• Journal of Korean Institute of Industrial Engineers
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• v.39 no.5
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• pp.361-366
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• 2013

Prediction of process parameters is very important in parameter design. If predictions are fairly accurate, the quality improvement process will be useful to save time and reduce cost. The concept of dual response approach based on response surface methodology has widely been investigated. Dual response approach may take advantages of optimization modeling for finding optimum setting of input factor by separately modeling mean and variance responses. This study proposes an alternative dual response approach based on machine learning techniques instead of statistical analysis tools. A hybrid neural network-genetic algorithm has been proposed for the purpose of parameter design. A neural network is first constructed to model the relationship between responses and input factors. Mean and variance responses correspond to output nodes while input factors are used for input nodes. Using empirical process data, process parameters can be predicted without performing real experimentations. A genetic algorithm is then applied to find the optimum settings of input factors, where the neural network is used to evaluate the mean and variance response. A drug formulation example from pharmaceutical industry has been studied to demonstrate the procedures and applicability of the proposed approach.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.4
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• pp.579-586
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• 2018

Short-term prediction of travel speed has been widely studied using data-driven non-parametric techniques. There is, however, a lack of research on the prediction aimed at urban areas due to their complex dynamics stemming from traffic signals and intersections. The purpose of this study is to develop a hybrid approach combining ensemble empirical mode decomposition (EEMD) and artificial neural network (ANN) for predicting urban travel speed. The EEMD decomposes the time-series data of travel speed into intrinsic mode functions (IMFs) and residue. The decomposed IMFs represent local characteristics of time-scale components and they are predicted using an ANN, respectively. The IMFs can be predicted more accurately than their original travel speed since they mitigate the complexity of the original data such as non-linearity, non-stationarity, and oscillation. The predicted IMFs are summed up to represent the predicted travel speed. To evaluate the proposed method, the travel speed data from the dedicated short range communication (DSRC) in Daegu City are used. Performance evaluations are conducted targeting on the links that are particularly hard to predict. The results show the developed model has the mean absolute error rate of 10.41% in the normal condition and 25.35% in the break down for the 15-min-ahead prediction, respectively, and it outperforms the simple ANN model. The developed model contributes to the provision of the reliable traffic information in urban transportation management systems.

• Journal of Korea Water Resources Association
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• v.55 no.9
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• pp.679-686
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• 2022

As the occurrences of flash floods have increased due to climate change, faster and more accurate precipitation observation using X-band radar has become important. Therefore, the Ministry of Environment installed two dual-pol X-band radars at Samcheok and Uljin. The radar data used in this study were obtained from two different elevation angles and composed to reduce the shielding effect. To obtain quantitative rainfall, quality control (QC), K_DP retrieval, and Hybrid Surface Rainfall (HSR) methods were sequentially applied. To improve the accuracy of the quantitative precipitation estimation (QPE) of the X-band radar, we retrieved parameters for the relationship between rainfall rate and specific differential phase, which is commonly called the R-K_DP relationship; hence, an empirical approach was developed using multiple rain gauges for those two radars. The newly suggested relationship, R = 27.4K^0.81_DP, slightly increased the correlation coefficient by 1% more than the relationship suggested by the previous study. The root mean square error significantly decreased from 3.88 mm/hr to 3.68 mm/hr, and the bias of the estimated precipitation also decreased from -1.72 mm/hr to -0.92 mm/hr for overall cases, showing the improvement of the new method.

• Journal of the Korean Society for Nondestructive Testing
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• v.29 no.1
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• pp.1-9
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• 2009

This paper presents stress distribution around a central crack tip in a tensile plate using phase-shifting photoelasticity and a power series stress function. Isochromatic data along the straight lines far from the crack tip were obtained by phase shifting photoelasticity and were used as input data of the hybrid experimental analysis. By using the complex-type power series stress equations, the photoelastic stress distribution fields in the vicinity of the crack and the mode I stress intensity factor were obtained. With the help of image processing software, accuracy and reliability was enhanced by twice multiplying and sharpening the measured isochromatics. Actual and reconstructed fringes were compared qualitatively. For quantitative comparison, percentage errors and standard deviations of the percentage errors were calculated for all measured input data by varying the number of terms in the stress function. The experimental results agreed with those predicted by finite element analysis and empirical equation within 2 percent error.

