• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.6
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• pp.679-686
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• 2020

In this thesis, an enhanced method for the feature extraction of vocal source signals and score combination using an MCE-Based weight estimation of the score of multiple feature vectors are proposed for the performance improvement of speaker recognition systems. The proposed feature vector is composed of perceptual linear predictive cepstral coefficients, skewness, and kurtosis extracted with lowpass filtered glottal flow signals to eliminate the flat spectrum region, which is a meaningless information section. The proposed feature was used to improve the conventional speaker recognition system utilizing the mel-frequency cepstral coefficients and the perceptual linear predictive cepstral coefficients extracted with the speech signals and Gaussian mixture models. In addition, to increase the reliability of the estimated scores, instead of estimating the weight using the probability distribution of the convectional score, the scores evaluated by the conventional vocal tract, and the proposed feature are fused by the MCE-Based score combination method to find the optimal speaker. The experimental results showed that the proposed feature vectors contained valid information to recognize the speaker. In addition, when speaker recognition is performed by combining the MCE-based multiple feature parameter scores, the recognition system outperformed the conventional one, particularly in low Gaussian mixture cases.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.49 no.12
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• pp.685-690
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• 2000

Multiple-lightning impulse currents are a general feature of the lightning ground f=flash. It is therefore necessary for lightning arresters used in power systems to be estimated by applying not only a single-lightning impulse current but also a multiple-lightning impulse currents. This paper presents the effects of multiple-lightning impulse currents on deterioration of ZnO arrester blocks. The multiple-lightning impulse generator which can produce quadruple 8/20$[\mus]$ 5[kA] with separation time of 30~120[ms] is designed and fabricated. The total energy applied to the arrester block at each impulse is about 1,200[J]. In experiment, various parameters such as leakage current component, reference voltage, and temperature are measured with the number of applied impulse current. Also, micro-structure changes of the ZnO blocks after applying the single and the multiple-lightning impulse currents of 200 times are compared. The experimental results indicate that the types of arrester blocks are more vulnerable to deterioration or damage by multiple-lightning impulse currents.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1450-1463
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• 2019

The problem towards crowd behavior recognition in a serious clustered scene is extremely challenged on account of variable scales with non-uniformity. This paper aims to propose a crowed behavior classification framework based on a transferring hybrid network blending 3D res-net with inception-v3. First, the 3D res-inception network is presented so as to learn the augmented visual feature of UCF 101. Then the target dataset is applied to fine-tune the network parameters in an attempt to classify the behavior of densely crowded scenes. Finally, a transferred entropy function is used to calculate the probability of multiple labels in accordance with these features. Experimental results show that the proposed method could greatly improve the accuracy of crowd behavior recognition and enhance the accuracy of multiple label classification.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.5
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• pp.355-369
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• 2018

LiDAR (Light Detection And Ranging) strip adjustment is process to improve geo-referencing of the ALS (Airborne Laser Scanner) strips that leads to seamless LiDAR data. Multiple strips are required to collect data over the large areas, thus the strips are overlapped in order to ensure data continuity. The LSA (LiDAR Strip Adjustment) consists of identifying corresponding features and minimizing discrepancies in the overlapping strips. The corresponding features are utilized as control features to estimate transformation parameters. This paper applied SURF (Speeded Up Robust Feature) to identify corresponding features. To improve determination of the corresponding feature, false matching points were removed by applying three schemes: (1) minimizing distance of the SURF feature vectors, (2) selecting reliable matching feature with high cross-correlation, and (3) reflecting geometric characteristics of the matching pattern. In the strip adjustment procedure, corresponding points having large residuals were removed iteratively that could achieve improvement of accuracy of the LSA eventually. Only a few iterations were required to reach reasonably high accuracy. The experiments with simulated and real data show that the proposed method is practical and effective to airborne LSA. At least 80 % accuracy improvement was achieved in terms of RMSE (Root Mean Square Error) after applying the proposed schemes.

• Computers and Concrete
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• v.32 no.3
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• pp.233-246
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• 2023

Accurate prediction of concrete compressive strength can minimize the need for extensive, time-consuming, and costly mixture optimization testing and analysis. This study attempts to enhance the prediction accuracy of compressive strength using stacking ensemble machine learning (ML) with feature engineering techniques. Seven alternative ML models of increasing complexity were implemented and compared, including linear regression, SVM, decision tree, multiple layer perceptron, random forest, Xgboost and Adaboost. To further improve the prediction accuracy, a ML pipeline was proposed in which the feature engineering technique was implemented, and a two-layer stacked model was developed. The k-fold cross-validation approach was employed to optimize model parameters and train the stacked model. The stacked model showed superior performance in predicting concrete compressive strength with a correlation of determination (R²) of 0.985. Feature (i.e., variable) importance was determined to demonstrate how useful the synthetic features are in prediction and provide better interpretability of the data and the model. The methodology in this study promotes a more thorough assessment of alternative ML algorithms and rather than focusing on any single ML model type for concrete compressive strength prediction.

