• Journal of KIISE:Databases
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• v.32 no.3
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• pp.219-227
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• 2005

In this paper, we propose an efficient algorithm which generates frequent itemsets by only one database scanning. A frequent itemset is subset of an itemset which is accessed by a transaction. For each item, if informations about transactions accessing the item are exist, it is possible to generate frequent itemsets only by the extraction of items haying an identical transaction ID. Proposed method in this paper generates the data structure which stores transaction ID for each item by only one database scanning and generates 2-frequent itemsets by using the hash technique at the same time. k(k$\geq$3)-frequent itemsets are simply found by comparing previously generated data structure and transaction ID. Proposed algorithm can efficiently generate frequent itemsets by only one database scanning .

• Water Engineering Research
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• v.3 no.1
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• pp.31-44
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• 2002

The effect of diurnal cycle, intermittent visit of observation satellite, sensor installation, partial coverage of remote sensing, heterogeneity of soil properties and precipitation to the soil moisture estimation error were analyzed to present the global sampling strategy of soil moisture. Three models, the theoretical soil moisture model, WGR model proposed Waymire of at. (1984) to generate rainfall, and Turning Band Method to generate two dimensional soil porosity, active soil depth and loss coefficient field were used to construct sufficient two-dimensional soil moisture data based on different scenarios. The sampling error is dominated by sampling interval and design scheme. The effect of heterogeneity of soil properties and rainfall to sampling error is smaller than that of temporal gap and spatial gap. Selecting a small sampling interval can dramatically reduce the sampling error generated by other factors such as heterogeneity of rainfall, soil properties, topography, and climatic conditions. If the annual mean of coverage portion is about 90%, the effect of partial coverage to sampling error can be disregarded. The water retention capacity of fields is very important in the sampling error. The smaller the water retention capacity of the field (small soil porosity and thin active soil depth), the greater the sampling error. These results indicate that the sampling error is very sensitive to water retention capacity. Block random installation gets more accurate data than random installation of soil moisture gages. The Walnut Gulch soil moisture data show that the diurnal variation of soil moisture causes sampling error between 1 and 4 % in daily estimation.

• Acta Via Serica
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• v.5 no.1
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• pp.153-186
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• 2020

The family as a social institution has survived most diverse political periods and appears resilient or at least able to reconstitute itself even in the aftermath of destructive events such as wars. Age at first marriage is one possibility to systematize the strategies that families follow in times of internal conflicts (e.g., civil wars), external interventions or peaceful times. The authors found that age at first marriage correlates with socio-political events whereas perceptions of insecurity lead to a decline in marital age. This paper is based on three case studies that the authors have conducted through ethnographic methods among Tajiks in the cities Kulob, Khujand, and Mazar-e Sharif in Tajikistan and Afghanistan. Combining Grounded Theory with the genealogical methods from social anthropology in order to generate demographic data, the authors introduce the method of grounded demography as a way to generate demographic data through ethnographic methods. Grounded demography offers a way to produce statistical data grounded in ethnographic research.

• Korean Journal of Remote Sensing
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• v.37 no.1
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• pp.139-151
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• 2021

By the end of December 2020, an earthquake with Mw about 6.4 hit Sisak-Moslavina County, Croatia. The town of Petrinja was the most affected region with major power outage and many buildings collapsed. The damage also affected neighbor countries such as Bosnia and Herzegovina and Slovenia. As a light of this devastating event, a deformation map due to this earthquake could be generated by using remote sensing imagery from Sentinel-1 SAR data. InSAR could be used as deformation map but still affected with noise factor that could problematize the exact deformation value for further research. Thus in this study, 17 SAR data from Sentinel-1 satellite is used in order to generate the multi-temporal interferometry utilize Stanford Method for Persistent Scatterers (StaMPS). Mean deformation map that has been compensated from error factors such as atmospheric, topographic, temporal, and baseline errors are generated. Okada model then applied to the mean deformation result to generate the modeled earthquake, resulting the deformation is mostly dominated by strike-slip with 3 meter deformation as right lateral strike-slip. The Okada sources are having 11.63 km in length, 2.45 km in width, and 5.46 km in depth with the dip angle are about 84.47° and strike angle are about 142.88° from the north direction. The results from this modeling can be used as learning material to understand the seismic activity in the latest 2020 Petrinja, Croatia Earthquake.

