• Journal of the Computational Structural Engineering Institute of Korea
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• v.35 no.2
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• pp.101-107
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• 2022

This study proposes the deconvolution method for the nanoindentation test results of geopolymer employing the Gaussian mixture model. Geopolymer has been studied extensively as an alternative construction material because it emits relatively lower CO₂ compared to ordinary Portland cement. Geopolymer is made of aluminosilicate and alkaline solution, and the Si/Al molar ratio affects its mechanical properties. Previous studies revealed that the Si/Al molar ratio of 1.8~2.0 results in the highest compressive strength, and the Si/Al molar ratio over 1.8 degrades the compressive strength of geopolymer severely; however the reason for the compressive strength degradation is still unclear. To understand the effect of the Si/Al molar ratio on the geopolymer structure, this study exploits the nanoindentation. The phase deconvolution of the indent modulus data is successful using the Gaussian mixture model, and it is observed that the Si/Al molar ratio alters the homogeneity of the geopolymer. Geopolymer becomes more homogeneous up to an Si/Al molar ratio of 1.8 at which geopolymer exhibits the highest compressive strength. The examination of this study is assumed to be adopted as evidence of strength degradation by the Si/Al ratio higher than the optimum value.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.2
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• pp.184-189
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• 2013

In this paper, we propose the injurious moving picture filtering system using certain sounds contained in the injurious moving picture to filter injurious moving picture which is distributed without limitation in internet and internet storage space. For this purpose, the Gaussian Mixture Model which can well represent the characteristics of the sound, is used and frame level likelihood estimation is used to calculate the likelihood between filtering target data and the sound models. Also, the pruning method which can real-time proceed by reducing the comparing number of data, is applied for real-time processing, and MWMR method which showed good performance from existing speaker identification, is applied for the distinguish performance of high precision. In the identification experiment result, in case of the frame rate which is the proportion of total frame to high likelihood frame, is set to 50%, identification error rate is 6.06%, and in case of frame rate is set to 60%, error rate is 3.03%. As the result, the proposed system can distinguish between general and injurious moving picture effectively.

• The Journal of the Acoustical Society of Korea
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• v.26 no.8
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• pp.390-396
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• 2007

In this letter, we propose a novel approach to improve the performance of speech/music classification for the selectable mode vocoder(SMV) of 3GPP2 using the Gaussian mixture model(GMM) which is based on the expectation-maximization(EM) algorithm. We first present an effective analysis of the features and the classification method adopted in the conventional SMV. And then feature vectors which are applied to the GMM are selected from relevant Parameters of the SMV for the efficient speech/music classification. The performance of the proposed algorithm is evaluated under various conditions and yields better results compared with the conventional scheme of the SMV.

• International Journal of Highway Engineering
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• v.14 no.3
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• pp.77-85
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• 2012

Most of Automatic Accident Detection Algorithm has a problem of detecting an accident as traffic congestion. Actually, center's managers deal with accidents depend on watching CCTV or accident report by drivers even though they run the Automatic Accident Detection system. It is because of the system's detecting errors such as detecting non-accidents as accidents, and it makes decreasing in the system's overall reliability. It means that Automatic Accident Detection Algorithm should not only have high detection probability but also have low false alarm probability, and it has to detect accurate accident spot. The study tries to verify and evaluate the effectiveness of using Gaussian Mixture Model and individual vehicle tracking to adapt Accident Detection Algorithm to Center Management System by measuring accident detection probability and false alarm probability's frequency in the real accident.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.6
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• pp.725-730
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• 2007

Gray-level histogram-based threshold selection methods such as Otsu's method, Huang and Wang's method, and etc. have been widely used for the threshold selection in image processing. They are simple and effective, but take too much time to determine the optimal multilevel threshold values as the number of thresholds are increased. In this paper, we measure correlation between gray-levels by using the Gaussian function and define a Gaussian-type finite mixture distribution which is combination of the Gaussian distribution function with the gray-level histogram, and propose a fast and effective threshold selection method using it. We show the effectiveness of the proposed through experimental results applied it to three images and the efficiency though comparison of the computational complexity of the proposed with that of Otsu's method.

