• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.5
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• pp.555-563
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• 2011

The goal of image calibration is to find a relation between image and world coordinates. Conventional image calibration uses physical camera model that is able to reflect camera's optical properties between image and world coordinates. In this paper, we try to calibrate images distortion using performance criterion-based polynomial model which assumes that the relation between image and world coordinates can be identified by polynomial equation and its order and parameters are able to be estimated with image and object coordinate values and performance criterion. In order to overcome existing limitations of the conventional image calibration model, namely, over-fitting feature, the performance criterion-based polynomial model is proposed. The efficiency of proposed method can be verified with 2D images that were taken by laser scan camera.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.1
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• pp.29-35
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• 2011

In this paper, an integrated simulation model for designing multi-agent system based DEMS (distributed energy management system) in stand-alone micro-grid system is proposed. In the design of the EMS(energy management system), the multi-agent based DEMS are more effective than conventional centralized EMS, but it is difficult to design the DEMS by general design tools for power system because of various states of a microgrid system during operation of stand-alone mode. Also, since performance test of the DEMS using a real system is inefficient, an integrated simulation model is required for designing an effective multi-agent system based DEMS. The proposed model consists of Matlab/SimPowerSystems based simulation model of the microgrid system and the Multi-agnet based DEMS designed by Matlab/StateFlow tool. In order to show the effect of the proposed model, the model outputs are analyzed for specified operation conditions.

• The Journal of Bigdata
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• v.5 no.1
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• pp.1-9
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• 2020

The study was conducted with funding from the government (Ministry of Agriculture, Food and Rural Affairs) in 2018 with support from the Agricultural, Food, and Rural Affairs Agency, 318069-03-HD040, and in based on artificial intelligence-based HPAI spread analysis and patterning. The model that is actively used in time series and text mining recently is LSTM (Long Short-Term Memory Models) model utilizing deep learning model structure. The LSTM model is a model that emerged to resolve the Long-Term Dependency Problem that occurs during the Backpropagation Through Time (BPTT) process of RNN. LSTM models have resolved the problem of forecasting very well using variable sequence data, and are still widely used.In this paper study, we used the data of the Call Detailed Record (CDR) provided by KT to identify the migration path of people who are expected to be closely related to the virus. Introduce the results of predicting the path of movement by learning the LSTM model using the path of the person concerned. The results of this study could be used to predict the route of HPAI propagation and to select routes or areas to focus on quarantine and to reduce HPAI spread.

• Korean Chemical Engineering Research
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• v.43 no.3
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• pp.371-379
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• 2005

Adsorption equilibria of the gases $H_2$, $CH_4$, and $C_2H_4$ and their binary mixtures on activated carbon (Calgon co.) have been measured by static volumetric method in the pressure range of 0 to 18 atm at temperatures of 293.15, 303.15, and 313.15 K. From the parameters obtained from single component adsorption isotherm, multi-component adsorption equilibria could be predicted and compared with experimental data. The binary experimental data were applied to four models : extended Langmuir, extended Langmuir-Freundlich, Ideal Adsorbed Solution theory (IAST), and Vacancy Solution Model (VSM). The models were found to describe the experimental data with a reasonable accuracy. Extended L-F model predicts equilibria of mixture better than any other model.

• Journal of Korea Game Society
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• v.13 no.2
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• pp.49-58
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• 2013

In this study, a tree growth model is designed to represent a variety of trees consisting of a outdoor terrain of game efficiently and naturally. The proposed tree growth model is an integrated tree growth model, and is configured using the following approaches: (1) the tree modeling method based on growth volume and the convolution sums of divisor functions, which is used to model a variety kind of trees more intuitively and naturally; (2) a rendering method using a level of detail of branch based on instancing for real-time processing of numerous trees with complicated structures; and (3) a combination of the above methods to efficiently implement a game landscape. The natural and diverse growths of trees that emerged using the proposed tree growth model is evaluated through experimentation, along with the possibility of implementing the natural game landscape and the efficiency of real-time processing.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.5
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• pp.608-615
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• 2006

This paper proposes a fuzzy model tree consisting of local linear models using fuzzy cluster for data modeling. First, cluster centers are calculated by fuzzy clustering method using all input and output attributes. And then, linear models are constructed at internal nodes with fuzzy membership values between centers and input attributes. The expansion of internal node is determined by comparing errors calculated in parent node with ones in child node, respectively. As a final step, data prediction is performed with a linear model having the highest fuzzy membership value between input attributes and cluster centers in leaf nodes. To show the effectiveness of the proposed method, we have applied our method to various dataset. Under various experiments, our proposed method shows better performance than conventional model tree and artificial neural networks.

