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Constructing Evaluation Model Based on AHP
Zhao Dan 보안공학연구지원센터 2016 International Journal of Grid and Distributed Comp Vol.9 No.8
First we analyzed the ways of evaluating practice teaching quality and the problems existing. Second based on AHP cleared the steps of constructing evaluating model. Then we analyzed the factors of effecting and restricting the quality and efficiency of the system for practice teaching. Evaluation index were selected abiding by operability、rationality、comprehensive and quantitative. Then according to questionnaire and statistical analysis of the relevant data we obtained the weight values of every factor based on AHP and set up scientific、quantitative evaluation model. Last it passed consistency test and showed that the model was scientific and rational.
Dan Zhao,Bilal Barakat 서울대학교 교육연구소 2015 Asia Pacific Education Review Vol.16 No.3
In the early 2000s, China’s Ministry of Education embarked on a program of school mapping restructure (SMR) that involved closing small rural schools and opening up larger centralized schools in towns and county seats.The stated aim of the policy was to improve educational resources and raise the human capital of rural students. Any progress that may have been achieved along these dimensions comes at a price, namely that many children lost the opportunity to learn in their own village schools. This study aims to understand the impact of SMR on the distance rural children are from schooling, in terms of physical, temporal and social measures. A particular focus rests on differential impact by child and family characteristics including socioeconomic status. The data are drawn from a combination of questionnaires, interviews and document analysis, collected in a rural mountainous area, specifically Xinfeng County in Guangdong Province located in the south of China. The authors analyze these data using geographical information systems, regression and classification tree analysis to estimate increased distance and travel time for students affected by SMR, in the context of an analysis of boarding versus commuting decisions and the choice of transportation mode by economic status. This study finds that, the physical distance increased by an average of about 8.3 miles through SMR, but through the increased tendency to board, the effect on average weekly travel distance was neutral; the average travel time increased by around 75 min for those students affected by SMR; even for children more likely to be boarding, the average increase in weekly travel time was estimated at over 2 h, specifically 130 min; social distance increased also; as students were moved from a tightly-knit community school to a somewhat more anonymous institution, this deterioration affected students who were previously ‘‘privileged’’ in this particular respect, and they are not at a disadvantage relative to their peers at their new school. In addition, the study uncovers nuanced effects of SMR on school travel behavior and calls into question that some behavioral assumptions implicit in the planning process. Policy implications of these findings are discussed, with specific reference to China’s current initiative of ‘‘balanced development’’ in the education sector.
( Dan Zhao ),( Baolong Guo ),( Yunyi Yan ) 한국인터넷정보학회 2018 KSII Transactions on Internet and Information Syst Vol.12 No.6
Unsupervised learning has shown good performance on image, video and audio classification tasks, and much progress has been made so far. It studies how systems can learn to represent particular input patterns in a way that reflects the statistical structure of the overall collection of input patterns. Many promising deep learning systems are commonly trained by the greedy layerwise unsupervised learning manner. The performance of these deep learning architectures benefits from the unsupervised learning ability to disentangling the abstractions and picking out the useful features. However, the existing unsupervised learning algorithms are often difficult to train partly because of the requirement of extensive hyperparameters. The tuning of these hyperparameters is a laborious task that requires expert knowledge, rules of thumb or extensive search. In this paper, we propose a simple and effective unsupervised feature learning algorithm for image classification, which exploits an explicit optimizing way for population and lifetime sparsity. Firstly, a sparse target matrix is built by the competitive rules. Then, the sparse features are optimized by means of minimizing the Euclidean norm (L<sub>2</sub>) error between the sparse target and the competitive layer outputs. Finally, a classifier is trained using the obtained sparse features. Experimental results show that the proposed method achieves good performance for image classification, and provides discriminative features that generalize well.
Synthesis of well dispersed uniform sub-4 nm Y2O3:Eu3+ colloidal nanocrystals.
Zhao, Dan,Seo, Seokjun,Zhang, Haibo,Bae, Byeong-Soo,Qin, Weiping American Scientific Publishers 2010 Journal of nanoscience and nanotechnology Vol.10 No.3
<P>Well dispersed uniform sub-4 nm Y2O3:Eu colloidal nanocrystals have been synthesized through the non-hydrolytic high-temperature thermal decomposition technique. The as-synthesized nanocrystals can be stably dispersed in nonpolar solvents due to the capping organic ligands on their surface. Compared with bulk materials, the nanocrystals exhibited different luminescence features, including the intensity enhancement of the 5D0 --> 7F4 transition observed in the emission spectrum.</P>