• The Journal of Asian Finance, Economics and Business
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• v.9 no.10
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• pp.159-168
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• 2022

The time series of foreign trade turnover is complex and variable and contains linear and nonlinear information. This paper proposes preprocessing the dataset by the EMD algorithm and combining the linear prediction advantage of the SARIMA model with the nonlinear prediction advantage of the EMD-LSTM model to construct the SARIMA-EMD-LSTM hybrid model by the weight assignment method. The forecast performance of the single models is compared with that of the hybrid models by using MAPE and RMSE metrics. Furthermore, it is confirmed that the weight assignment approach can benefit from the hybrid models. The results show that the SARIMA model can capture the fluctuation pattern of the time series, but it cannot effectively predict the sudden drop in foreign trade turnover caused by special reasons and has the lowest accuracy in long-term forecasting. The EMD-LSTM model successfully resolves the hysteresis phenomenon and has the highest forecast accuracy of all models, with a MAPE of 7.4304%. Therefore, it can be effectively used to forecast the Sino-Russia foreign trade turnover time series post-epidemic. Hybrid models cannot take advantage of SARIMA linear and LSTM nonlinear forecasting, so weight assignment is not the best method to construct hybrid models.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.17 no.3
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• pp.43-53
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• 2007

Internet becomes more and more popular, and most companies and institutes use web services for e-business and many other purposes. With the explosion of Internet, the occurrence of cyber terrorism has grown very rapidly. Simulation is one of the most widely used method to study internet worms. But, it is quite challenging to simulate very large-scale worm attacks because of various reasons. In this paper, we propose a hybrid modeling method for RCS(Random Constant Spreading) worm simulation. The proposed hybrid model simulates worm attacks by synchronizing modeling network and packet network. So, this model will be both detailed enough to generate realistic packet traffic, and efficient enough to model a worm spreading through the Internet. Moreover, our model have the capability of dynamic updates of the modeling parameters. Finally, we simulate the hybrid model with the CodeRed worm to show validity of our proposed model for RCS worm simulation.

• Journal of the Korean Applied Science and Technology
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• v.30 no.4
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• pp.622-634
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• 2013

The multi-origin of obesity and its associated diseases made it's a complex area of biomedical science research and severe health disorder. From the 1970s to onwards this health problem turned to an epidemic without having any report of declining yet and it created a red alert to the health sector. Meanwhile, many animal models have been developed to study the lethal effect of obesity. In consequence, many drugs, therapies and strategies have already been adopted based on the findings of those animal models. However, many complicated things based on molecular and generic mechanism has not been clarified to the date. Thus, it is important to develop a need based animal model for the better understanding and strategic planning to eliminate/avoid the obesity disorder. Therefore, the present review would unveil the pros and cons of presently established animal models for obesity research. In addition, it would indicate the required turning direction for further obesity and obesity based disease research.

• Animal cells and systems
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• v.16 no.5
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• pp.351-356
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• 2012

Food is important to any animal, and a large part of the behavioral repertoire is concerned with ensuring adequate nutrition. Two main nutritional sensations, hunger and satiety, produce opposite behaviors. Hungry animals seek food, increase exploratory behavior and continue feeding once they encounter food. Satiated animals decrease exploratory behavior, take rest, and stop feeding. The signals of hunger or satiety and their effects on physiology and behavior will depend not only on the animal's current nutritional status, but also on its experience and the environment in which the animal evolved. In our novel, nutritionally rich environment, improper control of appetite contributes to diseases from anorexia to the current epidemic of obesity. Despite extraordinary recent advances, genetic contribution to appetite control is still poorly understood partly due to lack of simple genetic model systems. In this review, we will discuss current understanding of molecular and cellular mechanisms by which animals regulate food intake depending on their nutritional status. Then, focusing on relatively less known muscarinic and cGMP signals, we will discuss how the molecular and behavioral aspects of hunger and satiety are conserved in a simple invertebrate model system, Caenorhabditis elegans so as for us to use it to understand the genetics of appetite control.

• International Journal of Computer Science & Network Security
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• v.22 no.1
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• pp.149-160
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• 2022

The article considers the peculiarities of developing a marketing strategy for the adaptation of small businesses to quarantine restrictions in the field of commercial entrepreneurship. The importance of reformatting the existing marketing strategy in connection with the change of key conditions of trade activity with the introduction of quarantine restrictions due to the covid19 virus epidemic is substantiated. Quarantine restrictions and the temporary introduction of lockdown in various countries around the world, including Ukraine, have not only caused a crisis for small businesses. But they became a shock therapy and accelerated the digitalization of retail. Trends in digitalization and development of digital infrastructure allow both to adapt the structures of commercial entrepreneurship to the current conditions, and set directions for development in the long run. Particular attention in the article is paid to changing the business model and automation of sales processes based on the introduction of vending. The preconditions and existing experience of vending in Ukraine are analyzed. An outline of the business model of the project for the sale of goods through vending machines has been developed.

