• 제목/요약/키워드: education model using the data

검색결과 1,777건 처리시간 0.031초

Intrusion Detection Scheme Using Traffic Prediction for Wireless Industrial Networks

  • Wei, Min;Kim, Kee-Cheon
    • Journal of Communications and Networks
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    • 제14권3호
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    • pp.310-318
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    • 2012
  • Detecting intrusion attacks accurately and rapidly in wireless networks is one of the most challenging security problems. Intrusion attacks of various types can be detected by the change in traffic flow that they induce. Wireless industrial networks based on the wireless networks for industrial automation-process automation (WIA-PA) standard use a superframe to schedule network communications. We propose an intrusion detection system for WIA-PA networks. After modeling and analyzing traffic flow data by time-sequence techniques, we propose a data traffic prediction model based on autoregressive moving average (ARMA) using the time series data. The model can quickly and precisely predict network traffic. We initialized the model with data traffic measurements taken by a 16-channel analyzer. Test results show that our scheme can effectively detect intrusion attacks, improve the overall network performance, and prolong the network lifetime.

멀티젠을 이용한 해상환경 DB개발 개선에 관한 연구 (A Study on the Improvement of Database contruction for Marine Environment by Multigen)

  • 김창제;김원욱;고성정
    • 해양환경안전학회지
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    • 제7권3호
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    • pp.85-92
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    • 2001
  • Ship handling simulator has much merit to provide trainees with real-like circumstances in doing virtual education and training. To improve the quality of the education, it should be included both the mathematical model which can explain complicated ship's manoeuvrability and graphic tools for 3D images which can embody the visual scenes of reality on screen. This paper is focused on how to construct the marine environment DB(data Base) using S-57 data of ENC(Electronic Navigational Chart).

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Evaluation of Predictive Models for Early Identification of Dropout Students

  • Lee, JongHyuk;Kim, Mihye;Kim, Daehak;Gil, Joon-Min
    • Journal of Information Processing Systems
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    • 제17권3호
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    • pp.630-644
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    • 2021
  • Educational data analysis is attracting increasing attention with the rise of the big data industry. The amounts and types of learning data available are increasing steadily, and the information technology required to analyze these data continues to develop. The early identification of potential dropout students is very important; education is important in terms of social movement and social achievement. Here, we analyze educational data and generate predictive models for student dropout using logistic regression, a decision tree, a naïve Bayes method, and a multilayer perceptron. The multilayer perceptron model using independent variables selected via the variance analysis showed better performance than the other models. In addition, we experimentally found that not only grades but also extracurricular activities were important in terms of preventing student dropout.

Inference on the Joint Center of Rotation by Covariance Pattern Models

  • Kim, Jinuk
    • 한국운동역학회지
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    • 제28권2호
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    • pp.127-134
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    • 2018
  • Objective: In a statistical linear model estimating the center of rotation of a human hip joint, which is the parameter related to the mean of response vectors, assumptions of homoscedasticity and independence of position vectors measured repeatedly over time in the model result in an inefficient parameter. We, therefore, should take into account the variance-covariance structure of longitudinal responses. The purpose of this study was to estimate the efficient center of rotation vector of the hip joint by using covariance pattern models. Method: The covariance pattern models are used to model various kinds of covariance matrices of error vectors to take into account longitudinal data. The data acquired from functional motions to estimate hip joint center were applied to the models. Results: The results showed that the data were better fitted using various covariance pattern models than the general linear model assuming homoscedasticity and independence. Conclusion: The estimated joint centers of the covariance pattern models showed slight differences from those of the general linear model. The estimated standard errors of the joint center for covariance pattern models showed a large difference with those of the general linear model.

Robust Multi-Layer Hierarchical Model for Digit Character Recognition

  • Yang, Jie;Sun, Yadong;Zhang, Liangjun;Zhang, Qingnian
    • Journal of Electrical Engineering and Technology
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    • 제10권2호
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    • pp.699-707
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    • 2015
  • Although digit character recognition has got a significant improvement in recent years, it is still challenging to achieve satisfied result if the data contains an amount of distracting factors. This paper proposes a novel digit character recognition approach using a multi-layer hierarchical model, Hybrid Restricted Boltzmann Machines (HRBMs), which allows the learning architecture to be robust to background distracting factors. The insight behind the proposed model is that useful high-level features appear more frequently than distracting factors during learning, thus the high-level features can be decompose into hybrid hierarchical structures by using only small label information. In order to extract robust and compact features, a stochastic 0-1 layer is employed, which enables the model's hidden nodes to independently capture the useful character features during training. Experiments on the variations of Mixed National Institute of Standards and Technology (MNIST) dataset show that improvements of the multi-layer hierarchical model can be achieved by the proposed method. Finally, the paper shows the proposed technique which is used in a real-world application, where it is able to identify digit characters under various complex background images.

Mixture model에 의한 홈쇼핑 이용자 시장세분화와 쇼핑성향 (A Study on Market Segmentations and Shopping Orientations of Home Shopping User: Based on Mixture Model)

  • 서정아;이진화;홍재원
    • 한국의류학회지
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    • 제32권7호
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    • pp.1023-1033
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    • 2008
  • The purpose of this study was to segment home-shopper market by using the demographic characteristics. This study enables a better unders landing of home-shoppers and improving the strategy of marketing. The specific objects of this study are as follow: First, it was to exam market segmentations by demographic factors using mixture model. Second, it was to exam shopping orientations of fashion merchandise according to segmentation groups. The data was collected from 637 subjects who had used the home shopping more than one time in a year. The data was analysised through frequencies, factor analysis, ANOVA, Duncan's mutiple range tests with SPSS 12.0 and Mixture model. The results of data are as follows: 1. The result of market segmentation as demographic factor using Mixture model was extracted to 4 market segments called 20's/ unmarried stage, 30's/ children bearing & rearing stage, 40's/ families with children's education stage, 50's/ aging stage. 2. Shopping orientations were extracted to 5 factors called a pleasure oriented, convenience oriented, off-line oriented, human oriented, thrift oriented.

