach of utilizing design of experiment (DOE) for response surface methodology (RSM). Thus, to optimize a facile and effective synthesis process for fluorescent carbon dots (CDs) derived from tapioca that is in line with green chemistry principles for sustainable synthesis. The predictions for fluorescent CDs synthesis from RSM were in excellent agreement with the artificial neural network (ANN) model prediction by the Levenberg–Marquardt back propagation (LMBP) algorithm. Considering R2, root mean square error (RMSE) and mean absolute error (MAE) have all revealed a positive hidden layer size. The best hidden layer of neurons were discovered at point 4-8, to confirm the validity of carbon dots, characterization of surface morphology and particles sizes of CDs were conducted with favorable confirmations of the unique characteristics and attributes of synthesized CDs by hydrothermal route.

scholarly journals Adaptive-SFSDAF for Spatiotemporal Image Fusion that Selectively Uses Class Abundance Change Information

2020 ◽  
Vol 12 (23) ◽  
pp. 3979
Author(s):  
Shuwei Hou ◽  
Wenfang Sun ◽  
Baolong Guo ◽  
Cheng Li ◽  
Xiaobo Li ◽  
...  
Keyword(s):  
Data Fusion ◽  
Image Fusion ◽  
Temporal Changes ◽  
Spectral Unmixing ◽  
Spatiotemporal Data ◽  
Fusion Method ◽  
Fusion Methods ◽  
Single Sensor ◽  
Primary Contribution ◽  
Detection Of Outliers

Many spatiotemporal image fusion methods in remote sensing have been developed to blend highly resolved spatial images and highly resolved temporal images to solve the problem of a trade-off between the spatial and temporal resolution from a single sensor. Yet, none of the spatiotemporal fusion methods considers how the various temporal changes between different pixels affect the performance of the fusion results; to develop an improved fusion method, these temporal changes need to be integrated into one framework. Adaptive-SFSDAF extends the existing fusion method that incorporates sub-pixel class fraction change information in Flexible Spatiotemporal DAta Fusion (SFSDAF) by modifying spectral unmixing to select spectral unmixing adaptively in order to greatly improve the efficiency of the algorithm. Accordingly, the main contributions of the proposed adaptive-SFSDAF method are twofold. One is to address the detection of outliers of temporal change in the image during the period between the origin and prediction dates, as these pixels are the most difficult to estimate and affect the performance of the spatiotemporal fusion methods. The other primary contribution is to establish an adaptive unmixing strategy according to the guided mask map, thus effectively eliminating a great number of insignificant unmixed pixels. The proposed method is compared with the state-of-the-art Flexible Spatiotemporal DAta Fusion (FSDAF), SFSDAF, FIT-FC, and Unmixing-Based Data Fusion (UBDF) methods, and the fusion accuracy is evaluated both quantitatively and visually. The experimental results show that adaptive-SFSDAF achieves outstanding performance in balancing computational efficiency and the accuracy of the fusion results.

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