Inicio  /  ISIJ INTERNATIONAL  /  Vol: 51 Núm: 9 Par: 0 (2011)  /  Artículo
ARTÍCULO
TITULO

Neural Network Prediction of Hardness in HAZ of Temper Bead Welding Using the Proposed Thermal Cycle Tempering Parameter (TCTP) 1506-1515

Lina Yu    
Yuma Nakabayashi    
Masato Sasa    
Shinsuke Itoh    
Masashi Kameyama    
Shinro Hirano    
Naoki Chigusa    
Kazuyoshi Saida    
Masahito Mochizuki and Kazutoshi Nishimoto    

Resumen

No disponible

 Artículos similares

       
 
Zengyu Cai, Chunchen Tan, Jianwei Zhang, Liang Zhu and Yuan Feng    
As network technology continues to develop, the popularity of various intelligent terminals has accelerated, leading to a rapid growth in the scale of wireless network traffic. This growth has resulted in significant pressure on resource consumption and ... ver más
Revista: Applied Sciences

 
Ahmed Skhiri, Ali Ferhi, Anis Bousselmi, Slaheddine Khlifi and Mohamed A. Mattar    
A correct determination of irrigation water requirements necessitates an adequate estimation of reference evapotranspiration (ETo). In this study, monthly ETo is estimated using artificial neural network (ANN) models. Eleven combinations of long-term ave... ver más
Revista: Water

 
Song Xue, Jingyan Chen, Sheng Li and Huaai Huang    
Early warning of safety risks downstream of small reservoirs is directly related to the safety of people?s lives and property and the economic and social development of the region. The lack of data and low collaboration in downstream safety management of... ver más
Revista: Water

 
Donghae Baek, Il Won Seo, Jun Song Kim, Sung Hyun Jung and Yuyoung Choi    
The dispersion coefficients are crucial in understanding the spreading of pollutant clouds in river flows, particularly in the context of the depth-averaged two-dimensional (2D) advection?dispersion equation (ADE). Traditionally, the 2D stream-tube routi... ver más
Revista: Water

 
Yong Liu, Xiaohui Yan, Wenying Du, Tianqi Zhang, Xiaopeng Bai and Ruichuan Nan    
The current work proposes a novel super-resolution convolutional transposed network (SRCTN) deep learning architecture for downscaling daily climatic variables. The algorithm was established based on a super-resolution convolutional neural network with t... ver más
Revista: Water