• KIPS Transactions on Computer and Communication Systems
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• v.10 no.12
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• pp.329-342
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• 2021

The pharmaceutical industry is experiencing a productivity crisis with a low probability of success despite a long period of time and enormous cost. As a strategy to solve the productivity crisis, the use cases of Artificial Intelligence(AI) and Bigdata are increasing worldwide and tangible results are coming out. However, domestic pharmaceutical companies are taking a wait-and-see attitude to adopt AI platform for drug research. This study proposed a research model that combines the Value-based Adoption Model and the Innovation Resistance Model to empirically study the effect of value perception and resistance factors on adopting AI Platform. As a result of empirical verification, usefulness, knowledge richness, complexity, and algorithmic opacity were found to have a significant effect on perceived values. And, usefulness, knowledge richness, algorithmic opacity, trialability, technology support infrastructure were found to have a significant effect on the innovation resistance.

• Journal of the Korea Convergence Society
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• v.13 no.2
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• pp.249-255
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• 2022

This study proposed an analysis framework for real-time prediction of CNC processing defects using machine learning-based models that are recently attracting attention as processing defect prediction methods, and applied it to CNC machines. Analysis shows that the XGBoost, CatBoost, and LightGBM models have the same best accuracy, precision, recall, F1 score, and AUC, of which the LightGBM model took the shortest execution time. This short run time has practical advantages such as reducing actual system deployment costs, reducing the probability of CNC machine damage due to rapid prediction of defects, and increasing overall CNC machine utilization, confirming that the LightGBM model is the most effective machine learning model for CNC machines with only basic sensors installed. In addition, it was confirmed that classification performance was maximized when an ensemble model consisting of LightGBM, ExtraTrees, k-Nearest Neighbors, and logistic regression models was applied in situations where there are no restrictions on execution time and computing power.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.14 no.5
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• pp.1-12
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• 2015

Length and the number of tunnels has increased 10% annually. Tunnel construction has positive effect in nature and driving condition. However, the structure of tunnels lead to a greater probability of major accidents. For this reason, tunnel is focusing its attention on the rapid incident handling and disaster management to build a tunnel management system in recently. In this study, tunnel management system adequacy analyzed in terms of operational safety using IPA and AHP analysis. IPA analysis results using the portfolio chart, incident management factors has a large gap between important and satisfaction. Disaster management is analyzed high ranking in priority. However, incident management factors are derived first priority in AHP analysis. This study determined that the results are meaningful to practitioners in the field is determined. In addition, practitioners comments should be reflected primarily for tunnel operational safely.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.43 no.6
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• pp.507-515
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• 2015

This paper proposes a filtering method for effective state vector estimation of highly maneuvering target. It is needed to hit the point called 'sweet spot' to increase the kill probability in missile interception. In paper, a filtering method estimates the length of a moving target tracked by a frequency modulated continuous wave (FMCW) radar. High resolution range profiles (HRRPs) is generated from the radar echo signal and then it's integrated into proposed filtering method. To simulate the radar measurement which is close to real, the study on the properties of scattering point of the missile-like target has been conducted with ISAR image for different angle. Also, it is hard to track the target efficiently with existing Kalman filters which has fixed measurement noise covariance matrix R. Therefore the proposed method continuously updates the covariance matrix R with sensor measurements and tracks the target. Numerical simulations on the proposed method shows reliable results under reasonable assumptions on the missile interception scenario.

• Journal of Korea Water Resources Association
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• v.46 no.2
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• pp.195-204
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• 2013

As drought is phenomenon of nature with unavoidability and repeated characteristic, it is necessary to plan to respond to it in advance and construct drought management system to minimize its damage. This study suggested standard for classification of drought, which is appropriate for our nation to respond to drought by assessing drought severity in the regions for this study. For data collection, 61 locations were selected - the locations keep precipitation data over 30 years of observation. And data for monthly precipitation for 37 years from 1973 were used. Based on this, this study classified unified drought interval into four levels using drought situation phases which are used in government. For standard for classification of drought severity fit to our nation, status of main drought was referred and these are classified based on accumulated probability of drought - 98~100% Exceptional Drought, 94~98% Extreme Drought, 90~94% Severe Drought, 86~90% Moderate Drought. Drought index (SPI, PDSI) was made in descending order and quantitative value of drought index fit to standard of classification for drought severity was calculated. To compare classification results of drought severity of SPI and PDSI with actual drought, comparison by year and month unit were analyzed. As a result, in comparison by year and comparison by month unit of SPI, drought index of each location was mostly identical each other between actual records and analyzed value. But in comparison by month unit of PDSI for same period, actual records did not correspond to analyzed values. This means that further study about mutual supplement for these indexes is necessary.