• Smart Media Journal
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• v.5 no.1
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• pp.78-87
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• 2016

The major problems of recent object tracking methods are related to the inefficient detection of moving objects due to occlusions, noisy background and inconsistent body motion. This paper presents a robust method for the detection and tracking of a moving in infrared animal videos. The tracking system is based on adaptive optical flow generation, Gaussian mixture and Kalman filtering. The adaptive Gaussian model of optical flow (GMOF) is used to extract foreground and noises are removed based on the object motion. Kalman filter enables the prediction of the object position in the presence of partial occlusions, and changes the size of the animal detected automatically along the image sequence. The presented method is evaluated in various environments of unstable background because of winds, and illuminations changes. The results show that our approach is more robust to background noises and performs better than previous methods.

• Korean Journal of Remote Sensing
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• v.35 no.1
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• pp.93-115
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• 2019

The availability of high-resolution laser scanning data and advanced machine learning algorithms has enabled an accurate potential rockfall source identification. However, the presence of other mass movements, such as landslides within the same region of interest, poses additional challenges to this task. Thus, this research presents a method based on an integration of Gaussian mixture model (GMM) and ensemble artificial neural network (bagging ANN [BANN]) for automatic detection of potential rockfall sources at Kinta Valley area, Malaysia. The GMM was utilised to determine slope angle thresholds of various geomorphological units. Different algorithms(ANN, support vector machine [SVM] and k nearest neighbour [kNN]) were individually tested with various ensemble models (bagging, voting and boosting). Grid search method was adopted to optimise the hyperparameters of the investigated base models. The proposed model achieves excellent results with success and prediction accuracies at 95% and 94%, respectively. In addition, this technique has achieved excellent accuracies (ROC = 95%) over other methods used. Moreover, the proposed model has achieved the optimal prediction accuracies (92%) on the basis of testing data, thereby indicating that the model can be generalised and replicated in different regions, and the proposed method can be applied to various landslide studies.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.371-373
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• 2015

This paper describes an analysis of hardware design conditions of moving object detection algorithm which is based on effective Gaussian mixture learning (EGML). The simulation model of EGML algorithm is implemented using OpenCV, and it is analyzed that the effects of parameter values on background learning time and moving object detection sensitivity for various images. In addition, optimal design conditions for hardware implementation of EGML-based MOD algorithm are extracted from fixed-point simulations for various bit-width parameters.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.5
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• pp.111-117
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• 2008

In this paper, we propose an approach to improve the performance of voiced/unvoiced (V/UV) decision under background noise environments for the selectable mode vocoder (SMV) of 3GPP2. We first present an effective analysis of the features and the classification method adopted in the SMV. And then feature vectors which are applied to the GMM are selected from relevant parameters of the SMV for the efficient voiced/unvoiced classification. For the purpose of evaluating the performance of the proposed algorithm, different experiments were carried out under various noise environments and yields better results compared with the conventional scheme of the SMV.

• The KIPS Transactions:PartB
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• v.12B no.4 s.100
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• pp.437-442
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• 2005

For the purpose of speaker normalization in speaker independent speech recognition systems, experiments are conducted on a method based on Gaussian mixture model(GMM). The method, which is an improvement of the previous study based on vector quantizer, consists of modeling the probability distribution of canonical feature vectors by a GMM with an appropriate number of clusters, and of estimating the warp factor of a test speaker by making use of the obtained probabilistic model. The purpose of this study is twofold: improving the existing ML based methods, and comparing the performance of what is called 'soft decision' method with that of the previous study based on vector quantizer. The effectiveness of the proposed method is investigated by recognition experiments on the TIMIT corpus. The experimental results showed that a little improvement could be obtained tv adjusting the number of clusters in GMM appropriately.