• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.57 no.3
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• pp.236-245
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• 2021

In this study, a drifting test using a experimental vessel (2,966 tons) in the northern waters of Jeju was carried out for the first time in order to obtain the fundamental data for drift. During the test, it was shown that the average leeway speed and direction by GPS position were 0.362 m/s and 155.54° respectively and the leeway rate for wind speed was 8.80%. The analysis of linear regression modes about leeway speed and direction of the experimental vessel indicated that wind or current (i.e. explanatory variable) had a greater influence upon response variable (e.g. leeway speed or direction) with the speed of the wind and current rather than their directions. On the other hand, the result of multiple regression model analysis was able to predict that the direction was negative, and it was demonstrated that predicted values of leeway speed and direction using an experimental vessel is to be more influential by current than wind while the leeway speed through variance and covariance was positive. In terms of the leeway direction of the experimental vessel, the same result of the leeway speed appeared except for a possibility of the existence of multi-collinearity. Then, it can be interpreted that the explanatory variables were less descriptive in the predicted values of the leeway direction. As a result, the prediction of leeway speed and direction can be demonstrated as following equations. Ŷ₁= 0.4031-0.0032X₁+0.0631X₂-0.0010X₃+0.4110X₄ Ŷ₂= 0.4031-0.6662X₁+27.1955X₂-0.6787X₃-420.4833X₄ However, many drift tests using actual vessels and various drifting objects will provide reasonable estimations, so that they can help search and rescue fishing gears as well.

• Journal of Korean Society for Atmospheric Environment
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• v.10 no.4
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• pp.224-232
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• 1994

A new neural network model has been developed to predict short-term air pollution concentration. In addition, a multiple regression model widely used in statistical analysis was tested. These models were applied for prediction of daily maximum ozone concentration in Seoul during the summer season of 1991. The time periods between May and September 1989 and 1990 were utilized to train set of learning patterns in neural network model, and to estimate multiple regression model. To evaluate the results of the different models, several Performance indices were used. The results indicated that the multiple regression model tended to underpredict the daily maximum ozone concentration with small r$^{2}$(0.38). Also, large errors were found in this model; 21.1 ppb for RMSE, 0.324 for NMSE, and -0.164 for MRE. On the other hand, the results obtained from the neural network model were very promising. Thus, we can know that this model has a prominent efficiency in the adaptive control for the non-linear multi- variable systems such as photochemical oxidants. Also, when the recent new information was added in the neural network model, prediction accuracy was increased. From the new model, the values of RMSE, NMSE and r$^{2}$ were 13.2ppb, 0.089, 0.003 and 0.55 respectively.

• International journal of advanced smart convergence
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• v.10 no.4
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• pp.206-214
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• 2021

The purpose of this study is to identify the level of polypharmacy use, drug knowledge, and drug misuse behavior in the elderly, and to understand the correlation between them and their effect on drug misuse behavior. The study design was a descriptive survey study, and the participants of the study were 215 elderly people from the local community center. The research tool used drug knowledge, drug misuse behavior, and the data collection period was from February 8 to 19, 2021. The data analysis were descriptive statistics, t-test, one-way ANOVA, Pearson's correlation coefficient, and regression analysis. As a result of the study, a significant correlation variable for the drug knowledge of the elderly showed a significant correlation with prescription and non-prescription, r=.145 (p<0.05), and r=.-. 136, which showed a negative significant correlation (p<0.05). As for the significant correlation variable in the drug misuse behavior of the elderly, when prescription and non-prescription were combined, there was a significant correlation with r=.256 (p<0.01), and when not using drugs, r=.-.225 was negative. showed a significant correlation (p<0.01). In terms of the effect on drug misuse behavior, chronic disease =.145, prescription and non-prescription use = .233, which had a positive effect, and non-prescription = -.328, indicating a negative and significant effect. The provision of education on the safe use of drugs by the elderly should first be provided in the community. In addition, we need systematic education and social support for the transmission of correct knowledge on multi-drug use by the elderly and for health management.

