• Journal of KIISE:Computing Practices and Letters
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• v.6 no.4
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• pp.383-392
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• 2000

Currently, multiprocessors are evaluated almost exclusively with scientific applications. Commercial applications are rarely explored because it is difficult to obtain the source codes of commercial DBMS. Even when the source code is available, such as for POSTGRES, understanding the source code enough to perform detailed meaningful performance evaluations is a daunting task for computer architects.To evaluate multiprocessors with commercial applications, we have developed our own DBMS, called EZDB. EZDB is a parallelized DBMS, loosely inspired from POSTGRES, and running on top of a software architecture simulator. It is capable of executing parallel programs written in SQL. Contrary to POSTGRES, EZDB is not intended as a prototype for a production-quality DBMS. Its purpose is to easily run and evaluate the performance of commercial applications on multiprocessor architectures. To illustrate the usefulness of EZDB, we showed the cache performance data collected for the TPC-B benchmark on a shared-memory multiprocessor. The simulation results showed that the data structures exhibited unique sharing characteristics and that their locality properties and working sets were very different from those in scientific applications.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.6C
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• pp.295-304
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• 2012

In this study, a resonant column testing system which is the largest in Korea has been developed to evaluate the dynamic deformation characteristics of coarse granular geomaterials, and the performance and the applicability of the testing system have been verified. The system has been developed as a typical Stokoe type device whose boundary conditions are fixed bottom and free top with additional mass, and can adopt a large specimen with 200 mm in diameter and 400 mm in height. The driving and measurement instruments are configured as high performance and precision systems, hence the automated testing system is appropriate to drive enough stress and to measure the behavior precisely for the test in practical manner. The dynamic response of the mechanical components and the applicability of the system have been evaluated using metal specimens as well as polyurethane specimens, and its precision was verified by comparing its results with those from other equipment and/or methods. To confirm the applicability of the large system for coarse geomaterials, the resonant column test results from both large and normal scale apparatus for the same material were compared and it was found that the result can be partially affected by scale. Finally, the dynamic deformation characteristics of coarse geomaterial which is used for construction of large dam was evaluated using the large system and its practicality could be confirmed.

• Journal of Korea Water Resources Association
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• v.45 no.2
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• pp.203-215
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• 2012

The objective of this study is to develop a catchment hydrologic cycle assessment model which can assess the impact of urban development and designing water cycle improvement facilities. Developed model might contribute to minimize the damage caused by urban development and to establish sustainableurban environments. The existing conceptual lumped models have a potential limitation in their capacity to simulate the hydrologic impacts of land use changes and assess diverse urban design. The distributed physics-based models under active study are data demanding; and much time is required to gather and check input data; and the cost of setting up a simulation and computational demand are required. The Catchment Hydrologic Cycle Assessment Tool (hereinafter the CAT) is a water cycle analysis model based on physical parameters and it has a link-node model structure. The CAT model can assess the characteristics of the short/long-term changes in water cycles before and after urbanization in the catchment. It supports the effective design of water cycle improvement facilities by supplementing the strengths and weaknesses of existing conceptual parameter-based lumped hydrologic models and physical parameter-based distributed hydrologic models. the model was applied to Seolma-cheon catchment, also calibrated and validated using 6 years (2002~2007) hourly streamflow data in Jeonjeokbigyo station, and the Nash-Sutcliffe model efficiencies were 0.75 (2002~2004) and 0.89 (2005~2007).

• Korean Journal of Agricultural and Forest Meteorology
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• v.19 no.3
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• pp.164-173
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• 2017

Alternative cropping systems would be one of climate change adaptation options. Suitable areas for a crop could be identified using a climate suitability model. The EcoCrop model has been used to assess climate suitability of crops using monthly climate surfaces, e.g., the digital climate map at high spatial resolution. Still, a high-performance computing approach would be needed for assessment of climate suitability to take into account a complex terrain in Korea, which requires considerably large climate data sets. The objectives of this study were to implement a script for R, which is an open source statistics analysis platform, in order to use the EcoCrop model under a parallel computing environment and to assess climate suitability of maize using digital climate maps at high spatial resolution, e.g., 1 km. The total running time reduced as the number of CPU (Central Processing Unit) core increased although the speedup with increasing number of CPU cores was not linear. For example, the wall clock time for assessing climate suitability index at 1 km spatial resolution reduced by 90% with 16 CPU cores. However, it took about 1.5 time to compute climate suitability index compared with a theoretical time for the given number of CPU. Implementation of climate suitability assessment system based on the MPI (Message Passing Interface) would allow support for the digital climate map at ultra-high spatial resolution, e.g., 30m, which would help site-specific design of cropping system for climate change adaptation.

• Journal of Bio-Environment Control
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• v.27 no.1
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• pp.27-33
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• 2018

This study was performed to review the domestic and international smart farm service model based on the convergence of agriculture and information & communication technology and derived various factors needed to improve the Korean smart greenhouse. Studies on modelling of crop growth environment in domestic smart farms were limited. And it took a lot of time to build research infrastructure. The cloud-based research platform as an alternative is needed. This platform can provide an infrastructure for comprehensive data storage and analysis as it manages the growth model of cloud-based integrated data, growth environment model, actuators control model, and farm management as well as knowledge-based expert systems and farm dashboard. Therefore, the cloud-based research platform can be applied as to quantify the relationships among various factors, such as the growth environment of crops, productivity, and actuators control. In addition, it will enable researchers to analyze quantitatively the growth environment model of crops, plants, and growth by utilizing big data, machine learning, and artificial intelligences.

