• Bulletin of the Korean Chemical Society
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• v.26 no.12
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• pp.2007-2016
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• 2005

An artificial neural network (ANN) is successfully presented for prediction acidity constant (pKa) of various benzoic acids and phenols with diverse chemical structures using a nonlinear quantitative structure-property relationship. A three-layered feed forward ANN with back-propagation of error was generated using six molecular descriptors appearing in the multi-parameter linear regression (MLR) model. The polarizability term $(\pi_1)$, most positive charge of acidic hydrogen atom $(q^+)$, molecular weight (MW), most negative charge of the acidic oxygen atom $(q^-)$, the hydrogen-bond accepting ability $(\epsilon_B)$ and partial charge weighted topological electronic (PCWTE) descriptors are inputs and its output is pKa. It was found that properly selected and trained neural network with 205 compounds could fairly represent dependence of the acidity constant on molecular descriptors. For evaluation of the predictive power of the generated ANN, an optimized network was applied for prediction pKa values of 37 compounds in the prediction set, which were not used in the optimization procedure. Squared correlation coefficient $(R^2)$ and root mean square error (RMSE) of 0.9147 and 0.9388 for prediction set by the MLR model should be compared with the values of 0.9939 and 0.2575 by the ANN model. These improvements are due to the fact that acidity constant of benzoic acids and phenols in water shows nonlinear correlations with the molecular descriptors.

• Proceedings of the KSR Conference
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• 2005.05a
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• pp.485-490
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• 2005

With the historical opening of the express rail, Korea has joined the league of France, Japan, Germany and Spain and entered into the super high-speed train era. Opening of the express rail will not only bring about enormous changes to the lives of Koreans, but it will also have a huge influence on the economic, social and cultural aspects of the country. With construction of the Seoul - Busan KTX line, railway passenger transportation capacity and freight transportation capacity will increase. Fast, safe, convenient and environmentally friendly, the express rail is a product of the latest technology and will secure its position as the newest and most preferred method of transportation for the next generation. As the traffic noise, train noise from KTX will become a social problems with the acceleration of speed and increase in the lines. In order to predict the train noise propagation from KTX, this paper presents the sound source system, the calculation model and the scale model experiment. Noise level unit patterns of a KTX that take the rolling noise, the motor noise and aerodynamic noise into consideration are simulated by the scale model experiment and numerical analysis.

• Journal of Electrical Engineering and Technology
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• v.2 no.1
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• pp.123-128
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• 2007

SPWPM traditional software development is surveyed and essential problems are investigated on the basis of system wireless link considerations. This paper presents the current state software planning tools for wireless LAN link optimization. The software directory is based on combination of MatLab and MapInfo software and measurement which gives the best grouping parameters to build up the software development. Among the requirements assumed, the WLAN site selections must be Line-of-sight (LOS) or near line of sight (NLOS) field strength prediction for either point to point or point to multi points. The results obtainable the out put of the program include two-dimensional (2D) and three dimensional (3D) plots for creating the link; design parameters through GUI representing the height and location for each antenna is depending on K-factor of the area and transmit antenna location.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.11
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• pp.64-70
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• 2003

This paper suggests a systematic methodology to predict chip forms using the experimental design technique and the neural network. Significant factors determined with ANOVA analysis are used as input variables of the neural network back-propagation algorithm. It has been shown that cutting conditions and cutting tool shapes have distinct effects on the chip forms, so chip breaking. Cutting tools are represented using the Z-map method, which differs from existing methods using some chip breaker parameters. After training the neural network with selected input variables, chip forms are predicted and compared with original chip forms obtained from experiments under same input conditions, showing that chip forms are same at all conditions. To verify the suggested model, one tool not used in training the model is chosen and input to the model. Under various cutting conditions, predicted chip forms agree well with those obtained from cutting experiments. The suggested method could reduce the cost and time significantly in designing cutting tools as well as replacing the“trial-and-error”design method.

• International Journal of Naval Architecture and Ocean Engineering
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• v.12 no.1
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• pp.354-366
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• 2020

This work mainly focuses on determination of the fully plastic J-integral solutions for welded center cracked plates subjected to remote tension loading. Detailed three-dimensional elasticeplastic Finite Element Analyses (FEA) were implemented to compute the fully plastic J-integral along the crack front for a wide range of crack geometries, material properties and weld strength mismatch ratios for 900 cases. According to the database generated from FEA, Back-propagation Neural Network (BPNN) model was proposed to predict the values and distributions of fully plastic J-integral along crack front based on the variables used in FEA. The determination coefficient R2 is greater than 0.99, indicating the robustness and goodness of fit of the developed BPNN model. The network model can accurately and efficiently predict the elastic-plastic J-integral for weld centerline crack, which can be used to perform fracture analyses and safety assessment for welded center cracked plates with varying strength mismatch conditions under uniaxial loading.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.664-667
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• 1997

For higher component density per chip, it is necessary to identify and control the semiconductor manufacturing process more stringently. Recently, neural networks have been identified as one of the most promising techniques for modeling and control of complicated processes such as plasma etching process. Since wafer states after each run using identical recipe may differ from each other, conventional neural network models utilizing input factors only cannot represent the actual state of process and equipment. In this paper, in addition to the input factors of the recipe, real-time tool data are utilized for modeling of 64M DRAM s-poly plasma etching process to reflect the actual state of process and equipment. For real-time tool data, we collect optical emission spectroscopy (OES) data. Through principal component analysis (PCA), we extract principal components from entire OES data. And then these principal components are included to input parameters of neural network model. Finally neural network model is trained using feed forward error back propagation (FFEBP) algorithm. As a results, simulation results exhibit good wafer state prediction capability after plasma etching process.

