• International Journal of Computer Science & Network Security
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• v.21 no.12
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• pp.149-156
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• 2021

This study intended to test the structure of the latent factor of an effectiveness scale and the stability of invariance across groups of students' classifications (gender and levels of education). In the large, non-clinical sample (850), students completed the effectiveness scale. The (CFA) confirmatory factor analysis was used to investigate the factor-structure of the measure, and multiple-group confirmatory factor analysis (MGCFA) model was used to test the stability of invariance across groups of students' classifications. The findings of the CFA indicated support for the original four-factor model. Additional analyses of the MGCFA method support the measurement (configural, metric and strong) invariant and practical invariant components of this model. There was an invariant across gender. There was partially invariant across groups of levels of education. The scale exists in groups of levels of education assess the same concepts of, excluding Items 15 and 10. Given that this study is the first investigation for the structure of the effectiveness scale.

• Smart Media Journal
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• v.12 no.7
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• pp.52-58
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• 2023

Alzheimer's disease is one type of dementia, the symptoms can be treated by detecting the disease at its early stages. Recently, many computer-aided diagnosis using magnetic resonance image(MRI) have shown a good results in the classification of AD. Taken these MRI images and feed to Free surfer software to extra the features. In consideration, using T1-weighted images and classifying using the convolution neural network (CNN) model are proposed. In this paper, taking the subjects from ADNI of subcortical and cortical features of 190 subjects. Consider the study to reduce the complexity of the model by using the single layer in the Res-Net, VGG, and Alex Net. Multi-class classification is used to classify four different stages, CN, EMCI, LMCI, AD. The following experiment shows for respective classification Res-Net, VGG, and Alex Net with the best accuracy with VGG at 96%, Res-Net, GoogLeNet and Alex Net at 91%, 93% and 89% respectively.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.11
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• pp.2108-2116
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• 2008

The paper concerns Fuzzy C-Means clustering based Radial Basis Function neural networks (FCM-RBFNN) and the optimization of the network is carried out by means of Particle Swarm Optimization(PSO). FCM-RBFNN is the extended architecture of Radial Basis Function Neural Network(RBFNN). In the proposed network, the membership functions of the premise part of fuzzy rules do not assume any explicit functional forms such as Gaussian, ellipsoidal, triangular, etc., so its resulting fitness values directly rely on the computation of the relevant distance between data points by means of FCM. Also, as the consequent part of fuzzy rules extracted by the FCM - RBFNN model, the order of four types of polynomials can be considered such as constant, linear, quadratic and modified quadratic. Weighted Least Square Estimator(WLSE) are used to estimates the coefficients of polynomial. Since the performance of FCM-RBFNN is affected by some parameters of FCM-RBFNN such as a specific subset of input variables, fuzzification coefficient of FCM, the number of rules and the order of polynomials of consequent part of fuzzy rule, we need the structural as well as parametric optimization of the network. In this study, the PSO is exploited to carry out the structural as well as parametric optimization of FCM-RBFNN. Moreover The proposed model is demonstrated with the use of numerical example and gas furnace data set.

• Journal of Digital Convergence
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• v.11 no.12
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• pp.1-10
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• 2013

The value added network service industry has played and important role in the telecommunication service industry and in the economic development of Korea. This study uses input-output analysis to investigate the role of value added network service sector in the Korean national economy for the period 2000, 2005, 2009, focusing on four topics in its application: production inducing effects, value-added inducing effect, employment-inducing effects by demand-driven model and supply shortage effect by supply-driven model, inflation impacts by the Leontief price model, finally analysing inter-industry linkage effects. The results of this study are as follows: production inducing effects analysis 2000 0.5253won to 2009 1.31314won, value-added inducing effects 0.25112won to 0.5337won employment inducing effects from 0.09749 to 0.21025 people grew, the supply shortage effect from 1.29003 to 2.12048won, price impact of Leontief price model was increased from 0.0022% to 0.00258%. Finally, inter-industry linkage effects, appeared to have the characteristics of final demand raw industrial.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.50 no.11
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• pp.506-513
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• 2001

