13,762 research outputs found
Solving multiple-criteria R&D project selection problems with a data-driven evidential reasoning rule
In this paper, a likelihood based evidence acquisition approach is proposed
to acquire evidence from experts'assessments as recorded in historical
datasets. Then a data-driven evidential reasoning rule based model is
introduced to R&D project selection process by combining multiple pieces of
evidence with different weights and reliabilities. As a result, the total
belief degrees and the overall performance can be generated for ranking and
selecting projects. Finally, a case study on the R&D project selection for the
National Science Foundation of China is conducted to show the effectiveness of
the proposed model. The data-driven evidential reasoning rule based model for
project evaluation and selection (1) utilizes experimental data to represent
experts' assessments by using belief distributions over the set of final
funding outcomes, and through this historic statistics it helps experts and
applicants to understand the funding probability to a given assessment grade,
(2) implies the mapping relationships between the evaluation grades and the
final funding outcomes by using historical data, and (3) provides a way to make
fair decisions by taking experts' reliabilities into account. In the
data-driven evidential reasoning rule based model, experts play different roles
in accordance with their reliabilities which are determined by their previous
review track records, and the selection process is made interpretable and
fairer. The newly proposed model reduces the time-consuming panel review work
for both managers and experts, and significantly improves the efficiency and
quality of project selection process. Although the model is demonstrated for
project selection in the NSFC, it can be generalized to other funding agencies
or industries.Comment: 20 pages, forthcoming in International Journal of Project Management
(2019
A study on Fast Predicting the Washability Curve of Coal
AbstractA pure new MATLAB-based image recognition system was developed to compute the coal particle picture of same grain class through the digital image processing method, 13 image feature parameters was selected to be most representative image characteristic parameters. Take the above parameters as the input of RBF neural network, the density level of coal particles could be estimated, combined with the real ash content of each density level, the washability curve could be drawed. Experement show,the absolute error of the total ash is 2.375%,which is Slightly big in the China standards of coal preparation (GB/T477 -1998); the related coefficients of each indicator in both actual and predicted float-and-sink material are all close to 1, while the curves of λ, β, θ and δ are very similar and the deviation of ξ curve is relatively large
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