• Smart Structures and Systems
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• v.10 no.4_5
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• pp.485-499
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• 2012

Mode shape expansion is useful in structural dynamic studies such as vibration based structural health monitoring; however most existing expansion methods can not consider the modelling errors in the finite element model and the measurement uncertainty in the modal properties identified from vibration data. This paper presents a reliable approach for expanding mode shapes with consideration of both the errors in analytical model and noise in measured modal data. The proposed approach takes the perturbed force as an unknown vector that contains the discrepancies in structural parameters between the analytical model and tested structure. A regularisation algorithm based on the Tikhonov solution incorporating the L-curve criterion is adopted to reduce the influence of measurement uncertainties and to produce smooth and optimised expansion estimates in the least squares sense. The Canton Tower benchmark problem established by the Hong Kong Polytechnic University is then utilised to demonstrate the applicability of the proposed expansion approach to the actual structure. The results from the benchmark problem studies show that the proposed approach can provide reliable predictions of mode shape expansion using only limited information on the operational modal data identified from the recorded ambient vibration measurements.

• Journal of the Korean Institute of Landscape Architecture
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• v.27 no.4
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• pp.143-150
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• 1999

The purpose of this research is to find som ways to model tree forms more efficiently in reference with surveying structural data and handling parameters in plant Editor of AccuRender, the AutoCAD-based rendering software adopting the procedural plant modeling technique. In case of modelling a new tree, because it is efficient to modify an existing tree data as a template, we attempted to classify 81 species' data from existing plant library including conifers and deciduous tree. According to the qualitative characteristics and quantitative parameters of geometrical and branching structure, 8 types of tree form were classified with factor and cluster analysis. Some critical aspects found in the distributions of standardized scores of parameters in each type were discussed for explaining the tree forms intuitively. For adaptability of the resulted classification and typical parameters, 10 species of tree were measured and modelled, and proved to be very similar to the real structures of tree forms. CG or CAD-based plant modelling technique would be recommended not only as a presentation tool but for planting design, landscape simulation and assessment.

• Communications of Mathematical Education
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• v.38 no.3
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• pp.353-378
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• 2024

The purpose of this study is to analyze the impact of a socio-critical modelling class on students' perception of the role of socially critical mathematics. Mathematics education involves cultivating the ability to understand and respond to the mathematics we use in our daily lives. Many elementary school students in Korea have doubts about why they need to learn mathematics, and in international assessments such as PISA and TIMSS, Korean students' affective dimensions are shown to be very negative. Researchers believed that a socio-critical modelling class would have an impact on students' perceptions and attitudes toward mathematics, and conducted a socio-critical modelling class for 20 sixth-grade elementary school students. Data collection and analysis were conducted inductively through students' words and actions during class, activity sheets, and interviews, based on the grounded theory of Strauss & Corbin (2014). As a result of the study, the socio-critical modelling class increased students' social critical awareness and made them re-aware of the role of mathematics.

• Journal of KIISE:Databases
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• v.30 no.3
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• pp.237-251
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• 2003

In this paper, we propose a video retrieval system to search logical structure and semantic contents of video data efficiently. The proposed system employs a layered modelling method that orBanifes video data in raw data layer, content layer and key frame layer. The layered modelling of the proposed system represents logical structures and semantic contents of video data in content layer. Also, the proposed system supports various types of searches such as text search, visual feature based similarity search, spatio-temporal relationship based similarity search and semantic contents search.

• Journal of Advanced Marine Engineering and Technology
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• v.18 no.1
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• pp.92-100
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• 1994

For generating NC machining data automatically, it is important to handle computer models such as geometric shape data including engineering specifications for the mechanical part to be manufactured. We proposed unique CAM system for a personal computer that can define the geometric shape in an ease manner and machine the sculptured surfaces of a mold cavity. In this paper, the theoretical basis of generation of high precision machining data for a mold cavity is obtained. The first is geometric modelling, and the second is high precision machining with an optimized tool path algorithm satisfying given tolerance limits. Especially, the bicubic Bezier basis function is adopted for a geometric modelling.

