• Maxillofacial Plastic and Reconstructive Surgery
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• v.16 no.2
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• pp.137-144
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• 1994

Inselecting scars for treatment, attention be paid not only to the features of the defect as seen objectively but also to the element of the defect most disturbing to the patient. We revised the scar tissues with simple elliptical excision, Z-plasty, modified Z-plasty, W-plasty and hand dermabrasion in varialbe pattern of scars and got the favorable results. The success rate of scar revision usually depends on the patient's subjective judgement. We must inform our patient the limitation of scar revision and importance of postoperative care. The oral and maxillofacial surgeons must resolve the variable scars which re involved in variable operations and traumas.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.2
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• pp.75-84
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• 2019

In this study, the random forest approach was used to predict the national mean rice yield of South Korea by using mean climatic factors at a national scale. A random forest model that used monthly climate variable and year as an important predictor in predicting crop yield. Annual yield change would be affected by technical improvement for crop management as well as climate. Year as prediction factor represent technical improvement. Thus, it is likely that the variables of importance identified for the random forest model could result in a large error in prediction of rice yield in practice. It was also found that elimination of the trend of yield data resulted in reasonable accuracy in prediction of yield using the random forest model. For example, yield prediction using the training set (data obtained from 1991 to 2005) had a relatively high degree of agreement statistics. Although the degree of agreement statistics for yield prediction for the test set (2006-2015) was not as good as those for the training set, the value of relative root mean square error (RRMSE) was less than 5%. In the variable importance plot, significant difference was noted in the importance of climate factors between the training and test sets. This difference could be attributed to the shifting of the transplanting date, which might have affected the growing season. This suggested that acceptable yield prediction could be achieved using random forest, when the data set included consistent planting or transplanting dates in the predicted area.

• Asia pacific journal of information systems
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• v.14 no.3
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• pp.77-92
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• 2004

When humans make decisions, they differentiate classifications of individual attribute variables that affect the decisions according to the importance and pattern of each attribute variables. The present study examines the practicality of the proposed Code Arrangement-Based Reasoning(CABR), which resembles the human's way of reasoning. To this end, we developed a CABR technique that classifies each attribute variable affecting significant impacts on the target variable into a cluster and assigns a code to the cluster. For verifying the proposed technique, both case-based reasoning and CABR were used for the customer continuance judgment problem of an automobile insurance company. Results indicated that the performance of CABR is close to the one of the case-based reasoning. The CABR also shows the possibility of using bio-informatics techniques for organizational data analysis in the future.

• Structural Engineering and Mechanics
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• v.60 no.4
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• pp.707-727
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• 2016

In this paper, thermal vibration of a nonlocal functionally graded (FG) plates with arbitrary boundary conditions under linear and non-linear temperature fields is explored by developing a refined shear deformation plate theory with an inverse cotangential function in which shear deformation effect was involved without the need for shear correction factors. The material properties of FG nanoplate are considered to be temperature-dependent and graded in the thickness direction according to the Mori-Tanaka model. On the basis of non-classical higher order plate model and Eringen's nonlocal elasticity theory, the small size influence was captured. Numerical examples show the importance of non-uniform thermal loadings, boundary conditions, gradient index, nonlocal parameter and aspect and side-to-thickness ratio on vibrational responses of size-dependent FG nanoplates.

• 한국경영정보학회:학술대회논문집
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• 2007.11a
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• pp.117-122
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• 2007

Recently, as the management environments are changing rapidly and the uncertainty is becoming larger, the needs of internal control for management and IS become stronger. In order to construct a new internal control system for IS, it is necessary to evaluate the former research of the system. This study emphasizes the importance of effective internal control system, presents a conceptual framework for the preceding factors to consider, and verifies empirically the framework. This study sets the organization citizenship behavior, IS innovation resistance, and IT capability from the viewpoint of Socio-Technical system as the preceding factors for the effectiveness of internal control system. A research model, affecting the above factors on IS capability as a mediating variable of the internal control effectiveness for the use of IS, is set up. PLS-Graph 3.0 is used to verify the model. We found that the internal control effectiveness have affirmative effect on information capability, a surrogate variable of the IS effectiveness and a mediation effect is meaningful.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.354-356
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• 2011

