2012, Vol.17, 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

These days news about natural disaster caused by earthquake, typhoon is broadcasted frequently. At this moment, structure' healthy is threatened by natural disaster, only way to minimize the casualties and property damage is doing accurate alert. In this research, I designed Expert system to reduce wrong-alert and elevate the accuracy. The expert system put many sensors close to each other as a one group. We focus on elevating reliability of monitoring system by comparing each nearby sensor's state not one single sensor. Providing accurate data which can decide safe to structure manager can minimize current damage by early response, also has advantage that can prevent additional damage can be occur in the future.

To achieve efficient code size reduction, this paper proposes a new compressed instruction set architecture for the OpenRISC architecture. The new instructions and their corresponding formats are designed by the profiling information of the existing instruction usage. New 16-bit instructions and 32-bit instructions are proposed to compressed the existing 32-bit instructions and instruction sequences, respectively. The proposed instructions can be classified into three types. The first is the new 16-bit instructions for the frequent normal 32-bit instructions such as add, load, store,branch, and jump instructions. The second type is the new 32-bit instructions for the consecutive two load instructions, two store instructions, and 32-bit data mov instructions. Finally, two new 32-bit instructions are proposed to compress function prolog and epilog code, respectively. OpenRISC hardware decoder is extended to support the new instructions. Experiments show that the efficiency of code size reduction improves by an average of 30.4% when compared to the OR1200 instruction set architecture without loss of execution performance.

In this paper, we propose a rule-based model to correct errors in a speech recognition result in the mobile device environment. The proposed model considers the mobile device environment with limited resources such as processing time and memory, as follows. In order to minimize the error correction processing time, the proposed model removes some processing steps such as morphological analysis and the composition and decomposition of syllable. Also, the proposed model utilizes the longest match rule selection method to generate one error correction candidate per point, assumed that an error occurs. For the purpose of deploying memory resource, the proposedmodel uses neither the Eojeol dictionary nor the morphological analyzer, and stores a combined rule list without any classification. Considering the modification and maintenance of the proposed model, the error correction rules are automatically extracted from a training corpus. Experimental results show that the proposed model improves 5.27% on the precision and 5.60% on the recall based on Eojoel unit for the speech recognition result.