Pooling shrinkage estimator of reliability for exponential failure model using the sampling plan (n, C, T)

  • Al-Hemyari, Z.A.;Jehel, A.K.
    • International Journal of Reliability and Applications
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    • 제12권1호
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    • pp.61-77
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    • 2011
  • One of the most important problems in the estimation of the parameter of the failure model, is the cost of experimental sampling units, which can be reduced by using any prior information available about ${\theta}$, and devising a two-stage pooling shrunken estimation procedure. We have proposed an estimator of the reliability function (R(t)) of the exponential model using two-stage time censored data when a prior value about the unknown parameter (${\theta}$) is available from the past. To compare the performance of the proposed estimator with the classical estimator, computer intensive calculations for bias, mean squared error, relative efficiency, expected sample size and percentage of the overall sample size saved expressions, were done for varying the constants involved in the proposed estimator (${\tilde{R}}$(t)).

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과학과 과학 교육에서 사용되는 모델에 관한 예비 초등 교사들의 인식 (Preservice Elementary Teachers' Perceptions on Models Used in Science and Science Education)

  • 오필석
    • 한국초등과학교육학회지:초등과학교육
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    • 제28권4호
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    • pp.450-466
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    • 2009
  • The purpose of this study was to explore preservice elementary teachers' perceptions on models used in science and science education. Participants were sixty-one undergraduate students who were enrolled in a science education course offered at a university of education located in a mid-sized city, Korea. Data were obtained from the participants at the beginning of the course when they provided their answers to a questionnaire about models. The analysis revealed that a large number of the preservice teachers perceived models as representative of physical realities. By contrast, a relatively small number of them viewed models as representations of ideas or things like theories or hypotheses. Lots of the participants were apt to define a model from the perspective of its functions and considered the purposes of models communication, teaching, and understanding as well as visualization, simplification, and clarification. Most of the preservice teachers believed that there could be multiple models for a single target, and all of them answered that models could be changed in science. It was therefore concluded that the preservice teachers perceived properly the multiplicity and variability of models. Nevertheless, they could not elaborate how a model is used and evaluated in the process of scientific inquiry, and just a few of them mentioned the detailed nature of models. The preservice teachers possessed teacher-centered views of using models in the science classroom, and a small number of them remarked that they were going to use models for students to develop their own models and perform scientific inquiry.

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Biosorption of Methylene Blue from Aqueous Solution Using Xanthoceras sorbifolia Seed Coat Pretreated by Steam Explosion

  • Yao, Zeng-Yu;Qi, Jian-Hua
    • Journal of Forest and Environmental Science
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    • 제32권3호
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    • pp.253-261
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    • 2016
  • Xanthoceras sorbifolia seed coat (XSSC) is a processing residue of the bioenergy crop. This work aimed to evaluate the applicability of using the steam explosion to modify the residue for dye biosorption from aqueous solutions by using methylene blue as a model cationic dye. Equilibrium, kinetic and thermodynamic parameters for the biosorption of methylene blue on the steam-exploded XSSC (SE-XSSC) were evaluated. The kinetic data followed the pseudo-second-order model, and the rate-limiting step was the chemical adsorption. Intraparticle diffusion was one of the rate-controlling factors. The equilibrium data agreed well with the Langmuir isotherm, and the biosorption was favorable. The steam-explosion pretreatment strongly affected the biosorption in some respects. It reduced the adsorption rate constant and the initial sorption rate of the pseudo-second-order model. It enhanced the adsorption capacity of methylene blue at higher temperatures while reduced the capacity at lower ones. It changed the biosorption from an exothermic process driven by both the enthalpy and the entropy to an endothermic one driven by entropy only. It increased the surface area and decreased the pH point of zero charge of the biomass. Compared with the native XSSC, SE-XSSC is preferable to MB biosorption from warmer dye effluents.

2008-2017 패널분석 결과에 나타난 개인-직무 적합성과 직무만족 간의 관계 (A Study on the Relationship between Person-Job Fit and Job Satisfaction shown in the Panel Data for 2008-2017)

  • 취칭칭;이정현
    • 아태비즈니스연구
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    • 제10권4호
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    • pp.87-118
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    • 2019
  • The purpose of this study is to examine the effects of person-job fit, which consists of educational fit and skill fit, on employees' intrinsic job satisfaction. To the end, the 10-year balanced panel data of the Korean Labor and Income Panel Study(KLIPS) by the Korea Labor Institute (KLI) for 2008-2017 are utilized. This study analyzes 12,730 observations by 1,273 employees by using fixed effect model, random effect model, and pooled OLS estimation method. The empirical results are as follows: First, it is founded that educational fit and skill fit seem affect job satisfaction positively. Second, the negative effects of over-education are clear and the negative effects of under-education are unclear, while the effects of over-skilled and under-skilled are insignificant statistically. Third, the results imply that the size of effect of over-education on intrinsic job satisfaction is larger than that of the effect of over-skilled. Forth, it is shown that the use of fixed effect model is more effective and trustworthy than that of random effect model and pooled OLS estimation method, implying that the effect size of coefficients which are estimated by pooled OLS method and random effect model are likely over-estimated. The empirical results above imply that firms and employees should focus on solving over-education issue before all in order to enhance employees' job satisfaction and it is needed to monitor regularly whether systemic job assignment process is done based on the employees' educational attainment and skill level and to provide more chances for job re-allocation and job rotation.