• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.5A
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• pp.341-346
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• 2009

M-WiMAX (Mobile-Worldwide Interoperability for Microwave Access) system, which uses OFDM (Orthogonal Frequency Division Multiplexing) technique, is known to be proper for mobile high-speed data transmission system. Nevertheless, M-WiMAX is seriously sensitive to IQ imbalance caused by the LO (Local Oscillator) at the receiver. In this paper, we analyze the effect of IQ imbalance on the system, and then propose a joint optimization scheme that can optimize DFE (Digital Front-end) of mobile modem by combining operation duplicated between AGC (Automatic Gain Control) and the estimation and compensation of IQ imbalance. Simulation results show that the proposed scheme achieves the same performance of the conventional scheme while reducing the complexity of the H/W implementation.

• Journal of the Korea Computer Industry Society
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• v.3 no.2
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• pp.235-242
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• 2002

To minimize restructuring cost of a tree, experiments were conducted to collect quantitative information of searching efficiencies varying degrees of imbalance in a binary search tree. Degrees of tree imbalance were measured by a balance factor, an absolute value of height difference of left subtree and right subtree in a binary search tree. The average number of comparisons increased (p＜0.01), and searching efficiency of O(n) was more appropriate rather than O(logn), as degrees of imbalance in a binary search tree deteriorated. However, there were no significant differences of searching efficiencies in height balanced trees and trees with subtrees to have height 3 less than the other (p＞0.05). Therefore, the findings would be applicable to maintain searching efficiency of a software with a binary search tree.

• Journal of the Korea Society of Computer and Information
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• v.15 no.7
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• pp.11-17
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• 2010

This paper proposes an improved SMO solving a quadratic optmization problem for class imbalanced learning. The SMO algorithm is aproporiate for solving the optimization problem of a support vector machine that assigns the different regularization values to the two classes, and the prosoposed SMO learning algorithm iterates the learning steps to find the current optimal solutions of only two Lagrange variables selected per class. The proposed algorithm is tested with the UCI benchmarking problems and compared to the experimental results of the SMO algorithm with the g-mean measure that considers class imbalanced distribution for gerneralization performance. In comparison to the SMO algorithm, the proposed algorithm is effective to improve the prediction rate of the minority class data and could shorthen the training time.

• The Korean Journal of Financial Management
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• v.9 no.2
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• pp.31-55
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• 1992

금융기관(金融機關)은 기업, 가계, 정부와 함께 우리 경제를 구성하는 주요한 부분이다. 금융기관은 최종적 차입자와 최종적 대부자를 중개하는 과정에서 여러가지 서어비스를 우리에게 제공하면서 존재한다. 금융기관이 제공하는 서어비스는 우리의 경제생활에 매우 중요한 영향을 미친다. 금융기관의 행동이 경제에 미치는 영향을 분석하기에 앞서 어떤 조건하에서 금융기관이 존립할 수 있는가를 알아보는 것도 금융기관의 행동을 이해하기 위해 의미있는 일일 것이다. 본 논문은 정보불균형(情報不均衡)의 관점에서 금융기관의 존립을 분석, 설명하는데 그 목적이 있다. 경제문제의 불확실성과 복잡성의 증대는 민간의 제한된 합리성을 더욱 제한되게 만들고 역으로 제한된 합리성 때문에 불확실성과 복잡성은 더욱 중요한 문제가 된다. 여기에다 인간의 이기주의가 결합하게 되면 정보의 유통이 불완전해져 정보가 불균등하게 분포하게 되는 현상이 생긴다. 정보불균형(情報不均衡)은 거래를 위촉시키고 극단적인 경우 시장실패(市場失敗)를 가져온다. 금융기관은 정보생산을 통해 거래위축이나 시장주패(市場朱敗)를 방지할 수 있는 역할을 한다. 금융기관이 정보생산을 한다고 해도 개별정보생산자나 직접 금융에 비해 정보생산비용면에서 우위를 가질 수 있어야 금융기관의 존립은 가능할 것이다. 즉 정보불균형(情報不均衡)을 해소하기 위한 정보생산이 금융기관이 존립할 수 있는 필요조건이라면, 정보생산의 경제성은 금융기관이 존립할 수 있는 필요충분조건이 된다고 할 수 있다. 금융기관이 개별정보생산자나 직접 금융보다 정보생산면에서의 경제성을 가질 수 있는 가능성은 첫째, 분산효과(分散效果)로 인한 대리비용(代理費用)의 감소(減小) 둘째, 분산효과(分散效果)로 인한 구성원보상(構成員補償)의 불확실성감소(不確實性減小) 세째, 금융기관 구성원간의 정보공유효과(情報共有效果)이다.

