• Journal of Environmental Impact Assessment
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• v.19 no.6
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• pp.617-623
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• 2010

It is the most important subject to figure out characteristics of the wastewater inflows of sewerage treatment plant(STP) when situation models are applied to operation of the biological processes and in the automatic control based on ICA(Instrument, Control and Automation). For the purposes, real-time influent monitoring method has been applied by using on-line monitoring equipments for the process optimization in conventional STP. Since, the influent of STP is consist of complex components such as, COD, BOD, TN, $NH_4$-N, $NO_3$-N, TP and $PO_4$-P. MRA2(Microbial Respiration Analyzer 2), which is capable of real-time analyzing of wastewater characteristics is used to overcome the limitations and defects of conventional online monitoring equipments in this study. Rapidity, accuracy and stability of developed MRA2 are evaluated and compared with the results from on-line monitoring equipments for seven months after installation in Full-scale STP.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.204-206
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• 2021

Real-time drilling parameters prediction is a considerably important study from the viewpoint of maximizing drilling efficiency. Among the methods of maximizing drilling, the method of improving the drilling speed is common, which is related to the rate of penetration, drillstring rotational speed, weight on bit, and drilling mud flow rate. This study proposes a method of predicting the drilling rate, one of the real-time drilling parameters, using a recurrent neural network-based deep learning model, and compares the existing physical-based drilling rate prediction model with a prediction model using deep learning.

• Journal of KIBIM
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• v.8 no.3
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• pp.12-19
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• 2018

The research aims to find implications of machine learning and urban big data as a way to construct the flexible transportation network system of smart city by responding the urban context changes. This research deals with a problem that existing a bus headway model is difficult to respond urban situations in real-time. Therefore, utilizing the urban big data and machine learning prototyping tool in weathers, traffics, and bus statues, this research presents a flexible headway model to predict bus delay and analyze the result. The prototyping model is composed by real-time data of buses. The data is gathered through public data portals and real time Application Program Interface (API) by the government. These data are fundamental resources to organize interval pattern models of bus operations as traffic environment factors (road speeds, station conditions, weathers, and bus information of operating in real-time). The prototyping model is implemented by the machine learning tool (RapidMiner Studio) and conducted several tests for bus delays prediction according to specific circumstances. As a result, possibilities of transportation system are discussed for promoting the urban efficiency and the citizens' convenience by responding to urban conditions.

• Tuberculosis and Respiratory Diseases
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• v.69 no.4
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• pp.250-255
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• 2010

Background: The purpose of this study was to evaluate recently developed real-time polymerase chain reaction (PCR) assay kit to detect Mycobacterium tuberculosis (MTB) and nontuberculous mycobacteria (NTM) in respiratory specimens. Methods: We assessed the positive rate of the real-time PCR assay to detect MTB and NTM in 87 culture-positive specimens (37 sputum, 50 bronchial washing), which were performed real-time PCR by using $Real-Q_{TM}$ MTB&NTM Kit from January 2009 to June 2009, at Gyeongsang University Hospital. To compare the efficacy with the TB-PCR assay, we evaluated 63 culture-positive specimens (19 sputum, 44 bronchial washing) for MTB or NTM, which were performed TB-PCR by using ABSOLUTETM MTB II PCR Kit from March 2008 to August 2008. Results: Among 87 specimens tested using real-time PCR, MTB and NTM were cultured in 58 and 29, respectively. The positive rate of real-time PCR assay to detect MTB was 71% (22/31) and 92.6% (25/27) in AFB stain-negative and stain-positive specimens. For NTM, the positive rate of real-time PCR was 11.1% (2/18) and 72.7% (8/11) in AFB stain-negative and stain-positive specimens. Among 63 specimens performed using TB-PCR, MTB and NTM were cultured in 46 and 17, respectively. The positive rate of TB-PCR was 61.7% (21/34) and 100% (12/12) in AFB stain-negative and stain-positive specimens. TB-PCR was negative in all NTM-cultured 17 specimens. Conclusion: TB/NTM real-time PCR assay is useful to differentiate MTB and NTM in AFB stain-positive respiratory specimens and it is as effective in detecting MTB with TB-PCR.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.33 no.3
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• pp.184-191
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• 2010

In the environment of 450mm wafers production known as the next-generation semiconductor production process, one of the most significant features is the full automation over the whole manufacturing processes involved. The full automation system for 450mm wafer production will minimize the human workers' involvement in the manufacturing process as much as possible. In addition, since the importance of an individual wafer processing increases noticeably, it is necessary to develop more robust scheduling systems in the whole manufacturing process than so ever. The scheduling systems for the next-generation semiconductor production processes also should be capable of monitoring individual wafers and collecting useful data on them in real time. Based on the information gathered from these processes, the system should finally have a real-time scheduling functions controlling whole the semiconductor manufacturing processes. In this study, preliminary investigations on the requirements and needed functions for constructing the real time scheduling system and transforming manufacturing environments for 300mm wafers to those of 400mm are conducted and through which the next generation semiconductor processes for efficient scheduling in a clustered production system architecture of the scheduler is proposed. Our scheduling architecture is composed of the modules for real-time scheduling, the clustered production type supporting, the optimal scheduling and so on. The specifications of modules to define the major required functions, capabilities, and the relationship between them are presented.