Renewable energy resources such as wind, wave, solar, micro hydro, tidal and biomass etc. are becoming importance stage by stage because of considering effect of the environment. Solar energy is one of the most successful sources of renewable energy for the production of electrical energy following solar energy. And, the solar/photovoltaic cell generators depend on the solar radiation, which is a random variable so this poses difficulty in the system scheduling and energy dispatching, as the schedule of the photovoltaic cell generators availability is not known in advance. This paper proposes to use the two-layered artificial neural networks for predicting the actual solar radiation from the previous values of the same variable.

• The Journal of the Acoustical Society of Korea
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• v.41 no.5
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• pp.492-499
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• 2022

This study experimentally investigates the effect of dimensionality reduction of vibration signal on fault diagnosis of a marine engine. By using the principal component analysis, a vibration signal having the dimension of 513 is converted into a low-dimensional signal having the dimension of 1 to 15, and the variation in fault diagnosis accuracy according to the dimensionality change is observed. The vibration signal measured from a full-scale marine generator diesel engine is used, and the contribution of the dimension-reduced signal is quantitatively evaluated using two kinds of variable importance analysis algorithms which are the integrated gradients and the feature permutation methods. As a result of experimental data analysis, the accuracy of the fault diagnosis is shown to improve as the number of dimensions used increases, and when the dimension approaches 10, near-perfect fault classification accuracy is achieved. This shows that the dimension of the vibration signal can be considerably reduced without degrading fault diagnosis accuracy. In the variable importance analysis, the dimension-reduced principal components show higher contribution than the conventional statistical features, which supports the effectiveness of the dimension-reduced signals on fault diagnosis.

• 한국연소학회:학술대회논문집
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• 2002.06a
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• pp.41-45
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• 2002

The zone-conditioned CMC equations are derived by taking an unconditional average of the generic conservation equations multiplied by delta and Heaviside functions in terms of mixture fraction and reaction progress variable. The resulting equations are essentially in the same form as the single zone CMC equations except for separate flow fields for burned and unburned gas. The zone-conditioned two-fluid equations are applied to a stagnating turbulent premixed flame brush of Cheng and Shepherd[5l. It is shown that the flame stretch factor is of crucial importance to accurately reproduce the measured mean reaction progress variable and conditional velocities. Further work is in progress for the relationship between surface and volume averages and extension to partially premixed combustion on the basis of a triple flame structure, e. g. in a lifted turbulent diffusion flame.

• Journal of information and communication convergence engineering
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• v.22 no.2
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• pp.133-138
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• 2024

Dissolved oxygen (DO) is an important factor in ecosystems. However, the analysis of DO is frequently rather complicated because of the nonlinear phenomenon of the river system. Therefore, a convenient model-free algorithm for DO variable is required. In this study, a data-driven algorithm for predicting DO was developed by combining XGBoost and an artificial neural network (ANN), called ANN-XGB. To train the model, two years of ecosystem data were collected in Anyang, Seoul using the Troll 9500 model. One advantage of the proposed algorithm is its ability to capture abrupt changes in climate-related features that arise from sudden events. Moreover, our algorithm can provide a feature importance analysis owing to the use of XGBoost. The results obtained using the ANN-XGB algorithm were compared with those obtained using the ANN algorithm in the Results Section. The predictions made by ANN-XGB were mostly in closer agreement with the measured DO values in the river than those made by the ANN.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.17 no.6
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• pp.587-595
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• 2005

The heat and flow characteristics in a parallel-flow heat exchanger were examined numerically to obtain its optimal design variables. A desirability function approach was introduced to optimize its performance with respect to the design parameters over the design domain. By varying the importance of heat transfer and pressure drop which are out put variables, the optimal values of the design parameters are examined. As a result, the us-age of the desirability function is very effective for the optimization of the design variables in a heat exchanger since the changes of optimal values are physically appropriate by varying the importance of each output variable.