• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.5
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• pp.16-23
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• 2011

Muzzle flash based on gunnery is the target that has huge energy. So, gunnery target in a long range over xx km is distinguishable in the IR(infrared) images, on the other hand, is not distinguishable in the CCD images. In this paper, we propose the classification method of gunnery targets in a infrared images and in a long range. The energy from gunnery have an effect on varous pixel values in infrared images as a property of infrared image sensor, distance, and atmosphere, etc. For this reason, it is difficult to classify gunnery targets using pixel values in infrared images. In proposed method, we take the profile of pixel values using high performance infrared sensor, and classify gunnery targets using modeling GMM and shape of profile. we experiment on the proposed method with infrared images in the ground and aviation. In experimental result, the proposed method provides about 93% classification rate.

• The Journal of the Korea Contents Association
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• v.10 no.7
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• pp.31-39
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• 2010

In this study, we prepared the real environmental sound data sets arising from people's movement comprising 9 different environment types. The environmental sounds are pre-processed with pre-emphasis and Hamming window, then go into the classification experiments with the extracted features using MFCC (Mel-Frequency Cepstral Coefficients). The GMM (Gaussian Mixture Model) classifier without post processing tends to yield abruptly changing classification results since it does not consider the results of the neighboring frames. Hence we proposed the post processing methods which suppress abruptly changing classification results by taking the probability or the rank of the neighboring frames into account. According to the experimental results, the method using the probability of neighboring frames improve the recognition performance by more than 10% when compared with the method without post processing.

• Journal of Korea Game Society
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• v.6 no.1
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• pp.3-10
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• 2006

In computer games, the level design and balance of characters are the key features for developing interesting games. Level designers make decision to change the parameters and opponent behaviors in order to avoid the player getting extremely frustrated with the improper level. Generally, opponent behavior is defined by static script, this causes the games to have static difficulty level and static environment. Therefore, it is difficult to keep track of the user playing interest, because a player can easily adapt to changeless repetition. In this paper, we propose a dynamic scripting method that able to maintain the level designers' intention where user enjoys the game by adjusting the opponent behavior while playing the game. The player's countermeasure pattern for dynamic level design is modeled using a Gaussian Mixture Model (GMM). The proposed method is applied to a shooting game, and the experimental results maintain the degree of interest intended by the level designer.

• Journal of Korean Society of Forest Science
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• v.112 no.1
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• pp.32-39
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• 2023

Using the logarithmic methods and the generalized method of moments (GMM), this study developed carbon storage equations for maple trees (Acer palmatum Thunb.) planted in an urban settlement area. A total of 20 maple trees of various ages and diameters were destructively harvested to determine their dry weight and carbon concentration by component. The allometric equations with DBH and DBH²×H as independent variables were developed to estimate the carbon storage for each tree component. The carbon concentration of tree components was the highest in stem wood (49.8%) and lowest in stem bark (46.5%). Allometric equations to estimate the carbon storage of tree components (stem, root, aboveground, and total) showed a similar coefficient of determinations (R²) between the allometric equations of the logarithmic method (0.7494-0.9036) and the GMM (0.7085-0.8847). However, the R² values of the leaves and branches were in the range of 0.3027 to 0.6380, lower than those of the R² of the other tree components. These results indicate that the carbon storage of maple trees growing in urban settlement areas can be efficiently predicted from the equations of GMM methods in the case of a small sample size or the heteroscedasticity of logarithmic equations.

• Journal of Environmental Impact Assessment
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• v.19 no.4
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• pp.421-429
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• 2010

The reliability of the measurement of ambient trace species is an important issue, especially, in background area such as Gosan in Jeju Island. In a previous episodic study, it was suggested that the PM10 measurement result by the gravimetric method(GMM) was not in agreement with the result by the ${\beta}$-ray absorption method(BAM). In this study, a systematic comparison was carried out for the data between 2001 and 2008 at Gosan(GMM and BAM) and Jeju city (BAM) which is near to Gosan. It was found that at Gosan the PM10 concentration by BAM was higher than GMM and the correlation between them was low. The BAM results at Gosan and Jeju city showed similar trend implying the discrepancy at Gosan was not caused by instrumental problem of the BAM at Gosan. Based on the previous studies two probable reasons for the discrepancy are identified; (1) negative measurement error by the evaporation of volatile ambient species at the filter in GMM such as nitrate and ammonium and (2) positive error by the absorption of water vapor during measurement in BAM. There was no heater at the inlet of BAM at Gosan during the sampling period. Based on the size-segregated measurement data, it was identified that the evaporation error was minor, if any. The relationship between the two methods did not vary with the ambient relative humidity. Thus, at present, it is not clear why the discrepancy had been occurring and when using the PM10 data at Gosan, one should be aware the possible errors.

