• Journal of the Korea Convergence Society
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• v.13 no.4
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• pp.1-6
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• 2022

Real-time systems using web cralwing data must provide users with data from the same database as remote data. To do this, the web crawler repeatedly sends HTTP(HtypeText Transfer Protocol) requests to the remote server to see if the remote data has changed. This process causes network load on the crawling server and remote server, causing problems such as excessive traffic generation. To solve this problem, in this paper, based on user events, we propose a real-time web crawling technique that can reduce the overload of the network while securing the reliability of maintaining the sameness between the data of the crawling server and data from multiple remote locations. The proposed method performs a crawling process based on an event that requests unit data and list data. The results show that the proposed method can reduce the overhead of network traffic in existing web crawlers and secure data reliability. In the future, research on the convergence of event-based crawling and time-based crawling is required.

• Journal of the Optical Society of Korea
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• v.17 no.6
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• pp.513-517
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• 2013

Recently, maskless lithography (ML) systems have become popular in digital manufacturing technologies. To achieve high-throughput manufacturing processes, digital micromirror devices (DMD) in ML systems must be driven to their operational limits, often in harsh conditions. We propose an instrument and algorithm to detect DMD malfunctions to ensure perfect mask image transfer to the photoresist in ML systems. DMD malfunctions are caused by either bad DMD pixels or data transfer errors. We detect bad DMD pixels with $20{\times}20$ pixel by white and black image tests. To analyze data transfer errors at high frame rates, we monitor changes in the frame rate of a target DMD pixel driven by the input data with a set frame rate of up to 28000 frames per second (fps). For our data transfer error detection method, we verified that there are no data transfer errors in the test by confirming the agreement between the input frame rate and the output frame rate within the measurement accuracy of 1 fps.

• Smart Structures and Systems
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• v.24 no.4
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• pp.507-524
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• 2019

A Bayesian dynamic linear model (BDLM) is presented for a data-driven analysis for response prediction and load effect separation of a revolving auditorium structure, where the main loads are self-weight and dead loads, temperature load, and audience load. Analyses are carried out based on the long-term monitoring data for static strains on several key members of the structure. Three improvements are introduced to the ordinary regression BDLM, which are a classificatory regression term to address the temporary audience load effect, improved inference for the variance of observation noise to be updated continuously, and component discount factors for effective load effect separation. The effects of those improvements are evaluated regarding the root mean square errors, standard deviations, and 95% confidence intervals of the predictions. Bayes factors are used for evaluating the probability distributions of the predictions, which are essential to structural condition assessments, such as outlier identification and reliability analysis. The performance of the present BDLM has been successfully verified based on the simulated data and the real data obtained from the structural health monitoring system installed on the revolving structure.

• Journal of Advanced Marine Engineering and Technology
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• v.30 no.2
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• pp.247-252
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• 2006

Almost ship monitoring systems are event driven alarm system which warn only when the measurement value is over or under set point. These kinds of system cannot warn until signal is growing to abnormal state that the signal is over or under the set point. therefore cannot play a role for preventive maintenance system. This paper proposes fault diagnosis method which is able to diagnose and forecast the fault from present operating condition by analyzing monitored signals with present ship monitoring system without any additional sensors. By analyzing the data with high correlation coefficient(CC), correlation level of interactive data can be defined. Knowledge base of abnormal detection can be built by referring level of CC(Fault Detection CC. FDCC) to detect abnormal data among monitored data from monitoring system and knowledge base of diagnosis built by referring CC among interactive data for related machine each other to diagnose fault part.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2005.06a
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• pp.281-286
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• 2005

Almost ship monitoring systems are event driven alarm system which warn only when the measurement value is over or under set point. These kinds of system cannot warn while signal is growing to abnormal state until the signal is over or under the set point and cannot play a role for preventive maintenance system. This paper proposes fault diagnosis method which is able to diagnose and forecast the fault from present operating condition by analyzing monitored signals with present ship monitoring system without additional sensors. By analyzing this data having high correlation coefficient(CC), correlation level of interactive data can be understood. Knowledge base of abnormal detection can be built by referring level of CC(Fault Detection CC, FDCC) to detect abnormal data among monitored data from monitoring system and knowledge base of diagnosis built by referring CC among interactive data for related machine each other to diagnose fault part.

