• Journal of KIISE:Computing Practices and Letters
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• v.16 no.12
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• pp.1199-1203
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• 2010

Data transfer between host system and storage device is based on the data unit called sector, which can be varied depending on computer systems. If NAND flash memory is used as a storage device, the variant sector size can affect storage system performance since its operation is much related to sector size and page size. In this paper, we propose an efficient FTL mapping management scheme to support multiple sector size within one NAND flash memory based storage device, and analyze the performance effect and management overhead. According to the proposed scheme, the management overhead of proposed FTL management is lower than conventional scheme when various sector sizes are configured in computer systems, while performance is less degraded in comparison with single sector size support system.

• Proceedings of the Korean Information Science Society Conference
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• 2010.06a
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• pp.271-272
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• 2010

• Korean Journal of Organic Agriculture
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• v.28 no.4
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• pp.535-553
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• 2020

In this study, the characteristics of the waste sector CDM project were analyzed through cluster analysis of the waste sector CDM project and the analysis of the CDM investment cost in waste sector using CDM project data registered with UNFCCC since 2008 when EU ETS phase 2 began. As of September 2020, 772 cases of CDM projects in waste disposal and disposal are registered. Biogas technology is the largest, followed by livestock manure processing and biomass production technology. The results of the cluster analysis are summarized as follows: First, on average, projects utilizing AWMS technology are small in size and relatively low in investment costs. This is judged to be relatively low investment costs due to previously attracted foreign investment capital. Second, the average investment cost of CDM projects considered along with waste (No.13), the energy industry (No.1) and agriculture (No.15) was higher than those involving only waste. The analysis of the factors determining the investment cost of the waste sector CDM project showed that, as with cluster analysis, the AWMS technology, which is a livestock manure treatment technology, was lower in the investment cost than those that use other technologies. As a result of multiple regression analysis, the investment cost of the CDM project was analyzed lower in the order of biomass, AWMS, LFG and biogas. Also, the higher the investment cost for CDM projects linked to waste, energy and agriculture, and the better the investment environment, the higher the investment cost. Although no statistical feasibility was obtained, the larger the annual emission reduction, the lower the CDM investment cost.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.43 no.3 s.345
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• pp.32-38
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• 2006

In this paper, an intercell interference (ICI) reduction technique is proposed for OFDM-based cellular systems using the concept of virtual multiple antenna where multiple antenna techniques are performed on a set of subcarriers, not on the actual antenna array. The proposed technique is especially effective for user terminals with a single antenna at cell boundary in fully-loaded OFDM cellular systems with a frequency reuse factor equal to 1. Proposed ICI reduction techniques developed for SISO and MISO environments are shown to be robust to symbol timing offsets and efficient for various cell environments by adjusting group size depending on the number of adjacent cells. Also, the concept of a virtual signature randomizer (VSR) is introduced to improve channel separability in the virtual MIMO approach. It is shown by simulation that the proposed techniques are effective in reducing ICI and inter-sector interference compared with the conventional methods.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.10
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• pp.531-541
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• 2020

This study aims to determine the nature of the association between dividend policy and a corporation's financial performance in emerging countries, as well as the main variables that may have an effect on financial performance. The study included 92 industrial and service sector companies listed on the Amman Stock Exchange (ASE) during the period from 2015 to 2019. The study used Panel Data Analysis and cross-sectional time-series data and simple and multiple linear regression models. A multiple regression model was also developed in order to test whether guess factors may have a possible impact on financial performance (such as Dividend Yield, Dividend Pay-out Ratio, Firm Size, Leverage Ratio, Current Ratio). The data was collected from the annual reports and information that was available on the ASE website covering the period from 2015 to 2019. The results detect a strong relation between DY, DPR, and FSIZE variables that explain firm performance. Also leverage ratio is negatively and significantly associated with ROA and AOE. Moreover, no relations were detected between current ratio and financial performance. The study's conclusion is that dividend policy explains a lot of a company's financial performance, meaning that the dividend policy has a statistically significant impact on company financial performance.

