scholarly journals Design Approach for the Optimal Model of an Energy-Efficient Timber Building with Enlarged Glazing Surface on the South Façade

2012 ◽  
Vol 11 (1) ◽  
pp. 71-78 ◽  
Author(s):  
Vesna Žegarac Leskovar ◽  
Miroslav Premrov
Keyword(s):  
Energy Efficient ◽  
Design Approach ◽  
Optimal Model ◽  
The South

Robust and energy-efficient carbon nanotube FET-based MVL gates: A novel design approach

2015 ◽  
Vol 46 (12) ◽  
pp. 1333-1342 ◽  
Author(s):  
Fazel Sharifi ◽  
Mohammad Hossein Moaiyeri ◽  
Keivan Navi ◽  
Nader Bagherzadeh
Keyword(s):  
Carbon Nanotube ◽  
Energy Efficient ◽  
Design Approach ◽  
Novel Design ◽  
Carbon Nanotube Fet

scholarly journals An approach to select an energy-efficient shading device for the south-oriented façades in heritage buildings in Alexandria, Egypt

2021 ◽  
Vol 7 ◽  
pp. 133-137
Author(s):  
Rehab M. Ahmad ◽  
Zeyad El-Sayed ◽  
Dina Taha ◽  
Hassan Shokry ◽  
Hatem Mahmoud
Keyword(s):  
Energy Efficient ◽  
The South ◽  
Heritage Buildings ◽  
Shading Device

Bulbous Bows for Energy Efficient Ships: Towards a Novel Design Approach

2015 ◽  
Author(s):  
A Grech La Rosa ◽  
◽  
G Thomas ◽  
E Muk-Pavic ◽  
T Dinham-Peren ◽  
...  
Keyword(s):  
Energy Efficient ◽  
Design Approach ◽  
Novel Design

scholarly journals Convolution Neural Network for the Prediction of Cochlodinium polykrikoides Bloom in the South Sea of Korea

2021 ◽  
Vol 10 (1) ◽  
pp. 31
Author(s):  
Youngjin Choi ◽  
Youngmin Park ◽  
Weol-Ae Lim ◽  
Seung-Hwan Min ◽  
Joon-Soo Lee
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Prediction Accuracy ◽  
Spatial Information ◽  
Experimental Results ◽  
Cochlodinium Polykrikoides ◽  
Optimal Model ◽  
The South ◽  
Model Configuration ◽  
South Sea

In this study, the occurrence of Cochlodinium polykrikoides bloom was predicted based on spatial information. The South Sea of Korea (SSK), where C. polykrikoides bloom occurs every year, was divided into three concentrated areas. For each domain, the optimal model configuration was determined by designing a verification experiment with 1–3 convolutional neural network (CNN) layers and 50–300 training times. Finally, we predicted the occurrence of C. polykrikoides bloom based on 3 CNN layers and 300 training times that showed the best results. The experimental results for the three areas showed that the average pixel accuracy was 96.22%, mean accuracy was 91.55%, mean IU was 81.5%, and frequency weighted IU was 84.57%, all of which showed above 80% prediction accuracy, indicating the achievement of appropriate performance. Our results show that the occurrence of C. polykrikoides bloom can be derived from atmosphere and ocean forecast information.

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