• Smart Structures and Systems
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• v.24 no.2
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• pp.243-256
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• 2019

Determination of the most meaningful structural modes and gaining insight into how these modes evolve are important issues for long-term structural health monitoring of the long-span bridges. To address this issue, modal parameters identified throughout the life of the bridge need to be compared and linked with each other, which is the process of mode tracking. The modal frequencies for a long-span bridge are typically closely-spaced, sensitive to the environment (e.g., temperature, wind, traffic, etc.), which makes the automated tracking of modal parameters a difficult process, often requiring human intervention. Machine learning methods are well-suited for uncovering complex underlying relationships between processes and thus have the potential to realize accurate and automated modal tracking. In this study, Gaussian mixture model (GMM), a popular unsupervised machine learning method, is employed to automatically determine and update baseline modal properties from the identified unlabeled modal parameters. On this foundation, a new mode tracking method is proposed for automated mode tracking for long-span bridges. Firstly, a numerical example for a three-degree-of-freedom system is employed to validate the feasibility of using GMM to automatically determine the baseline modal properties. Subsequently, the field monitoring data of a long-span bridge are utilized to illustrate the practical usage of GMM for automated determination of the baseline list. Finally, the continuously monitoring bridge acceleration data during strong typhoon events are employed to validate the reliability of proposed method in tracking the changing modal parameters. Results show that the proposed method can automatically track the modal parameters in disastrous scenarios and provide valuable references for condition assessment of the bridge structure.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.5
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• pp.239-246
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• 2023

In this paper, we propose an open-loop algorithm to classify speech and music signals using the spectral flux parameters and Mel Frequency Cepstral Coefficients(MFCC) parameters for the audio coder. To increase responsiveness, the MFCC was used as a short-term feature parameter and spectral fluxes were used as a long-term feature parameters to improve accuracy. The overall voice/music signal classification decision is made by combining the short-term classification method and the long-term classification method. The Gaussian Mixed Model (GMM) was used for pattern recognition and the optimal GMM parameters were extracted using the Expectation Maximization (EM) algorithm. The proposed long-term and short-term combined speech/music signal classification method showed an average classification error rate of 1.5% on various audio sound sources, and improved the classification error rate by 0.9% compared to the short-term single classification method and 0.6% compared to the long-term single classification method. The proposed speech/music signal classification method was able to improve the classification error rate performance by 9.1% in percussion music signals with attacks and 5.8% in voice signals compared to the Unified Speech Audio Coding (USAC) audio classification method.

• Journal of Industrial Technology
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• v.38 no.1
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• pp.41-52
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• 2018

Recently, object detection is a critical function for any system that uses computer vision and is widely used in various fields such as video surveillance and self-driving cars. However, the conventional methods can not detect the objects clearly because of the dynamic background change in the beach. In this paper, we propose a new technique to detect humans correctly in the dynamic videos like shores. A new background modeling method that combines spatial GMM (Gaussian Mixture Model) and temporal GMM is proposed to make more correct background image. Also, the proposed method improve the accuracy of people detection by using SVM (Support Vector Machine) to classify people from the objects and KCF (Kernelized Correlation Filter) Tracker to track people continuously in the complicated environment. The experimental result shows that our method can work well for detection and tracking of objects in videos containing dynamic factors and situations.

• Journal of Biomedical Engineering Research
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• v.33 no.2
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• pp.65-71
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• 2012

In this paper, the Gaussian Mixture Model(GMM) which is very robust modeling for pattern classification is proposed to classify wrist motions using surface electromyograms(EMG). EMG is widely used to recognize wrist motions such as up, down, left, right, rest, and is obtained from two electrodes placed on the flexor carpi ulnaris and extensor carpi ulnaris of 15 subjects under no strain condition during wrist motions. Also, EMG-based feature is derived from extracted EMG signals in time domain for fast processing. The estimated features based in difference absolute mean value(DAMV) are used for motion classification through GMM. The performance of our approach is evaluated by recognition rates and it is found that the proposed GMM-based method yields better results than conventional schemes including k-Nearest Neighbor(k-NN), Quadratic Discriminant Analysis(QDA) and Linear Discriminant Analysis(LDA).

• Journal of the Korea Convergence Society
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• v.7 no.4
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• pp.1-7
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• 2016

Structural MRI(sMRI) imaging is used to extract morphometric features after Grey Matter (GM), White Matter (WM) for several univariate and multivariate method, and Cerebro-spinal Fluid (CSF) segmentation. A new approach is applied for the diagnosis of very mild to mild AD. We propose the classification method of Alzheimer disease patients from normal controls by combining morphometric features and Gaussian Mixture Models parameters along with MMSE (Mini Mental State Examination) score. The combined features are fed into Multi-kernel SVM classifier after getting rid of curse of dimensionality using principal component analysis. The experimenral results of the proposed diagnosis method yield up to 96% stratification accuracy with Multi-kernel SVM along with high sensitivity and specificity above 90%.