• Journal of Korea Port Economic Association
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• v.23 no.4
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• pp.159-175
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• 2007

The purpose of this study is to examine the causal relationship between trading volume of freight and industrial growth in Korea ports, and to induce policy implications. In order to test whether time series data is stationary and the model is fitness or not, we put in operation unit root test, cointegration test. And we apply Granger causality based on an error correction model, Hsiao(1981) method and variance decomposition. The results indicate that the extent of causality between trading volume of freight and industrial growth is strong in order of Incheon port, Busan port, Gwang Yang port, Ulsan port. We can infer policy suggestions as follows; The port policy of government must be focused on re-adjusting investment among Korea ports and raising competitive power of Korea ports

• Journal of Advanced Navigation Technology
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• v.15 no.4
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• pp.543-548
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• 2011

In this paper, H-polarized electromagnetic scattering problems by a resistive strip grating over a grounded dielectric plane according to the strip width and grating period, the relative permittivity and thickness of a dielectric layer, and incident angles of a TE (transverse electric) plane wave are analyzed by applying the FGMM (Fourier-Galerkin Moment Method). The tapered resistivity of resistive strips in this paper varies from finite resistivity at one edge to zero resistivity at the other edge, then the induced surface current density on the resistive strip is expanded in a series of Jacobi polynomials of the order ${\alpha}=1$, ${\beta}=0$ as a kind of orthogonal polynomials. The numerical results of the normalized reflected power show in good agreement with those of existing papers.

• Corrosion Science and Technology
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• v.15 no.2
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• pp.58-68
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• 2016

Many industries need the universal standard or technique to obtain the identical CCT regardless of specimen geometries. This study aimed to determine an appropriate applied torque to the cylindrical specimen defining the apparatus and the procedure to measure the temperature of initiating crevice corrosion in tubular shape products such as pipes, tubes and round rods etc; the test method also proved applicable to the plate type specimen. A series of experiments for CCT measurements with the plate type and cylindrical stainless steel specimens of various diameters with different microstructures (austenitic and duplex) and PRENs were conducted to determine the relationship among geometries on CCT. Thus, the apparatus that could measure the CCT of stainless steels with both plate and cylindrical geometries was newly designed. The use of the apparatus facilitated the same CCT value for both geometries only if the specimens were made of the same alloy. The applied torque can be calculated for various diameters of the cylindrical specimens using the following relation; Applied torque, $Nm=-0.0012D^2+0.019D+2.4463$ (D; the diameter of cylindrical specimen, mm). However, upwards of 35 mm diameter cylindrical specimens require 1.58Nm, which is the same torque for the plate type specimen; in addition, this test method cannot be used for cylindrical specimens of less than 15 mm diameter.

• International Journal of Computer Science & Network Security
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• v.23 no.3
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• pp.151-160
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• 2023

The Hadith is the second source of Islamic jurisprudence after Qur'an. Both sources are indispensable for muslims to practice Islam. All Ahadith are collected and are written. But most books of Hadith contain Ahadith that can be weak or rejected. So, quite a long time, scholars of Hadith have defined laws, rules and principles of Hadith to know the correct Hadith (Sahih) from the fair (Hassen) and weak (Dhaif). Unfortunately, the application of these rules, laws and principles is done manually by the specialists or students until now. The work presented in this paper is part of the automatic treatment of Hadith, and more specifically, it aims to automatically process the chain of narrators (Hadith Isnad) to find its different components and affect for each component its own tag using a statistical method: the Hidden Markov Models (HMM). This method is a power abstraction for times series data and a robust tool for representing probability distributions over sequences of observations. In this paper, we describe an important tool in the Hadith isnad processing: A chunker with HMM. The role of this tool is to decompose the chain of narrators (Isnad) and determine the tag of each part of Isnad (POI). First, we have compiled a tagset containing 13 tags. Then, we have used these tags to manually conceive a corpus of 100 chains of narrators from "Sahih Alboukhari" and we have extracted a lexicon from this corpus. This lexicon is a set of XML documents based on HPSG features and it contains the information of 134 narrators. After that, we have designed and implemented an analyzer based on HMM that permit to assign for each part of Isnad its proper tag and for each narrator its features. The system was tested on 2661 not duplicated Isnad from "Sahih Alboukhari". The obtained result achieved F-scores of 93%.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.4
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• pp.420-425
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• 1999

The maximum demand controller is an electrical equipment installed at the consumer side of power system for monitoring the electrical energy consumed during every integrating period and preventing the target maximum demand (MD) being exceeded by disconnecting sheddable loads. By avoiding the peak loads and spreading the energy requirement the controller contributes to maximizing the utility factor of the generator systems. It results in not only saving the energy but also reducing the budget for constructing the natural base facilities by keeping thc number of generating plants ~ninimumT. he conventional MD controllers often bring about the large number of control actions during the every inteyating period and/or undesirable loaddisconnecting operations during the beginning stage of the integrating period. These make the users aviod the MD controllers. In this paper. fuzzy control technique is used to get around the disadvantages of the conventional MD control system. The proposed MD controller consists of the predictor module and the fuzzy MD control module. The proposed forecasting method uses the SOFM neural network model, differently from time series analysis, and thus it has inherent advantages of neural network such as parallel processing, generalization and robustness. The MD fuzzy controller determines the sensitivity of control action based on the time closed to the end of the integrating period and the urgency of the load interrupting action along the predicted demand reaching the target. The experimental results show that the proposed method has more accurate forecastinglcontrol performance than the previous methods.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.4
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• pp.149-158
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• 2023

