• Journal of the Korea Society of Computer and Information
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• v.24 no.6
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• pp.1-9
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• 2019

Hard disks, which have long been used as secondary storage in computing systems, are increasingly being replaced by solid state drives (SSDs), due to their relatively fast data input / output speeds and small, light weight. SSDs that use NAND flash memory as a storage medium are significantly different from hard disks in terms of physical operation and internal operation. In particular, there is a feature that data overwrite can not be performed, which causes erase operation before writing. In order to solve this problem, a hot data for frequently updating a data for a specific page is distinguished from a cold data for a relatively non-hot data. Hot data identification helps to improve overall performance by identifying and managing hot data separately. Among the various hot data identification methods known so far, there is a technique of recording consecutive write requests by using a Bloom filter and judging the values by hot data. However, the Bloom filter technique has a problem that a new bit array must be generated every time a set of items is changed. In addition, since it is judged based on a continuous write request, it is possible to make a wrong judgment. In this paper, we propose a method using a counting filter for accurate hot data verification. The proposed method examines consecutive write requests. It also records the number of times consecutive write requests occur. The proposed method enables more accurate hot data verification.

• Journal of Internet of Things and Convergence
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• v.8 no.5
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• pp.77-83
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• 2022

Nand flash memory requires an Erase-Before-Write operation structurally. In order to solve this problem, it can be solved by classifying a page (hot data page) where data update operation occurs frequently and storing it in a separate block. The MHF (Multi Hash Function Framework) technique records the frequency of data update requests in the system memory, and when the recorded value exceeds a certain standard, the data update request is judged as hot data. However, the method of simply counting only the frequency of the data update request has a limit in judging it as accurate hot data. In addition, in the case of a technique that determines the persistence of a data update request, the fact of the update request is recorded sequentially based on a time interval and then judged as hot data. In the case of such a persistence-based method, its implementation and operation are complicated, and there is a problem of inaccurate judgment if frequency is not considered in the update request. This paper proposes a lightweight hot data determination technique that considers both frequency and persistence in data update requests.

• Journal of the Korea Society of Computer and Information
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• v.22 no.2
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• pp.1-7
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• 2017

Recently, NAND flash memory, which is used as a storage medium, is replacing HDD (Hard Disk Drive) at a high speed due to various advantages such as fast access speed, low power, and easy portability. In order to apply NAND flash memory to a computer system, a Flash Translation Layer (FTL) is indispensably required. FTL provides a number of features such as address mapping, garbage collection, wear leveling, and hot data identification. In particular, hot data identification is an algorithm that identifies specific pages where data updates frequently occur. Hot data identification helps to improve overall performance by identifying and managing hot data separately. MHF (Multi hash framework) technique, known as hot data identification technique, records the number of write operations in memory. The recorded value is evaluated and judged as hot data. However, the method of counting the number of times in a write request is not enough to judge a page as a hot data page. In this paper, we propose hot data identification which considers not only the number of write requests but also the persistence of write requests.

• Journal of Information Technology Services
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• v.13 no.4
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• pp.359-375
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• 2014

Researchers cannot avoid missing data in collecting data, because some respondents arbitrarily or non-arbitrarily do not answer questions in studies and experiments. Missing data not only increase and distort standard deviations, but also impair the convenience of estimating parameters and the reliability of research results. Despite widespread use of hot deck, researchers have not been interested in it, since it handles missing data in ambiguous ways. Hot deck can be complemented using K-nn, a method of machine learning, which can organize donor groups closest to properties of missing data. Interested in the role of k-nn, this study was conducted to impute missing data based on the hot deck method using k-nn. After setting up imputation of missing data based on hot deck using k-nn as a study objective, deletion of listwise, mean, mode, linear regression, and svm imputation were compared and verified regarding nominal and ratio data types and then, data closest to original values were obtained reasonably. Simulations using different neighboring numbers and the distance measuring method were carried out and better performance of k-nn was accomplished. In this study, imputation of hot deck was re-discovered which has failed to attract the attention of researchers. As a result, this study shall be able to help select non-parametric methods which are less likely to be affected by the structure of missing data and its causes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.3935-3949
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• 2021

