• Journal of the Korea Safety Management & Science
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• v.12 no.3
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• pp.121-128
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• 2010

This is to show some basic data for introducing both circulated aggregate and recycled powder producing waste concrete. Standard-mixing design for 24MPa has been basically used and added and replaced normal aggregate with recycled powder made of waste concrete. In addition, polycarboxylate high-range water reducing agent has been used because recycled powder is missing adhesive strength and it is not compare with cement's adhesive strength. Compressive strength with powder mixture of 2%, 4%, 6%, 8%, and 10% has been decreased down to 80% of normal concrete material strength without recycled powder mixture. $200^{\circ}C$, $400^{\circ}C$ and $600^{\circ}C$ heated concrete were compressively tested in order to find out concrete strength resistant to high temperature. heat capacity was also tested, based on the expectancy of its low conductivity. In addition, thermal conduction test was tested in order to find out concrete insulation. According to this test, when concrete was tested by fire resistance, it using the circulation aggregate was same resulted by concrete using the natural aggregate. also, recycle powder was not effecting insulation performance. but it is fit to standard on concrete insulation of building law.

• Journal of KIISE:Databases
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• v.32 no.2
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• pp.129-141
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• 2005

Although spatio-temporal databases have received considerable attention recently, there has been little work on processing range sum queries on the historical records of moving objects despite their importance. Since to answer range sum queries, the direct access to a huge amount of data incurs prohibitive computation cost, materialization techniques based on existing index structures are recently suggested. A simple but effective solution is to apply the materialization technique to the MVR-tree known as the most efficient structure for window queries with spatio-temporal conditions. However, the MVR-tree has a difficulty in maintaining pre-aggregated results inside its internal nodes due to cyclic paths between nodes. Aggregate structures based on other index structures such as the HR-tree and the 3DR-tree do not provide satisfactory query performance. In this paper, we propose a new indexing technique called the Adaptive Partitioned Aggregate R-Tree (APART) and query processing algorithms to efficiently process range sum queries in many situations. Experimental results show that the performance of the APART is typically above 2 times better than existing aggregate structures in a wide range of scenarios.

• Journal of the Korean Data and Information Science Society
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• v.14 no.1
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• pp.37-44
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• 2003

One of the characteristics of both on-line analytical processing(OLAP) applications and decision support systems is to provide aggregated source data. The purpose of this study is to discuss on the aggregation of multi-dimensional data. In this paper, we (1) examine the SQL aggregate functions and the GROUP BY operator, (2) introduce the Data Cube and MDX, (3) present an example for the practical usage of the Data Cube and MDX using sample data.

• Journal of the Korea institute for structural maintenance and inspection
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• v.1 no.2
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• pp.123-130
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• 1997

The effect of experimental factors on the Freeze-Thaw durability in the High Strength Concrete has been analyzed and investigated with [DESIGN of EXPERIMENT: L16). The Experimental parameters included the type of aggregate and mixer, the conditions of aggregates, and the difference of mixing temperature, procedure and placing, etc. It is aim of this study to provide the fundamental data on the effect of various factors on the frost resistance of high strength concrete for the practical use and research data accumulation of durability inspection. The results of this experiment indicate that the freeze-thaw durability of high strength concrete is markedly affected by the coarse aggregate source, mixing temperature and curing conditions.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.16 no.2
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• pp.137-149
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• 2012

We propose an efficient method to measure the insurance risk of causality insurance companies by using the CreditRisk+ methodology. This method is superior to previous methods in several aspects. Its computation speed is very fast and the input data form is simple. It is able to aggregate both credit risk and insurance risk, so the insurance company can manage the risk in combined manner. In this paper, we propose a mathematical method to obtain the aggregate loss distribution of portfolios having correlation among products or business lines as a general case, and then suggest its implementation algorithm. Finally we apply this method to the real data from Korea Insurance Development Institute (KIDI) and discuss its availability to real applications.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.2911-2913
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• 1999

This paper suggest an optimal identification method to complex and nonlinear system modeling that is based on Fuzzy-Neural Network(FNN). The FNN modeling implements parameter identification using HCM algorithm and optimal identification algorithm structure combined with two types of optimization theories for nonlinear systems, we use a HCM Clustering Algorithm to find initial parameters of membership function. The parameters such as parameters of membership functions, learning rates and momentum coefficients are adjusted using optimal identification algorithm. The proposed optimal identification algorithm is carried out using both a genetic algorithm and the improved complex method. Also, an aggregate objective function(performance index) with weighted value is proposed to achieve a sound balance between approximation and generalization abilities of the model. To evaluate the performance of the proposed model, we use the time series data for gas furnace, the data of sewage treatment process and traffic route choice process.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.5
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• pp.487-496
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• 2000

