• Korea journal of population studies
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• v.26 no.2
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• pp.175-195
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• 2003

This paper investigates spatial distribution of the knowledge-information occupations by utilizing Geographical Information System(GIS). The knowledge-information occupations, comprised mainly of professionals, engineers and managers, have played a key role in the knowledge-based information society. The uneven development of bureaucratization and informatization among regions have resulted in unequal spatial distribution of the knowledge-information occupations. Analysis of 1995 and 2000 Census shows that these occupations tend to concentrate in some major metropolitan areas, while the other areas show rather traditional occupational structure. This spatial unequality has been also found in the occupational distribution within Seoul. This tendency of spatial concentration in the occupational distribution inherited from the industrial society and is not going to diminish in the knowledge-information society. More aggressive policies to make the most of decentralizing impacts of information and communication technologies should be implemented to counter-balance this tendency.

• Journal of the Korea Society of Computer and Information
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• v.25 no.2
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• pp.105-111
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• 2020

In this paper, we propose a scheme that minimizes the energy imbalance problem of solar-powered wireless sensor network (SP-WSN) using both a mobile sink capable of wireless power transfer and an efficient clustering scheme (including cluster head election). The proposed scheme charges the cluster head using wireless power transfer from a mobile sink and mitigates the energy hotspot of the nodes nearby the head. SP-WSNs can continuously harvest energy, alleviating the energy constraints of battery-based WSN. However, if a fixed sink is used, the energy imbalance problem, which is energy consumption rate of nodes located near the sink is relatively increased, cannot be solved. Thus, recent research approaches the energy imbalance problem by using a mobile sink in SP-WSN. Meanwhile, with the development of wireless power transmission technology, a mobile sink may play a role of energy charging through wireless power transmission as well as data gathering in a WSN. Simulation results demonstrate that increase the amount of collected data by the sink using the proposed scheme.

• The Transactions of the Korean Institute of Power Electronics
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• v.7 no.1
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• pp.65-73
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• 2002

This paper proposes a new carrier-based PWM method to solve the most serious problem of flying capacitor multi-level inverter that is the unbalance of capacitor voltages. The voltage unbalance occurs due to the difference of each capacitor's charging and discharging time applied to Flying Capacitor Inverter. New solution controls the variation of capacitor voltages into the mean '0'during some period by means of new carriers using the leg voltage redundancy in the flying capacitor inverter. The solution can be easily expanded to the multi-level inverter. The leg voltage redundancy in the new method makes the switching loss of device equals to the conduction loss of device. This paper will examine the unbalance of capacitor voltage and the conventional theory of self-balance using Phase-shifted carrier. And then the new method that is suitable to the flying capacitor inverter will be explained.

• Journal of Conservation Science
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• v.7 no.2
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• pp.68-79
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• 1998

The protecting-chamber for a standing Buddha of Mireuk-ri temple site at Jungwon is composed of granite of Cretaceous age which mainly consists of quartz, perthite, plagioclase, and biotite with minor amounts of muscovite, apatite, chlorite, sericite and opaque mineral. There are abundant cracks which may be developed by strong weathering and differential loading by structural unbalances of the whole protecting-chamber. Cracks can be divided into three types based on genesis as those formed by exfoliation, intrinsic, and pressure. The exfoliation occurred along the onion structure of the granite. The pressure cracks are generally superimposed on the exfoliation ones, which might be developed by structural unbalance of the protecting-chamber resulted from differential loading in places. The structural unbalance may be due to change in physical properties of the rocks according to strong weathering, differential settling of basement soil by difference in loading in places of protecting-chamber, westward creep of the basement soil below the West wall and related different resistance of the basement soil against the loading, and partial depression of the West wall. For the conservation of the protecting-chamber, it must be considered the method of stabilizing the basement and treatment of the cracks.

• Journal of KIISE:Software and Applications
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• v.32 no.2
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• pp.99-107
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• 2005

In this paper, wc propose a now missing imputation method for minimizing loss of information linkage disequilibrium-based and haplotype-based imputation method, which estimate missing values of the data based on the specificity of Single Nucleotide Polymorphism(SNP) genotype data. Method for imputing data is needed to minimize the loss of information caused by experimental missing data. In general, missing imputation of biological data has used major allele imputation method. but this approach is not optima]. 1'his method has high error rates of missing values estimation since the characteristics of the genotype data are not considered not take into consideration the specific structure of the data. In this paper, we show the results of the comparative evaluation of our model methods and major imputation method for the estimation of missing values.

