Data analysis framework for visual interactive product design under the background of cloud social speech environment

Author(s):  
Xiao Ye ◽  
Xin Lv
2021 ◽  
Vol 1122 (1) ◽  
pp. 012064
Author(s):  
Juliza Hidayati ◽  
Shelvy Riry Gusrina ◽  
Nazaruddin Matondang

2018 ◽  
Vol 36 (1) ◽  
pp. 015008 ◽  
Author(s):  
O J Piccinni ◽  
P Astone ◽  
S D’Antonio ◽  
S Frasca ◽  
G Intini ◽  
...  

Author(s):  
Xavier Fischer ◽  
Georges Fadel ◽  
Yann Ledoux

Author(s):  
K. Maddulapalli ◽  
S. Azarm ◽  
A. Boyars

We present an automated method to aid a Decision Maker (DM) in selecting the ‘most preferred’ from a set of design alternatives. The method assumes that the DM’s preferences reflect an implicit value function that is quasi-concave. The method is iterative, using three approaches in sequence to eliminate lower-value alternatives at each trial design. The method is interactive, with the DM stating preferences in the form of attribute tradeoffs at each trial design. We present an approach for finding a new trial design at each iteration. We provide an example, the design selection for a cordless electric drill, to demonstrate the method.


10.14311/1718 ◽  
2013 ◽  
Vol 53 (1) ◽  
Author(s):  
Aleksander Filip Żarnecki ◽  
Lech Wiktor Piotrowski ◽  
Lech Mankiewicz ◽  
Sebastian Małek

The Luiza analysis framework for GLORIA is based on the Marlin package, which was originally developed for data analysis in the new High Energy Physics (HEP) project, International Linear Collider (ILC). The HEP experiments have to deal with enormous amounts of data and distributed data analysis is therefore essential. The Marlin framework concept seems to be well suited for the needs of GLORIA. The idea (and large parts of the code) taken from Marlin is that every computing task is implemented as a processor (module) that analyzes the data stored in an internal data structure, and the additional output is also added to that collection. The advantage of this modular approach is that it keeps things as simple as possible. Each step of the full analysis chain, e.g. from raw images to light curves, can be processed step-by-step, and the output of each step is still self consistent and can be fed in to the next step without any manipulation.


2017 ◽  
Vol 2 (3/4) ◽  
pp. 150 ◽  
Author(s):  
Chun Hsiung Tseng ◽  
Yung Hui Chen ◽  
Yan Ru Jiang

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