• The KIPS Transactions:PartC
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• v.16C no.6
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• pp.783-790
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• 2009

The necessity of wireless sensor networks is increasing in the recent years. So many researches are studied in wireless sensor networks. The clustering algorithm provides an effective way to prolong the lifetime of the wireless sensor networks. The one-hop routing of LEACH algorithm is an inefficient way in the energy consumption of cluster-head, because it transmits a data to the BS(Base Station) with one-hop. On the other hand, other clustering algorithms transmit data to the BS with multi-hop, because the multi-hop transmission is an effective way. But the multi-hop routing of other clustering algorithms which transmits data to BS with multi-hop have a data bottleneck state problem. The unequal clustering algorithm solved a data bottleneck state problem by increasing the routing path. Most of the unequal clustering algorithms partition the nodes into clusters of unequal size, and clusters closer to the BS have small-size the those farther away from the BS. However, the energy consumption of cluster-head in unequal clustering algorithm is more increased than other clustering algorithms. In the thesis, I propose an energy efficient unequal clustering algorithm which decreases the energy consumption of cluster-head and solves the data bottleneck state problem. The basic idea is divided a three part. First of all I provide that the election of appropriate cluster-head. Next, I offer that the decision of cluster-size which consider the distance from the BS, the energy state of node and the number of neighborhood node. Finally, I provide that the election of assistant node which the transmit function substituted for cluster-head. As a result, the energy consumption of cluster-head is minimized, and the energy consumption of total network is minimized.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.20 no.1
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• pp.21-28
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• 2009

DFT-spread OFDM(Discrete Fourier Transform-Spread Orthogonal Frequency Division Multiplexing) is very effective for solving the PAPR(Peak-to-Average Power Ratio) problem. Therefore, the SC-FDMA(Single Carrier-Frequency Division Multiple Access) which is basically same to the DFT spread OFDM was adopted as the uplink standard of the 3GPP LTE ($3^{rd}$ Generation Partnership Project Long Term Evolution). Unlike the ordinary OFDM system, the SC-FDMA using DFT spreading method is vulnerable to the ICI(Inter-Carrier Interference) problem caused by the phase noise and IQ(In-phase/Quadrature) imbalance and effected FDE(Frequency Domain Equalizer). In this paper, the ICI effects from the phase noise and IQ imbalance which can be problems in uplink transmission are analyzed according the back-off level of HPA. Next, we propose the equalizer algorithm to remove the ICI effects. This proposed equalizer based on the FDE can be considered as up-graded and improved version of PNS(Phase Noise Suppression) algorithm. This proposed equalizer effectively compensates the ICI resulting from the phase noise and IQ imbalance. Finally, through the computer simulation, it can be shown that about SNR=14 dB is required for the $BER=10^{-4}$ after ICI compensation when the back-off is 4.5 dB, $\varepsilon=0.005$, $\phi=5^{\circ}$, and $pn=0.06\;rad^2$.

• Information Systems Review
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• v.9 no.1
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• pp.45-65
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• 2007

In medical industry, health insurance bill audit is unique and essential process in general hospitals. The health insurance bill audit process is very important because not only for hospital's profit but also hospital's reputation. Particularly, at the large general hospitals many related workers including analysts, nurses, and etc. have engaged in the health insurance bill audit process. This paper introduces a case of health insurance bill audit for finding reducible health insurance bill cases using decision tree induction techniques at a large general hospital in Korea. When supervised learning methods had been tried to be applied, one of major problems was data imbalance problem in the health insurance bill audit data. In other words, there were many normal(passing) cases and relatively small number of reduction cases in a bill audit dataset. To resolve the problem, in this study, well-known methods for imbalanced data sets including over sampling of rare cases, under sampling of major cases, and adjusting the misclassification cost are combined in several ways to find appropriate decision trees that satisfy required conditions in health insurance bill audit situation.

