• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.1
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• pp.495-505
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• 2014

The retention basin is a river-facility for the flood mitigation by storing the river flow temporarily. The new 3 retention basins are installed in these regions YeoJu, NaJu, YoungWol by the Large River Management Project. In this study, 1D and 2D numerical flow simulation are conducted to evaluate the reduction effect of the peak flood stage for the YeoJu retention basin. HEC-RAS and FLDWAV models are used for 1D simulation with the option of retention basin. CCHE2D model is used for 2D simulation with the same hydrograph used in 1D simulation. It is verified that the peak flood stage is reduced very largely about 0.13 m near the overtopping section of the levee in 1D simulation. It is verified that the peak flood stage is reduced very largely about 0.20 m at the upstream-end of the simulated reach in 2D simulation. 2D simulation for the retention basin is more reasonable because physical characteristics of topography in the model, and also more advantageous for the evaluation of the flow characteristics of the in- and outside of the retention basin on the results of simulation of this study.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.11
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• pp.672-682
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• 2020

The Korea Nowadays, which is research on digital twin technology for efficient operation in various industrial/manufacturing sites, is being actively conducted, and gradual depletion of fossil fuels and environmental pollution issues require new renewable/eco-friendly power generation methods, such as wave power plants. In wave power generation, however, which generates electricity from the energy of waves, it is very important to understand and predict the amount of power generation and operational efficiency factors, such as breakdown, because these are closely related by wave energy with high variability. Therefore, it is necessary to derive a meaningful correlation between highly volatile data, such as wave height data and sensor data in an oscillating water column (OWC) chamber. Secondly, the methodological study, which can predict the desired information, should be conducted by learning the prediction situation with the extracted data based on the derived correlation. This study designed a workflow-based training model using a machine learning framework to predict the pressure of the OWC. In addition, the validity of the pressure prediction analysis was verified through a verification and evaluation dataset using an IoT sensor data to enable smart operation and maintenance with the digital twin of the wave generation system.

• Journal of Navigation and Port Research
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• v.28 no.10 s.96
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• pp.933-940
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• 2004

A construction project of Incheon 2nd bridge, which is connected between the Incheon Song-Do New Town and the Incheon International Airport in Young-Jong-Do, has been proposed by the private capital in 1999. But the optimal width of the main span has not been decided in spite of the three investigations into the feasibility of ship's safe transit in this planned bridge. In this paper, we study the optimal width of the main span according to the traffic volume in the future traffic and the ship maneuverability of maximum size aspect. The result of this study, the channel in the main span of Incheon 2nd bridge is required to design two-way traffic scheme and the width of 1,000m, which will satisfy the safe transit from the viewpoint of the traffic volume in the future traffic and the ship maneuverability of maximum size.

• Korean Chemical Engineering Research
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• v.58 no.4
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• pp.588-595
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• 2020

Prediction and control of nitrogen oxides (NO_x) emission is of great interest in industry due to stricter environmental regulations. Herein, we propose an artificial intelligence (AI)-based framework for prediction of NO_x emission. The framework includes pre-processing of data for training of neural networks and evaluation of the AI-based models. In this work, Long-Short-Term Memory (LSTM), one of the recurrent neural networks, was adopted to reflect the time series characteristics of NO_x emissions. A decision tree was used to determine a time window of LSTM prior to training of the network. The neural network was trained with operational data from a heating furnace. The optimal model was obtained by optimizing hyper-parameters. The LSTM model provided a reliable prediction of NO_x emission for both training and test data, showing an accuracy of 93% or more. The application of the proposed AI-based framework will provide new opportunities for predicting the emission of various air pollutants with time series characteristics.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.3
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• pp.249-257
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• 2017

Filtering the data for travel time records obtained from DSRC probes is essential for a better estimation of the link travel time. This study addresses the major deficiency in the performance of TRANSGUIDE in removing anomalous data. This algorithm is unable to handle unstable traffic flow conditions for certain time intervals, where fluctuations are observed. In this regard, this study proposes an algorithm that is capable of overcoming the weaknesses of TRANSGUIDE. If TRANSGUIDE fails to validate sufficient number of observations inside one time interval, another process specifies a new validity range based on the median absolute deviation (MAD), a common statistical approach. The proposed algorithm suggests the parameters, ${\alpha}$ and ${\beta}$, to consider the maximum allowed outlier within a one-time interval to respond to certain traffic flow conditions. The parameter estimation relies on historical data because it needs to be updated frequently. To test the proposed algorithm, the DSRC probe travel time data were collected from a multilane highway road section. Calibration of the model was performed by statistical data analysis through using cumulative relative frequency. The qualitative evaluation shows satisfactory performance. The proposed model overcomes the deficiency associated with the rapid change in travel time.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.1
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• pp.34-45
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• 2013

Recently, world-wide research activities on new network architectures and protocols are being actively progressed. Furthermore, research activities on testbed and related technologies for supporting those activities are being actively performed. In domestic study, research on the OpenFlow protocol based future Internet testbed implementation and research on its virtualization for supporting independent research experiments are being performed at the same time. The network virtualization is a technology which maximizes the testbed utilization without interfering each other for the individual research when many researchers share the testbed. In this paper, we implemented a Enhanced FlowVisor by extending basic FlowVisor by adding QoS functions which are admission control and minimum bandwidth guarantee. On the testbed, we performed a real-time video streaming experiment for verifying the performance of the Enhnaced FlowVisor on the OpenFlow protocol based future internet testbed. The result shows that the Enhanced FlowVisor can provide reliable and stable QoS to the individual researches without disturbing each other traffic on virtualized networks of the future Internet testbed.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.36 no.9
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• pp.895-900
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• 2012

