• Journal of the Korean Geotechnical Society
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• v.20 no.3
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• pp.47-51
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• 2004

An unsaturated hydraulic conductivity function and a soil-water characteristic curve are the essential constitutive factors in studying unsaturated soils. In order to obtain the unsaturated hydraulic conductivity function, prediction functions, which are based on the soil-water characteristic curve, have been used because it is difficult to measure the unsaturated hydraulic conductivity function directly. In this study, a parameter estimation method using the flow pump technique is introduced to determine the unsaturated hydraulic conductivity function. This method provides more accurate and independent solution than previous methods for the determination of the unsaturated hydraulic conductivity function which is not subordinate to the soil-water characteristic curve or prediction models.

• Journal of the Korea Society of Computer and Information
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• v.8 no.3
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• pp.113-123
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• 2003

Many researches on the application of neural networks for making financial index prediction have proven their advantages over statistical and other methods. In this paper, a neural network model is proposed for the Buying, Holding or Selling timing prediction in stocks by the price index of stocks by inputting the closing price and volume of dealing in stocks and the technical indexes(MACD, Psychological Line). This model has an equalized multi-layer arithmetic function as well as the time series prediction function of backpropagation neural network algorithm. In the case that the numbers of learning data are unbalanced among the three categories (Buying, Holding or Selling), the neural network with conventional method has the problem that it tries to improve only the prediction accuracy of the most dominant category. Therefore, this paper, after describing the structure, working and learning algorithm of the neural network, shows the equalized multi-layer arithmetic method controlling the numbers of learning data by using information about the importance of each category for improving prediction accuracy of other category. Experimental results show that the financial index prediction using the equalized multi-layer arithmetic neural network has much higher correctness rate than the other conventional models.

• Korean Journal of Radiology
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• v.25 no.1
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• pp.24-32
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• 2024

Despite improvements in operative techniques and perioperative care, post-hepatectomy liver failure (PHLF) remains the most serious cause of morbidity and mortality after surgery, and several risk factors have been identified to predict PHLF. Although volumetric assessment using imaging contributes to surgical simulation by estimating the function of future liver remnants in predicting PHLF, liver function is assumed to be homogeneous throughout the liver. The combination of volumetric and functional analyses may be more useful for an accurate evaluation of liver function and prediction of PHLF than only volumetric analysis. Gadoxetic acid is a hepatocyte-specific magnetic resonance (MR) contrast agent that is taken up by hepatocytes via the OATP1 transporter after intravenous administration. Gadoxetic acid-enhanced MR imaging (MRI) offers information regarding both global and regional functions, leading to a more precise evaluation even in cases with heterogeneous liver function. Various indices, including signal intensity-based methods and MR relaxometry, have been proposed for the estimation of liver function and prediction of PHLF using gadoxetic acid-enhanced MRI. Recent developments in MR techniques, including high-resolution hepatobiliary phase images using deep learning image reconstruction and whole-liver T1 map acquisition, have enabled a more detailed and accurate estimation of liver function in gadoxetic acid-enhanced MRI.

• Journal of applied mathematics & informatics
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• v.28 no.1_2
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• pp.439-450
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• 2010

In this paper, we propose a new idea to evaluate the prediction accuracy of user's preference generated by memory-based collaborative filtering algorithm before prediction process in the recommender system. Our analysis results show the possibility of a pre-evaluation before the prediction process of users' preference of item's transaction on the web. Classification functions proposed in this study generate a user's rating pattern under certain conditions. In this research, we test whether classification functions select users who have lower prediction or higher prediction performance under collaborative filtering recommendation approach. The statistical test results will be based on the differences of the prediction accuracy of each user group which are classified by classification functions using the generative probability of specific rating. The characteristics of rating patterns of classified users will also be presented.

