• Proceedings of the Korea Concrete Institute Conference
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• 2002.05a
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• pp.545-550
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• 2002

Concrete is one of the essential structural materials in the construction. But, concrete consists of many materials and is affected by many factors such as properties of materials, site environmental situations, and skill of constructor. Therefore, concrete mixes depend on experiences of experts. However, it is more and more difficult to determine concrete mixes design by empirical means because more ingredients like mineral and chemical admixtures are included. Artificial Neural Networks(ANN) are a mimic models of human brain to solve a complex nonlinear problem. They are powerful pattern recognizers and classifiers, also their computing abilities have been proven in the fields of prediction, estimation and pattern recognition. Here, among them, the back propagation network and radial basis function network are used. Compositions of high-performance concrete mixes are eight components(water, cement, fine aggregate, coarse aggregate, fly ash, silica fume, superplasticizer and air-entrainer). Compressive strength and slump are measured. The results show that neural networks are proper tools to minimize the uncertainties of the design of concrete mixtures.

• Proceedings of the Korean Society of Applied Pharmacology
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• 1995.10a
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• pp.135-136
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• 1995

Resent advances in computer technology have introduced a sophisticated capability for computing the biological fate of toxicants in a biological system. This methodology, which has drastically altered risk assessment skill in toxicology, is designed using all the mechanistic information, and all claim better accuracy with extrapolating capability Iron animal to people than conventional pharmacokinetic methods. Biologically based mathematical models in which the specific mechanistic steps governing tissue disposition(pharmacokinetics) and toxic action (pharmacodynamics) of chemicals are constructed in quantitative terms by a set of equations loading to prediction of the outcome of specific toxicological experiments by computer simulation. pharmacokinetic and pharmacodynamic models are useful in risk assessment because their mechanistic biological basis permits the high-to-low dose, route to route and interspecies extrapolation of the tissue disposition and toxic action of chemicals.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.11 no.4
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• pp.31-38
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• 2012

In this paper, we selected primary cutting parameters that influence on surface roughness of cut bottom surface in end-milling for SM25C material. Those are overhang, depth of cut, feed rate and spindle speed. And then performed orthogonal array experiment and ANOVA by Taguchi method to determine that improved level combination of cutting parameters for betterment of working efficiency and surface roughness one of quality characteristics. And we verified a advisability of prediction model by verification test about level combination. From the results, main cutting parameter influences on surface roughness is spindle speed and the next is feed rate.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.14 no.10
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• pp.939-944
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• 2004

The numerical methods including the performance analysis and the noise prediction of the centrifugal compressor impeller are coupled with the optimization design skill, which consists of response surface method, statistical approach, and genetic algorithm. The flow-field Inside of a centrifugal compressor is obtained numerically by solving Wavier-Stokes equations. and then the propagating noise is estimated from the distributed surface pressure by using Ffowcs Williams-Hawkings formulation. The quadratic response surface model with D-optimal 3-level factorial experimental design points is constructed to optimize the impeller geometry for the advanced centrifugal compressor. The statistical analysis shows that the quadratic model exhibits a reasonable fitting quality resulting in the impeller blade design with high performance and low far-field noise level. The influences of selected design variables, objective functions, and constraints on the impeller performance and the impeller noise are also examined as a result of the optimization process.

• Atmosphere
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• v.21 no.4
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• pp.497-506
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• 2011

Korean mid- and upper-level aviation turbulence guidance (KTG) system is developed using the unified model (UM)-based regional data assimilation and prediction system (RDAPS) of the Korea Meteorological Administration. The KTG system includes three steps. First, the KTG system calculates a suite of diagnostics in the UM-RDAPS domain. Second, component diagnostics that have different units and numerical magnitudes are normalized into the values between 0 and 1, according to their own thresholds in the KTG system. Finally, normalized diagnostics are combined into one KTG predictor by measuring the weighting scores based on the probability of detection, which is calculated using the observed pilot reports (PIREPs) exclusively of moderate-or-greater (MOG) and null (NIL) intensities. To investigate the optimal performance of the KTG system, two types (RD-KTG and UM-KTG) of the KTG systems are developed and evaluated using the PIREPs over Korea and East Asia. Component diagnostics and their thresholds in the RD-KTG are founded on the 8-yrs (2002.12-2010.11) MM5-based RDAPS (previous version of the RDAPS; ${\Delta}x$ = 30 km) and PIREPs data, while those in the UM-KTG are based on the 6 months (2010.12-2011.5) UM-based RDAPS (${\Delta}x$ = 12 km) and PIREPs data. In comparison between the RD-KTG and UM-KTG, overall performance of the UM-KTG (0.815) is better than that of the RD-KTG (0.79) during the recent 6 months, because forecasting skill for the upper-level wind is higher in the UM-RDAPS than in the MM5-RDAPS. It is also found that the UM-KTG is more efficient than the RD-KTG according to the statistical evaluations and sensitivity tests to the number of component diagnostics.

