Abstract: Mechanical design of the thin-film solar framed
module and mounting system is important to enhance module
reliability and to increase areas of applications. The stress induced by
different mounting positions played a main role controlling the
stability of the whole mechanical structure. From the finite element
method, under the pressure from the back of module, the stress at Lc
(center point of the Long frame) increased and the stresses at Center,
Corner and Sc (center point of the Short frame) decreased while the
mounting position was away from the center of the module. In addition,
not only the stress of the glass but also the stress of the frame
decreased. Accordingly it was safer to mount in the position away
from the center of the module. The emphasis of designing frame
system of the module was on the upper support of the Short frame.
Strength of the overall structure and design of the corner were also
important due to the complexity of the stress in the Long frame.
Abstract: In this paper we present an efficient method for inverting an ideal in the ideal class group of a Cab curve by extending the method which is presented in [3]. More precisely we introduce a useful generator for the inverse ideal as a K[X]-module.
Abstract: This study is about an application of King Bhumibol
Adulyadej’s “Learn Wisely” (LW) concept in instructional design
and management process at the Faculty of Education, Suan Sunahdha
Rajabhat University. The concept suggests four strategies for true
learning. Related literature and significant LW methods in teaching
and learning are also reviewed and then applied in designing a
pedagogy learning module. The design has been implemented in
three classrooms with a total of 115 sophomore student teachers.
After one consecutive semester of managing and adjusting the
process by instructors and experts using collected data from minutes,
assessment of learning management, satisfaction and learning
achievement of the students, it is found that the effective SSRU
model of LW instructional method comprises of five steps.
Abstract: The prediction of Software quality during development life cycle of software project helps the development organization to make efficient use of available resource to produce the product of highest quality. “Whether a module is faulty or not" approach can be used to predict quality of a software module. There are numbers of software quality prediction models described in the literature based upon genetic algorithms, artificial neural network and other data mining algorithms. One of the promising aspects for quality prediction is based on clustering techniques. Most quality prediction models that are based on clustering techniques make use of K-means, Mixture-of-Guassians, Self-Organizing Map, Neural Gas and fuzzy K-means algorithm for prediction. In all these techniques a predefined structure is required that is number of neurons or clusters should be known before we start clustering process. But in case of Growing Neural Gas there is no need of predetermining the quantity of neurons and the topology of the structure to be used and it starts with a minimal neurons structure that is incremented during training until it reaches a maximum number user defined limits for clusters. Hence, in this work we have used Growing Neural Gas as underlying cluster algorithm that produces the initial set of labeled cluster from training data set and thereafter this set of clusters is used to predict the quality of test data set of software modules. The best testing results shows 80% accuracy in evaluating the quality of software modules. Hence, the proposed technique can be used by programmers in evaluating the quality of modules during software development.