• The Journal of Industrial Distribution & Business
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• v.15 no.5
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• pp.17-27
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• 2024

Purpose: The renminbi (RMB) has appreciated alongside the elevation of China's economic status, leading to increased exchange rate volatility. Moreover, China's medical industry saw a surge in import and export trade volume, with trade related to epidemic prevention and control in the medical sector significantly increasing its share. The medical device trade, in particular, occupies a substantial portion of this trade. Research design, data and methodology: This paper focuses on the import and export value of medical devices in the medical industry as a case study to explore the impact of RMB exchange rate fluctuations on the import and export trade of the medical industry during the pandemic. Additionally, it investigates whether the import and export trade of the medical industry can be a contributing factor to the fluctuations in the RMB exchange rate. Results: Through an empirical study on the import and export values of medical devices in the medical industry over the past three years, as well as the RMB exchange rate, this paper establishes a VAR model and conducts a series of tests including stationarity tests and cointegration tests. Conclusions: The conclusion is that fluctuations in the RMB exchange rate have a long-term impact on China's medical industry's import and export trade.

• Journal of Platform Technology
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• v.11 no.4
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• pp.3-12
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• 2023

In the post-COVID era, the importance of quarantine measures is greatly emphasized, and accordingly, research related to the detection of mask wearing conditions and prevention of other infectious diseases using deep learning is being conducted. However, research on the detection and tracking of visitors to cultural facilities to prevent the spread of diseases is equally important, so research on this should be conducted. In this paper, a convolutional neural network-based object detection model is trained through transfer learning using a pre-collected dataset. The weights of the trained detection model are then applied to a multi-object tracking model to monitor visitors. The visitor detection model demonstrates results with a precision of 96.3%, recall of 85.2%, and an F1-score of 90.4%. Quantitative results of the tracking model include a MOTA (Multiple Object Tracking Accuracy) of 65.6%, IDF1 (ID F1 Score) of 68.3%, and HOTA (Higher Order Tracking Accuracy) of 57.2%. Furthermore, a qualitative comparison with other multi-object tracking models showcased superior results for the model proposed in this paper. The research of this paper can be applied to the hygiene systems within cultural facilities in the post-COVID era.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.8
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• pp.1487-1493
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• 2016

The susceptible, exposed, infectious, and recovered model (SEIR) is used extensively in the field of epidemiology. On the other hand, dissemination information among users through internet grows exponentially. This information spreading can be modeled as an epidemic. In this paper, we derive the mathematical model of SEIR in viral communication from the view of optimal control theory. Overall the methods based on classical calculus, In order to solve the optimal control problem, proved to be more efficient and accurate. According to Pontryagin's minimum principle (PMP) the Hamiltonian function must be optimized by the control variables at all points along the solution trajectory. We present our method based on the PMP and forward backward algorithm. In this algorithm, one should integrate forward in time for the state equations then integrate backward in time for the adjoint equations resulting from the optimality conditions. The problem is mathematically analyzed and numerically solved as well.

• Communications for Statistical Applications and Methods
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• v.29 no.5
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• pp.499-512
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• 2022

Intervention measures have been implemented worldwide to reduce the spread of the COVID-19 outbreak. The COVID-19 outbreak has occured in several waves of infection, so this paper is divided into three groups, namely those countries who have passed the pandemic period, those countries who are still experiencing a single-wave pandemic, and those countries who are experiencing a multi-wave pandemic. The purpose of this study is to develop a multi-wave Richards model with several changepoint detection methods so as to obtain more accurate prediction results, especially for the multi-wave case. We investigated epidemiological trends in different countries from January 2020 to October 2021 to determine the temporal changes during the epidemic with respect to the intervention strategy used. In this article, we adjust the daily cumulative epidemiological data for COVID-19 using the logistic growth model and the multi-wave Richards curve development model. The changepoint detection methods used include the interpolation method, the Pruned Exact Linear Time (PELT) method, and the Binary Segmentation (BS) method. The results of the analysis using 9 countries show that the Richards model development can be used to analyze multi-wave data using changepoint detection so that the initial data used for prediction on the last wave can be determined precisely. The changepoint used is the coincident changepoint generated by the PELT and BS methods. The interpolation method is only used to find out how many pandemic waves have occurred in given a country. Several waves have been identified and can better describe the data. Our results can find the peak of the pandemic and when it will end in each country, both for a single-wave pandemic and a multi-wave pandemic.

• Genomics & Informatics
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• v.19 no.1
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• pp.11.1-11.8
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• 2021

For the novel coronavirus disease 2019 (COVID-19), predictive modeling, in the literature, uses broadly susceptible exposed infected recoverd (SEIR)/SIR, agent-based, curve-fitting models. Governments and legislative bodies rely on insights from prediction models to suggest new policies and to assess the effectiveness of enforced policies. Therefore, access to accurate outbreak prediction models is essential to obtain insights into the likely spread and consequences of infectious diseases. The objective of this study is to predict the future COVID-19 situation of Korea. Here, we employed 5 models for this analysis; SEIR, local linear regression (LLR), negative binomial (NB) regression, segment Poisson, deep-learning based long short-term memory models (LSTM) and tree based gradient boosting machine (GBM). After prediction, model performance comparison was evelauated using relative mean squared errors (RMSE) for two sets of train (January 20, 2020-December 31, 2020 and January 20, 2020-January 31, 2021) and testing data (January 1, 2021-February 28, 2021 and February 1, 2021-February 28, 2021) . Except for segmented Poisson model, the other models predicted a decline in the daily confirmed cases in the country for the coming future. RMSE values' comparison showed that LLR, GBM, SEIR, NB, and LSTM respectively, performed well in the forecasting of the pandemic situation of the country. A good understanding of the epidemic dynamics would greatly enhance the control and prevention of COVID-19 and other infectious diseases. Therefore, with increasing daily confirmed cases since this year, these results could help in the pandemic response by informing decisions about planning, resource allocation, and decision concerning social distancing policies.