• Korean Journal of Health Education and Promotion
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• v.32 no.1
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• pp.45-55
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• 2015

Objectives: This study is aimed at exploring the temporal developmental relationship of adolescent depression and parents' child-rearing attitudes, and to examine gender differences in the relationship. The middle school students of the 2011-2013 1st Korea Children and Youth Panel data were used for analysis and the sample consisted of 2.073 individuals. Methods: Research questions were answered through the Latent Growth Model and Autoregressive Cross-Lagged Model. Results: As the results of the Latent Growth Model show, adolescent depression declines as time goes by and there are differences in the depression felt by individuals. An autoregressive cross-lagged model and multiple group analysis were executed by gender. The results show significant gender differences in the relationship between depression and Parents' child-rearing attitudes. Parental neglect has shown differences influencing adolescents depression between males and females. However, in case of parental abuse, no differences between males and females were observed. Conclusion: The results of this study imply that the policy on depression should be carefully considered when preparing for interventions targeting adolescents by gender.

• Smart Structures and Systems
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• v.4 no.6
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• pp.759-777
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• 2008

A wireless sensing system is designed for application to structural monitoring and damage detection applications. Embedded in the wireless monitoring module is a two-tier prediction model, the auto-regressive (AR) and the autoregressive model with exogenous inputs (ARX), used to obtain damage sensitive features of a structure. To validate the performance of the proposed wireless monitoring and damage detection system, two near full scale single-story RC-frames, with and without brick wall system, are instrumented with the wireless monitoring system for real time damage detection during shaking table tests. White noise and seismic ground motion records are applied to the base of the structure using a shaking table. Pattern classification methods are then adopted to classify the structure as damaged or undamaged using time series coefficients as entities of a damage-sensitive feature vector. The demonstration of the damage detection methodology is shown to be capable of identifying damage using a wireless structural monitoring system. The accuracy and sensitivity of the MEMS-based wireless sensors employed are also verified through comparison to data recorded using a traditional wired monitoring system.

• Journal of Electrical Engineering and Technology
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• v.11 no.4
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• pp.869-877
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• 2016

Necessity has compelled man to improve upon the art of tapping wind energy for power generation; an apt reliever of strain exerted on the non-renewable fossil fuel. The power generation in a Wind Farm (WF) depends on site and wind velocity which varies with time and season which in turn determine wind power modeling. It implies, the development of an accurate wind speed model to predict wind power fluctuations at a particular site is significant. In this paper, Box-Jenkins ARIMA (Auto Regressive Integrated Moving Average) time series model for wind speed is developed for a 99MW wind farm in the southern region of India. Because of the uncertainty in wind power developed, the economic viability and reliability of power generation is significant. Life Cycle Costing (LCC) method is used to determine the economic viability of WF generated power. Reliability models of WF are developed with the help of load curve of the utility grid and Capacity Outage Probability Table (COPT). ARIMA wind speed model is used for developing COPT. The values of annual reliability indices and variations of risk index of the WF with system peak load are calculated. Such reliability models of large WF can be used in generation system planning.

• International Journal of Precision Engineering and Manufacturing
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• v.6 no.4
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• pp.37-43
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• 2005

This paper presents a hybrid control algorithm for the active noise control in the rectangular enclosure using an active/passive foam actuator. The hybrid control composes of the adaptive feedforward with feedback loop in which the adaptive feedforward control uses the well-known filtered-x LMS(least mean square) algorithm and the feedback loop consists of the sliding mode controller and observer. The hybrid control has its robustness for both transient and persistent external disturbances and increases the convergence speed due to the reduced variance of the jiltered-x signal by adding the feedback loop. The sliding mode control (SMC) is used to incorporate insensitivity to parameter variations and rejection of disturbances and the observer is used to get the state information in the controller deign. An active/passive smart foam actuator is used to minimize noise actively using an embedded PVDF film driven by an electrical input and passively using an absorption-foam. The error path dynamics is experimentally identified in the form of the auto-regressive and moving-average using the frequency domain identification technique. Experimental results demonstrate the effectiveness of the hybrid control and the feasibility of the smart foam actuator.

• The Journal of Asian Finance, Economics and Business
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• v.6 no.1
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• pp.121-128
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• 2019

This study analyses the relationship between the real estate market and economic growth in Vietnam, a country with a fledgling real estate market. Research data included economic growth rate and growth rate of the real estate market in Vietnam. The research used quarterly data for the period from 2005: Q1 to 2018: Q1. With the characteristics of Vietnam, there has been no real estate index up to now; therefore, the research used data on growth rates of the real estate market. In addition, the real estate market in Vietnam is still young, so the data series is very short, which is a limitation of this research. With qualitative and quantitative methods especially with the Vector Auto Regressive (VAR) model; the results of the study indicate new findings, unlike previous studies, including: (1) The real estate market positively impacts Vietnam's economic growth, most noticeably in the second quarter lag and the fourth quarter lag, and then its trend impacts inversely; (2) The real estate market and economic growth in Vietnam have fluctuated over time with many risks that are affected by the past shocks of these factors. From these findings, we proposed some managerial implications for managing the real estate market with economic growth in Vietnam sustainably.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.2
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• pp.21-34
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• 1998