The islanding detection for distributed resources (DR) becomes an important and emerging issue in power system protection since the distributed generator installations are rapidly increasing and most of the installed systems are interconnected with distribution network. In order to avoid the negative impacts from islanding operations of DR on protection, operation and management of distribution system, it is necessary to effectively detect the islanding operations of DR and rapidly disconnect it from distribution network. Generally, it is difficult to detect islanding operation by monitoring only one system parameter This paper presents a new logic based islanding detection method for distributed resources(DR) which are interconnected with distribution network. The proposed method detects the islanding operation by monitoring four system parameter: voltage variation, phase displacement, frequency variation, and the variation of total harmonic distortion(THD) of current; therefore, it effectively detects island operation of DR unit operating in parallel with the distribution network. We also verified the efficiency of the proposed algorithm using the radial distribution network of IEEE 34 bus model.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.3
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• pp.133-140
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• 1998

The neural network was applied to identify wear debris generated from the lubricated machine surface. The wear test was carried out under different experimental conditions. In order to describe characteristics of debris of various shapes and sizes, the four shape parameter(50％ volumetric diameter, aspect, roundness and reflectivity) of wear debris are used as inputs to the network and learned the friction condition of five values(material 3, applied load 1, sliding distance 1). It is shown that identification results depend on the ranges of these shape parameter learned. The three kinds of the wear debris had a different pattern characteristics and recognized the friction condition and materials very well by neural network.

• Communications for Statistical Applications and Methods
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• v.31 no.1
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• pp.105-127
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• 2024

We propose recurrent neural network (RNN) methods for forecasting realized volatility (RV). The data are RVs of ten major stock price indices, four from the US, and six from the EU. Forecasts are made for relative ratio of adjacent RVs instead of the RV itself in order to avoid the out-of-scale issue. Forecasts of RV ratios distribution are first constructed from which those of RVs are computed which are shown to be better than forecasts constructed directly from RV. The apparent asymmetry of RV ratio is addressed by the Piecewise Min-max (PM) normalization. The serial dependence of the ratio data renders us to consider two architectures, long short-term memory (LSTM) and gated recurrent unit (GRU). The hyperparameters of LSTM and GRU are tuned by the nested cross validation. The RNN forecast with the PM normalization and ratio transformation is shown to outperform other forecasts by other RNN models and by benchmarking models of the AR model, the support vector machine (SVM), the deep neural network (DNN), and the convolutional neural network (CNN).

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.1B
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• pp.52-63
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• 2004

Packet loss process of cdma2000 mobile Internet channel deployed in Korea is modeled as a two state Markov process known as Gilbert model. This paper proposes the procedures to derive four parameters of the our modified Gilbert model from packet loss trace taken from two major cdma2000 networks in Korea. These four parameters are derived in various situations, that is, with fixed and moving terminals, in open field and urban areas. They can be used to produce synthetic packet loss patterns for study of the channel. Moreover, if they are calculated on-line during multimedia service, they can be used to make loss protection controls adaptive to network condition.

• Journal of the Korean association of regional geographers
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• v.21 no.1
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• pp.21-38
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• 2015

This study attempts to estimate the complementarity between 21 cities in the Korean Southeastern Region using data on the network time distance and the volume of flow between the cities. Four types of flows recognized are people, commodities, information and finance. The first two types of flows are thought to be made on the transportation network while the last two are on the communication network. For the purpose of the study, the expected volumes of flows between cities are first estimated using the gravity-based regression and doubly-constrained entropy maximization models. These baseline volumes are then subtracted from the observed volumes of flows (of people and commodities) or the estimated volumes of flows (of information and finance) in order to identify positive differences or complementarities. The result shows that these four types of complementarity flows form distinctive urban networks in terms of spatial pattern and urban hierarchy. This suggests that more customized strategies to different types of complementarity are recommended to properly address the issues related to network infrastructure provision in the pursuit of the network city model in the region.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.7
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• pp.151-160
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• 2021

The purposes of this research were: 1) to analyze the confirmatory factors with the business operational model of entrepreneurs of small and medium enterprises (SMEs) in the textile and clothing industry, and 2) to verify the congruence of the model with the operational ways of the entrepreneurs of SMEs in the textile and clothing industry. The sample consisted of 500 small and medium enterprise entrepreneurs in the textile and clothing industry. This study was quantitative research and the instrument used to collect the data was a questionnaire. The data was analyzed using 1^st order and 2^nd order of confirmatory analysis (CFA). The findings of this research revealed that the model of SMEs in the textile and clothing industry was overall at a high level. Four main factors were used for the model of SMEs in the textile and clothing industry by their importance in descending order as follows: marketing mix (MM), collaboration network (CN), production inventory management (PIM), and creativity (CT). The results of verification of model congruence revealed the model of SMEs in the textile and clothing industry was fit and in accordance with the empirical data.