• Information Systems Review
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• v.18 no.2
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• pp.23-38
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• 2016

Previous studies on information systems (IS) and finance suggest that information on stock message boards influence the investment decisions of individual investors. However, how information on online stock message boards influences an individual investor's buy or sell decisions is unclear. To address this research question, we investigate the relationship between a number of posts on stock message boards and order imbalance in stock markets. Order imbalance is defined as the difference between the daily sum of buy-side shares traded and the daily sum of sell-side shares traded. Therefore, order imbalance can suggest the direction of trades and the strength of the direction with trading volumes. In this regard, this study examines how the number of posts (information on stock message boards) influences order imbalance (stock trading behavior). We collected about 46,077 messages of 40 companies on the Korea Composite Stock Price Index from Paxnet, the most popular Korean online stock message board. The messages we collected were divided based on in-trading and after-trading hours to examine the relationship between the numbers of posts and trading volumes. We also collected order imbalance data on individual investors. We then integrated the balanced panel data sets and analyzed them through vector regression. We found that the number of posts on online stock message boards is positively related to prior order imbalance. We believe that our findings contribute to knowledge in IS and finance. Furthermore, this study suggests that investors should carefully monitor information on stock message boards to understand stock market sentiments.

• Korea journal of population studies
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• v.20 no.1
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• pp.65-96
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• 1997

The main purpose of this study is to explore the determinants of sex ratio imbalance at birth in Taegu which has experienced the extremely imbalanced sex ratio at birth since mid-1980s. This paper attempts to compare the determinants of sex ratio imbalance at birth, such as sex discrimination against women, son preference, prenatal sex identification followes by sex-selective induced abortions, among married women aged 25 to 44 in Taegu with those in Bay area, California in USA. The research is based on the survey data which were conducted in Taegu, Repulic of Korea and Bay area, California in USA. The findings of this analysis suggest that married women in Taegu are more likely to feel sex discrimination against women than married women in Bay area. Furthermore, the percentage of married women's effort for son bearing before pregnancy is much higher than that of married women in Bay area. We also have found that the percentage of sex-selective induced abortion in Taegu is six times higher than that of married women in Bay area. According to the logistic regression analysis, the determinants of sex-selective induced abortion among married women in Taegu are discrimination against women, son preference, prenatal sex identification. On the other hand, age is the only variable which has an important impact on sex-selective induced abortion among married women in Bay area. From the findings of this study, we can conclude that son preference based on Cofucianism is the most important impact on sex ratio imbalance at birth in Taegu where son preference is much stronger than other regions in Korea. The phenomenon of extremely imbalanced sex ratio at birth in Taegu is the result of combination of these factors, such as strong son preference, seeking to have at least one son within small family size, and prenatal sex identification followed by sex-selective induced abortion.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.11
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• pp.3-10
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• 2014

Direct conversion receiver(DCR) gets noticed for integration and cost reduction of wireless communication systems instead of the heterodyne receiver which uses complex filter. But DCR has several factors in performance degradation. One of them is I/Q imbalance phenomenon, that is amplitude and phase mismatch between real and imaginary part of receiver. Accordingly, researches are being carried to improve the I/Q imbalance problem. However, the tendency of the broaden bandwidth of communication systems, low pass filter(LPF) mismatch problem affects severely in I/Q mismatch phenomenon at the DCR. To study this problem, we generated 10MHz broadband signal and shifted it ${\pm}8MHz$ from the center frequency. The signal is affected by LPF mismatch and it appears as frequency selective distortion. Thus, LPF mismatch model is added to I/Q imbalance model which conventionally dealt with amplitude and phase mismatches. In addition, we proposed the compensation method for each factors of mismatch. As the simulation results, the proposed I/Q mismatch compensator resolves the frequency selective distortion which occurred by the existing LPF mismatch.

