• Journal of Sensor Science and Technology
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• v.32 no.2
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• pp.105-109
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• 2023

In this study, we present a power-efficient driving method for gas sensor systems based on the analysis of input signal characteristics. The analysis of the gas sensor output signal characteristics in the frequency domain shows that most of the signal portions are distributed in a relatively low frequency region when extracting the gas sensor signal, which can lead to further performance improvement of the gas sensor system. Therefore, the proposed gas signal extracting technique changes the operating frequency of the read-out circuit based on the frequency characteristics of the output signal of the gas sensor, resulting in a reduction of power consumption at the whole system level. The proposed sensing technique, which can be applied to a general-purpose commercial gas sensor system, was implemented in a printed circuit board (PCB) to verify its effectiveness at the commercial level.

• The Journal of the Acoustical Society of Korea
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• v.29 no.2
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• pp.133-140
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• 2010

We improve the performance of cardiac disorder classification by adding new temporal features extracted from continuous heart sound signals. We add three kinds of novel temporal features to a conventional feature based on mel-frequency cepstral coefficients (MFCC): Heart sound envelope, murmur probabilities, and murmur amplitude variation. In cardiac disorder classification and detection experiments, we evaluate the contribution of the proposed features to classification accuracy and select proper temporal features using the sequential feature selection method. The selected features are shown to improve classification accuracy significantly and consistently for neural network-based pattern classifiers such as multi-layer perceptron (MLP), support vector machine (SVM), and extreme learning machine (ELM).

• Proceedings of the Korea Information Processing Society Conference
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• 2007.11a
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• pp.117-120
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• 2007

개인의 체질에 맞춰 의학적 행위를 시행하는 사상의학은 우리나라 고유의 전통의학으로 가치를 인정받고 있다. 이러한 사상의학에서 가장 중요한 것은 사상체질의 정확한 분류이다. 본 논문에서는 기존의 사상체질 분류 방법인 용모사기, 체형기상, QSCCII, 체질침 등이 임상의들의 직관에 의해 행해지고 있다는 문제점을 해결하기 위해 사상체질 분류의 정량화 및 객관화를 위한 연구를 수행하였다. 이를 위해 본 논문에서는 음성 신호 분석에서 발생하는 정보의 출력값에 의해 사상 체질을 분류하는 방법을 제안하였다. 이를 위해 40대 이상의 장년기 남성을 대상으로 사상체질 전문의의 진단표에서 뚜렷한 특징을 보유하고 있는 집단군을 구성하고 이들의 음성 특성을 분류하여 음성학적 요소를 추출하고자 한다. 또한 출력된 결과값을 토대로 체질 집단별 차이점과 유사성을 분류하여 사상 체질 분류를 행하였다.

• Journal of the Korean Chemical Society
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• v.59 no.3
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• pp.225-232
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• 2015

A liquid chromatography-electrospray ionization-tandem mass spectrometry method (LC-ESI-MS/MS) was used for determining seven pesticides (2,4-dichlorophenoxyacetic acid, methomyl, aldicarb, 2-methyl- 4-chlorophenoxy- acetic acid, molinate, carbaryl and carbofuran) and two synthetic materials (quinoline and bisphenol-A) in surface water. The analytes were extracted using solid-phase extraction (SPE). The eluate was concentrated by nitrogen gas. 100 microliters of 30% (v/v) methanol aqueous solution were used to dissolve the residue and an aliquot of the reconstituted solution was directly injected into LC-ESI-MS/MS after the filtration using 0.2 μm polytetrafluoroethylene (PTFE) syringe filter. Under the established condition, the calibration curves of the analytes were linear with correlation coefficients of above 0.997. The quantification limit was 0.002~0.011 μg/L and the relative standard deviations were less than 16.4%. In addition, accuracy was in the range of 84~107% and the recoveries were values between 56.2 and 98.6%. In this study, the developed method was applied to the analysis of real surface water samples.

• 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 Journal of The Korea Institute of Intelligent Transport Systems
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• v.14 no.5
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• pp.23-29
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• 2015

In this study, human is the subject of driving a car, the actual EEG is a biological information in a number of reactions that are displayed while driving the vehicle by using a measuring device, occurs during travel of the road EEG to be collected, number of experiments the collected material on the basis of changes associated with running time, extracts the factors such as changes due to road geometry, and analysis was performed. The required changes in the EEG occurring during traveling experiment analysis alpha (${\alpha}$) waves, beta (${\beta}$) wave, after the primary extraction in the form of gamma (${\gamma}$) faction, the brain wave frequency of the entire period of the experiment change rate extracts, to calculate the change in frequency in response to EEG characteristics by applying the regression model to observe a learning effect in response to an increase in the number of experiments, as a result, depending on the number of experiments, EEG changes due to individual differences. The show, by repeatedly driving a section like this, it was possible to verify that comfortably travels driver accustomed in accordance with the stored road geometry and signal, safety facilities.

