• Bulletin of the Korean Chemical Society
• /
• v.20 no.9
• /
• pp.1021-1025
• /
• 1999

Near-infrared (NIR) spectroscopy has been successfully utilized for the rapid identification of six typical petroleum products such as light straight-run (LSR), naphtha, kerosine, light gas oil (LGO), gasoline, and diesel. The spectral features of each product were reasonably differentiated in the NIR region, and the spectral differences provided enough qualitative spectral information for discrimination. For discrimination, principal component analysis (PCA) combined with Mahalanobis distance was used to identify each petroleum product from NIR spectra. The results showed that each product was accurately identified with an accuracy over 95%. Most noticeably, LSR, kerosine, gasoline, and diesel samples were predicted with identification accuracy of 99%. The overall results ensure that a portable NIR instrument combined with a multivariate qualitative discrimination method can be efficiently utilized for rapid and simple identification of petroleum products. This is especially important when local at-site measurements are necessary, such as accidental petroleum leakage and regulation of illegal product blending.

• Analytical Science and Technology
• /
• v.17 no.2
• /
• pp.192-197
• /
• 2004

The discrimination of bracken (Korean vs. Chinese) has been attempted using capillary electrophoresis (CE). Bracken (Pteridium aquilinum) was extracted with 30% methanol and separated on a uncoated fused-silica ($50{\mu}m{\times}27cm$) capillary. Conditions for optimal analysis include: temperature, $40^{\circ}C$; voltage, 8 kV; and pressure injection time, 5 sec. The optimal separation buffer was 0.3 M borate buffer (pH 8.5) containing 40 mM CHAPS with 30% ethylene glycol. Under the optimal conditions established for CE, the ratio of specific peak area (peak PA-1) to other peak area (peak PA-2) was effective in discrimination of Korean and Chinese bracken. The mean accuracy for discrimination of Korean and Chinese brackens were 80% and 86%, respectively.

• Nuclear Engineering and Technology
• /
• v.55 no.9
• /
• pp.3359-3366
• /
• 2023

In this study, a spiking cortical model (SCM) based n-g discrimination method is proposed. The SCM-based algorithm is compared with three other methods, namely: (i) the pulse-coupled neural network (PCNN), (ii) the charge comparison, and (iii) the zero-crossing. The objective evaluation criteria used for the comparison are the FoM-value and the time consumption of discrimination. Experimental results demonstrated that our proposed method outperforms the other methods significantly with the highest FoM-value. Specifically, the proposed method exhibits a 34.81% improvement compared with the PCNN, a 50.29% improvement compared with the charge comparison, and a 110.02% improvement compared with the zero-crossing. Additionally, the proposed method features the second-fastest discrimination time, where it is 75.67% faster than the PCNN, 70.65% faster than the charge comparison and 38.4% slower than the zero-crossing. Our study also discusses the role and change pattern of each parameter of the SCM to guide the selection process. It concludes that the SCM's outstanding ability to recognize the dynamic information in the pulse signal, improved accuracy when compared to the PCNN, and better computational complexity enables the SCM to exhibit excellent n-γ discrimination performance while consuming less time.

• MALSORI
• /
• no.46
• /
• pp.37-50
• /
• 2003

In this paper, we evaluate and compare the performance of speech/music discrimination based on various feature parameters and classifiers. As for feature parameters, we consider High Zero Crossing Rate Ratio (HZCRR), Low Short Time Energy Ratio (LSTER), Spectral Flux (SF), Line Spectral Pair (LSP) distance, entropy and dynamism. We also examine three classifiers: k Nearest Neighbor (k-NN), Gaussian Mixure Model (GMM), and Hidden Markov Model (HMM). According to our experiments, LSP distance and phoneme-recognizer-based feature set (entropy and dunamism) show good performance, while performance differences due to different classifiers are not significant. When all the six feature parameters are employed, average speech/music discrimination accuracy up to 96.6% is achieved.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.30 no.3C
• /
• pp.125-132
• /
• 2005

This paper describes a computer vision system based on active IR illumination for real-time gaze discrimination system. Unlike most of the existing gaze discrimination techniques, which often require assuming a static head to work well and require a cumbersome calibration process for each person, our gaze discrimination system can perform robust and accurate gaze estimation without calibration and under rather significant head movement. This is made possible by a new gaze calibration procedure that identifies the mapping from pupil parameters to screen coordinates using generalized regression neural networks (GRNNs). With GRNNs, the mapping does not have to be an analytical function and head movement is explicitly accounted for by the gaze mapping function. Futhermore, the mapping function can generalize to other individuals not used in the training. To further improve the gaze estimation accuracy, we employ a reclassification scheme that deals with the classes that tend to be misclassified. This leads to a 10% improvement in classification error. The angular gaze accuracy is about 5°horizontally and 8°vertically. The effectiveness of our gaze tracker is demonstrated by experiments that involve gaze-contingent interactive graphic display.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.3 no.1
• /
• pp.38-46
• /
• 2010