• Journal of Electrical Engineering and information Science
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• v.2 no.6
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• pp.20-27
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• 1997

We present a method of electromyographic(EMG) pattern recognition to identify motion commands for the control of a prosthetic arm by evidence accumulation with multiple parameters. Integral absolute value, variance, autoregressive(AR) model coefficients, linear cepstrum coefficients, and adaptive cepstrum vector are extracted as feature parameters from several time segments of the EMG signals. Pattern recognition is carried out through the evidence accumulation procedure using the distances measured with reference parameters. A fuzzy mapping function is designed to transform the distances for the application of the evidence accumulation method. Results are presented to support the feasibility of the suggested approach for EMG pattern recognition.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.505-508
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• 2008

Electrocardiogram (ECG) and blood pressure (BP) are main vital signs which are the standards in most medical settings in assessing the most basic body functions. Multi parameters are desired in providing more information for health professionals in order to detect or monitor medical problems of patients more precisely. This study urges us to develop a robust wireless healthcare monitoring system which has multiple physiological signs measurements on real time that applicable to various environments which integrates wireless sensor network technology and code division multiple access (CDMA) network with extended feature of locally standalone diagnosis algorithms that implemented in tell phone. ECG signal and BP parameter of the patients are routinely be monitored, processed and analyzed in details at cell phone locally to produce useful medical information to ease patients for tracking and future reference purposes. Any suspected or unknown patterns of signals will be immediately forwarded to hospital server using cell phone for doctors' evaluation. This feature enables the patients always recognize the importance of self-health checking so that the preventive actions can be taken earlier through this analytic information provided by this monitoring system because "Prevention is better than Cure".

• Journal of Internet Computing and Services
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• v.21 no.5
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• pp.21-29
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• 2020

Accurate estimation of human pose relies on backbone method in which its role is to extract feature map. Up to dated, the method of backbone feature extraction is conducted by the plain convolutional neural networks named by CNN and the residual neural networks named by Resnet, both of which have various architectures and performances. The CNN family network such as VGG which is well-known as a multiple stacked hidden layers architecture of deep learning methods, is base and simple while Resnet which is a bottleneck layers architecture yields fewer parameters and outperform. They have achieved inspired results as a backbone network in human pose estimation. However, they were used then followed by different pose estimation networks named by pose parsing module. Therefore, in this paper, we present a comparison between the plain CNN family network (VGG) and bottleneck network (Resnet) as a backbone method in the same pose parsing module. We investigate their performances such as number of parameters, loss score, precision and recall. We experiment them in the bottom-up method of human pose estimation system by adapted the pose parsing module of openpose. Our experimental results show that the backbone method using VGG network outperforms the Resent network with fewer parameter, lower loss score and higher accuracy of precision and recall.

• Current Optics and Photonics
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• v.4 no.6
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• pp.530-539
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• 2020

Hyperspectral images feature a relatively narrow band and are easily disturbed by noise. Accurate estimation of the types and parameters of noise in hyperspectral images can provide prior knowledge for subsequent image processing. Existing hyperspectral-noise estimation methods often pay more attention to the use of spectral information while ignoring the spatial information of hyperspectral images. To evaluate the noise in hyperspectral images more accurately, we have proposed a spectral-spatial cooperative noise-evaluation method. First, the feature of spatial information was extracted by Gabor-filter and K-means algorithms. Then, texture edges were extracted by the Otsu threshold algorithm, and homogeneous image blocks were automatically separated. After that, signal and noise values for each pixel in homogeneous blocks were split with a multiple-linear-regression model. By experiments with both simulated and real hyperspectral images, the proposed method was demonstrated to be effective and accurate, and the composition of the hyperspectral image was verified.

• The Journal of the Acoustical Society of Korea
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• v.18 no.8
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• pp.84-93
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• 1999

Determination of grain size of heat-treated stainless steel based fm digital ultrasonic signal processing technique is presented. This techniques consist in evidence accumulation with multiple feature parameters, difference absolute mean value(DAMV), variance(VAR), mean frequency (MEANF), auto regressive model coefficient(ARC) and linear cepstrum coefficient(LCC). Feature parameters were extracted from ultrasonic echo signal of heat-treated metals. It was found that a few parameters might not be sufficient to exactly evaluate the grain size of heat-treated metals. The determination of grain size of heat-treated metals was carried out through the evidence accumulation procedure using the distances measured with reference parameters. A fuzzy mapping function is designed to transform the distances for the application of the evidence accumulation method. In the work presented, heat-treated stainless steel samples with various grain sizes are examined. The processed experimental results supports the feasibility of the grain size determination technique presented.