• 한국직업건강간호학회지
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• 제26권1호
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• pp.19-29
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• 2017

Purpose: The purpose of this study was to define and clarify the concept of reality shock in new graduated nurses. Methods: The hybrid model was used to develop the concept of reality shock. The model included a field study. The participants were 9 newly graduated nurses with a nursing career spanning less than a year. Results: The reality shock in newly graduated nurses was identified to have three dimensions and seven attributes. Specifically: 1) the dimension of performance included two attributes (conflict between theory and practice, and being overwhelmed by the workload), 2) the dimension of relationship included three attributes (loss of support, embarrassment from interference, and relational withdrawal), 3) the dimension of expectations included two attributes(value confusions and incongruity in personal life). Conclusion: Newly graduated nurses' reality shock was defined as a state of incongruence in their entire life that the new nurses experienced owing to value confusions that occurred due to the conflicts between theory and practice in an unfamiliar work environment, getting overwhelmed by the workload, and withdrawing establishing relationships with others due to the loss of support and excessive interference. These findings could help develop intervention strategies to decrease reality shock in newly graduated nurses.

• 복식
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• 제51권6호
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• pp.117-128
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• 2001

The purposes of this study was to investigate discriminative factors of clothing buying intention on Internet shopping mall. The sample included 435 male(44.8%) and female(55.2% ) adults, and an instrument was developed based on previous studies. The statistical analysis used for this study were factor analysis, 1-test, and LISREL. The results of factor analysis showed that consumers evaluated apparel internet shopping attributes based on perceptional dimensions of internet shopping consisted of clothing quality and value. web service quality and value, and adoption of internet shopping. Each dimension has sub-factors as follows: (1) clothing quality was perceived as 'artistry' 'sociality' and 'practicality'. (2) web interface service quality was perceived as 'visuality', 'advantage', 'response', 'dependability' and 'buying-confidence'. (3) internet shopping adoption was perceived as 'usefulness' and 'convenience'. T-test revealed that consumer's buying intention, re-entry intention, and store attitude were differed concerning all sub-factors including 'usefulness' and 'convenience' in adoption of Internet shopping dimension. As a result of LISREL, clothing buying intention path model was set up as following path. (1) 'artistry', 'sociality' and 'practicality' of clothing quality affected clothing value perception positively. (2) 'visuality', 'advantage', 'response' and 'buying-confidence' of web service quality affected web service value perception positively. (3) clothing and web service value perception affected store attitude positively. (4) store attitude affected clothing buying intention positively. However, Adoption of Internet shopping dimension that was perceived as usefulness and convenience did not affect clothing buying intention path model. Therefore, consumers buying, intention model in internet circumstance could be used nearly the same as real market circumstance.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제12권1호
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• pp.1-5
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• 2012

Data reduction has been used widely in data mining for convenient analysis. Principal component analysis (PCA) and factor analysis (FA) methods are popular techniques. The PCA and FA reduce the number of variables to avoid the curse of dimensionality. The curse of dimensionality is to increase the computing time exponentially in proportion to the number of variables. So, many methods have been published for dimension reduction. Also, data augmentation is another approach to analyze data efficiently. Support vector machine (SVM) algorithm is a representative technique for dimension augmentation. The SVM maps original data to a feature space with high dimension to get the optimal decision plane. Both data reduction and augmentation have been used to solve diverse problems in data analysis. In this paper, we compare the strengths and weaknesses of dimension reduction and augmentation for classification and propose a classification method using data reduction for classification. We will carry out experiments for comparative studies to verify the performance of this research.

• 한국소성가공학회:학술대회논문집
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• 한국소성가공학회 2004년도 추계학술대회논문집
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• pp.338-341
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• 2004

The dimension of forged part is different with the die dimension by the various effects, such as, elastic deformation and thermal effect. And, the difference amounts are not same according to the forging conditions, for example, forging mode, flow stress, etc. Therefore, the use of FEA is effective to predict and update the required die dimension. However, the variables for FE simulation are also as many as variables in the experiment. The variables give very much effect to the accuracy of FE results. At first, the material model is very deeply affected to the estimated dimension of forged part. And the considering of loading and ejecting stages is also important to increase the dimensional accuracy. The experiment and FEA are performed to investigate the dimensional changes and accuracy in cold forging. Two types of upsetting are used to survey the effects of forging mode and stages.

