• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.14 no.1
• /
• pp.16-22
• /
• 2010

Electrocardiogram(ECG) analysis and arrhythmia recognition are critical for diagnosis and treatment of ill patients. Cardiac arrhythmia is a condition in which heart beat may be irregular and presents a serious threat to the patient recovering from ventricular tachycardia (VT) and ventricular fibrillation (VF). Other arrhythmias like atrial premature contraction (APC), Premature ventricular contraction (PVC) and superventricular tachycardia (SVT) are important in diagnosing the heart diseases. This paper presented new method to classify various arrhythmias contrary to other techniques which are limited to only two or three arrhythmias. ECG is decomposed into Intrinsic Mode Functions (IMFs) by Empirical Mode Decomposition (EMD). Burg algorithm was performed on IMFs to obtain AR coefficients which can reduce the dimension of feature vector and utilized as Multi-class SVM inputs which is basically extended from binary SVM. We chose optimal parameters for SVM classifier, applied to arrhythmias classification and achieved the accuracies of detecting NSR, APC, PVC, SVT, VT and VP were 96.8% to 99.5%. The results showed that EMD was useful for the preprocessing and feature extraction and multi-class SVM for classification of cardiac arrhythmias, with high usefulness.

• Journal of the Korean Institute of Telematics and Electronics S
• /
• v.36S no.6
• /
• pp.87-95
• /
• 1999

We propose a simulation model of 3-dimensional MPEG data over Asynchronous transfer Mode(ATM) networks. The model is based on a slice level and is named to Projected Vector Autoregressive(PVAR) model. The PVAR model is modeled using the Autoregressive(AR) model in order to meet the autocorrelation condition and fit the histogram, and maps real data by a projection function. For the projection function, we use the Cumulative Distribution Probability Function (CDPF), and the procedure is performed at each slice level. Our proposed model shows good performance in meeting the autocorrelation condition and fitting the histogram, and is found important in analyzing the performance of networks. In addiotion, we apply a smoothing method by which a periodic mean value. In general. the Quality of Service(QoS) depends on the Cell Loss Rate(CLR), which is related to the cell loss and a maximum delay in a buffer. Hence the proposed smoothing method can be used to improve the QoS.

• Journal of Aquaculture
• /
• v.9 no.1
• /
• pp.93-100
• /
• 1996

Previously, as an effort to make an autonomously replicating expression vector in fish, an ARS (autonomously replicating sequence) was cloned from MAR (matrix attachment region) of Misgurnus mizolepis. The DNA fragment composed of 443 base pairs contains ARS core consensus sequences, topoisomerase II consensus sequences, and A or T box sequences which are homologous to the known consensus sequences originated from other organisms. The clond ARS, as other DNA replication origins, contains inverted repeat sequences and several potential hairpin loop structures. These consensus sequences and hairpin structures may serve as recognition signals for regulatory proteins of DNA replication initiation.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.28 no.11C
• /
• pp.1140-1151
• /
• 2003

This paper proposes a face recognition method based on modified Otsu's binarization and Hu moment. Proposed method is robust to brightness, contrast, scale, rotation, and translation changes. As the proposed modified Otsu's binarization computes other thresholds from conventional Otsu's binarization, namely we create two binary images, we can extract higher dimensional feature vector. Here the feature vector has properties of robustness to brightness and contrast changes because the proposed method is based on Otsu's binarization. And our face recognition system is robust to scale, rotation, and translation changes because of using Hu moment. In the perspective of brightness, contrast, scale, rotation, and translation changes, experimental results with Olivetti Research Laboratory (ORL) database and the AR database showed that average recognition rates of conventional well-known principal component analysis (PCA) are 93.2% and 81.4%, respectively. Meanwhile, the proposed method for the same databases has superior performance of the average recognition rates of 93.2% and 81.4%, respectively.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.22 no.6
• /
• pp.768-773
• /
• 2012

One of the main reasons for serious road accidents is driving while drowsy. For this reason, drowsiness detection and warning system for drivers has recently become a very important issue. Monitoring physiological signals provides the possibility of detecting features of drowsiness and fatigue of drivers. One of the effective signals is to measure electroencephalogram (EEG) signals and electrooculogram (EOG) signals. The aim of this study is to extract drowsiness-related features from a set of EEG signals and to classify the features into three states: alertness, drowsiness, sleepiness. This paper proposes a drowsiness detection system using Linear Predictive Coding (LPC) coefficients and Support Vector Machine (SVM). Samples of EEG data from each predefined state were used to train the SVM program by using the proposed feature extraction algorithms. The trained SVM program was tested on unclassified EEG data and subsequently reviewed according to manual classification. The classification rate of the proposed system is over 96.5% for only very small number of samples (250ms, 64 samples). Therefore, it can be applied to real driving incident situation that can occur for a split second.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2008.11a
• /
• pp.561-562
• /
• 2008

