• KIPS Transactions on Software and Data Engineering
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• v.3 no.10
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• pp.451-456
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• 2014

The correlations between electroencephalographic (EEG) spectral power and emotional responses during affective sound clip listening are important parameters. Hemispheric asymmetry in prefrontal activation have been proposed in two decades ago, as measured by power value, is related to reactivity to affectively pleasure audio stimuli. In this study, we designed an emotional audio stimulus experiment in order to verify frontal EEG asymmetry by analyzing Event-related Spectral Perturbation (ERSP) results. Thirty healthy college male students volunteered the stimulus experiment with the standard IADS(International Affective Digital Sounds) clips. These affective sound clips are classified in three emotion states, high pleasure-high arousal (happy), middle pleasure-low arousal (neutral) and low pleasure-high arousal (fear). The analysis of the data was performed in both theta (4-8Hz) and alpha (8-13Hz) bands. ERSP maps in the alpha band revealed that there are the stronger power responses of high pleasure (happy) in the right frontal lobe, while the stronger power responses of middle-low pleasure (neutral and fear) in the left frontal lobe. Moreover, ERSP maps in the theta band revealed that there are the stronger power responses of high arousal (fear and happy) in the left pre-frontal lobe, while the stronger responses of low arousal (neutral) in the right pre-frontal lobe. However, the high pleasure emotions (happy) can elicit greater relative right EEG activity, while the low and middle pleasure emotions (fear and neutral) can elicit the greater relative left EEG activity. Additionally, the most differences of theta band have been found out in the medial frontal lobe, which is proved as the frontal midline theta. And there are the strongest responses of happy sounds in the alpha band around the whole frontal regions. These results are well suited for emotion recognition, and provide the evidences that theta and alpha powers may have the more important role in the emotion processing than previously believed.

• The Korean Journal of Applied Statistics
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• v.35 no.2
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• pp.229-250
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• 2022

Since the publication of Markowitz's (1952) mean-variance portfolio model, research on portfolio optimization has been conducted in many fields. The existing mean-variance portfolio model forms a nonlinear convex problem. Applying Dantzig's linear programming method, it was converted to a linear form, which can effectively reduce the algorithm computation time. In this paper, we proposed a Dantzig perturbation portfolio model that can reduce management costs and transaction costs by constructing a portfolio with stable and small (sparse) assets. The average return and risk were adjusted according to the purpose by applying a perturbation method in which a certain part is invested in the existing benchmark and the rest is invested in the assets proposed as a portfolio optimization model. For a covariance estimation, we proposed a Gaussian kernel weight covariance that considers time-dependent weights by reflecting time-series data characteristics. The performance of the proposed model was evaluated by comparing it with the benchmark portfolio with 5 real data sets. Empirical results show that the proposed portfolios provide higher expected returns or lower risks than the benchmark. Further, sparse and stable asset selection was obtained in the proposed portfolios.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.9
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• pp.679-685
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• 2016

This paper addresses an observer-based output-feedback sampled-data controller design problem for nonlinear systems in Takagi-Sugeno (T-S) form including singular perturbations. The design condition is represented in terms of linear matrix inequalities. The separation principle is also investigated.

• Phonetics and Speech Sciences
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• v.1 no.4
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• pp.147-157
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• 2009

The purpose of the study is to develop the normal voice database and to analyze the acoustic characteristics of Korean adults' voices by gender using MDVP. Eight categories in the 34 parameters of MDVP were analyzed in the voices of 170 Korean normal adults taken from /a/ vowel. Among them, Fundamental Frequency Parameters and Frequency Perturbation Parameters were significantly different by gender. In addition, Fundamental Frequency Parameters of our data were remarkably different from the data suggested in the MDVP program which currently used in clinics. Therefore, the data obtained from the current study can be effectively used for the diagnosis of voice disorders of Korean adults as the standard parameter values of MDVP.

• Journal of Communications and Networks
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• v.4 no.3
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• pp.189-198
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• 2002

Precoder has been shown to be able to provide source diversity and design flexibility. In this paper we employ precoding techniques for block transmission based on a multirate filterbank structure. To accommodate multiuser communication with variable data rates, different precoders with corresponding coefficients and up/down sampling rates are used. However, due to unknown multipath distortion, different interferences may exist in the received data, such as multiuser interference, intersymbol interference and interblock interference. To estimate channel parameters for a desired user, we employ all structured signature waveforms associated with different symbols of that user and apply subspace techniques. Therefore better performance of channel estimator can be achieved than the conventional subspace method based only on the signature of the current symbol. The delay for that user can also be jointly estimated. Channel identifiability conditions and asymptotic channel estimation error are investigated in detail. Numerical examples are provided to justify the proposed method. gest either multicode (MC) or multiple processing gain (MPG) mechanism [2], while requiring data rates to be integral multiples of some basic low-rate. In order to support variable rate transmission however, a comprehensive scheme needs to be investigated.

