• Journal of the Korean Society of Safety
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• v.27 no.6
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• pp.43-47
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• 2012

The unbalance currents flow the High Temperature Superconducting (HTS) power cable caused by asymmetrical fault, harmonic distortion and unbalanced load. That problem causes additional loss and leakage field in the HTS power cable, and deteriorates the electric power quality and stability. In addition, large amounts of unbalanced current can cause negative sequence and ground relays to operate. This paper presents an analysis unbalanced three-phase current distribution in HTS power cable caused by unbalanced load condition and grounding methods using PSCAD/EMTDC. The results obtained through the analysis would provide important data for the design of HTS power cables and valid information for their installation in power system.

• Proceedings of the KIEE Conference
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• 2005.07a
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• pp.459-461
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• 2005

This paper analyses the induced voltage characteristic of CCPU with unbalanced current from distribution line on underground transmission power cable systems. In switching surge strokes, in order to obtain the data of induced voltage/current on CCPU, the actual proof test carried out. This paper is expected to contribute the establishment of proper protection methods of CCPU against the unbalanced current from distribution line on underground transmission power cable systems.

• Journal of KIISE:Software and Applications
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• v.34 no.9
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• pp.804-816
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• 2007

Sample training data for machine learning often contain irrelevant information or redundant concept. It is also the case that the original data may include noise. If the information collected for constructing learning model is not reliable, it is difficult to obtain accurate information. So the system attempts to find relations or regulations between features and categories in the teaming phase. The feature selection is to remove irrelevant or redundant information before constructing teaming model. for improving its performance. Existing feature selection methods assume that the distribution of documents is balanced in terms of the number of documents for each class and the length of each document. In practice, however, it is difficult not only to prepare a set of documents with almost equal length, but also to define a number of classes with fixed number of document elements. In this paper, we propose a new feature selection method that considers the impurities among the words and unbalanced distribution of documents in categories. We could obtain feature candidates using the word impurity and eventually select the features through unbalanced distribution of documents. We demonstrate that our method performs better than other existing methods via some experiments.

• Journal of Biomedical Engineering Research
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• v.29 no.6
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• pp.466-476
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• 2008

The more people use ambulatory electrocardiogram(ECG) for arrhythmia detection, the more researchers report the automatic classification algorithms. Most of the previous studies don't consider the un-balanced data distribution. Even in patients, there are much more normal beats than abnormal beats among the data from 24 hours. To solve this problem, the hierarchical classification using 21 features was adopted for arrhythmia abnormal beat detection. The features include R-R intervals and data to describe the morphology of the wave. To validate the algorithm, 44 non-pacemaker recordings from physionet were used. The hierarchical classification model with 2 stages on domain knowledge was constructed. Using our suggested method, we could improve the performance in abnormal beat classification from the conventional multi-class classification method. In conclusion, the domain knowledge based hierarchical classification is useful to the ECG beat classification with unbalanced data distribution.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.286-287
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• 2015

Currently, Studies on improving the reliability of power supply is becoming an important issue because of the increase in demand of the electric power system. Therefore necessity of automation in distribution system is increasing day by day. However, a measured voltage data from FRTU of distribution automation system is incorrect because of installation space limits. Therefore there is a need of system analysis method by considering the characteristics of the distribution system. For a distribution system, applying the power flow method of transmission system has some problems, as distribution is radial system and it has unbalanced load. Therefore power flow by considering the characteristics of the distribution system have been studied. Existing power flow analysis of the distribution system has different methods like direct analysis method, backward/forward sweep method, modified method of newton raphson etc. In this paper, an improved power flow analysis method based on backward/forward sweep method is proposed in order to efficiently operate the distribution automation system. The proposed method of power flow has been verified through the result of case study.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.12
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• pp.445-452
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• 2022