• Journal of the Korea Safety Management & Science
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• v.14 no.2
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• pp.177-183
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• 2012

Obtaining and applying information is considered as a critical task in the modern informationized society. Finding the one's necessary information and processing it into a detailed knowledge are becoming more priortized in the enormous amount of information. Data modelling is the process that does not only reflect the demands of the user but the one that also facilitates the user's comprehension of the model itself. Ultimately, data modelling fully supports the processes that are requisite for the implementation of a data base and minimizes the alternations of the model during the development of applications.

• Journal of applied mathematics & informatics
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• v.40 no.3_4
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• pp.563-575
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• 2022

Many organizations seek statistical modelling facilitated by data analytics technologies for determining the prediction models associated with M&A (Merger and Acquisition). By combining these data analytics tool alongside with data collection approaches aids organizations towards M&A decision making, followed by achieving profitable insights as well. It promotes for better visibility, overall improvements and effective negotiation strategies for post-M&A integration. This paper explores on the impact of pre and post integration of M&A in a standard organizational setting via devising a suitable statistical model via employing techniques such as Naïve Bayes, K-nearest neighbour (KNN), and Decision Tree & Support Vector Machine (SVM).

• International journal of advanced smart convergence
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• v.4 no.1
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• pp.45-53
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• 2015

This paper is proposed mathematical load modelling based on system identification approach of energy consumption of residential air conditioning. Due to air conditioning is one of the significant equipment which consumes high energy and cause the peak load of power system especially in the summer time. The demand response is one of the solutions to decrease the load consumption and cutting peak load to avoid the reservation of power supply from power plant. In order to operate this solution, mathematical modelling of air conditioning which explains the behaviour is essential tool. The four type of linear model is selected for explanation the behaviour of this system. In order to obtain model, the experimental setup are performed by collecting input and output data every minute of 9,385 BTU/h air-conditioning split type with $25^{\circ}C$ thermostat setting of one sample house. The input data are composed of solar radiation ($W/m^2$) and ambient temperature ($^{\circ}C$). The output data are power and energy consumption of air conditioning. Both data are divided into two groups follow as training data and validation data for getting the exact model. The model is also verified with the other similar type of air condition by feed solar radiation and ambient temperature input data and compare the output energy consumption data. The best model in term of accuracy and model order is output error model with 70.78% accuracy and $17^{th}$ order. The model order reduction technique is used to reduce order of model to seven order for less complexity, then Kalman filtering technique is applied for remove white Gaussian noise for improve accuracy of model to be 72.66%. The obtained model can be also used for electrical load forecasting and designs the optimal size of renewable energy such photovoltaic system for supply the air conditioning.

• Journal of the Korean Society for Precision Engineering
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• v.9 no.1
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• pp.96-105
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• 1992

A study on the construction of a CAD/CAM system operated by 16 Bit PC basic language, for machining sculptured surface of die, was carried out. The system consists of 2 steps i.e., process for geometric modelling by wire frame and process for machining data generation. Geometric modelling for sculptured surface is made by the point data fitting, parallel sweeping, normal sweeping and linear connection of cross section curve. Machining data are gained by cutter off-set of geometric model data and machining carried out by DNC. This system is to be proved enough for rough cutting by actual machining experiment. But, for becoming a high level system, another method of cutter off-set has to be regarded and system must be reconstructed by another program language.

• International Journal of Contents
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• v.12 no.1
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• pp.49-53
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• 2016

"Hahoe Village" in Andong region is an UNESCO World Heritage Site. It should be protected against various disasters such as fire, flooding, earthquake, etc. Among these disasters, flooding has drastic impact on the lives and properties in a wide area. Since "Hahoe Village" is adjacent to Nakdong River, it is important to monitor the water level near the village. In this paper, we developed a hydrological modelling using multi-layer perceptron (MLP) to predict the water level of Nakdong River near "Hahoe Village". To develop the prediction model, error back-propagation (EBP) algorithm was used to train the MLP with water level data near the village and rainfall data at the upper reaches of the village. After training with data in 2012 and 2013, we verified the prediction performance of MLP with untrained data in 2014.