Abstract: Yi is an ethnic group mainly living in mainland China, with its own spoken and written language systems, after development of thousands of years. Ancient Yi is one of the six ancient languages in the world, which keeps a record of the history of the Yi people and offers documents valuable for research into human civilization. Recognition of the characters in ancient Yi helps to transform the documents into an electronic form, making their storage and spreading convenient. Due to historical and regional limitations, research on recognition of ancient characters is still inadequate. Thus, deep learning technology was applied to the recognition of such characters. Five models were developed on the basis of the four-layer convolutional neural network (CNN). Alpha-Beta divergence was taken as a penalty term to re-encode output neurons of the five models. Two fully connected layers fulfilled the compression of the features. Finally, at the softmax layer, the orthographic features of ancient Yi characters were re-evaluated, their probability distributions were obtained, and characters with features of the highest probability were recognized. Tests conducted show that the method has achieved higher precision compared with the traditional CNN model for handwriting recognition of the ancient Yi.
Abstract: Icons, or pictorial and graphical objects, are
commonly used in human-computer interaction (HCI) fields as the
mediator in order to communicate information to users. Yet there has
been little studies focusing on a majority of the world’s population –
semi-literate communities – in terms of the fundamental knowhow
for designing icons for such population. In this study, two sets of
icons belonging in different icon taxonomy – abstract and concrete –
are designed for a mobile application for semi-literate agricultural
communities. In this paper, we propose a triadic relationship of an
icon, namely meaning, task and mental image, which inherits the
triadic relationship of a sign. User testing with the application and a
post-pilot questionnaire are conducted as the experimental approach
in two rural villages in India. Icons belonging to concrete taxonomy
perform better than abstract icons on the premise that the design of
the icon fulfills the underlying rules of the proposed triadic
relationship.
Abstract: Modern managements of water distribution system
(WDS) need water quality models that are able to accurately predict
the dynamics of water quality variations within the distribution system
environment. Before water quality models can be applied to solve
system problems, they should be calibrated. Although former
researchers use GA solver to calibrate relative parameters, it is
difficult to apply on the large-scale or medium-scale real system for
long computational time. In this paper a new method is designed
which combines both macro and detailed model to optimize the water
quality parameters. This new combinational algorithm uses radial
basis function (RBF) metamodeling as a surrogate to be optimized for
the purpose of decreasing the times of time-consuming water quality
simulation and can realize rapidly the calibration of pipe wall reaction
coefficients of chlorine model of large-scaled WDS. After two cases
study this method is testified to be more efficient and promising, and
deserve to generalize in the future.