• Journal of Hydrospace Technology
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• v.2 no.2
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• pp.24-35
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• 1996

This paper describes parameter identification techniques formulated for the estimation of maneuvering coefficients of a submerged body. The first part of this paper is concerned with the identifiability of the system parameters. The relationship between a stochastic linear time-invariant system and the equivalent dynamic system is investigated. The second is concerned with the development of the numerically stable identification technique. Two identification techniques are tested; one is the ma7mum likelihood (ML) methods using the Holder & Mead simplex search method and using the modified Newton-Raphson method, and the other is the modified extended Kalman filter (MEKF) method with a square-root algorithm, which can improve the numerical accuracy of the extended Kalman filter. As a results, it is said that the equations of motion for a submerged body have higher probability to generate simultaneous drift phenomenon compared to general state equations and only the ML method using the Holder & Mead simplex search method and the MEKF method with a square-root algorithm gives acceptable results.

• Journal of Korea Multimedia Society
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• v.10 no.11
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• pp.1530-1537
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• 2007

In this paper, we implemented a speech emotion recognition system that can distinguish human emotional states from recorded speech captured by a single microphone and classify them into four categories: neutrality, happiness, sadness and anger. In general, a speech recorded with a microphone contains background noises due to the speaker environment and the microphone characteristic, which can result in serious system performance degradation. In order to minimize the effect of these noises and to improve the system performance, a MA(Moving Average) filter with a relatively simple structure and low computational complexity was adopted. Then a SFS(Sequential Forward Selection) feature optimization method was implemented to further improve and stabilize the system performance. For speech emotion classification, a SVM pattern classifier is used. The experimental results indicate the emotional classification performance around 65% in the computer simulation and 62% on the ARM platform.

• The Journal of the Acoustical Society of Korea
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• v.25 no.6
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• pp.269-276
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• 2006

In this paper, we propose an emotion recognition system that can discriminate human emotional state into neutral or anger from the speech captured by a cellular-phone in real time. In general. the speech through the mobile network contains environment noise and network noise, thus it can causes serious System performance degradation due to the distortion in emotional features of the query speech. In order to minimize the effect of these noise and so improve the system performance, we adopt a simple MA (Moving Average) filter which has relatively simple structure and low computational complexity, to alleviate the distortion in the emotional feature vector. Then a SFS (Sequential Forward Selection) feature optimization method is implemented to further improve and stabilize the system performance. Two pattern recognition method such as k-NN and SVM is compared for emotional state classification. The experimental results indicate that the proposed method provides very stable and successful emotional classification performance such as 86.5%. so that it will be very useful in application areas such as customer call-center.

• Journal of Electrical Engineering and Technology
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• v.11 no.3
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• pp.618-628
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• 2016

Active power filter (APF) has been proved as a flexible solution for compensating the harmonic distortion caused by nonlinear loads in power distribution power systems. Digital repetitive control can achieve zero steady-state error tracking of any periodic signal while the sampling points within one repetitive cycle must be a known integer. However, the compensation performance of the APF would be degradation when the grid frequency varies. In this paper, an improved repetitive control scheme with frequency adaptive capability is presented to track any periodic signal with variable grid frequency, where the variable delay items caused by time-varying grid frequency are approximated with Pade approximants. Additionally, the stability criterion of proposed repetitive control scheme is given. A three-phase shunt APF experimental platform with proposed repetitive control scheme is built in our laboratory. Simulation and experimental results demonstrate the effectiveness of the proposed repetitive control scheme.

• Proceedings of the KSME Conference
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• 2004.04a
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• pp.1801-1806
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• 2004

Stochastic nature of subgrid-scale stress causes the predictability problem in large eddy simulation (LES) by which the LES solution field decorrelates with field from filtered directnumerical simulation (DNS). In order to evaluate the predictability limit in a priori sense, the information on the interplay between resolved scale and subgrid-scale (SGS) is required. In this study, the analysis on the inter-scale interaction is performed by applying tophat and cutoff filters to DNS database of flow over a circular cylinder at Reynolds number of 3900. The effect of filter shape is investigated on the interpretation of correlation between scales. A critique is given on the use of tophat filter for SGS analysis using DNS database. It is shown that correlations between Karman vortex and SGS kinetic energy drastically decrease when the cutoff filter is used, which implies that the small scale universality holds even in the presence of the large scale coherent structure.

