• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.27 no.6
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• pp.566-576
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• 2016

Inverse synthetic aperture radar(ISAR) image generated from bistatic radar(Bi-ISAR) represents two-dimensional scattering distribution of a target, and the Bi-ISAR can be used for bistatic target identification. However, Bi-ISAR has large variability in scattering mechanisms depending on bistatic configurations and do not represent exact range-Doppler information of a target due to inherent distortion. Thus, an efficient training DB construction is the most important factor in target identification using Bi-ISARs. Recently, a database construction method based on realistic flight scenarios of a target, which provides a reliable identification performance for the monostatic target identification, was applied to target identification using high resolution range profiles(HRRPs) generated from bistatic radar(Bi-HRRPs), to construct efficient training DB under bistatic configurations. Consequently, high identification performance was achieved using only small amount of training Bi-HRRPs, when the target is a considerable distance away from the bistatic radar. Thus, flight scenarios based training DB construction is applied to target identification using Bi-ISARs. Then, the capability and efficiency of the method is analyzed.

• Fire Science and Engineering
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• v.32 no.1
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• pp.108-115
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• 2018

The purpose of this study was to classify VR programs according to their characteristics and associations by the disaster management phase and firefighting school curriculum, and set priorities among VR fire training and training programs using AHP analysis., The relative priority is given considering its importance and urgency. As a result of the study, firefighting education and training classes showed the highest level of response. The relative priority of the subcategories was highest in the prevention stage. The simulation implementation for the evacuation experience was highest in closed spaces, such as a subway, and the simulation for the special firefighting vehicle was highest in the contrast phase. In the response phase, real fire simulation training was the highest. In the recovery phase, virtual reality training was the most effective in determining the fire situation based on the fire database.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.3
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• pp.135-138
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• 2014

This paper devices a facial expression recognition method robust to overfitting using 2D-DCT and EHMM algorithm. In particular, this paper achieves enhanced recognition performance by setting up a large window size for 2D-DCT feature extraction and extracting the observation vectors of EHMM. The experimental results on the CK facial expression database and the JAFFE facial expression database showed that the facial expression recognition accuracy was improved according as window size is large. Also, the proposed method revealed the recognition accuracy of 87.79% and showed enhanced recognition performance ranging from 46.01% to 50.05% in comparison to previous approaches based on histogram feature, when CK database is employed for training and JAFFE database is used to test the recognition accuracy.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.20 no.3
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• pp.289-295
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• 2009

HRR(High Resolution Range) profiles show one-dimensional radar images including electromagnetic scattering phenomena of a target. Thus, they are not only robust to noise, but also easily obtainable in a real-time. However, in order to construct a training database for the success of radar target identification, a huge amount of HRR profiles are needed because HRR profiles are highly dependent on the relative angle between the radar and the target. In order to alleviate this difficulty, a database construction method based on the scenarios of target's movement is proposed. The proposed method is able to provide a reliable target identification performance even with a small amount of training database.

• Journal of the Korea Society for Simulation
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• v.28 no.2
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• pp.159-167
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• 2019

In the field of defense M&S, we are actively pursuing researches that interoperable multiple war game models to simulate various combat environments at the same time. Although the 'unit DB(Database)' for operating the war game models is originated from the identical data, it has been recognized that the method of expressing the attribute of the data is different and the cross reference is impossible. As a result, it makes unnecessary time and effort in establishing the same unit DB in the organizations that operate the war game model. In this study, a method of reusing the unit DB of the training war game model to the analysis war game model with similar resolution and simulated logic was applied to the actual field. For this purpose, we defined the procedure for converting the unit DB by analyzing metadata of the 'Chang-Jo21', a combat training model for corps and division, and the 'Vision21', an analysis model for corps and division operation plan. And we introduced an algorithm that can map different metadata of two unit DBs. This study was meaningful as the first attempt to map and integrate heterogeneous metadata semantically for the reuse of unit DB between different war game models in defense M&S field. Also, it provided implications for the necessity of paradigm shift that reuse of the unit DB between two different war game models is possible and the need for standardization of the unit DB metadata in the defense M&S filed.