In this paper, the Multi-FNN(Fuzzy-Neural Networks) model is identified and optimized using HCM(Hard C-Means) clustering method and genetic algorithms. The proposed Multi-FNN is based on Yamakawa's FNN and uses simplified inference as fuzzy inference method and error back propagation algorithm as learning rules. We use a HCM clustering and Genetic Algorithms(GAs) to identify both the structure and the parameters of a Multi-FNN model. Here, HCM clustering method, which is carried out for the process data preprocessing of system modeling, is utilized to determine the structure of Multi-FNN according to the divisions of input-output space using I/O process data. Also, the parameters of Multi-FNN model such as apexes of membership function, learning rates and momentum coefficients are adjusted using genetic algorithms. A aggregate performance index with a weighting factor is used to achieve a sound balance between approximation and generalization abilities of the model. The aggregate performance index stands for an aggregate objective function with a weighting factor to consider a mutual balance and dependency between approximation and predictive abilities. According to the selection and adjustment of a weighting factor of this aggregate abjective function which depends on the number of data and a certain degree of nonlinearity, we show that it is available and effective to design an optimal Multi-FNN model. To evaluate the performance of the proposed model, we use the time series data for gas furnace and the numerical data of nonlinear function.

• Environmental and Resource Economics Review
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• v.20 no.2
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• pp.255-289
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• 2011

This study made linked Energy IO Table 00-05-08 of 76 sectors in intermediate sectors and analyzed structural decomposition analysis in energy consumption change in industrial sector with both by aggregate data and micro data. Structural decomposition analysis focused value added level change, value added share change of each industry, output structural change of each industry and energy intensity change of each industry as factors. Supply side model based on Ghosh inverse matrix was applied as empirical model because Korea has export driven industrial structure. Empirical results with aggregate data showed that value added change increased energy consumption and output structural change of each industry decreased energy consumption in both 2000~2005 and 2005~2008. However value added share change and energy intensity change caused opposite direction in energy consumption change with time. Policy based on aggregate data can not evaluate effort of each industry in energy efficiency and make effective results because aggregate data delete character of each industry.

• East Asian Economic Review
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• v.18 no.4
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• pp.395-423
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• 2014

This study examines the role of trade finance in the trade collapse of 2008-09 from the perspective of the Korean economy. We use two approaches. Firstly, as background to a more formal analysis, we make a casual observation on the behavior of aggregate data on trade finance, on which Korea has relatively abundant data. Aggregate data do not convincingly support the view that trade finance played an active role in causing the trade collapse. The measures of trade finance and the value of trade both dropped sharply, but the ratio of trade finance over trade was stable and in some cases increased during the crisis period. Secondly, using quarterly data on listed firms in Korea, we conduct panel estimations to test whether firms that are more dependent on external finance experienced greater export contraction during the crisis. Our regression analysis suggests that the financial vulnerability of firms, measured by various financial ratios, did not contribute to export contraction during the financial crisis. This observation largely applies even to smaller firms, who are usually thought of as being more vulnerable financially. However, we find that small exporters that relied heavily on cross-border trade payables or receivables suffered larger drops in export growth during the crisis.

• Journal of the Korea Concrete Institute
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• v.23 no.5
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• pp.551-558
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• 2011

The effect of the maximum aggregate size and substitution rate of natural sand on the mechanical properties of concrete is evaluated using 15 lightweight aggregate concrete mixes. For mechanical properties of concrete, compressive strength increase with respect to age, tensile resistance, elastic modulus, rupture modulus, and stress-strain relationship were measured. The experimental data were compared with the design equations specified in ACI 318-08, EC2, and/or CEB-FIP code provisions and empirical equations proposed by Slate et al., Yang et al., and Wang et al. The test results showed that compressive strength of lightweight concrete decreased with increase in maximum aggregate size and amount of lightweight fine aggregates. The parameters to predict the compressive strength development could be empirically formulated as a function of specific gravity of coarse aggregates and substitution rate of natural sand. The measured rupture modulus and tensile strength of concrete were commonly less than the prediction values obtained from code provisions or empirical equations, which can be attributed to the tensile resistance of lightweight aggregate concrete being significantly affected by its density as well as compressive strength.