With the rapid development of Internet and big data technology, various online social network platforms have been established, producing massive information every day. Hot topic discovery aims to dig out meaningful content that users commonly concern about from the massive information on the Internet. Most of the existing hot topic discovery methods focus on a single network data source, and can hardly grasp hot spots as a whole, nor meet the challenges of text sparsity and topic hotness evaluation in cross-network scenarios. This paper proposes a novel hot topic discovery method across social network based on an im-proved LDA model, which first integrates the text information from multiple social network platforms into a unified data set, then obtains the potential topic distribution in the text through the improved LDA model. Finally, it adopts a heat evaluation method based on the word frequency of topic label words to take the latent topic with the highest heat value as a hot topic. This paper obtains data from the online social networks and constructs a cross-network topic discovery data set. The experimental results demonstrate the superiority of the proposed method compared to baseline methods.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.3 s.174
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• pp.735-742
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• 2000

Densification behaviors of mixed metal powder under high temperature were investigated. Experimental data of mixed copper and tool steel powder with various volume fractions of Cu powder were obtained under hot isostatic pressing and hot pressing. By mixing the creep potentials of McMeeking and co-workers and of Abouaf and co-workers originally for pure powder, the mixed creep potentials with various volume fractions of Cu powder were employed in the constitutive models. The constitutive equations were implemented into a finite element program (ABAQUS) to compare with experimental data for densification of mixed powder under hot isostatic pressing and hot pressing. Finite element calculations by using the creep potentials of Abouaf and co-workers agreed reasonably well with experimental data, however, those by McMeeking and co-workers underestimate experimental data as observed in the case of pure metal powders.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.6
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• pp.738-744
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• 2018

This paper proposes a low cost hot swap operation circuit of a parallel operation power supply for servers. Hot swap function for server power system is essential in 24 hour operation system such as internet data center, server, factory and etc. Server power supplies used in internet data centers have two or more parallel operations with the hot swap operation. However, the cost of the power supply is high because the controller IC for hot swap operation is very expensive. Therefore, this paper proposes a parallel-operated power supply with a low-cost hot swap controller for servers. The proposed system can operate hot swap function by using discrete devices and reduce the cost by more than 50%. A 1.2kW prototype system is implemented to verify the proposed low cost hot swap controller.

• Journal of KIISE:Computer Systems and Theory
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• v.35 no.1
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• pp.18-29
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• 2008

NAND Flash memory has been growing popular storage device for the last years because of its low power consumption, fast access speed, shock resistance and light weight properties. However, it has the distinct characteristics such as erase-before-write architecture, asymmetric read/write/erase speed, and the limitation on the number of erasure per block. Due to these limitations, various Flash Translation Layers (FTLs) have been proposed to effectively use NAND flash memory. The systems that adopted the conventional FTL may result in severe performance degradation by the hot data which are frequently requested data for overwrite in the same logical address. In this paper, we propose a novel FTL algorithm called Adaptive Flash Translation Layer (AFTL) which uses sector mapping method for hot data and log-based block mapping method for cold data. Our system removes the redundant write operations and the erase operations by the separating hot data from cold data. Moreover, the read performance is enhanced according to sector translation that tends to use a few read operations. A series of experiments was organized to inspect the performance of the proposed method, and they show very impressive results.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.12
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• pp.3061-3071
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• 2000

Densification behavior of titanium alloy powder was investigated under hot pressing at various pressures and temperatures. Experimental date were obtained for densification of titanium alloy powder under an instantaneous loading and subsequent creep deformation during hot pressing. The constitutive models of Fleck et al. and the modified Gurson were employed for thermo-phastic deformation under the instantaneous loading and that f Abouaf and co-workers for creep deformation of titanium alloy powder during hot pressing. By implementing these constitutive equations into a finite element program(ABAQUS), finite element results were compared with experimental data during hot pressing. To investigate the effect of friction between the power and die wall, density distributions of power compacts were measured and compared with finite element calculations. Finite element results from the models of Fleck et al. and the modified Gurson agreed reasonably good with experimental data for densification and density distribution of titanium alloy powder under the instantaneous loading during hot pressing. Finite element results from the model of Abouaf and co-workers, however, somewhat overestimate experimental data for creep deformation of power compacts during hot pressing.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.6
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• pp.761-767
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• 2010

In the case of NAND flash memory, a whole block needs to be erased for update operations because update-in- place operations are not supported in NAND flash memory. Blocks of NAND flash memory have the limited erasure cycles, so frequently updated data (hot data) easily makes blocks worn out. As the result, the capacity of NAND flash memory will be reduced by hot data. In this paper, we propose a wear-leveling algorithm by discriminating hot and cold data based on the update patterns of data. When we applied this scheme to NAND flash memory, we confirmed that the erase counts of blocks became more uniform by mapping hot data to a block with a low erase count and cold data to block with a high erase count.