• Journal of Information Technology Services
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• v.22 no.4
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• pp.111-122
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• 2023

Since the organization of civil servants has been divided and stratified according to the characteristics of the bureaucracy, it is inevitable that the organization and personnel will increase when new tasks arise. Even in the process of informatization, only the processing method was brought online while leaving the existing business processing procedures as they were, so there was no reduction in manpower through informatization. In order to maintain or upgrade the current administrative services while reducing the number of civil servants, it is inevitable to use AI technology. By using data and AI to integrate the 'powers and responsibilities assigned to the officials in charge', manpower can be reduced, and the reduced costs can be reinvested in the collection, analysis, and utilization of on-site data to further promote intelligent informatization. In this study, as a way for the government's success in intelligent informatization innovation, we proposed a 'Civil Servants-AI Collaboration Platform'. This Platform based on the civil servant proposal system as a reward system and the characteristics of intelligent informatization that are different from the informatization. By establishing a 'Civil Servants-AI Collaboration Platform', the performance evaluation system of the short-term evaluation method by superiors can be improved to a data-driven always-on evaluation method, thereby alleviating the rigid hierarchy of government organizations. In addition, through the operation of Collaboration Platform, it will become common to define and solve problems using data and AI, and the intelligence informatization of government organizations will be activated.

• Journal of Korea Water Resources Association
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• v.41 no.3
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• pp.341-351
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• 2008

Since the damage from the torrential rain increases recently due to climate change and global warming, the significance of flood forecasting and warning becomes important in medium and small streams as well as large river. Through the preprocess and main processes for estimating runoff, diverse errors occur and are accumulated, so that the outcome contains the errors in the existing flood forecasting and warning method. And estimating the parameters needed for runoff models requires a lot of data and the processes contain various uncertainty. In order to overcome the difficulties of the existing flood forecasting and warning system and the uncertainty problem, ANFIS(Adaptive Neuro-Fuzzy Inference System) technique has been presented in this study. ANFIS, a data driven model using the fuzzy inference theory with neural network, can forecast stream level only by using the precipitation and stream level data in catchment without using a lot of physical data that are necessary in existing physical model. Time series data for precipitation and stream level are used as input, and stream levels for t+1, t+2, and t+3 are forecasted with this model. The applicability and the appropriateness of the model is examined by actual rainfall and stream level data from 2003 to 2005 in the Tancheon catchment area. The results of applying ANFIS to the Tancheon catchment area for the actual data show that the stream level can be simulated without large error.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.27 no.6
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• pp.787-793
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• 1994

It has been demonstrated for the finite-difference ocean circulation model that the problem of uncertain forcing and input data can be tackled with an optimization techniques. The uncertainty problem in interesting flow properties is exploring a finite difference ocean circulation model due to the uncertainty in the driving boundary conditions. The mathematical procedure is based upon optimization method by the conjugate gradient method using the simulated data and a simple barotropic model. An example for the ocean circulation model is discussed in which wind forcing and the steady-state circulation are determined from a simulated stream function.

• Proceedings of the Korean Geotechical Society Conference
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• 2005.03a
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• pp.677-686
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• 2005

The allowable bearing capacity of a pile, the most important factor in stability estimation, is determined by applying safety factor to the ultimate load or yield load. There are several but contradictory methods available in current design codes to estimate the allowable bearing capacity and the safety factor. This paper analyzes load-settlement curves obtained from 19 static load tests measured from 11 sites. At all tests, the load is applied until apparent failure is observed. The validity of the ultimate and yield load estimation method and load caculated from the settlement criterion is investigated through comparison with the measured data. In addition, a new procedure to estimate allowable load and safety factor is proposed. Additional data from field static load tests, such as those incorporated in this study, are needed to more reliably apply the proposed method in design practice.

• Language Facts and Perspectives
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• v.47
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• pp.177-203
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• 2019

The purpose of this study is to suggest the possibility of using online corpora for teaching synonyms in Korean. The research included how to develop the effective concordance learning materials for teaching synonyms in Korean using data driven learning(DDL). Because synonyms are similar in meaning and usage, even native speaker can not clearly explain the difference in synonyms. Furthermore, it is not easy to provide proper example sentences for each word, and it is a reality that the differentiation of the synonyms are not sufficiently provided in the Korean textbooks. In recent years, it has been claimed that DDL helps students produce vocabulary as well as comprehend vocabulary. Nevertheless, it is hard to find how the concordance materials should be made for them. In this study, we extract concordance examples from the various kinds of online corpora; written and spoken corpora, korean textbooks, newspapers. We presented how to make corpus-designed activities using concordance materials for teaching Korean synonyms. In order to examine the effects of DDL, five experimental lessons were given to a group of 15 advanced korean learners in the university and follow-up surveys(attitude-questionnaire) were conducted. This study is meaningful in that it proposed a new teaching method in Korean synonym education.