• Ocean Science Journal
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• v.42 no.3
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• pp.153-163
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• 2007

This study is based on a series of numerical modeling experiments to understand the tidal circulation in the Kangjin Bay (KB). The tidal circulation in the KB is mostly controlled by the inflow from two channels, Noryang and Daebang which introduce the open ocean water into the northern part of the KB with relatively strong tidal current, while in the southern part of the KB, shallowest region of the entire study area, weak tidal current prevails. The model prediction of the sea level agrees with observed records at skill scores exceeding 90 % in terms of the four major tidal constituents (M2, S2, K1, O1). However, the skill scores for the tidal current show relatively lower values of 87, 99, 59, 23 for the semi-major axes of the constituents, respectively. The tidal ellipse parameters in the KB are such that the semi-major axes of the ellipse for M2 range from 1.7 to 38.5 cm/s and those for S2 range from 0.5 to 14.4 cm/s. The orientations of the major-axes show parallel with the local isobath. The eccentricity values at various grid points of ellipses for M2 and S2 are very low with 0.2 and 0.06 on the average, respectively illustrating that the tidal current in the KB is strongly rectilinear. The magnitude of the tidal residual current speed in the KB is on the order of a few cm/s and its distribution pattern is very complex. One of the most prominent features is found to be the counter-clockwise eddy recirculation cell at the mouth of the Daebang Channel.

• The Journal of Korean Physical Therapy
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• v.23 no.1
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• pp.37-43
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• 2011

Purpose: We studied the changes in motor response time and stop signal response time following visuomotor skill learning of a stop signal task in young healthy subjects. This study also was designed to determine what an effective practice is for different stop signals in the stop signal task (SST). Methods: Forty-five right-handed normal volunteers without a history of neurological dysfunction were recruited. They all gave written informed consent. In all subjects, motor reaction time (RT) and stop signal reaction time (SSRT) were measured for the stop signal task. Tasks were classified into three categories: predictable-stop signal task (P-SST) practice group random-stop signal task (R-SST) practice group control group. Results: Motor reaction time in the P-SST was significantly reduced when comparing pre- and post-tests (p<0.05). Stop signal reaction times in the P-SST and the R-SST were significantly reduced following motor skill learning (p<0.05). Also, the reaction time of the R-SST was shorter than that of the P-SST. Conclusion: These findings indicate that practice of an SST improves motor performance and stop function for some stop signals in the SST. P-SST practice was effective in the stop function of regular movement because of faster of the motor prediction and preparation but the R-SST was effective in the stop function of movements because of faster motor selection.

• Journal of Navigation and Port Research
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• v.43 no.3
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• pp.160-165
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• 2019

The conduct of seafarer is major cause of marine accidents. This study models the behavior of the seafarer based on the Hidden Markov Model (HMM). Additionally, through the path analysis of the behavior estimated by the model, the kind of situations, procedures and errors that may have caused the marine accidents were interpreted. To successfully implement the model, the seafarer behaviors were observed by means of the summarized verdict reports issued by the Korean Maritime Safety Tribunal, and the observed results converted into behavior data suitable for HMM learning through the behavior classification framework based on the SRKBB (Skill-, Rule-, and Knowledge-Based Behavior). As a result of modeling the seafarer behaviors by the type of vessels, it was established that there was a difference between the models, and the possibility of identifying the preferred path of the seafarer behaviors. Through these results, it is expected that the model implementation technique proposed in this study can be applied to the prediction of the behavior of the seafarer as well as contribute to the prioritization of the behavior correction among seafarers, which is necessary for the prevention of marine accidents.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.391-404
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• 2021

This study developed a long-term prediction model for the potential yield of garlic based on a genetic algorithm (GA) by utilizing global climate reanalysis data. The GA is used for digging the inherent signals from global climate reanalysis data which are both directly and indirectly connected with the garlic yield potential. Our results indicate that both deterministic and probabilistic forecasts reasonably capture the inter-annual variability of crop yields with temporal correlation coefficients significant at 99% confidence level and superior categorical forecast skill with a hit rate of 93.3% for 2 × 2 and 73.3% for 3 × 3 contingency tables. Furthermore, the GA method, which considers linear and non-linear relationships between predictors and predictands, shows superiority of forecast skill in terms of both stability and skill scores compared with linear method. Since our result can predict the potential yield before the start of farming, it is expected to help establish a long-term plan to stabilize the demand and price of agricultural products and prepare countermeasures for possible problems in advance.

• Journal of Korean Society of Disaster and Security
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• v.14 no.4
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• pp.17-27
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• 2021

Streamflow prediction is a very vital disaster mitigation approach for effective flood management and water resources planning. Lately, torrential rainfall caused by climate change has been reported to have increased globally, thereby causing enormous infrastructural loss, properties and lives. This study evaluates the contribution of rainfall to streamflow prediction in normal and peak rainfall scenarios, typical of the recent flood at Piney Resort in Vernon, Hickman County, Tennessee, United States. Daily streamflow, water level, and rainfall data for 20 years (2000-2019) from two USGS gage stations (03602500 upstream and 03599500 downstream) of the Piney River watershed were obtained, preprocesssed and fitted with Long short term memory (LSTM) model. Tensorflow and Keras machine learning frameworks were used with Python to predict streamflow values with a sequence size of 14 days, to determine whether the model could have predicted the flooding event in August 21, 2021. Model skill analysis showed that LSTM model with full data (water level, streamflow and rainfall) performed better than the Naive Model except some rainfall models, indicating that only rainfall is insufficient for streamflow prediction. The final LSTM model recorded optimal NSE and RMSE values of 0.68 and 13.84 m³/s and predicted peak flow with the lowest prediction error of 11.6%, indicating that the final model could have predicted the flood on August 24, 2021 given a peak rainfall scenario. Adequate knowledge of rainfall patterns will guide hydrologists and disaster prevention managers in designing efficient early warning systems and policies aimed at mitigating flood risks.