• International conference on construction engineering and project management
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• 2009.05a
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• pp.464-471
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• 2009

Robot and application of IT skill of construction industry are slow comparatively than another thing industry by the feature. This research proposes progress management and real time information gathering through construction automation and RFID focused on steel structure construction. Building for RTPM system, must consider various variables and surrounding situation in construction field and it is the most important and difficult matter that draw right requirement and grasp relation between this requirements to accomplish one suitable task considering these environment. Therefore, in this study analyzes requirement and target for RTPM system based on scenario that is easy to draw requirement and apply this to use case model. Presented method suggests that represent relation between goals and way that refines goal systematically from requirement of RTPM system. And it could express for visualization through the Way that attaches nonfunctional elements of system with system internal goal.

• Korea Trade Review
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• v.45 no.4
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• pp.95-105
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• 2020

Vietnam has experienced a high economic growth since early 2000s. One of the reasons for this successful economic growth is foreign direct investment that has been invested mainly in manufacturer sector in Vietnam. In this paper, we examine the impacts of foreign direct investment to Vietnam on its exports using quarterly data from 2000:1 to 2017:4. Since all the variables in our model is subject to I(1), we apply Fully Modified OLS(FMOLS) to estimate a cointegration vectors. Our results show that there exists a long-run relationship among Export, FDI, Exchange rate and G20 countries' GDP. Also, we find that FDI has a positive effect on Vietnam's export, which was statistically significant. Our results support the hypothesis that the FDI to Vietnam since 2000 has an export-oriented feature.

• Korean Journal of Psychosomatic Medicine
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• v.27 no.2
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• pp.101-110
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• 2019

Objectives : It is not clear which clinical variables are most closely associated with delirium in the Intensive Care Unit (ICU). By comparing clinical data of ICU delirium and non-delirium patients, we sought to identify variables that most effectively differentiate delirium from non-delirium. Methods : Medical records of 6,386 ICU patients were reviewed. Random Subset Feature Selection and Principal Component Analysis were utilized to select a set of clinical variables with the highest discriminatory capacity. Statistical analyses were employed to determine the separation capacity of two models-one using just the selected few clinical variables and the other using all clinical variables associated with delirium. Results : There was a significant difference between delirium and non-delirium individuals across 32 clinical variables. Richmond Agitation Sedation Scale (RASS), urinary catheterization, vascular catheterization, Hamilton Anxiety Rating Scale (HAM-A), Blood urea nitrogen, and Acute Physiology and Chronic Health Examination II most effectively differentiated delirium from non-delirium. Multivariable logistic regression analysis showed that, with the exception of vascular catheterization, these clinical variables were independent risk factors associated with delirium. Separation capacity of the logistic regression model using just 6 clinical variables was measured with Receiver Operating Characteristic curve, with Area Under the Curve (AUC) of 0.818. Same analyses were performed using all 32 clinical variables;the AUC was 0.881, denoting a very high separation capacity. Conclusions : The six aforementioned variables most effectively separate delirium from non-delirium. This highlights the importance of close monitoring of patients who received invasive medical procedures and were rated with very low RASS and HAM-A scores.

• The Journal of the Korea Contents Association
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• v.15 no.7
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• pp.84-92
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• 2015

Advertisement market using the augmented reality has been rapidly increasing recently due to integration with LBS(location based services). This study was conducted to examine the effect and acceptance intention of mobile augmented reality advertisement through TAM(Technology Acceptance Model). 'Contextual feature,' which is a characteristic of augmented reality advertisement, was selected as an outside effective variable, and perceived usefulness and perceived ease of use, which are the core variables of TAM, and attitude of advertisement and advertisement acceptance intention were selected as important evidences of Technology Acceptance Process. Also, the relationship between each evidences were investigated. As a result of the investigation of the outside influence on the acceptance model of augmented reality advertisement, it was shown that the contextual feature of augmented reality advertisement had meaningful influence on perceived usefulness and perceived ease of use, and it was also shown that it has positive influence on the attitude of advertisement and acceptance intention of advertisement.

• Journal of the Korea Convergence Society
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• v.7 no.6
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• pp.7-11
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• 2016

Voice recognition systems which use a white noise and voice recognition environment are not correct voice recognition with variable voice mixture. Therefore in this paper, we propose a method using the convergence of Bayesian technique and selecting voice for effective voice recognition. we make use of bank frequency response coefficient for selective voice extraction, Using variables observed for the combination of all the possible two observations for this purpose, and has an voice signal noise information to the speech characteristic extraction selectively is obtained by the energy ratio on the output. It provide a noise elimination and recognition rates are improved with combine voice recognition of bayesian methode. The result which we confirmed that the recognition rate of 2.3% is higher than HMM and CHMM methods in vocabulary recognition, respectively.