• The Journal of the Acoustical Society of Korea
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• v.40 no.4
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• pp.337-346
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• 2021

Detection of abnormal signal generally can be done by using features of normal signals as main information because of data imbalance. This paper propose an efficient method for abnormal signal detection using parallel AutoEncoder (AE) which can use features of abnormal signals as well. The proposed Parallel AE (PAE) is composed of a normal and an abnormal reconstructors having identical AE structure and train features of normal and abnormal signals, respectively. The PAE can effectively solve the imbalanced data problem by sequentially training normal and abnormal data. For further detection performance improvement, additional binary classifier can be added to the PAE. Through experiments using public acoustic data, we obtain that the proposed PAE shows Area Under Curve (AUC) improvement of minimum 22 % at the expenses of training time increased by 1.31 ~ 1.61 times to the single AE. Furthermore, the PAE shows 93 % AUC improvement in detecting abnormal underwater acoustic signal when pre-trained PAE is transferred to train open underwater acoustic data.

• International Area Studies Review
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• v.16 no.1
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• pp.275-301
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• 2012

This study analyzes the regional disparity in India between 24 states over the period 1980 to 2009. The traditional regressive and spatial autoregressive models are used that includes measures of spatial effects. The results provide no evidence that convergence is valid in India. However, the results indicate that spatial interaction is an important element of state growth in India. The result of spatial analysis excluded two outliner states reveals more strong relationship between the weighted spatial income level and the state growth rates. Moreover, the results find that the coefficients of spatial lag of initial per capital and error terms are significantly negative. The coefficient of variation measures that the distribution of state income level has diverged over time. Therefore, this study concludes that the growth of regional state income does not have a tendency to converge rater than diverge. The results is rational because as the Indian economy is growing rapidly, some states grow faster than the others while initial poor states become the poorest ones, which increases regional disparity in India.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.6
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• pp.1353-1364
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• 2018

This study proposed a method to detect Cochlodinium polykrikoides red tide pixels in satellite images using a logistic regression model of machine learning technique under Imbalanced data. The spectral profiles extracted from red tide, clear water, and turbid water were used as training dataset. 70% of the entire data set was extracted and used for as model training, and the classification accuracy of the model was evaluated using the remaining 30%. At this time, the white noise was added to the spectral profile of the red tide, which has a relatively small number of data compared to the clear water and the turbid water, and over-sampling was performed to solve the unbalanced data problem. As a result of the accuracy evaluation, the proposed algorithm showed about 94% classification accuracy.

• Journal of the Korea Convergence Society
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• v.12 no.1
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• pp.49-55
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• 2021

Recently, with the development of database, it is possible to store a lot of data generated in finance, security, and networks. These data are being analyzed through classifiers based on machine learning. The main problem at this time is data imbalance. When we train imbalanced data, it may happen that classification accuracy is degraded due to over-fitting with majority class data. To overcome the problem of data imbalance, oversampling strategy that increases the quantity of data of minority class data is widely used. It requires to tuning process about suitable method and parameters for data distribution. To improve the process, In this study, we propose a strategy to explore and optimize oversampling combinations and ratio based on various methods such as synthetic minority oversampling technique and generative adversarial networks through genetic algorithms. After sampling credit card fraud detection which is a representative case of data imbalance, with the proposed strategy and single oversampling strategies, we compare the performance of trained classifiers with each data. As a result, a strategy that is optimized by exploring for ratio of each method with genetic algorithms was superior to previous strategies.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.12
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• pp.1588-1594
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• 2020

In this paper, we discuss a data balancing method for learning a neural network that generates digital holograms using a deep neural network (DNN). Deep neural networks are based on deep learning (DL) technology and use a generative adversarial network (GAN) series. The fringe pattern, which is the basic unit of a hologram to be created through a deep neural network, has very different data types depending on the hologram plane and the position of the object. However, because the criteria for classifying the data are not clear, an imbalance in the training data may occur. The imbalance of learning data acts as a factor of instability in learning. Therefore, it presents a method for classifying and balancing data for which the classification criteria are not clear. And it shows that learning is stabilized through this.