• Journal of Korean Tunnelling and Underground Space Association
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• v.20 no.6
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• pp.1091-1104
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• 2018

Wear prediction of TBM disc cutters is a very important issue during design as well as construction stages for hard rock TBMs as the cutter head intervention is directly related to the time and cost of tunneling. For that, some methods such as NTNU, CSM and Gehring models were used to predict disc cutter wear and intervention interval. There are however some problems to be addressed in these models in terms of accuracy and time for testing, so that a NAT (New Abrasion Tester) model has been developed in order to achieve simplicity and reliability together at the same time (Farrokh and Kim, 2018). On the basis, the proposed NAT model has been applied to ${\bigcirc}{\bigcirc}$ project in Korea. A comparative study was performed to compare with the conventional methods and as a result the NAT model showed a very good agreement with actual cutter life. The NAT model will be further applied to other projects to establish credibility.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.639-644
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• 2024

Recent developments in the use of drones on battlefields, extending beyond reconnaissance to firepower support, have greatly increased the importance of technologies for early automatic drone identification. In this study, to identify an effective image classification model that can distinguish drones from other aerial targets of similar size and appearance, such as birds and balloons, we utilized a dataset of 3,600 images collected from the internet. We adopted a transfer learning approach that combines the feature extraction capabilities of three pre-trained convolutional neural network models (VGG16, ResNet50, InceptionV3) with an additional classifier. Specifically, we conducted a comparative analysis of the performance of these three pre-trained models to determine the most effective one. The results showed that the InceptionV3 model achieved the highest accuracy at 99.66%. This research represents a new endeavor in utilizing existing convolutional neural network models and transfer learning for drone identification, which is expected to make a significant contribution to the advancement of drone identification technologies.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.4
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• pp.268-278
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• 2020

Flowering time has been put forward due to the recent abnormally warm winter, which often caused damages of flower buds by late frosts persistently. In the present study, cumulative chill unit and cumulative heat unit of Niitaka pear, which are required for releasing the endogenous dormancy and for flowering after breaking dormancy, respectively, were compared between flowering time prediction models used in South K orea. Observation weather data were collected at eight locations for the recent three years from 2018-2020. The dates of full bloom were also collected to determine the confidence level of models including DVR, mDVR and CD models. It was found that mDVR model tended to have smaller values (8.4%) of the coefficient of variation (cv) of chill units than any other models. The CD model tended to have a low value of cv (17.5%) for calculation of heat unit required to reach flowering after breaking dormancy. The mDVR model had the most accurate prediction of full bloom during the study period compared with the other models. The DVR model usually had poor skills in prediction of full bloom dates. In particular, the error of the DVR model was large especially in southern coastal areas (e.g., Ulju and Sacheon) where the temperature was warm. Our results indicated that the mDVR model had relatively consistent accuracy in prediction of full bloom dates over region and years of interest. When observation data for full bloom date are compiled for an extended period, the full bloom date can be predicted with greater accuracy improving the mDVR model further.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.37 no.10
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• pp.953-959
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• 2013

In this study, a multibody dynamic model of the cervical spine was developed for a virtual in-vitro cadaveric experiment. The dynamic cervical spine model was reconstructed based on Korean CT images and the material properties of joints and soft tissue obtained from in-vitro experimental literature. The model was validated by comparing the inter-segmental rotation, multi-segmental rotations, load-displacement behavior, ligament force, and facet contact force with the published in-vitro experimental data. The results from the model were similar to published experimental data. The developed dynamic model of the cervical spine can be useful for injury analysis to predict the loads and deformations of the individual soft-tissue elements as well as for virtual in-vitro cadaveric experiments.