• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.2
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• pp.331-336
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• 2018

In this paper, the life cycle cost of the auxiliary power unit in the conventional 8200 series electric locomotive is evaluated and an effective life cycle cost reduction method is sought. For this, a life cycle cost evaluation model was proposed using IEC 60300-3-3 standard. As a result of analysis, material cost which accounted for a large percentage of preventive maintenance cost, accounted for 64% of total cost, and breakdown maintenance cost was as high as 27%. Except for the cost of preventive maintenance, the breakdown maintenance cost ratio was the highest. In order to reduce the LCC of the auxiliary power unit(APU) of the 8200 series in the future, it is necessary to reduce the material cost in case of development and to secure the high reliability according to the parts manufacturing so as to minimize the maintenance cost.

• Journal of Electrical Engineering and Technology
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• v.10 no.2
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• pp.518-528
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• 2015

This article presents a closed loop control of DC series motor fed by DC chopper controlled by an PID controller based intelligent control using ANN (Artificial Neural Network). The PID-ANN controller performances are analyzed in both steady state and dynamic operating condition with various set speed and various load torque. Here two different motor parameters are taken for analysis (220V and 110V motor parameters). The static and dynamic performances are taken for comparison with conventional PID controller and existing work. The steady state stability analysis of the system also made using the transfer function model. The equation model is also done to analysis the performances by set speed change and load torque change. The proposed controller have better control over the conventional PID controller and the reported existing work. This system is initially simulated using MATLAB / Simulink and then experimental setup done using P89V51RD2BN microcontroller.

• Journal of the Korean Operations Research and Management Science Society
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• v.21 no.3
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• pp.197-214
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• 1996

Financial analysis of stock data usually involves extensive computation of large amount of time series data sets. To handle the large size of the data sets and complexity of the analyses, database management systems have been increasingly adaopted for efficient management of stock data. Specially, relational database management system is employed more widely due to its simplistic data management approach. However, the normalized two-dimensional tables and the structured query language of the relational system turn out to be less effective than expected in accommodating time series stock data as well as the various computational operations. This paper explores a new data management approach to stock data management on the basis of an object-oriented database management system (ODBMS), and proposes a data model supporting times series data storage and incorporating a set of financial analysis functions. In terms of functional stock data analysis, it specially focuses on a primitive set of operations such as variance of stock data. In accomplishing this, we first point out the problems of a relational approach to the management of stock data and show the strength of the ODBMS. We secondly propose an object model delineating the structural relationships among objects used in the stock data management and behavioral operations involved in the financial analysis. A prototype system is developed using a commercial ODBMS.

• Journal of the Korean Data and Information Science Society
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• v.22 no.3
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• pp.449-458
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• 2011

Illegal waste dumping is one of the major problems that the government agency monitoring water quality has to face. Recently government agency installed COD (chemical oxygen demand) auto-monitering machines in river. In this article we provide an outlier detection algorithm using R based on the time series intervention model that detects some outlier values among those COD time series values generated from an auto-monitering machine. Through this algorithm using R, we can achieve an automatic algorithm that does not need manual intervention in each step, and that can further be used in simulation study.

• BMB Reports
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• v.29 no.1
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• pp.1-10
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• 1996

The binding interaction of ethidium to a series of synthetic deoxyoligonucleotides containing a B-Z junction between left-handed Z-DNA and right-handed B-DNA, was studied. The series of deoxyoligonucleotides was designed so as to vary a dinucleotide step immediately adjacent to a B-Z junction region. Ethidium binds to the right-handed DNA forms and hybrid B-Z forms which contain a B-Z junction, in a highly cooperative manner. In a series of deoxyoligonucleotides, the binding affinity of ethidium with DNA forms which were initially hybrid B-Z forms shows over an order of magnitude higher than that with any other DNA forms, which were entirely in B-form DNA The cooperativity of binding isotherms were described by an allosteric binding model and by a neighbor exclusion model. The binding data were statistically compared for two models. The conformation of allosterically converted DNA forms under binding with ethidium is found to be different from that of the initial B-form DNA as examined by CD spectra. The ratio of the binding constant was interestingly correlated to the free energy of base unstacking and the conformational conversion of the dinucleotide. The more the base stacking of the dinucleotide is unstable, or the harder the conversion of B to A conformation, the higher the ratio of the binding constant of ethidium with the allosterically converted DNA forms and with the initial B-Z hybrid forms. DNA sequence around a B-Z junction region affects the binding affinity of ethidium. The results in this study demonstrate that ethidium could preferentially interact with unusual DNA structures.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.3
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• pp.186-199
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• 2013