Class distribution of unbalanced data is an important part of the digital world and is a significant part of cybersecurity. Abnormal activity of unbalanced data should be found and problems solved. Although a system capable of tracking patterns in all transactions is needed, machine learning with disproportionate data, which typically has abnormal patterns, can ignore and degrade performance for minority layers, and predictive models can be inaccurately biased. In this paper, we predict target variables and improve accuracy by combining estimates using Synthetic Minority Oversampling Technique (SMOTE) and Light GBM algorithms as an approach to address unbalanced datasets. Experimental results were compared with logistic regression, decision tree, KNN, Random Forest, and XGBoost algorithms. The performance was similar in accuracy and reproduction rate, but in precision, two algorithms performed at Random Forest 80.76% and Light GBM 97.16%, and in F1-score, Random Forest 84.67% and Light GBM 91.96%. As a result of this experiment, it was confirmed that Light GBM's performance was similar without deviation or improved by up to 16% compared to five algorithms.

• Journal of the Optical Society of Korea
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• v.18 no.5
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• pp.466-471
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• 2014

An asynchronous optical packet-switching (AOPS) node with load-balancing capability can achieve better performance in reducing the high packet-loss ratio (PLR) and time delay caused by unbalanced traffic. This paper proposes a novel dynamic load-balancing algorithm for an AOPS node with limited buffer and without wavelength converters, and considering the data flow distribution and service levels. By calculating the occupancy state of the output ports, load state of the input ports, and priorities for data flow, the traffic is balanced accordingly. Simulations demonstrate that asynchronous variant data packets and output traffic can be automatically balanced according to service levels and the data flow distribution. A PLR of less than 0.01% can be achieved, as well as an average time delay of less than 0.46 ns.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.11
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• pp.1417-1423
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• 1999

Distribution system can experience the diverse events instantly and permanently. Also, it can experience high impedance fault or line drop under unbalanced situation, Accordingly, it is difficulty to identify the fault location because that data collected from distribution SCADA system may include uncertainty. This paper proposes an expert system, which can infer the faulted location the quickly and exactly for the diverse events in the distribution system. The expert system utilizes distribution SCADA function and collected data, especially, the monitoring mechanism for the normal open position switches is adopted newly in order to recognize the fault type exactly. Also, automated fault location diagnosis strategy is developed in order to minimize the spreading effect of fault obtained from the error of the system operator. The proposed strategy is implemented in C language. Especially, in order to prove the effectiveness of proposed expert system, the several scenario is simulated for the given model system. The real feeders are selected as model system for the simulation.

• The Korean Journal of Food And Nutrition
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• v.2 no.2
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• pp.31-39
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• 1989

The objective of this study is to collect basic data about sex bias in intrahousehold food distribution, by investigating 124 housekeepers living in Seoul . This Survey was performed using questionnaire about the experience of male-female dietary disparity and the attitudes about sex-differential nutrition Results were summarized as follows ; 1. 38% of housekeepers had been experienced in male-female disparity of food distribution at meal time in her childhood. Protein foods(meats & fishes) and special foods were not distributed evenly to both sexes. 2. The major causes of these unbalanced, sex-differential food distribution was rather masculine priority than food shortage. 3. Compared with the past, male-female dietary disparity was disappeared. However, 47.7% of housekeepers took more care for son's meals than daughter's, consciously or unconsciously.

• Korean Journal of Remote Sensing
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• v.36 no.1
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• pp.83-92
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• 2020

The purpose of this work was to apply geographical information system (GIS) for geostatistical analyzing by selecting a semi-variogram model to quantify the spatial correlation of the population distribution with residential neighborhoods in the both sides of Mosul city. Two hundred and sixty-eight sample sites in 240 ㎢ are adopted. After determining the population distribution with respect to neighborhoods, data were inserted to ArcGIS10.3 software. Afterward, the datasets was subjected to the semi-variogram model using ordinary kriging interpolation. The results obtained from interpolation method showed that among the various models, Spherical model gives best fit of the data by cross-validation. The kriging prediction map obtained by this study, shows a particular spatial dependence of the population distribution with the neighborhoods. The results obtained from interpolation method also indicates an unbalanced population distribution, as there is no balance between the size of the population neighborhoods and their share of the size of the population, where the results showed that the right side is more densely populated because of the small area of residential homes which occupied by more than one family, as well as the right side is concentrated in economic and social activities.