Abstract: Result from the constant dwindle in natural resources,
the alternative way to reduce the costs in our daily life would be urgent
to be found in the near future. As the ancient technique based on the
theory of solar chimney since roman times, the double-skin façade are
simply composed of two large glass panels in purpose of daylighting
and also natural ventilation in the daytime. Double-skin façade is
generally installed on the exterior side of buildings as function as the
window, so there is always a huge amount of passive solar energy the
façade would receive to induce the airflow every sunny day. Therefore,
this article imposes a domestic double-skin window for residential
usage and attempts to improve the volume flow rate inside the cavity
between the panels by the frame geometry design, the installation of
outlet guide plate and the solar energy collection system. Note that the
numerical analyses are applied to investigate the characteristics of flow
field, and the boundary conditions in the simulation are totally based
on the practical experiment of the original prototype. Then we
redesign the prototype from the knowledge of the numerical results
and fluid dynamic theory, and later the experiments of modified
prototype will be conducted to verify the simulation results. The
velocities at the inlet of each case are increase by 5%, 45% and 15%
from the experimental data, and also the numerical simulation results
reported 20% improvement in volume flow rate both for the frame
geometry design and installation of outlet guide plate.
Abstract: Avoiding learning failures in mathematics e-learning environments caused by emotional problems in students with autism has become an important topic for combining of special education with information and communications technology. This study presents an adaptive emotional adjustment model in mathematics e-learning for students with autism, emphasizing the lack of emotional perception in mathematics e-learning systems. In addition, an emotion classification for students with autism was developed by inducing emotions in mathematical learning environments to record changes in the physiological signals and facial expressions of students. Using these methods, 58 emotional features were obtained. These features were then processed using one-way ANOVA and information gain (IG). After reducing the feature dimension, methods of support vector machines (SVM), k-nearest neighbors (KNN), and classification and regression trees (CART) were used to classify four emotional categories: baseline, happy, angry, and anxious. After testing and comparisons, in a situation without feature selection, the accuracy rate of the SVM classification can reach as high as 79.3-%. After using IG to reduce the feature dimension, with only 28 features remaining, SVM still has a classification accuracy of 78.2-%. The results of this research could enhance the effectiveness of eLearning in special education.