• KIISE Transactions on Computing Practices
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• v.23 no.9
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• pp.557-563
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• 2017

When large-scale gene expression data are analyzed using lasso regression, the estimation of regression coefficients may be unstable due to the highly correlated expression values between associated genes. This irregularity, in which the coefficients are reduced by L1 regularization, causes difficulty in variable selection. To address this problem, we propose a regression model which exploits the repetitive bootstrapping of gene expression values prior to lasso regression. The genes selected with high frequency were used to build each regression model. Our experimental results show that several genes were consistently selected in all regression models and we verified that these genes were not false positives. We also identified that the sign distribution of the regression coefficients of the selected genes from each model was correlated to the real dependent variables.

• Journal of the Korea Convergence Society
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• v.11 no.11
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• pp.257-267
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• 2020

In this study, we predicted the employment ratio by the export industry using various machine learning methods and verified whether the prediction performance is improved by applying the constrained forecast combination method to these predicted values. In particular, the constrained forecast combination method is known to improve the prediction accuracy and stability by imposing the sum of predicted values' weights up to one. In addition, this study considered various variables affecting the employment ratio of each industry, and so we adopted recursive feature elimination method that allows efficient use of machine learning methods. As a result, the constrained forecast combination showed more accurate prediction performance than the predicted values of the machine learning methods, and in particular, the stability of the prediction performance of the constrained forecast combination was higher than that of other machine learning methods.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.40 no.3
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• pp.144-154
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• 2003

In this paper we present Logical Evolution method which is a new teaming algorithm for the concepts expressed as binary logic function. We try to solve some problems of Inductive Learning algorithms through Logical Evolution. First, to be less affected from limited prior knowledge, it generates features using the gained informations during learning process and learns the concepts with these features. Second, the teaming is done using not the whole example set but the individual example, so even if new problem or new input-output variables are given, it can use the previously generated features. In some cases these old features can make the teaming process more efficient. Logical Evolution method consists of 5 operations which are selected and performed by the logical evaluation procedure for feature generation and learning process. To evaluate the performance of the present algorithm, we make experiments on MONK data set and a newly defined problem.

• Science of Emotion and Sensibility
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• v.21 no.1
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• pp.45-58
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• 2018

As information and communication technologies (ICTs) become more advanced, consumers are able to experience retailing activities such as searching for products and services in online retail shops and for Internet-exclusive branded contents. Specifically, fashion retailers are facing the need to develop more novel experiential design than one another to maximize customers' experience in Internet websites and secure sustainable competency. Confirming methods of organic integration of experiential and visual features of both online and mobile channels is an important aspect of the study of extended consumers' interfaces of retail channels. Mehrabian and Russell's stimulus-organism-response (S-O-R) paradigm and Sugiyama and Andree's attention, interest, search, action, and share (AISAS) model were used for this research. Specifically, the present study considered the effect of e-commerce website features on consumers' emotional reactions (pleasure and arousal) and the consequent impact on online consumer behaviors (search, action, and share). Hence, plus the self-reported survey methods, each subject's psychophysiological indicators (i.e., pleasure and arousal) were measured to obtain more objective and reliable data and to redeem the results of the self-reported survey. Findings revealed the implications of the e-commerce website feature by comprehending the S-O-R paradigm and AISAS model and extending the understanding of the role of variables associated with comprehended frameworks based on psychophysiological data.

• KIISE Transactions on Computing Practices
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• v.21 no.3
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• pp.183-191
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• 2015

Recent developments in IT have made real-time ECG monitoring possible, and this represents a promising application for the emerging HL7 standard for the exchange of clinical information. However, applying the HL7 standard directly to real-time ECG monitoring causes problems, because the partial duplication of data within an HL7 message increases the amount of data to be transmitted, and the time taken to process it. We reduce these overheads by Feature Scaling, by standardizing the range of independent variables or features of data, while nevertheless generating HL7-compliant messages. We also use a De-Duplication algorithm to eliminate the partial repetition of the OBX field in an HL7 ORU message. Our strategy shortens the time required to create messages by 51%, and reduces the size of messages by 1/8, compared to naive HL7 coding.