• The Journal of the Korea Contents Association
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• v.12 no.2
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• pp.472-482
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• 2012

There are many investors in the stock market, and more and more people get interested in the stock investment. In order to avoid risks and make profit in the stock investment, we have to determine several aspects using various information. That is, we have to select profitable stocks and determine appropriate buying/selling prices and holding period. This paper proposes a data mining tool for the investors' decision support. The data mining tool makes stock investors apply machine learning techniques and generate stock price prediction model. Also it helps determine buying/selling prices and holding period. It supports individual investor's own decision making using past data. Using the proposed tool, users can manage stock data, generate their own stock price prediction models, and establish trading policy via investment simulation. Users can select technical indicators which they think affect future stock price. Then they can generate stock price prediction models using the indicators and test the models. They also perform investment simulation using proper models to find appropriate trading policy consisting of buying/selling prices and holding period. Using the proposed data mining tool, stock investors can expect more profit with the help of stock price prediction model and trading policy validated on past data, instead of with an emotional decision.

• Journal of Korean Society for Geospatial Information Science
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• v.14 no.1 s.35
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• pp.49-55
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• 2006

Nowadays, flood hazard maps have been generated to minimize the damages from the flooding. To generate such flood hazard maps, LiDAR data can be used as data source with higher data accuracy. LiDAR data, however, requires relatively higher cost and longer processing time. In this background, this study proposed DEM generation using NGIS digital topographic maps. For that, breaklines were processed to count directions of water flows. In addition, the river profile data, unique data source to represent real topography of the river area, were integrated to the breaklines to generate DEM. City of Kuri in Kyunggi Province was selected for this study and 1:1,000 and 1:5,000 topographic maps were integrated to process breaklines and river profile data were also linked to generate DEM. The generated DEM showed relatively lower vertical accuracy from mixing 1:1,000 and 1:5,000 topographic maps since 1:1,000 topographic maps were not available for some portion of the area. However, the DEM generated demonstrated reasonable accuracy and resolution for flood map generation as well as higher cost saving effects. On the contrary, for more efficient utilization of NGIS topographic maps, periodic map updating needs to be made including technical consideration in building breaklines and applying interpolation methods.

• Journal of Software Engineering Society
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• v.25 no.1
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• pp.1-9
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• 2012

RFID systems have been widely used in various fields such as logistics, distribution, food, security, traffic and others. A RFID middleware, one of the key components of the RFID system, perform an important role in many functions such as filtering, grouping, reporting tag data according to given user specifications and so on. However, manual test data generation is very hard because the inputs of the RFID middleware are generated according to the RFID middleware standards and complex encoding rules. To solve this problem, in this paper, we propose a black box test technique based on RFID middleware standards. Firstly, we define ten types of input conversion rules to generate new test data from existing test data based on the standard specifications. And then, using these input conversion rules, we generate various additional test data automatically. To validate the effectiveness of generated test data, we measure coverage of generated test data on actual RFID middleware. The results show that our test data achieve 78% statement coverage and 58% branch coverage in the classes of filtering and grouping, 79% statement coverage and 64% branch coverage in the classes of reporting.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.6
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• pp.257-264
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• 2019

Recently, deep neural network based approaches have shown a good performance for various fields of natural language processing. A huge amount of training data is essential for building a deep neural network model. However, collecting a large size of training data is a costly and time-consuming job. A data augmentation is one of the solutions to this problem. The data augmentation of text data is more difficult than that of image data because texts consist of tokens with discrete values. Generative adversarial networks (GANs) are widely used for image generation. In this work, we generate sentimental texts by using one of the GANs, CS-GAN model that has a discriminator as well as a classifier. We evaluate the usefulness of generated sentimental texts according to various measurements. CS-GAN model not only can generate texts with more diversity but also can improve the performance of its classifier.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 2002.10b
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• pp.249-250
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• 2002

In this paper, the relationship among the 3-D surface topography parameters are studied. Several surface topography parameters that are important in tribology are calculated against various surface topography data. 3-D surface data with desired properties are generated by using the non-causal 2-D auto-regressive (AR) model. The non-causal 2-D AR model is a random 3-D surface topography model that can generate 3-D surface topography data with specified parameters.

• 한국데이터정보과학회:학술대회논문집
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• 2006.11a
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• pp.97-112
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• 2006

FRACAS(Failure Reporting, Analysis and Corrective Action System) is intended to provide management visibility and control for reliability and maintainability improvement of hardware and associated software by timely and disciplined utilization of failure and maintenance data to generate and implement effective corrective actions to prevent failure recurrence and to simplify or reduce the maintenance tasks. This process applies to acquisition for the design, development, fabrication, test, and operation or military systems, equipment, and associated computer programs. This paper shows the FRACAS development process and developed FRACAS system for a defense equipment.