• Journal of Information Technology Services
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• v.14 no.4
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• pp.185-197
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• 2015

As the uncertainty of demand in B2B electronics market has increased, firms need a strong method to estimate the market demand. An accurate prediction on the market demand is crucial for a firm not to overproduce or underproduce its goods, which would influence the performance of the firm. However, it is complicated to estimate the demand in a B2B market, particularly for the private sector, because firms are very diverse in terms of size, industry, and types of business. This study proposes both qualitative and quantitative demand forecasting approaches for B2B PC products. Four different measures for predicting PC products in B2B market with consideration of the different PC uses-personal work, common work, promotion, and welfare-are developed as the qualitative model's input variables. These measures are verified by survey data collected from experts in 139 firms, and can be applied when individual firms estimate the demand of PC goods in a B2B market. As the quantitative approach, the multiple regression model is proposed and it includes variables of region, type of industry, and size of the firm. The regression model can be applied when the aggregated demand for overall domestic PC market needs to be estimated.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.5
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• pp.417-425
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• 2003

H.264, which is the latest video coding standard of both ITU-T(International Telecommunication Union-Telecommunication standardization sector) and MPEG(Moving Picture Experts Group), adopts new video coding tools such as variable block size motion estimation, multiple reference frames, quarter-pel motion estimation/compensation(ME/MC), 4${\times}$4 Integer DCT(Discrete Cosine Transform), and Rate-Distortion Optimization, etc. These new video coding tools provide good coding of efficiency compared with existing video coding standards as H.263, MPEG-4, etc. However, these new coding tools require the increase of encoder complexity. Therefore, in order to apply H.264 to many real applications, fast algorithms are required for H.264 coding tools. In this paper, when encoder MacroBlock(MB) mode is decided by rate-distortion optimization tool, fast mode decision algorithm by skipping variable block size ME/MC and spatial-predictive coding, which occupies most encoder complexity, is proposed. In terms of computational complexity, the proposed method runs about 4 times as far as JM(Joint Model) 42 encoder of H.264, while the PSNR(peak signal-to-noise ratio)s of the decoded images are maintained.

• Asia pacific journal of information systems
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• v.14 no.1
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• pp.23-41
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• 2004

This paper proposes an empirical evidence about contextual factors which determine perceived business performances of small firms resulted from IT investment. In this paper, small firms are defined as firms of which total employees are below fifty. These small firms account for 95% of total number of private companies in Korea. We used a perceived IT performance model based on Balanced Scorecard framework to evaluate IT performance of small firms. And data were collected by Web and e-mail survey method with multiple screening. Statistical results show that business performance of small firms are differentiated in terms of firm size, location, longevity, age of owner, education level of owner, while industry sector, profitability, sex of owner don't make significant differences.

• International Journal of Computer Science & Network Security
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• v.21 no.7
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• pp.324-330
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• 2021

Agriculture is one of the essential needs of human life on planet Earth. It is the source of food and earnings for many individuals around the world. The economy of many countries is associated with the agriculture sector. Lots of diseases exist that attack various fruits and crops. Apple Tree Leaves also suffer different types of pathological conditions that affect their production. These pathological conditions include apple scab, cedar apple rust, or multiple diseases, etc. In this paper, an automatic detection framework based on deep learning is investigated for apple leaves disease classification. Different pre-trained models, VGG16, ResNetV2, InceptionV3, and MobileNetV2, are considered for transfer learning. A combination of parameters like learning rate, batch size, and optimizer is analyzed, and the best combination of ResNetV2 with Adam optimizer provided the best classification accuracy of 94%.

• Journal of Digital Contents Society
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• v.18 no.1
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• pp.149-158
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• 2017

Big data have been presenting us with exciting opportunities and challenges in economic development. For instance, in the agriculture sector, mixing up of various agricultural data (e.g., weather data, soil data, etc.), and subsequently analyzing these data deliver valuable and helpful information to farmers and agribusinesses. However, massive data in agriculture are generated in every minute through multiple kinds of devices and services such as sensors and agricultural web markets. It leads to the challenges of big data problem including data collection, data storage, and data analysis. Although some systems have been proposed to address this problem, they are still restricted either in the type of data, the type of storage, or the size of data they can handle. In this paper, we propose a novel design of a platform for collecting and analyzing agricultural big data. The proposed platform supports (1) multiple methods of collecting data from various data sources using Flume and MapReduce; (2) multiple choices of data storage including HDFS, HBase, and Hive; and (3) big data analysis modules with Spark and Hadoop.