• Proceedings of the KIPE Conference
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• 2019.07a
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• pp.142-144
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• 2019

Nowadays, multi-MHz wireless power transfer (WPT) system has received a great concern of study due to its desirable characteristics such as user convenience, system compact and better safety as compared to the conventional DC-DC with cord. This paper presents a solution for WPT Lithium Batteries charger with Constant Current (CC) and Constant Voltage (CV) charging process. The proposed system consists of a high frequency class D power amplifier, a pair of PCB coil, transformable high-order resonant network and a full-bridge rectifier. The charger can be implemented CC /CV charging profile thanks to automatic reconfigurable resonant compensator. Therefore, the battery can be fully charged without the help of an additional DC/DC converter. The simulation and 50W-6.78-MHz hardware experimental results are presented to verify the feasibility of the proposed method and to evaluate the performance of the proposed wireless battery charger.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.23.3-23
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• 2001

The main object of this work is the development of an intelligent multi-sensor integration and fusion model that uses fuzzy inference system. Sensor data from different types of sensors are integrated and fused together based on the confidence which is not typically used in traditional data fusion methods. The information is fed as input to a fuzzy inference system(FIS). The output of the FIS is weights that are assigned to the different sensor data reflecting the confidence En the sensor´s behavior and performance. We interpret a type of fuzzy inference system as an interpolator of B-spline hypersurfaces. B-spline basis functions of different orders are regarded as a class of membership functions. This paper presents a model that ...

• Proceedings of the KSR Conference
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• 1998.11a
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• pp.202-209
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• 1998

This paper presents a modelling of railway facilities and signalling system based on object-oriented software development technique to simulate multi-train movements on the complex railway network. Block and interlocking functions of signalling system is modelled using Node-Link model of railway network and signal control logic, which can be used to set the train routes and control the train movement. A brief explanation of class design about these model is provided.

• Transactions on Control, Automation and Systems Engineering
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• v.2 no.2
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• pp.130-135
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• 2000

A two parameter model of a single fictitious weak acid with unknown dissociation constant has been successfully applied to design a neutralization system for many multi-component acid streams. But there are some processes for which above two parameter model is not satisfactory due to poor approxmation of the nonlinearity of pH process. Here, for etter control of wide class of multi-component acid streams, a three parameter model of a strong acid and a weak acid with unknown dissociation constant is proposed. The model approximates effectively three types of largest gain variation nonlinearities. Based on this model a nonlinear pH control system is designed. Parameters can eeasily estimated since their combinations appear linearly in the model equations and nonlinear adaptive control system may also be constructed just as with the two parameter model.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.38 no.4
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• pp.289-309
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• 2014

A rotor blade is an important device that converts kinetic energy of wind into mechanical energy. Rotor blades affect the power performance, energy conversion efficiency, and loading and dynamic stability of wind turbines. Therefore, considering the characteristics of a wind turbine system is important for achieving optimal blade design. This study examined the general blade design procedure for a wind turbine system and aero-structure design results for a 2-MW class wind turbine blade (KR40.1b). As suggested above, a rotor blade cannot be designed independently, because its ultimate and fatigue loads are highly dependent on system operating conditions. Thus, a reference 2-MW wind turbine system was also developed for the system integrated load calculations. All calculations were performed in accordance with IEC 61400-1 and the KR guidelines for wind turbines.

• Journal of Korean Academy of Nursing
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• v.20 no.1
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• pp.16-37
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• 1990

A cost analysis for hospitalized patients was carried out based upon Patient Classification System(PCS) in order to determine an appropriate nursing fee. The data were collected from 21 nursing units of three teaching hospitals from April 1 to June 30, 1989. first, all of the 22,056 inpatients were classified into mildly ill(Class Ⅰ), moderately ill(Class Ⅱ), acutely ill(Class Ⅲ), and critically ill(Class Ⅳ) by the PCS which had been carefully developed to be suitable for the Korean nursing units. Second. PCS cost accounting was applied to the above data. The distribution of inpatients, nursing costs, and nursing productivity were as follows : 1) Patient distribution ranged from 45% to class Ⅰ, 36% to class Ⅱ, 15% to class Ⅲ, and 4% to class Ⅳ, the proportion of class Ⅳ in ‘H’ Hospital was greater than that of the other two hospitals. 2) The proportion of Class Ⅲ and Ⅳ in the medical nursing units was greater than that of surgical nursing units. 3) The number of inpatients was greatest on Tuesdays, and least on Sundays. 4) The average nursing cost per hour was W 3,164 for ‘S’ hospital, W 3,511 for ‘H’ hospital and W 4,824 for ‘K’ hospital. The average nursing cost per patient per day was W 14,126 for ‘S’ Hospital, W 15,842 for ‘H’ hospital and W 21,525 for ‘K’ hospital. 5) The average nursing cost calculated by the PCS was W 13,232 for class Ⅰ, W 18,478 for class Ⅱ, W 23,000 for class Ⅲ, and W 25,469 for class Ⅳ. 6) The average nursing cost for the medical and surgical nursing units was W 13,180 and W 13,303 respetively for class Ⅰ, W 18,248 and W 18,707 for class Ⅱ, W 22,303 and W 23,696 for class Ⅲ, and W 24,331 and W 26,606 for class Ⅳ. 7) The nursing costs were composed of 85% for wages and fringe benefits, 3% for material supplies and 12% for overhead. The proportion of wages and fringe benefits among the three Hospitals ranged from 75%, 92% and 98% for the ‘S’, ‘H’, ‘K’ hospitals respectively These findings explain why the average nursing cost of ‘K’ hospital was higher than the others. 8) According to a multi- regression analysis, wages and fringe benefits, material supplies, and overhead had an equal influence on determining the nursing cost while the nursing hours had less influence. 9) The productivity of the medical nursing units were higher than the surgical nursing units, productivity of the D(TS) - nursing units was the lowest while the K(Med) - nursing unit was the highest in 'S' hospital. In ‘H’ hospital, productivity was related to the number of inpatients rather than to the characteristics of the nursing units. The ‘K’ hospital showed the same trend as ‘S’ hospital, that the productivity of the medical nursing unit was higher than the surgical nursing unit. The productivity of ‘S’ hospital was evaluated the highest followed by ‘H’ hospital and ‘K’ hospital. Future research on nursing costs should be extended to the other special nursing areas such as pediatric and psychiatric nursing units, and to ICU or operating rooms. Further, the PCS tool should be carefully evaluated for its appropriateness to all levels of institutions(primary, secondary, tertiary). This study took account only of the quantity of nursing services when developing the PCS tool for evaluating the productivity of nursing units. Future research should also consider the quality of nursing services including the appropriateness of nursing activities.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.23-45
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• 2020