• Journal of Digital Convergence
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• v.15 no.12
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• pp.313-321
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• 2017

Traffic accidents in the country have steadily increased. Although IOT technologies have been applied so as to prevent the primary accident, practical solutions to prevent the secondary accident have not been suggested. A general guideline is simply recommended. In this paper, utilizing existing communication technology, we implement a proposed model and its simulation to prevent the secondary accident. When it is possible for a driver to secure visibility, the secondary accident can be prevented; In areas like tunnel, mountain terrain, and curve road with heavy traffic, where the driver has difficulty in securing the visibility, the secondary accident rates after the primary accident have been increasing. Therefore, we implement an accident prevention system that determines the primary accident utilizing sensor technology and prevents the secondary accident communicating through V2V or V2I. After the simulation, we found that the proposed model and the existing model made no difference with regard to the secondary accident rates when the visibility of the driver is secured; With the application of the proposed model, however, the accident rates decreased for 3-7 percent even though the visibility and communication were not secured.

• Journal of Digital Convergence
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• v.20 no.3
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• pp.325-331
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• 2022

As IoT devices are used in various ways in an edge network environment, multiple studies are being conducted that utilizes the information collected from IoT devices in various applications. However, it is not easy to apply accurate IoT data immediately as IoT data collected according to network environment (interference, interference, etc.) are frequently missed or error occurs. In order to minimize mistakes in IoT data collected in an edge network environment, this paper proposes a management technique that ensures the reliability of IoT data by randomly generating signature values of IoT data and allocating only Security Information (SI) values to IoT data in bit form. The proposed technique binds IoT data into a blockchain by applying multiple hash chains to asymmetrically link and process data collected from IoT devices. In this case, the blockchainized IoT data uses a probability function to which a weight is applied according to a correlation index based on deep learning. In addition, the proposed technique can expand and operate grouped IoT data into an n-layer structure to lower the integrity and processing cost of IoT data.

• Journal of the Korea Society for Simulation
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• v.30 no.2
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• pp.1-9
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• 2021

Many factors such as wind direction, wind strength, temperature, and obstacles affect a munition's trajectory. Since these factors eventually determines the probability of hit and the hitting point of a target, these factors should be considered to obtain reliable weapon effectiveness data. In this study, we propose the extension of the MSDL(Military Scenario Definition Language) to reflect these factors to improve the reliability of weapon effectiveness data. Based on the existing MSDL, which has been used to set the initial condition of a military simulation scenarios, the newly identified subelements are added in ScenarioID, Environment, Organizations, and Installations as a scenario schema. Also, DamageAssessment and DesignOfExperiments element are added to make weapon effectiveness data easily. The extended MSDL enables to automatically generate the simulation scenarios that reflect various factors which affect the probability of hit or kill. This extended MSDL is applied to an integrated simulation software of weapon systems, named AddSIM version 4.0 for generation of weapon effectiveness data.

• Korean Journal of Culture and Social Issue
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• v.14 no.4
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• pp.1-18
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• 2008

The present study examined the accuracy of polygraph tests through two types of statistical methods with unknown ground truth. One method evaluated the accuracy based on the rates of agreements between polygraph test results of crime suspects and prosecutors' indictment decisions for them. Those crime suspects were tested with polygraph by the Prosecutors' Office of the Republic of Korea between 2000 and 2004. The other method estimated the accuracy by using the latent class analysis based on the frequency distributions of the polygraph results and indictments during 2006. Excluding cases that were 'inconclusive' on the polygraph test, the study showed that the accuracy of the polygraph tests is .914 (SE=.004) for the 2000-2004 data, and .885 (SE=.021) for the 2006 data. With the inclusion of 'inconclusive' cases in the 2006 data, the results from the latent class analysis showed the accuracy in the range between .707 and .734 (SE=.027~.031), with false positives between .078 and .087 (SE=.019~.023), and false negatives between .029 and .078 (SE=.010~.023). The probability that the polygraph test correctly classifies subjects appeared to be in the range between .912 and .925 (SE=.013-.016) for those who lie, and in the range between .867 to .955 (SE=.011-.040) for those who tell the truth.

• Journal of the Korean Geographical Society
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• v.49 no.5
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• pp.761-778
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• 2014

There has been relatively little study to model price for commercial property because of its low transaction volume in the market. Despite of this thin market character, this paper tried to estimate prices for commercial lots as accurate as possible. We constructed a model whose components consist of mean structure(global trend), exponential covariance function and a pure error term, and applied it to actual sales price data of Seoul. We explicitly took account of spatial autocorrelation of land price by utilizing a kriging technique, a representative method of spatial interpolation, because the land price of commercial lots has feature of differential price forming pattern depending on submarkets they belong to. In addition, we chose to apply a bayesian kriging to overcome data scarcity by incorporating experts' knowledge into prior probability distribution. The chosen model's excellent performance was verified by the result from validation data. We confirmed that the excellence of the model is attributed to incorporating both autocorexperts' knowledge and spatial autocorrelation in the model construction. This paper is differentiated from previous studies in the sense that it applied the bayesian kriging technique to estimate price for commercial lots and explicitly combined experts' knowledge with data. It is expected that the result of this paper would provide a useful guide for the circumstances under which property price has to be estimated reliably based on sparse transaction data.