• Journal of the Korean Society of Food Science and Nutrition
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• v.46 no.6
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• pp.681-687
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• 2017

This study was focused on the anti-inflammatory activities of Annona muricata leaf ethanol extracts (AME). Inflammation of macrophages was induced by lipopolysaccharide (LPS) treatment, and various inflammation-mediated factors [cytokines and nitric oxide (NO)] were measured. AME treatment significantly reduced LPS-induced NO, cytokine levels [interleukin (IL)-6, tumor necrosis $factor-{\alpha}$ and $IL-1{\beta}$], and expression of inducible NO synthase and cyclooxygenase-2 in a dose-dependent manner. Mechanical studies showed that AME treatment inhibited activation of mitogen-activated protein kinase and nuclear factor $(NF)-{\kappa}B$ in macrophages treated with LPS. From these results, AME treatment strongly inhibits LPS-induced inflammation through inhibition of $NF-{\kappa}B$ activation, suggesting AME could be a potential candidate for treatment of inflammatory disease as a nutraceutical drug.

• Korean Journal of Remote Sensing
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• v.29 no.1
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• pp.21-34
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• 2013

Permanent Scatterer InSAR (PSInSAR) technique extracts permanent scatterers exhibiting high phase stability over the entire observation period and calculates precise time-series deformation at Permanent Scatterer (PS) points by using single master interferograms. This technique is not a good method to apply on nature environment such as forest area where permanent scatterers cannot be identified. Another muti-temporal Interferometric Synthetic Aperture Radar (InSAR), Small BAseline Subset (SBAS) technique using multi master interferograms with short baselines, can be effective to detect deformation in forest area. However, because of the error induced from phase unwrapping, the technique sometimes fails to estimate correct deformation from a stack of interferograms. To overcome those problems, we introduced new multi-temporal InSAR technique, called Temporarily Coherence Point InSAR (TCPInSAR), in this paper. This technique utilizes multi master interferograms with short baseline and without phase unwrapping. To compare with traditional multi-temporal InSAR techniques, we retrieved spatially changing deformation because PSs have been found enough in forest area with TCPInSAR technique and time-series deformation without phase unwrapping error. For this study, we acquired ERS-1 and ERS-2 SAR dataset on Augustine volcano, Alaska and detected deformation in study area for the period 1992-2005 with SBAS and TCPInSAR techniques.

• Journal of Broadcast Engineering
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• v.23 no.3
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• pp.421-430
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• 2018

Many voice recognition systems use methods such as MFCC, HMM to acknowledge human voice. This recognition method is designed to analyze only a targeted sound which normally appears between a human and a device one. However, the recognition capability is limited when there is a group sound formed with diversity in wider frequency range such as dog barking and indoor sounds. The frequency of overlapped sound resides in a wide range, up to 20KHz, which is higher than a voice. This paper proposes the new recognition method which provides wider frequency range by conjugating the Wideband Sound Spectrogram and the Keras Sequential Model based on DNN. The wideband sound spectrogram is adopted to analyze and verify diverse sounds from wide frequency range as it is designed to extract features and also classify as explained. The KSM is employed for the pattern recognition using extracted features from the WSS to improve sound recognition quality. The experiment verified that the proposed WSS and KSM excellently classified the targeted sound among noisy environment; overlapped sounds such as dog barking and indoor sounds. Furthermore, the paper shows a stage by stage analyzation and comparison of the factors' influences on the recognition and its characteristics according to various levels of noise.

• Journal of KIISE:Computing Practices and Letters
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• v.13 no.3
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• pp.165-178
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• 2007

The recent progress in multimedia signal processing and transmission technologies has contributed to the extensive use of multimedia devices to watch sports games with small LCD panel. However, the most of video sequences are captured for normal viewing on standard TV or HDTV, for cost reasons, merely resized and delivered without additional editing. This may give the small-display-viewers uncomfortable experiences in understanding what is happening in a scene. For instance, in a soccer video sequence taken by a long-shot camera techniques, the tiny objects (e.g., soccer ball and players) may not be clearly viewed on the small LCD panel. Moreover, it is also difficult to recognize the contents of the scorebox which contains the elapsed time and scores. This renuires intelligent display technique to provide small-display-viewers with better experience. To this end, one of the key technologies is to determine region of interest (ROI) and display the magnified ROI on the screen, where ROI is a part of the scene that viewers pay more attention to than other regions. Examples include a region surrounding a ball in long-shot and a scorebox located in the comer of each frame. In this paper, we propose a scheme for raising viewing experiences of multimedia mobile device users. Instead of taking generic approaches utilizing visually salient features for extraction of ROI in a scene, we take domain-specific approach to exploit unique attributes of the soccer video. The proposed scheme consists of two modules: ROI determination and scorebox extraction. The experimental results show that the proposed scheme offers useful tools for intelligent video display on multimedia mobile devices.