• Journal of Integrative Natural Science
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• v.12 no.4
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• pp.105-115
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• 2019

In this paper, a prediction system is proposed to control the brightness of smart street lamps by predicting the moving path through the reduction of consumption power and information of pedestrian's past moving direction while meeting the function of existing smart street lamps. The brightness of smart street lamps is adjusted by utilizing the walk tracking vector and soft hand-off characteristics obtained through the motion sensing sensor of smart street lamps. In addition, the motion vector is used to analyze and predict the pedestrian path, and the GPU is used for high-speed computation. Pedestrians were detected using adaptive Gaussian mixing, weighted difference imaging, and motion vectors, and motions of pedestrians were analyzed using the extracted motion vectors. The preprocessing process using linear interpolation is performed to improve the performance of the proposed prediction system. Fuzzy prediction system and neural network prediction system are designed in parallel to improve efficiency and rough set is used for error correction.

• Genomics & Informatics
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• v.6 no.4
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• pp.166-172
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• 2008

A multitude of protein-coding sequence variations (CVs) in the human genome have been revealed as a result of major initiatives, including the Human Variome Project, the 1000 Genomes Project, and the International Cancer Genome Consortium. This naturally has led to debate over how to accurately assess the functional consequences of CVs, because predicting the functional effects of CVs and their relevance to disease phenotypes is becoming increasingly important. This article surveys and compares variation databases and in silico prediction programs that assess the effects of CVs on protein function. We also introduce a combinatorial approach that uses machine learning algorithms to improve prediction performance.

• Transactions of Materials Processing
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• v.21 no.7
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• pp.420-425
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• 2012

A new model is suggested for the prediction of radial displacements of a roll in order to analyze multi-high rolling mills. The model was developed from predictions based on finite element simulations. This model utilizes the compliance coefficient, which is expressed as a function of three dimensionless parameters, and is approximated by using the same interpolation function as used in the finite element method. The prediction accuracy of the model is demonstrated through comparison with the predictions from the FE model.

• The Korean Journal of Ecology
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• v.25 no.1
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• pp.29-31
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• 2002

The purpose of this study is to estimate height growth patterns and site index cuties (base index age 50 years) for Japanese red cedar trees(Cryptomeria japonica D. Don) grown in southern regions of Korea. The Chapman-Richards growth function was selected for stand height prediction using on the results of stem analysis data sets. Anamorphic base age invariant site index cuties were presented based on this height prediction equation. The resulting site index prediction equation can provide an indication of the productivity of the site quality based on Japanese red cedar trees plantation ages planted in southern regions of Korea.

• Proceedings of the Korea Concrete Institute Conference
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• 1995.10a
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• pp.124-128
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• 1995

It is the "maturity rule" that concrete of the same mix, at the same maturity, has the same strength. In this study, the Nurse-Saul function which was proposed to account for the effects of temperature and time on strength development is used in computing maturity. After existing various functions to relate concrete strength to the maturity value are considered, new strenth-maturity function is proposed. Tests are conducted in order to compare prediction value with measured concrete strength. The constants in proposed prediction equation are determined by standard specimens(cylinders) test, and the equation is adopted to predict strength of slab. The slab was cast in the laboratory from the same batch of mole, and cores are cut from slab in order to estimate the actual strength. Tehese values are used to compare with proposed equation. equation.

• Journal of the Korean Society for Railway
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• v.9 no.4 s.35
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• pp.419-424
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• 2006

In this paper we study on a method to predict and to demonstrate the reliability of the Korea high speed train control system in quantitative point of view. For the prediction of the reliability in train control system which is composed of electronic parts, Relax Software 7.7 automation tool is employed and MIL-HDBK-217 Handbook that is a standard for the prediction of the failure rate in electronic components is used. Mean Time Between Failure (MTBF) is predicted based on the failure rate of the subsystems, State Modeling and Markov Modeling method is used to express a reliability function of the train control system composed by hardware redundancy as a function of time. We propose a Reliability Test which is performed on the level of the subsystems and Failure Report, Analysing, Correction action system which use the test operation data to prove the predicted reliability.