Big data is creating in a wide variety of fields such as medical care, manufacturing, logistics, sales site, SNS, and the dataset characteristics are also diverse. In order to secure the competitiveness of companies, it is necessary to improve decision-making capacity using a classification algorithm. However, most of them do not have sufficient knowledge on what kind of classification algorithm is appropriate for a specific problem area. In other words, determining which classification algorithm is appropriate depending on the characteristics of the dataset was has been a task that required expertise and effort. This is because the relationship between the characteristics of datasets (called meta-features) and the performance of classification algorithms has not been fully understood. Moreover, there has been little research on meta-features reflecting the characteristics of multi-class. Therefore, the purpose of this study is to empirically analyze whether meta-features of multi-class datasets have a significant effect on the performance of classification algorithms. In this study, meta-features of multi-class datasets were identified into two factors, (the data structure and the data complexity,) and seven representative meta-features were selected. Among those, we included the Herfindahl-Hirschman Index (HHI), originally a market concentration measurement index, in the meta-features to replace IR(Imbalanced Ratio). Also, we developed a new index called Reverse ReLU Silhouette Score into the meta-feature set. Among the UCI Machine Learning Repository data, six representative datasets (Balance Scale, PageBlocks, Car Evaluation, User Knowledge-Modeling, Wine Quality(red), Contraceptive Method Choice) were selected. The class of each dataset was classified by using the classification algorithms (KNN, Logistic Regression, Nave Bayes, Random Forest, and SVM) selected in the study. For each dataset, we applied 10-fold cross validation method. 10% to 100% oversampling method is applied for each fold and meta-features of the dataset is measured. The meta-features selected are HHI, Number of Classes, Number of Features, Entropy, Reverse ReLU Silhouette Score, Nonlinearity of Linear Classifier, Hub Score. F1-score was selected as the dependent variable. As a result, the results of this study showed that the six meta-features including Reverse ReLU Silhouette Score and HHI proposed in this study have a significant effect on the classification performance. (1) The meta-features HHI proposed in this study was significant in the classification performance. (2) The number of variables has a significant effect on the classification performance, unlike the number of classes, but it has a positive effect. (3) The number of classes has a negative effect on the performance of classification. (4) Entropy has a significant effect on the performance of classification. (5) The Reverse ReLU Silhouette Score also significantly affects the classification performance at a significant level of 0.01. (6) The nonlinearity of linear classifiers has a significant negative effect on classification performance. In addition, the results of the analysis by the classification algorithms were also consistent. In the regression analysis by classification algorithm, Naïve Bayes algorithm does not have a significant effect on the number of variables unlike other classification algorithms. This study has two theoretical contributions: (1) two new meta-features (HHI, Reverse ReLU Silhouette score) was proved to be significant. (2) The effects of data characteristics on the performance of classification were investigated using meta-features. The practical contribution points (1) can be utilized in the development of classification algorithm recommendation system according to the characteristics of datasets. (2) Many data scientists are often testing by adjusting the parameters of the algorithm to find the optimal algorithm for the situation because the characteristics of the data are different. In this process, excessive waste of resources occurs due to hardware, cost, time, and manpower. This study is expected to be useful for machine learning, data mining researchers, practitioners, and machine learning-based system developers. The composition of this study consists of introduction, related research, research model, experiment, conclusion and discussion.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.42 no.7
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• pp.622-627
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• 2014

A pico-class satellite has limitation to generate power from the solar cells due to its limited accommodation area to install the solar cells. The variation of incidence angle between solar panels and sunlight induced by the revolution and rotation of the satellite is one of the key parameters to determine the power generation. In this study, we proposed a concentrating photovoltaic system for pico-class satellite applications to enhance power generation when the ${\beta}$ angle between the sunlight and the solar panel is zero by effectively concentrating solar energy on solar panels. The feasibility of the conceptual idea has been demonstrated by power measurement test using solar simulator and commercial multi-array lens system.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.05a
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• pp.1101-1103
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• 2006

For a broad class of discrete-time FIFO queueing systems with D-MAP (discrete-time Markovian arrival process) arrivals, we present a distributional Little's law that relates the distribution of the stationary number of customers in system (queue) with that of the stationary number of slots a customer spends in system (queue). Taking the multi-server D-MAP/D/c queue for example, we illustrate how to utilize this relation to get the desired distribution of the number of customers.

• Bulletin of the Korean Mathematical Society
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• v.46 no.1
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• pp.107-116
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• 2009

We consider a single server multi-class queueing model with Poisson arrivals and relative priorities. For this queue, we derive a system of equations for the transform of the queue length distribution. Using this system of equations we find the moments of the queue length distribution as a solution of linear equations.