As data utilization is becoming more important recently, the importance of data centers is also increasing. However, the data center is a problem in terms of environment and economy because it is a massive power-consuming facility that runs 24 hours a day. Recently, studies using deep learning techniques to reduce power used in data centers or servers or predict traffic have been conducted from various perspectives. However, the amount of traffic data processed by the server is anomalous, which makes it difficult to manage the server. In addition, many studies on dynamic server management techniques are still required. Therefore, in this paper, we propose a dynamic server management technique based on Long-Term Short Memory (LSTM), which is robust to time series data prediction. The proposed model allows servers to be managed more reliably and efficiently in the field environment than before, and reduces power used by servers more effectively. For verification of the proposed model, we collect transmission and reception traffic data from six of Wikipedia's data centers, and then analyze and experiment with statistical-based analysis on the relationship of each traffic data. Experimental results show that the proposed model is helpful for reliably and efficiently running servers.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.11
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• pp.527-536
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• 2017

With the recent development of smart grid industry, the necessity for efficient EMS(Energy Management System) has been increased. In particular, in order to reduce electric load and energy cost, sophisticated electric load forecasting and efficient smart grid operation strategy are required. In this paper, for more accurate electric load forecasting, we extend the data collected at demand time into high time resolution and construct an artificial neural network-based forecasting model appropriate for the high time resolution data. Furthermore, to improve the accuracy of electric load forecasting, time series data of sequence form are transformed into continuous data of two-dimensional space to solve that problem that machine learning methods cannot reflect the periodicity of time series data. In addition, to consider external factors such as temperature and humidity in accordance with the time resolution, we estimate their value at the time resolution using linear interpolation method. Finally, we apply the PCA(Principal Component Analysis) algorithm to the feature vector composed of external factors to remove data which have little correlation with the power data. Finally, we perform the evaluation of our model through 5-fold cross-validation. The results show that forecasting based on higher time resolution improve the accuracy and the best error rate of 3.71% was achieved at the 3-min resolution.

• Korean Journal of Social Welfare Studies
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• no.37
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• pp.117-144
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• 2008

This is a timeseries study on the riving forces behind the changes of Korean welfare state. There are a few previous studies on the determinants of korean welfare state. These previous studies have some limitations in terms of reliability of the data source and validity of the statistical method used. Using the Comparative Social Policy Data-set(CSPD), we try to overcome the limitation of these previous studies. And adapting the time series regression, we examine the hypotheses about the changes of korean welfare state. In this study, four dependent variables are examined: the ratio of public social welfare expenditure to the GDP(WELGDP), the ratio of public social welfare expenditure to the government budget(WELGOV), the ratio of social expenditure to the GDP(SOCX), social welfare expenditure per capita. And independent variables were selected based on the theoretical background on the changes of welfare state. The results of this study as follows: First, the variables based on structural functionalism (industrialization) are the major driving forces behind the changes of korean welfare state since 1960s. Second, the effect of unemployment variable may be reasonably interpreted as reflecting the residual characteristics of korean welfare state. Third, the politics of the left based on power resource theory should be restrictedly interpreted. Ultimately, korean welfare state is still at rudimentary stage where the theory of industrialization is well applied as a driving forces behind the changes of welfare state.

• The Korean Journal of Applied Statistics
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• v.32 no.1
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• pp.161-171
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• 2019

Varied methods have been researched continuously because the past as the daily maximum electricity demand expectation has been a crucial task in the nation's electrical supply and demand. Forecasting the daily peak electricity demand accurately can prepare the daily operating program about the generating unit, and contribute the reduction of the consumption of the unnecessary energy source through efficient operating facilities. This method also has the advantage that can prepare anticipatively in the reserve margin reduced problem due to the power consumption superabundant by heating and air conditioning that can estimate the daily peak load. This paper researched a model that can forecast the next day's daily peak load when considering the influence of temperature and weekday, weekend, and holidays in the Seasonal ARIMA, TBATS, Seasonal Reg-ARIMA, and NNETAR model. The results of the forecasting performance test on the model of this paper for a Seasonal Reg-ARIMA model and NNETAR model that can consider the day of the week, and temperature showed better forecasting performance than a model that cannot consider these factors. The forecasting performance of the NNETAR model that utilized the artificial neural network was most outstanding.

• The Journal of the Korea Contents Association
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• v.22 no.9
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• pp.19-28
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• 2022

This study aimed at public officials who are most strongly demanded to tear up the corruption and diagnosed what they really think about 'the corruption'. So, a qualitative research method called 'subjectivity study' or 'Q methodology' was used to typing on the perception of corruption. In other words, 30 Q-samples related to corruption and P-samples composed of 30 public officials were investigated to derive the analysis results. As a result of the analysis, three types of perceptions of corruption were defined. That is, showed a high distribution of civil servants such as 'educational administration' and 'teacher', and and showed a high distribution of 'general administrative positions' civil servants. Also, among the respondents of , it was found that the distribution of 'high' was higher for the level of corruption in our society, and the distribution of 'medium' for and was found in the case of the respondents with high factor weight. The overall explanatory power was high at 62.11%, and based on the series of results, a hypothesis could be found that 'the perception of corruption differs according to the characteristics of the work of public officials'. By the results, the commonly recognized terms for 'corruption' were 'politician/politician' and 'solicitation'. Therefore, based on a series of results, this study is expected to be the 'Priming' for finding ways to move toward a more transparent society by diagnosing and reflecting on the thoughts of corruption in our society once again.