• The Korean Journal of Applied Statistics
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• v.32 no.3
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• pp.349-374
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• 2019

A class imbalance problem arises when one class outnumbers the other class by a large proportion in binary data. Studies such as transforming the learning data have been conducted to solve this imbalance problem. In this study, we compared resampling methods among methods to deal with an imbalance in the classification problem. We sought to find a way to more effectively detect the minority class in the data. Through simulation, a total of 20 methods of over-sampling, under-sampling, and combined method of over- and under-sampling were compared. The logistic regression, support vector machine, and random forest models, which are commonly used in classification problems, were used as classifiers. The simulation results showed that the random under sampling (RUS) method had the highest sensitivity with an accuracy over 0.5. The next most sensitive method was an over-sampling adaptive synthetic sampling approach. This revealed that the RUS method was suitable for finding minority class values. The results of applying to some real data sets were similar to those of the simulation.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.7
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• pp.295-302
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• 2023

Software requirements written in natural language may have different meanings from the stakeholders' viewpoint. When designing an architecture based on quality attributes, it is necessary to accurately classify quality attribute requirements because the efficient design is possible only when appropriate architectural tactics for each quality attribute are selected. As a result, although many natural language processing models have been studied for the classification of requirements, which is a high-cost task, few topics improve classification performance with the imbalanced quality attribute datasets. In this study, we first show that the classification model can automatically classify the Korean requirement dataset through experiments. Based on these results, we explain that data augmentation through EDA(Easy Data Augmentation) techniques and undersampling strategies can improve the imbalance of quality attribute datasets, and show that they are effective in classifying requirements. The results improved by 5.24%p on F1-score, indicating that handling imbalanced data helps classify Korean requirements of classification models. Furthermore, detailed experiments of EDA illustrate operations that help improve classification performance.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.29-45
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• 2015

Response modeling is a well-known research issue for those who have tried to get more superior performance in the capability of predicting the customers' response for the marketing promotion. The response model for customers would reduce the marketing cost by identifying prospective customers from very large customer database and predicting the purchasing intention of the selected customers while the promotion which is derived from an undifferentiated marketing strategy results in unnecessary cost. In addition, the big data environment has accelerated developing the response model with data mining techniques such as CBR, neural networks and support vector machines. And CBR is one of the most major tools in business because it is known as simple and robust to apply to the response model. However, CBR is an attractive data mining technique for data mining applications in business even though it hasn't shown high performance compared to other machine learning techniques. Thus many studies have tried to improve CBR and utilized in business data mining with the enhanced algorithms or the support of other techniques such as genetic algorithm, decision tree and AHP (Analytic Process Hierarchy). Ahn and Kim(2008) utilized logit, neural networks, CBR to predict that which customers would purchase the items promoted by marketing department and tried to optimized the number of k for k-nearest neighbor with genetic algorithm for the purpose of improving the performance of the integrated model. Hong and Park(2009) noted that the integrated approach with CBR for logit, neural networks, and Support Vector Machine (SVM) showed more improved prediction ability for response of customers to marketing promotion than each data mining models such as logit, neural networks, and SVM. This paper presented an approach to predict customers' response of marketing promotion with Case Based Reasoning. The proposed model was developed by applying different weights to each feature. We deployed logit model with a database including the promotion and the purchasing data of bath soap. After that, the coefficients were used to give different weights of CBR. We analyzed the performance of proposed weighted CBR based model compared to neural networks and pure CBR based model empirically and found that the proposed weighted CBR based model showed more superior performance than pure CBR model. Imbalanced data is a common problem to build data mining model to classify a class with real data such as bankruptcy prediction, intrusion detection, fraud detection, churn management, and response modeling. Imbalanced data means that the number of instance in one class is remarkably small or large compared to the number of instance in other classes. The classification model such as response modeling has a lot of trouble to recognize the pattern from data through learning because the model tends to ignore a small number of classes while classifying a large number of classes correctly. To resolve the problem caused from imbalanced data distribution, sampling method is one of the most representative approach. The sampling method could be categorized to under sampling and over sampling. However, CBR is not sensitive to data distribution because it doesn't learn from data unlike machine learning algorithm. In this study, we investigated the robustness of our proposed model while changing the ratio of response customers and nonresponse customers to the promotion program because the response customers for the suggested promotion is always a small part of nonresponse customers in the real world. We simulated the proposed model 100 times to validate the robustness with different ratio of response customers to response customers under the imbalanced data distribution. Finally, we found that our proposed CBR based model showed superior performance than compared models under the imbalanced data sets. Our study is expected to improve the performance of response model for the promotion program with CBR under imbalanced data distribution in the real world.

• KOREAN POULTRY JOURNAL
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• v.43 no.12
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• pp.126-129
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• 2011