• Journal of Fashion Business
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• v.24 no.1
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• pp.88-101
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• 2020

The music videos of BTS go beyond the limit of media promoting music and shows their meaning in various ways and complete the visual message of music through fashion style. BTS' fashion style in the music videos shows a change in symbolic representation of the genre of each album and song, of which gender images are changing aligned with the music messages of BTS. The purpose of this study was to derive gender images of fashion style in BTS music videos and to interpret their meaning based on Judith Butler's theory that performativity creates discourse through iterative process. It is conducted as a research method, an analytical study was conducted in parallel with literature studies and empirical case analysis. The scope of the study was limited to 301 costumes that appeared in 21 official music videos from debut single album '2Cool 4 Skool' released in 2013 to the mini album 'Map of the Soul: Persona' released in 2019. As a result of the analysis, the controversial fashion style, challenging fashion style, boyish fashion style, hybrid fashion style, the playful fashion style were revealed. The conclusion of studying the gender image of BTS, interpreted by this analysis using Judith Butler's theory, is as follows. The gender image of BTS is the traditional image that identifies with the dominant gender discourse, the resistive gender image that intentionally distances mainstream culture, the eclectic image parodying the gender of the opposing term, and the deconstructive image that transcends the dominant gender discourse.

• Geomechanics and Engineering
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• v.31 no.2
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• pp.129-147
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• 2022

The properties of soil are naturally highly variable and thus, to ensure proper safety and reliability, we need to test a large number of samples across the length and depth. In pile foundations, conducting field tests are highly expensive and the traditional empirical relations too have been proven to be poor in performance. The study proposes a state-of-art Particle Swarm Optimization (PSO) hybridized Artificial Neural Network (ANN), Extreme Learning Machine (ELM) and Adaptive Neuro Fuzzy Inference System (ANFIS); and comparative analysis of metaheuristic models (ANN-PSO, ELM-PSO, ANFIS-PSO) for prediction of bearing capacity of pile foundation trained and tested on dataset of nearly 300 dynamic pile tests from the literature. A novel ensemble model of three hybrid models is constructed to combine and enhance the predictions of the individual models effectively. The authenticity of the dataset is confirmed using descriptive statistics, correlation matrix and sensitivity analysis. Ram weight and diameter of pile are found to be most influential input parameter. The comparative analysis reveals that ANFIS-PSO is the best performing model in testing phase (R² = 0.85, RMSE = 0.01) while ELM-PSO performs best in training phase (R² = 0.88, RMSE = 0.08); while the ensemble provided overall best performance based on the rank score. The performance of ANN-PSO is least satisfactory compared to the other two models. The findings were confirmed using Taylor diagram, error matrix and uncertainty analysis. Based on the results ELM-PSO and ANFIS-PSO is proposed to be used for the prediction of bearing capacity of piles and ensemble learning method of joining the outputs of individual models should be encouraged. The study possesses the potential to assist geotechnical engineers in the design phase of civil engineering projects.

• The Journal of the Acoustical Society of Korea
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• v.39 no.4
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• pp.351-363
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• 2020

Launch vehicles are subject to airborne acoustic loads during atmospheric flight and these effects become pronounced especially in transonic region. As the vibration due to the acoustic loads can cause malfunction of payloads, it is essential to predict and reduce the acoustic loads. In this study, a complete process has been developed for predicting airborne vibro-acoustic environment inside the payload pairing and subsequent noise reduction procedure employing acoustic blankets and Helmholtz resonators. Acoustic loads were predicted by Reynolds-Averaged Navier-Stokes (RANS) analysis and a semi-empirical model for pressure fluctuation inside turbulent boundary layer. Coupled vibro-acoustic analysis was performed using VA One SEA's Finite Element Statistical Energy Analysis (FE-SEA) hybrid module and ANSYS APDL. The process has been applied to a hammerhead launch vehicle to evaluate the effect of acoustic load reduction and accordingly to verify the effectiveness of the process. The presently developed process enables to obtain quick analysis result with reasonable accuracy and thus is expected to be useful in the initial design phase of a launch vehicle.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.7
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• pp.191-198
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• 2021

Since the size of training dataset become large and the model is getting deeper to achieve high accuracy in deep learning, the deep neural network training requires a lot of computation and it takes too much time with a single node. Therefore, distributed deep learning is proposed to reduce the training time by distributing computation across multiple nodes. In this study, we propose hybrid allreduce strategy that considers the characteristics of each layer and communication and computational overlapping technique for synchronization of distributed deep learning. Since the convolution layer has fewer parameters than the fully-connected layer as well as it is located at the upper, only short overlapping time is allowed. Thus, butterfly allreduce is used to synchronize the convolution layer. On the other hand, fully-connecter layer is synchronized using ring all-reduce. The empirical experiment results on PyTorch with our proposed scheme shows that the proposed method reduced the training time by up to 33% compared to the baseline PyTorch.