• Asia-Pacific Journal of Business
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• v.14 no.3
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• pp.221-240
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• 2023

Purpose - This paper investigates how business cycle impacts on corporate credit spreads since global financial crisis. Furthermore, it tests how the impact changes by the phase of the cycle. Design/methodology/approach - This study collected dataset from Barclays Global Aggregate Bond Index through the Bloomberg. It conducted multi-regression analysis by projecting business cycle using Hodrick-Prescott filtering and various cyclical variables, while ran dynamic analysis of 5-variable Vector Error Correction Model to confirm the robustness of the test. Findings - First, it proves to be statistically significant that corporate credit spreads have moved countercyclicaly since the crisis. Second, It indicates that the corporate credit spread's countercyclicality to the macroeconomic changes works symmetrically by the phase of the cycle. Third, the VECM supports that business cycle's impact on the spreads maintains more sustainably than other explanatory variable does in the model. Research implications or Originality - It becomes more appealing to accurately measure the real economic impact on corporate credit spreads as the interaction between credit and business cycle deepens. The economic impact on the spreads works symmetrically by boom and bust, which implies that the market stress could impact as another negative driver during the bust. Finally, the business cycle's sustainable impact on the spreads supports the fact that the economic recovery is the key driver for the resilience of credit cycle.

• The Journal of Asian Finance, Economics and Business
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• v.6 no.1
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• pp.195-203
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• 2019

The study aims to identify Service Quality factors that affect purchase decision on Click and Collect service through the mediating variable of customer emotions at Mobile World stores in Ho Chi Minh City. This study employs a mixed methods research design. Data were collected through online self-completion questionnaire distributed to 316 customers who used to experience Click and Collect service at the Mobile World stores in Ho Chi Minh City, Vietnam. The theoretical model was tested through two-stage regression analysis (PATH model). The findings show that factors of service quality such as Reliability, Responsiveness, Assurance, Empathy, Tangibility, and Emotions affect the decision to purchase online and receive products directly at Mobile World stores in Ho Chi Minh City. Responsiveness and Assurance have a significant positive impact on the customer's emotions. Consequently, these factors should be considered and addressed when conducting multi-channel services. Obviously, employees must first be trained to be able to deliver the promise of the retailer to their customers. Based on the results of the study, the authors provide managerial implications for retailers in Vietnam in the multi-channel retail environment to develop Click and Collect at retail stores across the country and the world.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.5
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• pp.1035-1040
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• 2024

The main objective of inspecting structures is to ensure the safety of all entities that utilize these structures as cracks in structures if not attended to could lead to serious calamities. With that objective in mind, artificial intelligence (AI) based technologies to assist human inspectors are needed especially for retaining walls in structures. In this paper, we predict the crack displacement of retaining walls using an Polynomial Regressive (PR) analysis model, as well as Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) deep learning models, and compare their performance. For the performance comparison, we apply multi-variable feature inputs, by utilizing temperature and rainfall data that may affect the crack displacement of the retaining wall. The training and inference data were collected through measuring sensors such as inclinometers, thermometers, and rain gauges. The results show that the multi-variable feature model had a MAE of 0.00186, 0.00450 and 0.00842, which outperformed the single variable feature model at 0.00393, 0.00556 and 0.00929 for the polynomial regression model, LSTM model and the GRU model respectively from the evaluation performed.

• Journal of the Semiconductor & Display Technology
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• v.20 no.4
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• pp.146-150
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• 2021

Plasma information based virtual metrology (PI-VM) that predicts wafer-to-wafer etch rate variation after wet cleaning of plasma facing parts was developed. As input parameters, plasma information (PI) variables such as electron temperature, fluorine density and hydrogen density were extracted from optical emission spectroscopy (OES) data for etch plasma. The PI-VM model was trained by stepwise variable selection method and multi-linear regression method. The expected etch rate by PI-VM showed high correlation coefficient with measured etch rate from SEM image analysis. The PI-VM model revealed that the root cause of etch rate variation after the wet cleaning was desorption of hydrogen from the cleaned parts as hydrogen combined with fluorine and decreased etchant density and etch rate.