This paper aims to examine the effect of nanodroplets on pool-type absorption heat transfer enhancement and to find the relationship between the dispersion stability and the absorption performance. The concentrations of oil and surfactant are considered as the key parameters. $C_{12}E_4$ and Tween20 are used as the surfactants and N-decane oil is added to the $NH_3/H_2O$ solution to make the binary nanoemulsion fluids. Binary nanoemulsion fluids are dispersed by the ultrasonic vibrator and the stirrer under specific conditions. The dispersion stability of binary nanoemulsion fluids for each oil concentrations is evaluated from the droplet size and Tyndall effect analysis. The absorption performance of binary nanoemulsion fluids is compared with the result of dispersion stability. In addition, it is found that the binary nanoemulsion fluid is a strong candidate as a new working fluid for absorption applications.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.10
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• pp.4678-4702
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• 2018

Traffic Matrix estimation has always caught attention from researchers for better network management and future planning. With the advent of high traffic loads due to Cloud Computing platforms and Software Defined Networking based tunable routing and traffic management algorithms on the Internet, it is more necessary as ever to be able to predict current and future traffic volumes on the network. For large networks such origin-destination traffic prediction problem takes the form of a large under- constrained and under-determined system of equations with a dynamic measurement matrix. Previously, the researchers had relied on the assumption that the measurement (routing) matrix is stationary due to which the schemes are not suitable for modern software defined networks. In this work, we present our Compressed Sensing with Dynamic Model Estimation (CS-DME) architecture suitable for modern software defined networks. Our main contributions are: (1) we formulate an approach in which measurement matrix in the compressed sensing scheme can be accurately and dynamically estimated through a reformulation of the problem based on traffic demands. (2) We show that the problem formulation using a dynamic measurement matrix based on instantaneous traffic demands may be used instead of a stationary binary routing matrix which is more suitable to modern Software Defined Networks that are constantly evolving in terms of routing by inspection of its Eigen Spectrum using two real world datasets. (3) We also show that linking this compressed measurement matrix dynamically with the measured parameters can lead to acceptable estimation of Origin Destination (OD) Traffic flows with marginally poor results with other state-of-art schemes relying on fixed measurement matrices. (4) Furthermore, using this compressed reformulated problem, a new strategy for selection of vantage points for most efficient traffic matrix estimation is also presented through a secondary compression technique based on subset of link measurements. Experimental evaluation of proposed technique using real world datasets Abilene and GEANT shows that the technique is practical to be used in modern software defined networks. Further, the performance of the scheme is compared with recent state of the art techniques proposed in research literature.

• Journal of Advanced Navigation Technology
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• v.16 no.5
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• pp.864-870
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• 2012

Voice pattern recognition technology to solve the problems of the existing problems and common issues that you may have in language learning content analysis. This is the first problem of language-learning content, online learning posture. Game open another web page through the lesson, but the concentration of the students fell. Have not been able to determine the second issue according Speaking has made the learning process actually reads. Third got a problem with the mechanical process by a learning management system, as well by the teacher in the evaluation of students and students who are learning progress between the difference in the two. Finally, the biggest problem, while maintaining their existing content made to be able to solve the above problem. Speaking learning dedicated learning programs under this background, voice pattern recognition technology learning process for speech recognition and voice recognition capabilities for learning itself has been used in the recognition process the data of the learner's utterance as an audio file of the desired change to a transfer to a specific location of the server or SQL server may be easily inserted into any system or program, any and all applicable content that has already been created without damaging the entire component because the new features were available. Contributed to this paper, active participation in class more interactive teaching methods to change.

• Korean Journal of Food Science and Technology
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• v.36 no.6
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• pp.995-1000
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• 2004

Nutritional composition and antioxidative capacity of mulberry fruit (Ficus-4x) were investigated for evaluation as new red-colored fruit. Contents of moisture, crude fat, crude protein, and vitamin C were similar, whereas that of crude ash was higher, to those of other berry fruits. Contents of minerals (Ca, 14.33 mg/100 g; P, 39.98 mg/100 g; Fe, 6.01 mg/100 g; Zn, 4.04 mg/100 g; Mn, 2.26 mg/100 g), particularly Fe, were higher than those of other berry fruits. Hardness, springness, cohesiveness, gumminess, and chewiness of mulberry fruit were higher, and color values (L, 36.03; a, 1.80; b, 1.54) were lower than those of strawberry. Relative scavenging activities of mulberry fruit methanol extract and its cyanidine 3-glucoside on 1, 1-diphenyl-2-picrylhydrazy radical (DPPH) were 35.7 and 78.2%, respectively, using butylated hydroxyanisole (BHA) as standard. Antioxidant activities in corn oil (peroxide values and conjugated dienoic acid) were tertiary butylhydroquinone (TBHQ) > mulberry fruit ethanol extract > mulberry fruit water extract > butylated hydroxytoluene (BHT) > tocopherol. Results show mulberry fruit can be very useful red-colored fruit for development of functional foods with beneficial effects on radical scavenging and antioxidative capacities.