• The Journal of the Acoustical Society of Korea
• /
• v.24 no.4
• /
• pp.217-223
• /
• 2005

In this paper, we propose a predictive block constrained trellis coded quantization (BC-TCQ) to quantize cepstral coefficients for the distributed speech recognition. For Prediction of the cepstral coefficients. the 1st order auto-regressive (AR) predictor is used. To quantize the prediction error signal effectively. we use a BC-TCQ. The performance is compared to the split vector quantizers used in the ETSI standard, demonstrating reduction in the cepstral distance and computational complexity.

• International Commerce and Information Review
• /
• v.16 no.3
• /
• pp.73-96
• /
• 2014

The purpose of this research is to evaluate a short-term export demand forecasting model reflecting individual passenger vehicle brands and market characteristics by using Vector Autoregressive (VAR) models that are based on multivariate time-series model. The short-term export demand forecasting model was created by discerning theoretical potential factors that affect the short-term export demand of individual passenger vehicle brands. Quarterly short-term export demand forecasting model for two Korean small vehicle brands (Accent and Avante) were created by using VAR model. Predictive value at t+1 quarter calculated with the forecasting models for each passenger vehicle brand and the actual amount of sales were compared and evaluated by altering subject period by one quarter. As a result, RMSE % of Accent and Avante was 4.3% and 20.0% respectively. They amount to 3.9 days for Accent and 18.4 days for Avante when calculated per daily sales amount. This shows that the short-term export demand forecasting model of this research is highly usable in terms of prediction and consistency.

• The Transactions of the Korean Institute of Electrical Engineers A
• /
• v.48 no.12
• /
• pp.1544-1553
• /
• 1999

Recently, ultrasonic testing techniques have been widely used in the evaluation of the quality of metal. In this experiment, six heat-treated temperature of specimen have been considered : 0, 1200, 1250, 1300, 1350 and 1387$^{\circ}C$. As heat-treated temperature increases, the grain size of stainless steel also increases and then, eventually make it destroy. In this paper, a pattern recognition method is proposed to identify the heat-treated temperature of metals by evidence accumulation based on artificial intelligence with multiple feature parameters; difference absolute mean value(DAMV), variance(VAR), mean frequency(MEANF), auto regressive model coefficient(ARC), linear cepstrum coefficient(LCC) and adaptive cepstrum vector(ACV). The grain signal pattern recognition is carried out through the evidence accumulation procedure using the distances measured with reference parameters. Especially ACV is superior to the other parameters. The results (96% successful pattern classification) are presented to support the feasibility of the suggested approach for ultrasonic grain signal pattern recognition.

• Nuclear Engineering and Technology
• /
• v.52 no.6
• /
• pp.1327-1333
• /
• 2020

The global warming phenomenon emerges from the issue of climate change, which attracts the attention of intellectuals towards clean energy sources from dirty energy sources. Among clean sources, nuclear energy is getting immense attention among policymakers. However, the role of nuclear energy in pollution emissions reduction has remained inconclusive and demand for further investigation. Therefore, the current study contributes to extend knowledge by investigating the nexus between nuclear energy, economic growth, and CO₂ emissions in a developing country context such as Pakistan for the period between 1973 and 2017. The auto-regressive distributive lag model summarizes the nuclear energy has negative effect on environmental pollution as it releases carbon emission in the environment. Moreover, vector error correction Granger causality provides evidence for bidirectional causality between nuclear energy and carbon emissions. These interesting findings provide new insight, and policy guidelines provided based on these results.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2000.06a
• /
• pp.246-250
• /
• 2000

A distinctive feature in vibration control of a large civil infrastructure is the existence of large disturbances, such as wind, earthquake, and sea wave forces. Those disturbances govern the behavior of the structure, however, they cannot be precisely measured, especially for the case of wind-induced vibration control. The sliding mode fuzzy control (SMFC), which is of interest in this study, may use not only the structural response measurement but also the wind force measurement. Hence, an adaptive disturbance estimation filter is introduced to generate a wind force vector at each time instance based on the measured structural response and the stochastic information of the wind force. The structure of the filter is constructed based on an auto-regressive with auxiliary input model. A numerical simulation is carried out on a benchmark problem of a wind-excited building. The results indicate that the overall performance of the proposed SMFC is as good as the other methods and that most of the performance indices improve as the adaptive disturbance estimation filter is introduced.

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

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

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

• The Journal of Asian Finance, Economics and Business
• /
• v.8 no.4
• /
• pp.113-124
• /
• 2021

The study investigates how financial markets respond to a shock to tone and semantic similarity of the Bank of Thailand press releases. The techniques in natural language processing are employed to quantify the tone and the semantic similarity of 69 press releases from 2010 to 2018. The corpus of the press releases is accessible to the general public. Stock market returns and bond yields are measured by logged return on SET50 and short-term and long-term government bonds, respectively. Data are daily from January 4, 2010, to August 8, 2019. The study uses the Structural Vector Auto Regressive model (SVAR) to analyze the effects of unanticipated and temporary shocks to the tone and the semantic similarity on bond yields and stock market returns. Impulse response functions are also constructed for the analysis. The results show that 1-month, 3-month, 6-month and 1-year bond yields significantly increase in response to a positive shock to the tone of press releases and 1-month, 3-month, 6-month, 1-year and 25-year bond yields significantly increase in response to a positive shock to the semantic similarity. Interestingly, stock market returns obtained from the SET50 index insignificantly respond to the shocks from the tone and the semantic similarity of the press releases.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.2
• /
• pp.467-479
• /
• 2022

Working memory (WM), which plays a vital role in daily activities, is a memory system that temporarily stores and processes information when people are engaged in complex cognitive activities. The influence of music on WM has been widely studied. In this work, we conducted a series of n-back memory experiments with different task difficulties and multiple trials on 14 subjects under the condition of no music and Alpha wave leading music. The analysis of behavioral data show that the change of music condition has significant effect on the accuracy and time of memory reaction (p<0.01), both of which are improved after the stimulation of Alpha wave music. Behavioral results also suggest that short-term training has no significant impact on working memory. In the further analysis of electrophysiology (EEG) data recorded in the experiment, auto-regressive (AR) model is employed to extract features, after which an average classification accuracy of 82.9% is achieved with support vector machine (SVM) classifier in distinguishing between before and after WM enhancement. The above findings indicate that Alpha wave leading music can improve WM, and the combination of AR model and SVM classifier is effective in detecting the brain activity changes resulting from music stimulation.