• The Journal of Asian Finance, Economics and Business
• /
• v.9 no.4
• /
• pp.173-183
• /
• 2022

The objective of this research is to examine the influence of the COVID-19 pandemic on stock markets in a few developing and developed countries. This study uses daily data from January 2020 to May 2021 and obtained from World Health Organization and Thomson Reuters. The secondary data was evaluated through panel econometric methodology that includes different unit root tests, and to analyze the long-run relationship between variables, panel cointegration techniques were applied. The long-run causality among variables was examined through Panel Vector Error Correction Model. The overall findings of this study suggest a long-run association exists between several cases and death with the stock returns of the GCC and other stock markets. Furthermore, the VECM model also identified a long-run causality running from COVID cases and death towards the stock rerun of both sets of stock markets. However, a subsequent Wald test yielded mixed results, indicating no short-run causality between cases and deaths and stock returns in both groups; however, in the case of GCC, several COVID-19 cases are having a causal impact on stock markets, which is notable in light of the fact that the death rate in GCC is significantly lower than in many developed and developing countries.

• The Korean Journal of Applied Statistics
• /
• v.36 no.1
• /
• pp.21-32
• /
• 2023

The amount of money processed in medicine from the Korea Consumer Agency was studied by the various time series models. The medical data set from the Korea Consumer Agency were consisted of counseling, damage relief and conciliation. For the analysis of time series, autoregressive moving average model, vector autoregressive model and the transfer function model were used. We considered the stationarity and cross correlation function for the identification and fitting. As a result, the transfer function model showed a better prediction. Whereas, the vector autoregressive model also provided good information for the degree and duration of the influence of variables.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.5
• /
• pp.1414-1430
• /
• 2022

Due to the Internet of Things popularity, many agricultural data are collected by sensors automatically. The abundance of agricultural data makes precise prediction of rice yield possible. Because the climate factors have an essential effect on the rice yield, we considered the climate factors in the prediction model. Accordingly, this paper proposes a machine learning model for rice yield prediction in Taiwan, including the genetic algorithm and support vector regression model. The dataset of this study includes the meteorological data from the Central Weather Bureau and rice yield of Taiwan from 2003 to 2019. The experimental results show the performance of the proposed model is nearly 30% better than MARS, RF, ANN, and SVR models. The most important climate factors affecting the rice yield are the total sunshine hours, the number of rainfall days, and the temperature.The proposed model also offers three advantages: (a) the proposed model can be used in different geographical regions with high prediction accuracies; (b) the proposed model has a high explanatory ability because it could select the important climate factors which affect rice yield; (c) the proposed model is more suitable for predicting rice yield because it provides higher reliability and stability for predicting. The proposed model can assist the government in making sustainable agricultural policies.

• Journal of Advanced Navigation Technology
• /
• v.16 no.6
• /
• pp.1123-1133
• /
• 2012

This study configuration Vector Control System which is stable and has outstanding Dynamic Characteristics in Very Low Speed Region and Low Speed Region, and proposes Instantaneous Speed Observer and Very Low Speed Control method and vector control system of the speed estimation a using Reduced-Dimensional State Observer. The Observer proposed in this system, by appling Reduced-Dimensional State Observer to Load-Torque estimation and using for speed estimation, implements system composition simply and is capable of accurate Instantaneous Speed estimation in Very Low Speed Region. Also, this study reduces influence by System Noise and suggests an induction motor speed control system which is effective in Load Disturbance, modeling error, estimation noise and so on without changing pole of an Observer.

• Journal of Advanced Navigation Technology
• /
• v.16 no.6
• /
• pp.1005-1013
• /
• 2012

In this paper, the induction motor in indirect vector control method modeling, from indirect vector control method it undergoes an influence to the $1/{\tau}$. When changing a rotor resistance because being like this quality Matlab/Simulink where it will make what kind of effect in speed presumption it led proposed control system used microprosser TMS320C31 DSP for high speed processing. The effectiveness of the proposed system is verified by simulation and experimental results. This result shows highly characteristic speed estimation and robust character of load regulation. and the flux which it follows in change of parameter and speed presumption it was under simulation and get the good result which it comes to get it analyzed.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.12 no.3
• /
• pp.1289-1301
• /
• 2011

In this paper, flux estimation method at the Induction motor is applied to stability flux estimate of possibility in overall speed domain. angle operation has voltage and current and speed information using the Induction motor direct control method. Induction motor direct control is material to flux information. Exact flux estimation method to using current model flux estimator of low-speed domain and voltage model flux estimator of high-speed domain. Speed and current and flux controller using PI controller. And error of integral requital for add to Anti-Windup PI controller. Verified to performance of Current model Flux controller and voltage model flux controller using Matlab / Simulink. Analysis has parameter influence of direct vector control and indirect vector control at the Induction motor vector control. So, verified to minute control. Analyzed to simulation result and proof to validity of presented algorithm.

