2015, Vol.20, No.10

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

pISSN : 1598-849X / eISSN : 2383-9945
https://journal.kci.go.kr/jksci

Based on the dual hardware and software with distributed recovery blocks, the centralized type fault tolerant store management system(SMS) was proposed. As a result of trade off study related to mutiplex hardware system design, dual single board computer(SBC) was adapted. To verify redundancy function of the proposed structure, the prototype SMS and weapon simulator were used. The proposed SMS operated normally without being affected by a primary SBC failure. The switching time from primary SBC to shadow SBC was within 200 ms. The reliability of the proposed SMS was predicted and compared with the non fault tolerant SMS, thereby it was proved that the proposed SMS has a higher reliability than the non fault tolerant system within effective range.

Wearable device, next generation smart device, is consistently growing. The flexible display will be a kind of display in the wearable device. The flexible display technology is now evolving with end-user requirement such as portability and easy installation. Previous wearable display products still have some difficulties in manufacturing and in flexibility whole device. But it can be a flexible display with LED device and utilized in commercial area. In this paper, we propose a driver to control the LED display and implement a flexible LED display system.

In this paper, we propose a stable automatic signature verification algorithm applicable to various smart devices. The proposed algorithm uses real and forgery data all together, which can improve the verification rate dramatically. As a tool for signature acquisition in a smart device, two applications, one using touch with a finger and the other using a pressure-sensing-stylus pen, are developed. The verification core is based on SVM and some modifications are made to include the characteristics of signatures. As shown in experimental results, the minimum error rate was 1.84% in the SVM based method, which can easily defeat 4.38% error rate with the previous parametric approach. Even more, 2.43% error rate was achieved with the features excluding pressure-related features, better than the previous approach including pressure-related features and only about 0.6% more error than the best result, which means that the proposed algorithm can be applied to a smart device with or without pressure-sensing-stylus pens and used for security purposes.