• Communications for Statistical Applications and Methods
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• 제26권5호
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• pp.497-506
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• 2019

Forecasting the U.S. employment level is made using machine learning methods of the artificial neural network: deep neural network, long short term memory (LSTM), gated recurrent unit (GRU). We consider the big data of the federal reserve economic data among which 105 important macroeconomic variables chosen by McCracken and Ng (Journal of Business and Economic Statistics, 34, 574-589, 2016) are considered as predictors. We investigate the influence of the two statistical issues of the dimension reduction and time series differencing on the machine learning forecast. An out-of-sample forecast comparison shows that (LSTM, GRU) with differencing performs better than the autoregressive model and the dimension reduction improves long-term forecasts and some short-term forecasts.

• Journal of Computational Design and Engineering
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• 제1권2호
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• pp.96-102
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• 2014

In the field of design and manufacturing, there are many problems with managing dynamic states of three-dimensional (3D) objects. In order to solve these problems, the four-dimensional (4D) mesh model and its modeling system have been proposed. The 4D mesh model is defined as a 4D object model that is bounded by tetrahedral cells, and can represent spatio-temporal changes of a 3D object continuously. The 4D mesh model helps to solve dynamic problems of 3D models as geometric problems. However, the construction of the 4D mesh model is limited on the time-series 3D voxel data based method. This method is memory-hogging and requires much computing time. In this research, we propose a new method of constructing the 4D mesh model that derives from the 3D mesh model with continuous rigid body movement. This method is realized by making a swept shape of a 3D mesh model in the fourth dimension and its tetrahedralization. Here, the rigid body movement is a screwed movement, which is a combination of translational and rotational movement.

• 산업기술연구
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• 제22권A호
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• pp.219-228
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• 2002

The divided channel is not often used on the river and when the installation is for the controlling of the flow quantity. The determination of the channel size is not a easy task. Model tests are examined to confirm the variation of distribution rate by the method of the channel installation and the position of the structure and the adjustment of numerical simulation is executed by the comparing of the results. This study is to execute numerical model according to installation of divided channel by using AQUADYN program, the 2nd dimension numerical model, and HEC-RAS program, the 1st dimension numerical model, by the shape of divided channel. Also, it compares with difference by method about each case.

• 한국통계학회:학술대회논문집
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• 한국통계학회 2003년도 추계 학술발표회 논문집
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• pp.9-14
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• 2003

A Bayesian model-based clustering method is proposed for clustering objects on the basis of dissimilarites. This combines two basic ideas. The first is that tile objects have latent positions in a Euclidean space, and that the observed dissimilarities are measurements of the Euclidean distances with error. The second idea is that the latent positions are generated from a mixture of multivariate normal distributions, each one corresponding to a cluster. We estimate the resulting model in a Bayesian way using Markov chain Monte Carlo. The method carries out multidimensional scaling and model-based clustering simultaneously, and yields good object configurations and good clustering results with reasonable measures of clustering uncertainties. In the examples we studied, the clustering results based on low-dimensional configurations were almost as good as those based on high-dimensional ones. Thus tile method can be used as a tool for dimension reduction when clustering high-dimensional objects, which may be useful especially for visual inspection of clusters. We also propose a Bayesian criterion for choosing the dimension of the object configuration and the number of clusters simultaneously. This is easy to compute and works reasonably well in simulations and real examples.

• Journal of Mechanical Science and Technology
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• 제16권1호
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• pp.60-74
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• 2002

In this study, normal, log-normal, triangular, uniform. Weibull, Erlang and unit beta probability density functions are tried to represent the behaviour of frequency distributions of workpiece dimensions collected from various manufacturing firms. Among the distribution functions, the unit beta distribution function is found to be the best fit using the chi-square test of fit. An attempt is made for the adoption of the unit beta model to x-bar charts of quality control in manufacturing. In this direction, upper and lower control limits (UCL and LCL) of x-bar control charts of dimension measurements are estimated for the beta model, and the observed differences between the beta and normal model control limits are discussed for the measurement sets.

• Journal of information and communication convergence engineering
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• 제7권3호
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• pp.361-365
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• 2009

This paper has been presented the transport characteristics of FinFET using the analytical potential model based on the Poisson's equation in subthreshold and threshold region. The threshold voltage is the most important factor of device design since threshold voltage decides ON/OFF of transistor. We have investigated the variations of threshold voltage and drain induced barrier lowing according to the variation of geometry such as the length, width and thickness of channel. The analytical potential model derived from the three dimensional Poisson's equation has been used since the channel electrostatics under threshold and subthreshold region is governed by the Poisson's equation. The appropriate boundary conditions for source/drain and gates has been also used to solve analytically the three dimensional Poisson's equation. Since the model is validated by comparing with the three dimensional numerical simulation, the subthreshold current is derived from this potential model. The threshold voltage is obtained from calculating the front gate bias when the drain current is $10^{-6}A$.