The numerical analysis of sound radiation by vibrating structure is a well known and mature technology used in many industries. Accurate methods based on the boundary or finite element method have been successfully developed over the last two decades and are now available in standard CAE tools. These methods are however known to require significant computational resources which, furthermore, very quickly increase with the frequency of interest. The low speed of most current methods is a main obstacle for a systematic use of acoustic CAE in industrial design processes. In this paper we are going to present a set of innovative techniques that significantly speed-up the calculation of acoustic radiation indicators (acoustic pressure, velocity, intensity and power; contribution vectors). The modeling is based on the well known combination of finite elements and infinite elements but also combines the following ingredients to obtain a very high performance: o a multi-frontal massively parallel sparse direct solver; o a multi-frequency solver based on the Krylov method; o the use of pellicular acoustic modes as a vector basis for representing acoustic excitations; o the numerical evaluation of Green functions related to the specific geometry of the problem under investigation. All these ingredients are embedded in the ACTRAN/AR CAE tool which provides unprecedented performance for acoustic radiation analysis. The method will be demonstrated on several applications taken from various industries.

• Journal of The Korean Astronomical Society
• /
• v.33 no.1
• /
• pp.47-55
• /
• 2000

Nonpotential characteristics of magnetic fields in AR 5747 are examined using Mees Solar Observatory magnetograms taken on Oct. 20, 1989 to Oct. 22, 1989. The active region showed such violent flaring activities during the observational span that strong X-ray flares took place including a 2B/X3 flare. The magnetogram data were obtained by the Haleakala Stokes Polarimeter which provides simultaneous Stokes profiles of the Fe I doublet 6301.5 and 6302.5. A nonlinear least square method was adopted to derive the magnetic field vectors from the observed Stokes profiles and a multi-step ambiguity solution method was employed to resolve the $180^{\circ}$ ambiguity. From the ambiguity-resolved vector magnetograms, we have derived a set of physical quantities characterizing the field configuration, which are magnetic flux, vertical current density, magnetic shear angle, angular shear, magnetic free energy density, a measure of magnetic field discontinuity MAD and linear force-free coefficient. Our results show that (1) magnetic nonpotentiality is concentrated near the inversion line in the flaring sites, (2) all the physical parameters decreased with time, which may imply that the active region was in a relaxation stage of its evolution, (3) 2-D MAD has similar patterns with other nonpotential parameters, demonstrating that it can be utilized as an useful parameter of flare producing active region, and (4) the linear force-free coefficient could be a evolutionary indicator with a merit as a global nonpotential parameter.

• The Journal of the Acoustical Society of Korea
• /
• v.13 no.2
• /
• pp.50-59
• /
• 1994

This paper presents a speech recognition method utilizing third-order cumulants as a feature vector and a neural network for recognition. The use of higher-order cumulants provides desirable uncoupling between the gaussian noise and speech, which enables us to estimate the coefficients of AR model without bias. Unlike the conventional method using second-order statistics, the proposed one exhibits low bias even in SNR as low as 0 dB at the expense of higher variance. It is confirmed through computer simulation that recognition rate of korean single-vowels with the cumulant-based method is much higher than the results with the conventional method even in low SNR.

• Smart Structures and Systems
• /
• v.4 no.6
• /
• pp.759-777
• /
• 2008

A wireless sensing system is designed for application to structural monitoring and damage detection applications. Embedded in the wireless monitoring module is a two-tier prediction model, the auto-regressive (AR) and the autoregressive model with exogenous inputs (ARX), used to obtain damage sensitive features of a structure. To validate the performance of the proposed wireless monitoring and damage detection system, two near full scale single-story RC-frames, with and without brick wall system, are instrumented with the wireless monitoring system for real time damage detection during shaking table tests. White noise and seismic ground motion records are applied to the base of the structure using a shaking table. Pattern classification methods are then adopted to classify the structure as damaged or undamaged using time series coefficients as entities of a damage-sensitive feature vector. The demonstration of the damage detection methodology is shown to be capable of identifying damage using a wireless structural monitoring system. The accuracy and sensitivity of the MEMS-based wireless sensors employed are also verified through comparison to data recorded using a traditional wired monitoring system.

• Smart Structures and Systems
• /
• v.5 no.1
• /
• pp.95-117
• /
• 2009

In this paper, the time series based damage detection algorithms developed by Nair, et al. (2006) and Nair and Kiremidjian (2007) are applied to the benchmark experimental data from the National Center for Research on Earthquake Engineering (NCREE) in Taipei, Taiwan. Both acceleration and strain data are analyzed. The data are modeled as autoregressive (AR) processes, and damage sensitive features (DSF) and feature vectors are defined in terms of the first three AR coefficients. In the first algorithm developed by Nair, et al. (2006), hypothesis tests using the t-statistic are applied to evaluate the damaged state. A damage measure (DM) is defined to measure the damage extent. The results show that the DSF's from the acceleration data can detect damage while the DSF from the strain data can be used to localize the damage. The DM can be used for damage quantification. In the second algorithm developed by Nair and Kiremidjian (2007) a Gaussian Mixture Model (GMM) is used to model the feature vector, and the Mahalanobis distance is defined to measure damage extent. Additional distance measures are defined and applied in this paper to quantify damage. The results show that damage measures can be used to detect, quantify, and localize the damage for the high intensity and the bidirectional loading cases.