We pose pattern classification as a density estimation problem where we consider mixtures of generative models under partially labeled data setups. Unlike traditional approaches that estimate density everywhere in data space, we focus on the density along the decision boundary that can yield more discriminative models with superior classification performance. We extend our earlier work on the recursive estimation method for discriminative mixture models to semi-supervised learning setups where some of the data points lack class labels. Our model exploits the mixture structure in the functional gradient framework: it searches for the base mixture component model in a greedy fashion, maximizing the conditional class likelihoods for the labeled data and at the same time minimizing the uncertainty of class label prediction for unlabeled data points. The objective can be effectively imposed as individual mixture component learning on weighted data, hence our mixture learning typically becomes highly efficient for popular base generative models like Gaussians or hidden Markov models. Moreover, apart from the expectation-maximization algorithm, the proposed recursive estimation has several advantages including the lack of need for a pre-determined mixture order and robustness to the choice of initial parameters. We demonstrate the benefits of the proposed approach on a comprehensive set of evaluations consisting of diverse time-series classification problems in semi-supervised scenarios.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.55 no.7
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• pp.281-289
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• 2006

Advanced controllers among Flexible AC Transmission System(FACTS) devices employ self-commutated switching converters, VSI (Voltage Sourced Inverters), as the synchronous voltage source. Such controllers are SSSC (Static Synchronous Series Compensator), STATCOM (Static Synchronous Compensator) and UPFC (Unified Power Flow Controller). UPFC is series-shunt combined controller. Its series and shunt inverters can be modeled as SSSC and STATCOM but the dependant relation between the inverters is very complex. For that reason, the complexity makes it difficult to develop the UPFC model by simply combining the SSSC and STATOM models when we apply the model for conventional power system dynamic simulation algorithm. Therefore, we need each relevant models of VSI type FACTS devices for power system analysis. This paper proposes a modeling approach which can be applied to modeling of VSI type FACTS devices. The proposed method using Newton-type current injection method can be used to make UPFC, SSSC, and STATCOM models. The proposed models are used for 2-area test system simulation, and the results verify their effectiveness.

• Journal of Electrical Engineering and Technology
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• v.13 no.5
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• pp.1841-1851
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• 2018

For the safe and stable operation of the power system, accurate wind power prediction is of great significance. A wind power prediction method based on empirical mode decomposition and improved extreme learning machine is proposed in this paper. Firstly, wind power time series is decomposed into several components with different frequency by empirical mode decomposition, which can reduce the non-stationary of time series. The components after decomposing remove the long correlation and promote the different local characteristics of original wind power time series. Secondly, an improved extreme learning machine prediction model is introduced to overcome the sample data updating disadvantages of standard extreme learning machine. Different improved extreme learning machine prediction model of each component is established. Finally, the prediction value of each component is superimposed to obtain the final result. Compared with other prediction models, the simulation results demonstrate that the proposed prediction method has better prediction accuracy for wind power.

• International Journal of Quality Innovation
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• v.3 no.2
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• pp.174-182
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• 2002

System identification and controller formulation are essential in dynamic process control. In system identification, data for system identification are obtained, and then they are analyzed so that the system model of the process is built, identified, and diagnosed. In controller formulation, the control equation is derived based on the result of the system identification. There has been much theoretical research on system identification and controller formulation. These theories are very useful when they are appropriately applied. To our regret, however, these theories are not always effectively applied in practice because the engineers and the operators who manage the process often do not have the necessary understanding of required time series analysis methods. On the other hand, because of widespread use of statistical packages, system identification such as estimating ARMA models can be done with little understanding of time series analysis methods. Therefore, it might be said that the most theoretically difficult part in practice is the controller formulation. In this paper, lists of control equations are proposed as a useful tool for practitioners to use. The tool supports bridging the gap between theory and practice in dynamic process control. Also, for some models, the generalized control equations are obtained.

• Steel and Composite Structures
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• v.29 no.5
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• pp.623-633
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• 2018

A robust element is essential for successful design of steel frames with Direct analysis (DA) method. To this end, an innovative and efficient curved-quartic-function (CQF) beam-column element using the fourth-order polynomial shape function with end-springs in series is proposed for practical applications of DA. The member initial imperfection is explicitly integrated into the element formulation, and, therefore, the P-${\delta}$ effect can be directly captured in the analysis. The series of zero-length springs are placed at the element ends to model the effects of semi-rigid joints and material yielding. One-element-per-member model is adopted for design bringing considerable savings in computer expense. The incremental secant stiffness method allowing for large deflections is used to describe the kinematic motion. Finally, several problems are studied in this paper for examining and validating the accuracy of the present formulations. The proposed element is believed to make DA simpler to use than existing elements, which is essential for its successful and widespread adoption by engineers.