[n this paper, we propose a hierarchical self-organizing fuzzy logic controller to improve the performance of the FARMA(Fuzzy auto-regressive moving average) SOC(Self-organizing fuzzy logic controller) when the system parameters change. The proposed controller contains the FARMA SC)C in the lower level and has a coordinator in the higher level, which evaluates convergence. and when it senses the degradation of system performance it compensates the control input by a look-up table. The proposed controller shows good perforniance over the FARMA SOC when the system parameters change. We executed some computer simulations on the regulation problem of an inlrerted pendulum system and compared the results with those of the FARMA SOC. As a result, it ha:; been shown that the proposed controller outperformed the FARMA SOC when the changes of the system parameters occurred.

• Nuclear Engineering and Technology
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• v.53 no.6
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• pp.2056-2065
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• 2021

The transition toward clean energy is an issue of great importance with growing debate in climate change mitigation. The complex nature of nuclear energy-CO₂ emissions nexus makes it difficult to predict whether or not nuclear acts as a clean energy source. Hence, we examined the relationship between nuclear energy consumption and CO₂ emissions in the context of the IPAT and Environmental Kuznets Curve (EKC) framework. Dynamic Auto-regressive Distributive Lag (DARDL), a newly modified econometric tool, is employed for estimation of long- and short-run dynamics by using yearly data spanning from 1971 to 2018. The empirical findings of the study revealed an instantaneous increase in nuclear energy reduces environmental pollution, which highlights that more nuclear energy power in the Indian energy system would be beneficial for climate change mitigation. The results further demonstrate that the overarching effect of population density in the IPAT equation stimulates carbon emissions. Finally, nuclear energy and population density contribute to form the EKC curve. To achieving a cleaner environment, results point out governmental policies toward the transition of nuclear energy that favours environmental sustainability.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.5
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• pp.37-41
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• 2021

This study aims to investigate the relationship between the exchange rate and Trade Balance. Trade Balance is used as the dependent variable, and the independent variables are Exchange Rate, Gross Domestic Product, and Inflation. Augmented Dickey-Fuller unit root test was adopted to test the stationary property of time series data, Auto Regressive Distributed Lag model was employed to find the long run and short-run relationship and long-run adjustment, Bound test approach, the unrestricted Error Correction Model and Granger Causality Test are used to analyze the data from 1977 to 2019. The research findings suggest that inflation has a positive impact on the trade balance in the short run. The exchange rate and the Gross Domestic Product have adverse effects on Trade balance in the long run. The coefficient of ER in the previous year is negative, and the coefficient of TB in the previous year is positive and significant. This is consistent with the J-Curve phenomenon, which states that devaluation may not improve trade balance in the immediate period, but will significantly impact the trade balance improvement in subsequent periods. Hence Marshall Lerner Condition exists in Sri Lanka.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.5
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• pp.221-229
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• 2021

This study aims to forecast the exchange rate of the Chinese Yuan against the US Dollar by a combination of different models as proposed by Poon and Granger (2003) during the Covid-19 pandemic. For this purpose, we include three uni-variate time series models, i.e., ARIMA, Naïve, Exponential smoothing, and one multivariate model, i.e., NARDL. This is the first of its kind endeavor to combine univariate models along with NARDL to the best of our knowledge. Utilizing monthly data from January 2011 to December 2020, we predict the Chinese Yuan against the US dollar by two combination criteria i.e. var-cor and equal weightage. After finding out the individual accuracy, the models are then assessed through equal weightage and var-cor methods. Our results suggest that Naïve outperforms all individual & combination of time series models. Similarly, the combination of NARDL and Naïve model again outperformed all of the individual as well as combined models except the Naïve model, with the lowest MAPE value of 0764. The results suggesting that the Chinese Yuan exchange rate against the US Dollar is dependent upon the recent observations of the time series. Further evidence shows that the combination of models plays a vital role in forecasting which commensurate with the literature.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.5
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• pp.913-921
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• 2021

The objective of the study is to investigate the impact of the COVID-19 pandemic on Saudi Arabia stock market. The study relied on the data of the daily closing stock market price index Tadawul All Share Index (TASI), and the number of daily cases infected with COVID-19 during the period from March 15, 2020, to August 10, 2020. The study employs the Vector Auto-Regressive (VAR) model, the Impulse Response Function (IRF) and Autoregressive Conditional Heteroscedasticity (ARCH) models. The results of the correlation matrix and the Impulse Response Function (IRF) show that stock market returns responded negatively to the growth in COVID-19 infected cases during the pandemic. The results of ARCH model confirmed the negative impact of COVID-19 pandemic on KSA stock market returns. The results also showed that the negative market reaction was strong during the early days of the COVID-19 pandemic. The study concluded that stock market in KSA responded quickly to the COVID-19 pandemic; the response varies over time according to the stage of the pandemic. However, the Saudi government's response time and size of the stimulus package have played an important role in alleviating the impacts of the COVID-19 pandemic on Saudi Arabia Stock Market.