• Journal of Advanced Navigation Technology
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• v.12 no.3
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• pp.280-285
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• 2008

The traditional face detection method is to use difference picture method are used to detect movement. However, most do not consider this mathematical approach using real-time or real-time implementation of the algorithm is complicated, not easy. This paper, the first to detect real-time facial image is converted YCbCr and RGB video input. Next, you convert the difference between video images of two adjacent to obtain and then to conduct Glassfire Labeling. Labeling value compared to the threshold behavior Area recognizes and converts video extracts. Actions to convert video to conduct face detection, and detection of facial characteristics required for the extraction and use of AdaBoost algorithm.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.165-166
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• 2017

It is demanding to receive information of data in real time anywhere and analyze it with meaningful data. Research on the platform for such analysis is actively underway. In this paper, we try to find out what are important factors in solving the problems of collecting and analyzing IoT data in real time. How much better than existing data collection methods and analytical methods can be the basis for judging the value of the data. It is important to accurately collect and store data more quickly and quickly from many sensors in real time in real time, and analytical methods that can derive values from the stored data. Therefore, an important requirement of the analysis platform in the IoT environment is to process large amount of data in real time and to centralize and manage it.

• Journal of Korea Water Resources Association
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• v.30 no.5
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• pp.431-439
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• 1997

This paper describes the development of a decision support system (DSS) for the real time reservoir operation that aims to maximize the flood control effect. In the decision support system, model base and real time data processing subsystem are included along with the graphical user interface(GUI) that is able to visualize the forecasted runoff hydrographs at the flood control points and reservoir water levels resulting from the model run as well as the current hydrologic status. The system was verified through the pseudo real time applications to the Taechong reservoir operation with the historical flood events of the Kum river basin occurred in July, 1987 and August, 1995, Decision making processes were performed using the developed system and the results were compared with the real operations at that time. The reservoir operation using the pseudo real time application of DSS were simulated by the flood runoff simulation model, that shows the reservoir operation by DSS were successful in flood control for the lower Kum River.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.5
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• pp.615-621
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• 1999

In this paper, we consider the robust stability of uncertain linear systems with time-delay in the time domain. The considered uncertainties are both the unstructured uncertainty which is only Known its norm bound and the structured uncertainty which is known its structured. Based on Lyapunov stability theorem and{{{{ { H}_{$\infty$ } }}}} theory known as Strictly Bounded Real Lemma (SBRL), we present new conditions that guarantee the robust stability of system. Also, we extend this to multiple time-varying delays systems and large-scale systems, respectively. Finally, we show the usefulness of our results by numerical examples.

• Journal of Microbiology and Biotechnology
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• v.24 no.8
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• pp.1044-1050
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• 2014

Since there is no consensus about the most reliable assays to detect invasive aspergillosis from samples obtained by minimally invasive or noninvasive methods, we compared the efficacy of an enzyme-linked immunosorbent assay (ELISA) for galactomannan (GM) detection and quantitative real-time PCR assay (qRT-PCR) for the diagnosis of invasive pulmonary aspergillosis. Neutropenic, male Sprague-Dawley rats (specific pathogen free; 8 weeks old; weight, $200{\pm}20g$) were immunosuppressed with cyclophosphamide and infected with Aspergillus fumigatus intratracheally. Tissue and whole blood samples were harvested on days 1, 3, 5, and 7 post-infection and examined with GM ELISA and qRT-PCR. The A. fumigatus DNA detection sequence was detected in the following number of samples from 12 immunosuppressed, infected rats examined on the scheduled days: day 1 (0/12), day 3 (0/12), day 5 (6/12), and day 7 (8/12) post-infection. The sensitivity and specificity of the qRT-PCR assay was 29.2% and 100%, respectively. Receiver operating characteristic curve (ROC) analysis indicated a Ct (cycle threshold) cut-off value of 15.35, and the area under the curve (AUC) was 0.627. The GM assay detected antigen in sera obtained on day 1 (5/12), day 3 (9/12), day 5 (12/12), and day 7 (12/12) post-infection, and thus had a sensitivity of 79.2% and a specificity of 100%. The ROC of the GM assay indicated that the optimal Ct cut-off value was 1.40 (AUC, 0.919). The GM assay was more sensitive than the qRT-PCR assay in diagnosing invasive pulmonary aspergillosis in rats.