• Journal of Power Electronics
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• v.12 no.5
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• pp.800-812
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• 2012

This paper analyses the mathematical model and control strategies of a Hybrid Active Power Filter with Injection Circuit (IHAPF). The control strategy based on the load harmonic current detection is selected. A novel control method for a IHAPF, which is based on the analyzed control mathematical model, is proposed. It consists of two closed-control loops. The upper closed-control loop consists of a single fuzzy logic controller and the IHAPF model, while the lower closed-control loop is composed of an Adaptive Network based Fuzzy Inference System (ANFIS) controller, a Neural Generalized Predictive (NGP) regulator and the IHAPF model. The purpose of the lower closed-control loop is to improve the performance of the upper closed-control loop. When compared to other control methods, the simulation and experimental results show that the proposed control method has the advantages of a shorter response time, good online control and very effective harmonics reduction.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.6
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• pp.1207-1214
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• 2019

Technologies of the 4th industrial revolution have been unknowingly seeping into our lives. Many IoT based home security systems are using the convolutional neural network(CNN) as good biometrics to recognize a face and protect home and family from intruders since CNN has demonstrated its excellent ability in image recognition. In this paper, three layouts of CNN for 2D and 2.5D image of small dataset with various input image size and filter size are explored. The simulation results show that the layout of CNN with 50*50 input size of 2.5D image, 2 convolution and max pooling layer, and 3*3 filter size for small dataset of 2.5D image is optimal for a home security system with recognition accuracy of 0.966. In addition, the longest CPU time consumption for one input image is 0.057S. The proposed layout of CNN for a face recognition is suitable to control the actuators in the home security system because a home security system requires good face recognition and short recognition time.

• The Transactions of the Korean Institute of Power Electronics
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• v.9 no.1
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• pp.42-49
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• 2004

This paper presents a new method of external load disturbance compensation using deadbeat load torque observer and gain compensation by parameter estimator. The response of the permanent magnet synchronous motor(PMSM) follows the nominal plant. The load torque compensation method is composed of a deadbeat observer. To reduce the noise effect, the post-filter implemented by moving average(MA) process is adopted. The parameter compensator with recursive least square method(RLSM) parameter estimator is suggested to make the new system work as same as the name plate system which in used to take gains. The proposed estimator is combined with a high performance load torque observer to resolve the problems. As a result, the proposed control system has a robust and precise system against the load torque and the parameter variation. A stability and usefulness are verified by computer simulation and experiment.

• Bulletin of the Korean Chemical Society
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• v.23 no.4
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• pp.575-579
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• 2002

Two-dimensionally cross-linked ultrathin films of poly(maleic acid-alt-methyl vinyl ether) (MA-MVE) and poly(allylamine) (PAA) were produced by using sodium dioctadecyl sulfate (2C18S) as the monolayer template for Langmuir-Blodgett (LB) depositio n. The template molecules were subsequently removed by thermal treatment followed by extraction. The polyion-complexed monolayers of three components, i.e., template 2C18S, co-spread PAA, and subphase MA-MVE, were formed at the air-water interface. Their monolayer properties were studied by the surface pressure-area isotherm. The monolayers were transferred on solid substrates as Y type. The polyion-complexed LB films and the resulting network films were characterized by FT-IR spectroscopy, X-ray photoelectron spectroscopy (XPS), and scanning electron microscopy (SEM). The cross-linking to form a polymer network was achieved by amide or imide formation through heat treatment under a vacuum. SEM observation of the film on a porous fluorocarbon membrane filter (pore diameter 0.1 ㎛) showed covering of the pores by four layers in the polyion complex state. Extraction by chloroform followed by heat treatment produced hole defects in the film.

• The Journal of Korean Institute of Information Technology
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• v.16 no.12
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• pp.25-30
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• 2018

It is extremely lacking and urgently required that the method of constructing the Gene Regulatory Network (GRN) from RNA-Sequencing data (RNA-Seq) because of Big-Data and GRN in Big-Data has obtained substantial observation as the interactions among relevant featured genes and their regulations. We propose newly the computational comparative feature patterns selection method by implementing a minimum-redundancy maximum-relevancy (MRMR) filter the support vector machine-recursive feature elimination (SVM-RFE) with Intensity-dependent normalization (DEGSEQ) as a preprocessor for emphasizing equal preciseness in RNA-seq in Big-Data. We found out the proposed algorithm might be more scalable and convenient because of all libraries in R package and be more improved in terms of the time consuming in Big-Data and minimum-redundancy maximum-relevancy of a set of feature patterns at the same time.