• Journal of Korea Multimedia Society
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• v.13 no.3
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• pp.369-377
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• 2010

In vocabulary recognition systems based on HMM(Hidden Markov Model)s, training process unseen model bring on show a low recognition rate. If recognition vocabulary modify and make an addition then recreated modeling of executed database collected and training sequence on account of bring on additional expenses and take more time. This study suggest efficient context dependent process modeling method using decision tree-based state tying. On study suggest method is reduce recreated of model and it's offered that robustness and accuracy of context dependent acoustic modeling. Also reduce amount of model and offered training process unseen model as concerns context dependent a likely phoneme model has been used unseen model solve the matter. System performance as a result of represent vocabulary dependence recognition rate of 98.01%, vocabulary independence recognition rate of 97.38%.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.20 no.9
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• pp.18-23
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• 2006

This paper presents an operator training simulator for power system restoration against massive blackout. The system is designed especially focused on the generality and convenient setting up for initial condition of simulation. The former is accomplished by using power flow calculation methodology, and PSS/E data is used to set up the initial state for easy setting. The proposed simulator consists of three major components-a power flow(PF), a data conversion(CONV), and, a GUI module. The PF module calculates power flow, and then checks over-voltages of buses and overloads of lines. The CONV module composes a Y-Bus array and a database at each restoration action. The initial Y-Bus array is composed from PSS/E data. A user friendly GUI module is developed including a graphic editor and a built-in operation manual. The maximum processing time for one step operation is 15 seconds, which is adequate for training purpose.

• Proceedings of the KSRS Conference
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• v.2
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• pp.710-712
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• 2006

The aim of this study was to analyze limestone deposits potential using an artificial neural network and a Geographic Information System (GIS) environment to identify areas that have not been subjected to the same degree of exploration. For this, a variety of spatial geological data were compiled, evaluated and integrated to produce a map of potential deposits in the Gangreung area, Korea. A spatial database considering deposit, topographic, geologic, geophysical and geochemical data was constructed for the study area using a GIS. The factors relating to 44 limestone deposits were the geological data, geochemical data and geophysical data. These factors were used with an artificial neural network to analyze mineral potential. Each factor’s weight was determined by the back-propagation training method. Training area was applied to analyze and verify the effect of training. Then the mineral deposit potential indices were calculated using the trained back-propagation weights, and potential map was constructed from GIS data. The mineral potential map was then verified by comparison with the known mineral deposit areas. The verification result gave accuracy of 87.31% for training area.

• Proceedings of the KSPS conference
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• 2006.05a
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• pp.37-40
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• 2006

In this paper, we propose a VQ codebook design of speech recognition feature parameters in order to improve the performance of a distributed speech recognition system. For the context-dependent HMMs, a VQ codebook should be correlated with phonetic distributions in the training data for HMMs. Thus, we focus on a selection method of training data based on phonetic distribution instead of using all the training data for an efficient VQ codebook design. From the speech recognition experiments using the Aurora 4 database, the distributed speech recognition system employing a VQ codebook designed by the proposed method reduced the word error rate (WER) by 10% when compared with that using a VQ codebook trained with the whole training data.

• Journal of the Korea institute for structural maintenance and inspection
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• v.7 no.1
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• pp.267-277
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• 2003

The purpose of this study makes a retrofit and rehabilitation practice trough the analysis and the improvement for the underlying problem of current retrofit and rehabilitation methods. Therefore, the deterioration process, the damage cause, the condition classification, the fatigue mechanism and the applied quantity of strengthening methods for slab bridge decks were analysed. Artificial neural networks are efficient computing techniqures that are widely used to solve complex problems in many fields. In this study, a back-propagation neural network model for estimating a management on existing slab bridge decks from damage cause, damage type, and integrity assessment at the initial stsge is need. The training and testing of the network were based on a database of 36. Four different network models werw used to study the ability of the neural network to predict the desirable output of increasing degree of accuracy. The neural networks is trained by modifying the weights of the neurons in response to the errors between the actual output values and the target output value. Training was done iteratively until the average sum squared errors over all the training patterms were minimized. This generally occurred after about 5,000 cycles of training.