• The Journal of the Korea Contents Association
• /
• v.13 no.6
• /
• pp.20-29
• /
• 2013

The compression of video data is intended for real-time transmission of band-limited channels. Compressed video bit-streams are very sensitive to transmission error. If we lose packets or receive them with errors during transmission, not only the current frame will be corrupted, but also the error will propagate to succeeding frames due to the spatio-temporal predictive coding structure of sequences. Error detection and concealment is a good approach to reduce the bad influence on the reconstructed visual quality. To increase concealment efficiency, we need to get some more accurate error detection algorithm. In this paper, We hide specific data into the motion vector difference of each macro-block, which is obtained from the procedure of inter prediction mode in H.264/AVC. Then, the location of errors can be detected easily by checking transmitted specific data in decoder. We verified that the proposed algorithm generates good performances in PSNR and subjective visual quality through the computer simulation by H.324M mobile simulation tool.

• Parasites, Hosts and Diseases
• /
• v.49 no.4
• /
• pp.431-436
• /
• 2011

The onset, severity, and ultimate outcome of malaria infection are influenced by parasite-expressed virulence factors as well as by individual host responses to these determinants. In both humans and mice, liver injury follows parasite entry, persisting to the erythrocytic stage in the case of infection with the fatal strain of Plasmodium falciparum. Hepatic nuclear factor (HNF)-$1{\alpha}$ is a master regulator of not only the liver damage and adaptive responses but also diverse metabolic functions. In this study, we analyzed the expression of host HNF-$1{\alpha}$ in relation to malaria infection and evaluated its interaction with the 5'-untranslated region of subtilisin-like protease 2 (subtilase, Sub2). Recombinant human HNF-$1{\alpha}$ expressed by a lentiviral vector (LV HNF-$1{\alpha}$) was introduced into mice. Interestingly, differences in the activity of the 5'-untranslated region of the Pf-Sub2 promoter were detected in 293T cells, and LV HNF-$1{\alpha}$ was observed to influence promoter activity, suggesting that host HNF-$1{\alpha}$ interacts with the Sub2 gene.

• Journal of the Korea Society of Computer and Information
• /
• v.12 no.4
• /
• pp.87-94
• /
• 2007

Imbalances in focus, luminance and color between stereo Pairs could cause disparity vector estimation error and increment of transmission data. If the distribution of errors in residual image is large, it may influence to lowering of compression performance. Therefore, in this paper, we propose an efficient balancing method between stereo pairs to reduce the effect. For this, we registrated stereo images using a FFT based method to consider the pixels in the occluded region, we eliminated the pixels of blocks which has large error of disparity vector estimation in balancing function estimation. The balancing function has estimated using histogram specification, local information of target image and residual image between stereo images. Experiments show that the proposed method is effective in error distribution, PSNR and disparity vector estimation. We expect that our method can be improving compression efficiency in stereo coding system.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.6 no.6
• /
• pp.1496-1521
• /
• 2012

Uncertainty is ubiquitous in target tracking wireless sensor networks due to environmental noise, randomness of target mobility and other factors. Sensing results are always unreliable. This paper considers unreliability as it occurs in wireless sensor networks and its impact on target-tracking accuracy. Firstly, we map intersection pairwise sensors' uncertain boundaries, which divides the monitor area into faces. Each face has a unique signature vector. For each target localization, a sampling vector is built after multiple grouping samplings determine whether the RSS (Received Signal Strength) for a pairwise nodes' is ordinal or flipped. A Fault-Tolerant Target-Tracking (FTTT) strategy is proposed, which transforms the tracking problem into a vector matching process that increases the tracking flexibility and accuracy while reducing the influence of in-the-filed factors. In addition, a heuristic matching algorithm is introduced to reduce the computational complexity. The fault tolerance of FTTT is also discussed. An extension of FTTT is then proposed by quantifying the pairwise uncertainty to further enhance robustness. Results show FTTT is more flexible, more robust and more accurate than parallel approaches.