• Communications for Statistical Applications and Methods
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• v.23 no.6
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• pp.445-466
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• 2016

Nonparametric Bayesian methods have seen rapid and sustained growth over the past 25 years. We present a gentle introduction to the methods, motivating the methods through the twin perspectives of consistency and false consistency. We then step through the various constructions of the Dirichlet process, outline a number of the basic properties of this process and move on to the mixture of Dirichlet processes model, including a quick discussion of the computational methods used to fit the model. We touch on the main philosophies for nonparametric Bayesian data analysis and then reanalyze a famous data set. The reanalysis illustrates the concept of admissibility through a novel perturbation of the problem and data, showing the benefit of shrinkage estimation and the much greater benefit of nonparametric Bayesian modelling. We conclude with a too-brief survey of fancier nonparametric Bayesian methods.

• Communications for Statistical Applications and Methods
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• v.27 no.2
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• pp.225-239
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• 2020

Analysis approaches for single compositional data are well established; however, effective analysis strategies for paired compositional data remain to be investigated. The current project was motivated by studies of age-related hearing loss (presbyacusis), where subjects are classified into four audiometric phenotypes that need to be ranked within these phenotypes based on their paired compositional data. We address this challenge by formulating this problem as a classification problem and integrating a penalized multinomial logistic regression model with compositional data analysis approaches. We utilize Elastic Net for a penalty function, while considering average, absolute difference, and perturbation operators for compositional data. We applied the proposed approach to the presbyacusis study of 532 subjects with probabilities that each ear of a subject belongs to each of four presbyacusis subtypes. We further investigated the ranking of presbyacusis subjects using the proposed approach based on previous literature. The data analysis results indicate that the proposed approach is effective for ranking subjects based on paired compositional data.

• Journal of the Earthquake Engineering Society of Korea
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• v.3 no.3
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• pp.9-20
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• 1999

A method is proposed to estimate the joint damages of a steel structure from modal data using the neural networks technique. The beam-to-column connection in a steel frame structure is represented by a zero-length rotational spring of the end of the beam element, and the connection fixity factor is defined based on the rotational stiffness so that the factor may be in the range 0~1.0. Then, the severity of joint damage is defined as the reduction ratio of the connection fixity factor. Several advanced techniques are employed to develop the robust damage identification technique using neural networks. The concept of the substructural indentification is used for the localized damage assessment in the large structure. The noise-injection learning algorithm is used to reduce the effects of the noise in the modal data. The data perturbation scheme is also employed to assess the confidence in the estimated damages based on a few sets of actual measurement data. The feasibility of the proposed method is examined through a numerical simulation study on a 2-bay 10-story structure and an experimental study on a 2-story structure. It has been found that the joint damages can be reasonably estimated even for the case where the measured modal vectors are limited to a localized substructure and the data are severely corrupted with noise.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2461-2466
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• 2003

This paper presents a simple sensor fusion algorithm using neural network for navigation of mobile robots with obstacle avoidance and trap recovery. The multiple sensors input sensor data to the input layer of neural network activating the input nodes. The multiple sensors used include optical encoders, ultrasonic sensors, infrared sensors, a magnetic compass sensor, and GPS sensors. The proposed sensor fusion algorithm is combined with the VFH(Vector Field Histogram) algorithm for obstacle avoidance and AGPM(Adaptive Goal Perturbation Method) which sets adaptive virtual goals to escape trap situations. The experiment results show that the proposed low-level fusion algorithm is effective for real-time navigation of mobile robot.

• Korean Management Science Review
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• v.15 no.1
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• pp.49-62
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• 1998

The purpose of this paper is to develope a large-scale simplex method program LPAKO. Various up-to-date techniques are argued and implemented. In LPAKO, basis matrices are stored in a LU factorized form, and Reid's method is used to update LU maintaining high sparsity and numerical stability, and further Markowitz's ordering is used in factorizing a basis matrix into a sparse LU form. As the data structures of basis matrix, Gustavson's data structure and row-column linked list structure are considered. The various criteria for reinversion are also discussed. The dynamic steepest-edge simplex algorithm is used for selection of an entering variable, and a new variation of the MINOS' perturbation technique is suggested for the resolution of degeneracy. Many preprocessing and scaling techniques are implemented. In addition, a new, effective initial basis construction method are suggested, and the criteria for optimality and infeasibility are suggested respectively. Finally, LPAKO is compared with MINOS by test results.