2015, Vol.20, No.11

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

Flash memory has advantages in that it is fast access speed, low-power, and low-price. Therefore, they are widely used in electronics industry sectors. However, the flash memory has weak points, which are the limited number of erase operations and non-in-place update problem. To overcome the limited number of erase operations, many wear leveling techniques are studied. They use many tables storing information such as erase count of blocks, hot and cold block indicators, reference count of pages, and so on. These tables occupy some space of main memory for the wear leveling techniques. Accordingly, they are not appropriate for low-power devices limited main memory. In order to resolve it, a wear leveling technique using bit array and Bit Set Threshold (BST) for flash memory. The proposing technique reduces the used space of main memory using a bit array table, which saves the history of block erase operations. To enhance accuracy of cold block information, we use BST, which is calculated by using the number of invalid pages of the blocks in a one-to-many mode, where one bit is related to many blocks. The performance results illustrate that the proposed wear leveling technique improve life time of flash memory to about 6%, compared with previous wear leveling techniques using a bit array table in our experiment.

We propose a hybrid pattern recognition method that effectively combines two different features for improving data classification. We first extract the PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis) features, both of which are widely used in pattern recognition, to construct a set of basic features, and then evaluate the separability of each basic feature. According to the results of evaluation, we select only the basic features that contain a large amount of discriminative information for construction of the combined features. The experimental results for the various data sets in the UCI machine learning repository show that using the proposed combined features give better recognition rates than when solely using the PCA or LDA features

In this paper, we propose a plural subject account of group beliefs using grounding concept, which bridges the gap between the two main approaches of ACLs, the mentalistic accounts and the social commitment accounts. The key idea of this Gilbert's plural subject account of group beliefs is to extend the grounding logic into the common ground framework for the semantics of ACLs which is fully verifiable, fully formalized and easily applicable. Thus, we formalize the proper group belief concept using common ground framework and we define the semantics of the primitive speech acts of FIPA-ACL such as inform, request and confirm as case studies. Also, we illustrate our framework on an e-commerce agent purchase negotiation and compare this proposed framework with two traditional semantics for ACLs.