• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.103-128
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• 2021

Recently, investors' interest and the influence of stock-related information dissemination are being considered as significant factors that explain stock returns and volume. Besides, companies that develop, distribute, or utilize innovative new technologies such as artificial intelligence have a problem that it is difficult to accurately predict a company's future stock returns and volatility due to macro-environment and market uncertainty. Market uncertainty is recognized as an obstacle to the activation and spread of artificial intelligence technology, so research is needed to mitigate this. Hence, the purpose of this study is to propose a machine learning model that predicts the volatility of a company's stock price by using the internet search volume of artificial intelligence-related technology keywords as a measure of the interest of investors. To this end, for predicting the stock market, we using the VAR(Vector Auto Regression) and deep neural network LSTM (Long Short-Term Memory). And the stock price prediction performance using keyword search volume is compared according to the technology's social acceptance stage. In addition, we also conduct the analysis of sub-technology of artificial intelligence technology to examine the change in the search volume of detailed technology keywords according to the technology acceptance stage and the effect of interest in specific technology on the stock market forecast. To this end, in this study, the words artificial intelligence, deep learning, machine learning were selected as keywords. Next, we investigated how many keywords each week appeared in online documents for five years from January 1, 2015, to December 31, 2019. The stock price and transaction volume data of KOSDAQ listed companies were also collected and used for analysis. As a result, we found that the keyword search volume for artificial intelligence technology increased as the social acceptance of artificial intelligence technology increased. In particular, starting from AlphaGo Shock, the keyword search volume for artificial intelligence itself and detailed technologies such as machine learning and deep learning appeared to increase. Also, the keyword search volume for artificial intelligence technology increases as the social acceptance stage progresses. It showed high accuracy, and it was confirmed that the acceptance stages showing the best prediction performance were different for each keyword. As a result of stock price prediction based on keyword search volume for each social acceptance stage of artificial intelligence technologies classified in this study, the awareness stage's prediction accuracy was found to be the highest. The prediction accuracy was different according to the keywords used in the stock price prediction model for each social acceptance stage. Therefore, when constructing a stock price prediction model using technology keywords, it is necessary to consider social acceptance of the technology and sub-technology classification. The results of this study provide the following implications. First, to predict the return on investment for companies based on innovative technology, it is most important to capture the recognition stage in which public interest rapidly increases in social acceptance of the technology. Second, the change in keyword search volume and the accuracy of the prediction model varies according to the social acceptance of technology should be considered in developing a Decision Support System for investment such as the big data-based Robo-advisor recently introduced by the financial sector.

• Journal of Distribution Science
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• v.13 no.5
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• pp.23-31
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• 2015

Purpose - This study investigated competitive counteractions necessary for continuous growth in a rapidly changing logistics environment in Northeast Asia. Research Design, Data, and Methodology - Using a time series analysis, the study first investigated Northeast Asian trade scale and volume by examining online and offline material from the Ministry of Ocean and Fisheries, Busan Port Authority, and other government agencies. A literature survey was done to investigate the state and prospect of the logistics environment in Northeast Asia including changes in freight volume at major ports in the three Northeast Asian countries. Result - The results of the study suggest using the Trans-Korean Railroad (TKR), as well as promoting the North Pole and South Pole routes, to compete against changes in trade volume and the logistics environment in Northeast Asia. A SWOT analysis was done to examine the effectiveness of these strategies. Conclusions - The findings indicate that the TKR impact, using the Busan Port connecting the Trans-China Railway (TCR), the Trans-Siberian (TSR), and the North Pole Route, may be uncertain in practice considering the uncertainty in international politics.

• Journal of the Korean Mathematical Society
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• v.61 no.5
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• pp.875-898
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• 2024

This study estimates the true price of an asset and finds the optimal bid/ask prices for market makers. We provide a novel state-space model based on the exponential Ornstein-Uhlenbeck volatility and the Heston models with Gaussian noise, where the traded price and volume are available, but the true price is not observable. An objective of this study is to use Bayesian filtering to estimate the posterior distribution of the true price, given the traded price and volume. Because the posterior density is intractable, we employ the guided particle filtering algorithm, with which adaptive rejection metropolis sampling is used to generate samples from the density function of an unknown distribution. Given a simulated sample path, the posterior expectation of the true price outperforms the traded price in estimating the true price in terms of both the mean absolute error and root-mean-square error metrics. Another objective is to determine the optimal bid/ask prices for a market maker. The profit-and-loss of the market maker is the difference between the true price and its bid/ask prices multiplied by the traded volume or bid/ask size of the market maker. The market maker maximizes the expected utility of the PnL under the posterior distribution. We numerically calculate the optimal bid/ask prices using the Monte Carlo method, finding that its spread widens as the market maker becomes more risk-averse, and the bid/ask size and the level of uncertainty increase.

