• The Journal of the Korea Contents Association
• /
• v.21 no.1
• /
• pp.311-320
• /
• 2021

At present, the secondary school teacher employment examination(SSTEE) is the only gateway to become a national and public secondary teacher in Korea, and after the revision from the 2014 academic year, all the questions of the exam have been converted to supply-type test items, requiring more definitive, accurate, and solid answers. Compared to the selection-type test items that measure recognition memory, the supply-type questions, testing recall memory, require constant memorization and retrieval practices to furnish answers; however, there is not enough learning tools available to support the practices. At this juncture, this study invented a mobile app, called ONE PASS, for the SSTEE. By unpacking the functional mechanisms of the brain, the basis of cognitive processing, this ONE PASS app offers a set of tools that feature brain-based learning principles, such as a personalized study planner, motivation measurement scales, mind mapping, brainstorming, and sample questions from previous tests. This study is expected to contribute to the research on the development of learning contents for applications, and at the same time, it hopes to be of some help for candidates in their exam preparation process.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.42 no.5 s.305
• /
• pp.55-62
• /
• 2005

In this paper, we propose the face recognition system using HNMA(Hippocampal Neuron Modeling Algorithm) which can remodel the cerebral cortex and hippocampal neuron as a principle of a man's brain in engineering, then it can learn the feature-vector of the face images very fast and construct the optimized feature each image. The system is composed of two parts. One is feature-extraction and the other is teaming and recognition. In the feature extraction part, it can construct good-classified features applying PCA(Principal Component Analysis) and LDA(Linear Discriminants Analysis) in order. In the learning part, it cm table the features of the image data which are inputted according to the order of hippocampal neuron structure to reaction-pattern according to the adjustment of a good impression in the dentate gyrus region and remove the noise through the associate memory in the CA3 region. In the CA1 region receiving the information of the CA3, it can make long-term memory learned by neuron. Experiments confirm the each recognition rate, that are face changes, pose changes and low quality image. The experimental results show that we can compare a feature extraction and learning method proposed in this paper of any other methods, and we can confirm that the proposed method is superior to existing methods.