• Journal of Korean Academy of Nursing Administration
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• v.5 no.1
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• pp.63-76
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• 1999

The main objective of this study was to develop a concept of service marketing promotion in nursing that is derived from the concepts of service marketing theory. This research was a descriptive study, at the factor isolation level. The principle of concept derivation suggested by Walker and Avant (1988) and the Hybrid model suggested by Schwarz-Barcott and Kim (1993) were employed as the research method. The data were collected from December, 1997 to April. 1998 at a large general hospital located in Seoul. The procedures of this study were as follows: First. at the theoretical phase: the meaning, attributes, and definition of service marketing promotion were identified through an extensive review of the literature. Second, at the empirical phase: fieldwork was done to identify the promotional activities and events in nursing. Top nurse managers from 4 units (Director of Nursing, Head nurses of inpatient nursing unit, outpatient nursing unit. and home care nursing unit) were interviewed and the content of the interview was analyzed to identify the meaning and attributes of promotion in nursing. Other methods such as brochures and other audio-visual materials which were relevant to nursing promotion were used to supplement the interviews. Finally, the results of the theoretical and empirical analyses were intergrated to develop a concept of service marketing in nursing practice. A final definition of service marketing promotion in nursing was identified as follows. 1. Promotion as a marketing function in nursing service is concerned with communication to target markets on all information related to nursing service in order to satisfy the objectives of both a nursing service organization and the target markets. 2. The goals of nursing service promotion include: 1) increasing visibility of nursing services and delivering the information on nursing services, 2) affirming the value of nursing services, so it can contribute to formulation of reimbursement policy for nursing services. 3) advancing the general image of the nursing profession and nursing services. 4) achieving and attaining a desirable positioning for nurses among health care professionals. and 5) creating and stimulating the demand for nursing services. 3. In order to obtain these goals it is necessary to provide information on nursing services, to persuade target markets. to remind them about nursing services. and to establish a collaborative relationship with related departments. 4. The tools used to carry out the above functions of promotion in nursing are the providing nursing services, public relations and publicity. QA of nursing, advertising, and sales promotion. 5. The target markets of nursing service include the nursing customer markets. the internal markets, the influence markets. the recruitment markets. the supplier markets. and the nursing referral markets. In conclusion, the concept of promotion in other service marketing areas can be applied to the promotion of nursing service marketing. The promotion of nursing service is more than just effective communication in nursing service. it is the effective use of the concepts of service marketing promotion. Promotion of nursing service will contribute to create and expand nursing services.

• Steel and Composite Structures
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• v.34 no.5
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• pp.743-767
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• 2020

Due to the impressive flexural performance, enhanced compressive strength and more constrained crack propagation, Fibre-reinforced concrete (FRC) have been widely employed in the construction application. Majority of experimental studies have focused on the seismic behavior of FRC columns. Based on the valid experimental data obtained from the previous studies, the current study has evaluated the seismic response and compressive strength of FRC rectangular columns while following hybrid metaheuristic techniques. Due to the non-linearity of seismic data, Adaptive neuro-fuzzy inference system (ANFIS) has been incorporated with metaheuristic algorithms. 317 different datasets from FRC column tests has been applied as one database in order to determine the most influential factor on the ultimate strengths of FRC rectangular columns subjected to the simulated seismic loading. ANFIS has been used with the incorporation of Particle Swarm Optimization (PSO) and Genetic algorithm (GA). For the analysis of the attained results, Extreme learning machine (ELM) as an authentic prediction method has been concurrently used. The variable selection procedure is to choose the most dominant parameters affecting the ultimate strengths of FRC rectangular columns subjected to simulated seismic loading. Accordingly, the results have shown that ANFIS-PSO has successfully predicted the seismic lateral load with R² = 0.857 and 0.902 for the test and train phase, respectively, nominated as the lateral load prediction estimator. On the other hand, in case of compressive strength prediction, ELM is to predict the compressive strength with R² = 0.657 and 0.862 for test and train phase, respectively. The results have shown that the seismic lateral force trend is more predictable than the compressive strength of FRC rectangular columns, in which the best results belong to the lateral force prediction. Compressive strength prediction has illustrated a significant deviation above 40 Mpa which could be related to the considerable non-linearity and possible empirical shortcomings. Finally, employing ANFIS-GA and ANFIS-PSO techniques to evaluate the seismic response of FRC are a promising reliable approach to be replaced for high cost and time-consuming experimental tests.