• Journal of Ocean Engineering and Technology
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• v.33 no.4
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• pp.313-321
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• 2019

The prediction of the hydrodynamic performance of a planing hull vessel is an important and challenging topic for computational fluid dynamic (CFD) applications to naval hydrodynamics. In this paper, the resistance and planing attitude analysis for a Fridsma hull, which is a prismatic planing hull, in still water are numerically studied using OpenFOAM. OpenFOAM is an open source code package based on C++ libraries and the finite volume method (FVM) for the discretization of the RANS equation. The volume of fluid method (VOF) is used to capture the water-air interface and the SST ${\kappa}-{\omega}$ model is used for the turbulence simulation. The overset mesh method is used to capture the large motion of the hull at higher speeds. Before the extensive analysis, uncertainty analyses using various time steps and grid sizes were performed for one ship speed case of Fn = 1.19. The results of the present study are compared with those of a model test, other CFD research, and Savitsky's empirical formula. The results of the present study, following the trend of other CFD results, slightly over predict the resistance and under predict the sinkage and, more significantly, the trim.

• Journal of the Korean Society of Marine Environment & Safety
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• v.25 no.1
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• pp.27-33
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• 2019

Several studies have been completed on the topic of container terminals in Northern Vietnam. Few of them, however, deal with competition in terms of costs related to vessel waiting time or cargo handling. This paper estimates the average waiting cost per TEU for all the container terminals in Northern Vietnam. After average waiting time was first estimated by applying queuing theory, uncertainty theory was applied to estimated vessel daily cost. A simulation was performed to create a series of data representing waiting cost per TEU in relation to the rate of volume handled/capacity of each terminal. Non-linear regression based on this series was used to present a function for the relationship between the average waiting cost of each terminal and the rate of volume handled /capacity.

• Journal of Dental Anesthesia and Pain Medicine
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• v.22 no.2
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• pp.129-143
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• 2022

Background: Patient-controlled analgesia (PCA) has been widely used as an effective medical treatment for pain and for postoperative analgesia. However, improper dose errors in intravenous (IV) administration of narcotic analgesics from a PCA infusion pump can cause patient harm. Furthermore, opioid overdose is considered one of the highest risk factors for patients receiving pain medications. Therefore, accurate delivery of opioid analgesics is a critical function of PCA infusion pumps. Methods: We designed a microbalance method that consisted of a closed acrylic chamber containing a layer and an oil layer with an electronic balance. A commercially available infusion analyzer (IDA-5, Fluke Co., Everett, WA, USA) was used to measure the accuracy of the infusion flow rate from a commercially available smart PCA infusion pump (PS-1000, UNIMEDICS, Co., Ltd., Seoul, Korea) and compared with the results of the microbalance method. We evaluated the uncertainty of the flow rate measurement using the ISO guide (GUM:1995 part3). The battery life, delay time of the occlusion alarm, and bolus function of the PCA pump were also tested. Results: The microbalance method was good in the short-term 2 h measurement, and IDA-5 was good in the long-term 24 h measurement. The two measurement systems can complement each other in the case of the measurement time. Regarding battery performance, PS-1000 lasted approximately 5 days in a 1 ml/hr flow rate condition without recharging the battery. The occlusion pressure alarm delays of PS-1000 satisfied the conventional alarm threshold of occlusion pressure (300-800 mmHg). Average accuracy bolus volume was measured as 63%, 95%, and 98.5% with 0.1 ml, 1 ml, and 2 ml bolus volume presets, respectively. A 1 ml/hr flow rate measurement was evaluated as 2.08% of expanded uncertainty, with a 95% confidence level. Conclusion: PS-1000 showed a flow accuracy to be within the infusion pump standard, which is ± 5% of flow accuracy. Occlusion alarm of PS-1000 was quickly transmitted, resulting in better safety for patients receiving IV infusion of opioids. PS-1000 is sufficient for a portable smart PCA infusion pump.