• Korean Journal of Agricultural and Forest Meteorology
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• v.8 no.4
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• pp.275-278
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• 2006

Google Earth enables people to easily find information linked to geographical locations. Google Earth consists of a collection of zoomable satellite images laid over a 3-D Earth model and any geographically referenced information can be uploaded to the Web and then downloaded directly into Google Earth. This can be achieved by encoding in Google's open file format, KML (Keyhole Markup Language), where it is visible as a new layer superimposed on the satellite images. We used KML to create and share fine resolution gridded temperature data projected to 3 climatological normal years between 2011-2100 to visualize the site-specific warming and the resultant earlier blooming of spring flowers over the Korean Peninsula. Gridded temperature and phonology data were initially prepared in ArcGIS GRID format and converted to image files (.png), which can be loaded as new layers on Google Earth. We used a high resolution LCD monitor with a 2,560 by 1,600 resolution driven by a dual link DVI card to facilitate visual effects during the demonstration.

• Journal of Astronomy and Space Sciences
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• v.21 no.4
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• pp.441-452
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• 2004

Nowcast and forecast based on realtime data are quite essential for space weather monitoring. We have developed the web pages (http://sun.kao.re.kr) of the KAO Space Weather Monitoring system by using ION (IDL on the Net). They display latest solar and geomagnetic data, and present their expected effects on satellite, communications and ground power system. In addition, daily NOAA/SEC prediction reports on the probability of solar X-ray flares, proton events and geomagnetic storms are provided. To predict the arrival times of interplanetary shocks and CMEs, two different types of prediction models are also implemented. A work is in progress to develop web-based database of several solar and geomagnetic activities. These data are automatically downloaded to our data server in every minute, or every day using IDL and FTP programs. In this paper, we will introduce more details on the development of the KAO Space Weather Monitoring system.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.11
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• pp.53-61
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• 2017

Recently, there has been an increase in the number of web-based three-dimensional (3D) printing-related service platforms, which allow consumers to collect 3D modeling data, make requests for production, and receive goods through a distribution service using the service platform. The application of 3D printing technology has been expanded to the construction field, yet no guidelines for the related service platform or operation examples can be found. Therefore, the functions of 10 web-based 3D printing service platforms actively used in other industries were investigated and analyzed in this study, and the analysis results were used as a guideline to develop a 3D printing service platform for the construction industry. In addition, the design, construction and distribution services to be equipped with the construction 3D printing service integration platform were presented by creating the driving scenario of the platform. As 3D printing technology develops, the overall construction and architectural paradigms for design, construction and distribution will change. To prepare for such changes and to pioneer the digital construction market in the future, the role of the 3D printing service platform is expected to increase continually.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.42 no.1
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• pp.9-26
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• 2005

In the pursuit of ever higher levels of performance, recent computer systems have made use of deep pipeline, dynamic scheduling and multi-issue superscalar processor technologies. In this situations, branch prediction schemes are an essential part of modem microarchitectures because the penalty for a branch misprediction increases as pipelines deepen and the number of instructions issued per cycle increases. In this paper, we propose a novel branch prediction scheme, direction-gshare(d-gshare), to improve the prediction accuracy. At first, we model a neural network with the components that possibly affect the branch prediction accuracy, and analyze the variation of their weights based on the neural network information. Then, we newly add the component that has a high weight value to an original gshare scheme. We simulate our branch prediction scheme using Simple Scalar, a powerful event-driven simulator, and analyze the simulation results. Our results show that, compared to bimodal, two-level adaptive and gshare predictor, direction-gshare predictor(d-gshare. 3) outperforms, without additional hardware costs, by up to 4.1% and 1.5% in average for the default mont of embedded direction, and 11.8% in maximum and 3.7% in average for the optimal one.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.2
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• pp.236-244
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• 2005

In this study, we introduce a new topology of Fuzzy Polynomial Neural Networks(FPNN) that is based on fuzzy relation and evolutionally optimized Multi-Layer Perceptron, discuss a comprehensive design methodology and carry out a series of numeric experiments. The construction of the evolutionally optimized FPNN(EFPNN) exploits fundamental technologies of Computational Intelligence. The architecture of the resulting EFPNN results from a synergistic usage of the genetic optimization-driven hybrid system generated by combining rule-based Fuzzy Neural Networks(FNN) with polynomial neural networks(PNN). FNN contributes to the formation of the premise part of the overall rule-based structure of the EFPNN. The consequence part of the EFPNN is designed using PNN. As the consequence part of the EFPNN, the development of the genetically optimized PNN(gPNN) dwells on two general optimization mechanism: the structural optimization is realized via GAs whereas in case of the parametric optimization we proceed with a standard least square method-based learning. To evaluate the performance of the EFPNN, the models are experimented with the use of several representative numerical examples. A comparative analysis shows that the proposed EFPNN are models with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.