• Korean Journal of Remote Sensing
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• v.36 no.5_1
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• pp.807-821
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• 2020

The objective of this study is to evaluate the applicability of representative spatio-temporal fusion models developed for the fusion of mid- and low-resolution satellite images in order to construct a set of time-series high-resolution images for crop monitoring. Particularly, the effects of the characteristics of input image pairs on the prediction performance are investigated by considering the principle of spatio-temporal fusion. An experiment on the fusion of multi-temporal Sentinel-2 and RapidEye images in agricultural fields was conducted to evaluate the prediction performance. Three representative fusion models, including Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), SParse-representation-based SpatioTemporal reflectance Fusion Model (SPSTFM), and Flexible Spatiotemporal DAta Fusion (FSDAF), were applied to this comparative experiment. The three spatio-temporal fusion models exhibited different prediction performance in terms of prediction errors and spatial similarity. However, regardless of the model types, the correlation between coarse resolution images acquired on the pair dates and the prediction date was more significant than the difference between the pair dates and the prediction date to improve the prediction performance. In addition, using vegetation index as input for spatio-temporal fusion showed better prediction performance by alleviating error propagation problems, compared with using fused reflectance values in the calculation of vegetation index. These experimental results can be used as basic information for both the selection of optimal image pairs and input types, and the development of an advanced model in spatio-temporal fusion for crop monitoring.

• Journal of The Korean Society of Agricultural Engineers
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• v.52 no.6
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• pp.63-73
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• 2010

This study is to develop a downstream flood damage prediction model for efficient confrontation in case of extreme and flash flood by future probable small agricultural dam break situation. For a Changri reservoir (0.419 million $m^3$) located in Yongin city of Gyeonggi province, a dam break scenario was prepared. With the probable maximum flood (PMF) condition calculated from the probable maximum precipitation (PMP), the flood condition by dam break was generated by using the HEC-HMS (Hydrologic Engineering Center - Hydrologic Modeling System) model. The flood propagation to the 1.12 km section of Hwagok downstream was simulated using HEC-RAS (Hydrologic Engineering Center - River Analysis System) model. The flood damaged areas were generated by overtopping from the levees and the boundaries were extracted for flood damage prediction, and the degree of flood damage was evaluated using IDEM (Inundation Damage Estimation Method) by modifying MD-FDA (Multi-Dimensional Flood Damage Analysis) and regression analysis simple method. The result of flood analysis by dam-break was predicted to occurred flood depth of 0.4m in interior floodplain by overtopping under PMF scenario, and maximum flood depth was predicted up to 1.1 m. Moreover, for the downstream of the Changri reservoir, the total amount of the maximum flood damage by dam-break was calculated nearly 1.2 billion won by IDEM.

• Computers and Concrete
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• v.19 no.5
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• pp.457-465
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• 2017

The Taiwanese liquid crystal display (LCD) industry has traditionally produced a huge amount of waste glass that is placed in landfills. Waste glass recycling can reduce the material costs of concrete and promote sustainable environmental protection activities. Concrete is always utilized as structural material; thus, the concrete compressive strength with a variety of mixtures must be studied using predictive models to achieve more precise results. To create an efficient waste LCD glass concrete (WLGC) design proportion, the related studies utilized a multivariable regression analysis to develop a compressive strength waste LCD glass concrete equation. The mix design proportion for waste LCD glass and the compressive strength relationship is complex and nonlinear. This results in a prediction weakness for the multivariable regression model during the initial growing phase of the compressive strength of waste LCD glass concrete. Thus, the R ratio for the predictive multivariable regression model is 0.96. Neural networks (NN) have a superior ability to handle nonlinear relationships between multiple variables by incorporating supervised learning. This study developed a multivariable prediction model for the determination of waste LCD glass concrete compressive strength by analyzing a series of laboratory test results and utilizing a neural network algorithm that was obtained in a related prior study. The current study also trained the prediction model for the compressive strength of waste LCD glass by calculating the effects of several types of factor combinations, such as the different number of input variables and the relevant filter for input variables. These types of factor combinations have been adjusted to enhance the predictive ability based on the training mechanism of the NN and the characteristics of waste LCD glass concrete. The selection priority of the input variable strategy is that evaluating relevance is better than adding dimensions for the NN prediction of the compressive strength of WLGC. The prediction ability of the model is examined using test results from the same data pool. The R ratio was determined to be approximately 0.996. Using the appropriate input variables from neural networks, the model validation results indicated that the model prediction attains greater accuracy than the multivariable regression model during the initial growing phase of compressive strength. Therefore, the neural-based predictive model for compressive strength promotes the application of waste LCD glass concrete.

• Proceedings of the KSR Conference
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• 1998.11a
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• pp.361-368
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• 1998

This paper sums up the study of the soundproof facilities to be placed on the test track section within the Seoul-Pusan H.S.T project. The objective of this study is to determine optimum design of soundproof including height, length, location, sound absorbing materials for test track(chonan-taejon). This paper shows the model to design the shape and materials of noise barrier for high speed trains(TGV, ICE, etc). Noise prediction is to be conducted by MITHRA. Various parameters affecting the noise propagation outdoors are surveyed and discussed in relation to H.S.T. noise.