• Korean Journal of Environmental Agriculture
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• v.30 no.2
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• pp.169-178
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• 2011

BACKGROUND: This study aimed to estimate the measurement uncertainty associated with determination of cadmium and lead from tomato paste by ICP/MS. The sources of measurement uncertainty (i.e. sample weight, final volume, standard weight, purity, molecular weight, working standard solution, calibration curve, recovery and repeatability) in associated with the analysis of cadmium and lead were evaluated. METHODS AND RESULTS: The guide to the expression of uncertainty was used for the GUM (Guide to the expression of Uncertainty in Measurement) and Draft EURACHEM/CITAC (EURACHEM: A network of organization for analytical chemistry in Europe/Co-Operation on International Traceability in Analytical Chemistry) Guide with mathematical calculation and statistical analysis. The uncertainty components were evaluated by either Type A or Type B methods and the combined standard uncertainty were calculated by statistical analysis using several factors. Expected uncertainty of cadmium and lead was $0.106{\pm}0.015$ mg/kg (k=2.09) and $0.302{\pm}0.029$ mg/kg (k=2.16), on basis of 95% confidence of Certified Reference Material (CRM) which was within certification range of $0.112{\pm}0.007$ mg/kg for cadmium (k=2.03) and $0.316{\pm}0.021$ mg/kg for lead (k=2.01), respectively. CONCLUSION(s): The most influential components in the uncertainty of heavy metals analysis were confirmed as recovery, standard calibration curve and standard solution were identified as the most influential components causing uncertainty of heavy metal analysis. Therefore, more careful consideration is required in these steps to reduce uncertainty of heavy metals analysis in tomato paste.

• Journal of the Korean Vacuum Society
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• v.22 no.5
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• pp.231-237
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• 2013

New method of volume measurement for reference weights of a pressure balance using a gas pycnometer is proposed. The result of volume measurement of proposed method shows the uncertainties of approximately 0.2% at the level of confidence of 95% for reference weights in the ranges of 1 kg, 2 kg, and 5 kg. This measuring system consists of a sample chamber, an expansion chamber, a precision pressure gage, a precison thermometer, a vacuum pump, and helium as a medium gas. The measurement principle of this proposed method is based on Boyle's law. This method will contribute a reliability of the volume measurements of reference weights for a pressure balance to the national measurement standard.

• Tunnel and Underground Space
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• v.29 no.2
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• pp.89-107
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• 2019

Rock joints have an extremely important role in analyzing the mechanical stability and hydraulic characteristics of rock mass structures. Most rock joint parameters are generally indicated as a distribution by statistical techniques. In this research, calculation technique of Joint Center Volume (JCV) is analyzed, which is required for estimating the size distribution having the largest uncertainty among the joint parameters, then a new technique is proposed which is applicable regardless of the shape of survey window. The existing theoretical JCV calculation technique can be applied only to the plane window, and the complete enumeration techniques show the limitations in joint trace type and analysis time. This research aims to overcome the limitations in survey window shape and joint trace type through calculating JCV by using Monte Carlo simulation. The applicability of proposed technique is validated through the estimation results at non-planar survey windows such as curved surface and tunnel surface.

• Progress in Medical Physics
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• v.28 no.4
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• pp.144-148
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• 2017

When a high density metallic implant is placed in the path of the proton beam, spatial heterogeneity can be caused due to artifacts in three dimensional (3D) computed tomography (CT) scans. These artifacts result in range uncertainty in dose calculation in treatment planning system (TPS). And this uncertainty may cause significant underdosing to the target volume or overdosing to normal tissue beyond the target. In clinical cases, metal implants must be placed in the beam path in order to preserve organ at risk (OARs) and increase target coverage for tumors. So we should introduce Ti-mesh. In this paper, we measured the lateral dose profile for proton beam using an EBT3 film to confirm dosimetric impact of Ti-mesh when the Ti-mesh plate was placed in the proton beam pathway. The effect of Ti-mesh on the proton beam was investigated by comparing the lateral dose profile calculated from TPS with the film-measured value under the same conditions.