• 사회경제평론
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• no.38
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• pp.51-83
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• 2012

Consider an n goods production economy. Assume the equilibrium condition of Sraffa's price system, a balanced growth condition and the goods market clearing conditions. If both equations are given to determine a real wage rate and investment, the economic system is over-determined. It suggests that there exists no long-period equilibrium to satisfy both labor market and goods market conditions. This paper interprets this situation of over-determinacy as a disequilibrium state, and attempts to solve it through disequilibrium dynamics. It constructs a model of accumulation and real wage rates consistent with Lotka-Volterra system, and shows that the overall growth path fluctuates endogenously around a resting point of long-period disequilibrium.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.1
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• pp.49-54
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• 2024

Data imbalance is a common issue encountered in classification problems, stemming from a significant disparity in the number of samples between classes within the dataset. Such data imbalance typically leads to problems in classification models, including overfitting, underfitting, and misinterpretation of performance metrics. Methods to address this issue include resampling, augmentation, regularization techniques, and adjustment of loss functions. In this paper, we focus on loss function adjustment, particularly comparing the performance of various configurations of loss functions (Cross Entropy, Balanced Cross Entropy, two settings of Focal Loss: 𝛼 = 1 and 𝛼 = Balanced, Asymmetric Loss) on Multi-Class black-box video data with imbalance issues. The comparison is conducted using the I3D, and R3D_18 models.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.18 no.7
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• pp.757-765
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• 2007

OFDM system is an excellent high speed transmission method but it is seriously sensitive to the phase noise and IQ imbalance. Therefore, in this paper, we analyze the communication performance of the OFDM communication system with IQ imbalance and phase noise. Phase noise's variance can be calculated by integral calculus of phase noise power spectrum. From simulation results, it can be shown that the BER performances show different change according to the phase noise variance and IQ imbalance amount. When amplitude imbalance is ${\varepsilon}$=0.2; 0.3; 0.4 and phase imbalance is ${\phi}=10^0$, and distribution of phase noise is ${\sigma}^2=0.012$, BER is degraded by 2.88 dB, 3.61 dB, 4.09 dB in $10^{-5}$ in the respect of the SNR penalty.

• Journal of Intelligence and Information Systems
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• v.15 no.3
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• pp.1-15
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• 2009

In a classification problem, data imbalance occurs when the number of instances in one class greatly outnumbers the number of instances in the other class. Such data sets often cause a default classifier to be built due to skewed boundary and thus the reduction in the classification accuracy of such a classifier. This paper proposes a Geometric Mean-based Boosting (GM-Boost) to resolve the problem of data imbalance. Since GM-Boost introduces the notion of geometric mean, it can perform learning process considering both majority and minority sides, and reinforce the learning on misclassified data. An empirical study with bankruptcy prediction on Korea companies shows that GM-Boost has the higher classification accuracy than previous methods including Under-sampling, Over-Sampling, and AdaBoost, used in imbalanced data and robust learning performance regardless of the degree of data imbalance.

• The Korean Journal of Applied Statistics
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• v.27 no.3
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• pp.357-371
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• 2014

There are many studies related to imbalanced data in which the class distribution is highly skewed. To address the problem of imbalanced data, previous studies deal with resampling techniques which correct the skewness of the class distribution in each sampled subset by using under-sampling, over-sampling or hybrid-sampling such as SMOTE. Ensemble methods have also alleviated the problem of class imbalanced data. In this paper, we compare around a dozen algorithms that combine the ensemble methods and resampling techniques based on simulated data sets generated by the Backbone model, which can handle the imbalance rate. The results on various real imbalanced data sets are also presented to compare the effectiveness of algorithms. As a result, we highly recommend the resampling technique combining ensemble methods for imbalanced data in which the proportion of the minority class is less than 10%. We also find that each ensemble method has a well-matched sampling technique. The algorithms which combine bagging or random forest ensembles with random undersampling tend to perform well; however, the boosting ensemble appears to perform better with over-sampling. All ensemble methods combined with SMOTE outperform in most situations.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.2
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• pp.18-26
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• 2015

In this paper, we studied the method for analyzing and estimating the parameters that induce I/Q imbalance in the repeater using direct conversion RF. In repeater, amplitude, phase, and filter mismatch are generated in the receiving-end which converts RF signal to baseband signal. And amplitude and phase mismatch are generated in the transmitting-end which converts baseband signal to RF signal. Accordingly, we modeled the parameters that cause I/Q imbalance in the structure of the repeater in order, and proposed a feedback test structure from the transmitting-end to the receiving-end for estimating the corresponding parameters. By comparing the test transmitting signal and received signal, it is possible to estimate the I/Q imbalance parameters which occurred from mixed components of real and imaginary part. And it was confirmed that I/Q imbalance phenomenon has been properly compensated with estimated parameters at the direct conversion RF repeater.