This paper describes a computer vision system based on active IR illumination for real-time gaze discrimination system. Unlike most of the existing gaze discrimination techniques, which often require assuming a static head to work well and require a cumbersome calibration process for each person, our gaze discrimination system can perform robust and accurate gaze estimation without calibration and under rather significant head movement. This is made possible by a new gaze calibration procedure that identifies the mapping from pupil parameters to screen coordinates using generalized regression neural networks (GRNNs). With GRNNs, the mapping does not have to be an analytical function and head movement is explicitly accounted for by the gaze mapping function. Furthermore, the mapping function can generalize to other individuals not used in the training. To further improve the gaze estimation accuracy, we employ a reclassification scheme that deals with the classes that tend to be misclassified. This leads to a 10% improvement in classification error. The angular gaze accuracy is about $5^{\circ}$horizontally and $8^{\circ}$vertically. The effectiveness of our gaze tracker is demonstrated by experiments that involve gaze-contingent interactive graphic display.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.9 no.2
• /
• pp.322-329
• /
• 2005

This paper describes a computer vision system based on active IR illumination for real-time gaze discrimination system. Unlike most of the existing gaze discrimination techniques, which often require assuming a static head to work well and require a cumbersome calibration process for each person, our gaze discrimination system can perform robust and accurate gaze estimation without calibration and under rather significant head movement. This is made possible by a new gaze calibration procedure that identifies the mapping from pupil parameters to screen coordinates using generalized regression neural networks (GRNNS). With GRNNS, the mapping does not have to be an analytical function and head movement is explicitly accounted for by the gaze mapping function. furthermore, the mapping function can generalize to other individuals not used in the training. To further improve the gaze estimation accuracy, we employ a reclassification scheme that deals with the classes that tend to be misclassified. This leads to a 10$\%$ improvement in classification error. The angular gaze accuracy is about $5^{circ}$horizontally and $8^{circ}$vertically. The effectiveness of our gaze tracker is demonstrated by experiments that involve gaze-contingent interactive graphic display.

• Phonetics and Speech Sciences
• /
• v.10 no.1
• /
• pp.75-81
• /
• 2018

Diplophonia is generally defined as the perception of more than one fundamental frequency component in a voice. Its perceptual aspect has traditionally been used to evaluate diplophonia because the perceptions can be easily evaluated, but there are limitations in the validity of the reliability of the intra- and inter-raters, examination situation, and variation of voice sample. Therefore, the purpose of this study is to confirm the reliability and accuracy of auditory perceptual evaluation by comparing non-invasive indirect assessment methods (sound waveform and EGG analysis), and to identify their usefulness with diplophonia. A total of 28 diplophonic voices and 39 non-periodic voices were assessed. Three raters assessed the diplophonia by performing an auditory perception evaluation and identifying the quasi-periodic perturbations of the acoustic waveform and EGG. Among the three discrimination methods, intra- and inter-rater reliability, sensitivity, specificity, accuracy, positive likelihood ratio, and negative likelihood ratio were examined, and the McNemar test was performed to compare the discriminant agreement. The accuracy of the auditory perceptual evaluation (86.57%) was not significantly different from that of sound waveform acoustic (88.06%), but it was significantly different from that of EGG (83.33%). The reading time (6.02 s) for the auditory perceptual evaluation was significantly different from that for sound waveform analysis (30.15 s) and EGG analysis (16.41 s). In the discrimination of diplophonia, auditory perceptual evaluation has sufficient reliability and accuracy as compared to sound waveform and EGG. Since immediate feedback is possible, auditory perceptual evaluation is more convenient. Therefore, it can continue to be used as a tool to discriminate diplophonia in clinical practice.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.27 no.2
• /
• pp.1-13
• /
• 2002

In this paper, we compares the business failure prediction accuracy among Linear Programming Discriminant Analysis(LPDA) model, Multivariate Discriminant Analysis (MDA) model and logit analysis model. The Data for 417 companies analyzed were gathered from KIS-FAS Published by Korea Information Service in 1999. The result of comparison for four time horizons shows that LPDA Is advantageous in prediction accuracy over the other two models when over all tilt ratio and business failure accuracy are considered simultaneously.

• Journal of the Microelectronics and Packaging Society
• /
• v.31 no.2
• /
• pp.45-53
• /
• 2024

The maintenance of semiconductor equipment is crucial for the continuous growth of the semiconductor market. System management is imperative given the anticipated increase in the capacity and complexity of industrial equipment. Ensuring optimal operation of manufacturing processes is essential to maintaining a steady supply of numerous parts. Particularly, monitoring the status of substrate transfer robots, which play a central role in these processes, is crucial. Diagnosing failures of their major components is vital for preventive maintenance. Fault diagnosis methods can be broadly categorized into physics-based and data-driven approaches. This study focuses on data-driven fault diagnosis methods due to the limitations of physics-based approaches. We propose a methodology for data acquisition and preprocessing for robot fault diagnosis. Data is gathered from vibration sensors, and the data preprocessing method is applied to the vibration signals. Subsequently, the dataset is trained using Gradient Tree-based XGBoost machine learning classification algorithms. The effectiveness of the proposed model is validated through performance evaluation metrics, including accuracy, F1 score, and confusion matrix. The XGBoost classifiers achieve an accuracy of approximately 92.76% and an equivalent F1 score. ROC curves indicate exceptional performance in class discrimination, with 100% discrimination for the normal class and 98% discrimination for abnormal classes.