• The Journal of Industrial Distribution & Business
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• v.11 no.8
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• pp.7-19
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• 2020

Purpose: Prior studies rarely investigated the effects of the Word of Mouse (WoM) information on consumers' motivation and willingness to purchase a product. Furthermore, few scholars have studied how word-of-mouth information works and they fail to find consistent results. Research design, data and methodology: This study uses a multivariate regression model to investigate the influence of WoM on consumer attitudes and consumer decision-making. It categorizes the quality of WoM into source level and acceptance level, to analyze its influence from a new perspective. A total of 400 surveys were completed, resulting in 336 usable questionnaires for analysis. It was collected in 14 cities from all regions in China. This study constructs a theoretical model of WoM influence on consumers' purchase willingness based on a systematic review of the related literature on WoM quality, perceived value, customer trust, and consumers' purchase willingness. Results: Empirical results reveal that the Internet WoM (consumer's source level and acceptance level) indirectly affects consumer behavior by influencing consumer attitudes. Conclusions: This study provides practical significance and value for merchants to develop better WoM marketing and to establish the reliability of WoM websites. Companies should consider online WoM from the perspective of consumers, thereby improving existing marketing strategies.

• Steel and Composite Structures
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• v.36 no.4
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• pp.417-426
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• 2020

Drilling processes in fiber-reinforced polymer composites are essential for the assembly and fabrication of composite structural parts. The economic impact of rejecting the drilled part is significant considering the associated loss when it reaches the assembly stage. Therefore, this article tends to illustrate the effect of cutting conditions (feed and speed), and laminate thickness on thrust force, torque, and delamination in drilling woven E-glass fiber reinforced epoxy (GFRE) composites. Four feeds (0.025, 0.05, 0.1, and 0.2 mm/r) and three speeds (400, 800, and 1600 RPM) are exploited to drill square specimens of 36.6×36.6 mm, by using CNC machine model "Deckel Maho DMG DMC 1035 V, ecoline". The composite laminates with thicknesses of 2.6 mm, 5.3 mm, and 7.7 mm are constructed respectively from 8, 16, and 24 glass fiber layers with a fiber volume fraction of about 40%. The drilled specimen is scanned using a high-resolution flatbed color scanner, then, the image is analyzed using CorelDraw software to evaluate the delamination factor. Multi-variable regression analysis is performed to present the significant coefficients and contribution of each variable on the thrust force and delamination. Results illustrate that the drilling parameters and laminate thickness have significant effects on thrust force, torque, and delamination factor.

• The Journal of Society for e-Business Studies
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• v.22 no.2
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• pp.123-136
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• 2017

The evaluation and analysis method of information system (IS) is studied from the system perspective, the user perspective, and the management viewpoint. The detailed analysis method performs qualitative evaluation by user questionnaire or expert opinion. In this study, Measures the productivity and the effect of building administrative information systems. In the previous study, qualitative productivity and universal effect indicators were used, but in this study, quantitative productivity indicators and indicators specific to administrative complaints were selected. KONEPS, an administrative service system, used electronic contract records and information recorded in the intermediate process. The information was converted into the number of days, and the productivity based on the input manpower was calculated. The effect analysis analyzed the questionnaire related to civil affairs, which is the goal of the administrative work system. Each factor was divided into reflective structural variable and formal structural variable, and internal consistency and multi-collinearity were diagnosed. In order to verify the model, the influence of the work was set as a hypothesis, the reliability was verified according to the descriptive statistics method, the influence was measured through the regression analysis, and the model was analyzed by the multiple regression model path coefficient. Model validation methods are Chi-square (df, p), RMR, GFI, AGFI, NFI, CFI and GFI as indicators according to CFA.