• Proceedings of the KSR Conference
• /
• 2006.11b
• /
• pp.1405-1410
• /
• 2006

Purpose of this research is that offers basic data for optimized design using neural network method to calculate consolidation settlement in study area. In this research, preformed the neural network method that analyzed the settlement characteristics of soft ground nearby study area. Thus, data base established on ground properties and consolidation settlement of neighboring area. In addition, designed the optimum neural network model for prediction of settlement through network learning and consolidation settlement prediction using consolidation settlement DB and ground properties DB. Optimized neural network model decided by repeated learning for various case of hidden layers. In this study, proposed that the optimized consolidation settlement calculation method using neural network and verified which is the optimized consolidation settlement calculation method using neural network.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.9 no.3
• /
• pp.580-586
• /
• 2008

This paper addresses the prediction of the material property continuities of a microstructure. Prediction was made by measuring the dynamic responses distribution of the fabricated materials used in the microstructures. When these distributional material properties were used in estimating the mechanical performances of microstructures, the differences between the computer simulation and the experimental result of microstructures could be reduced and their reliability design could be made.

• Advances in aircraft and spacecraft science
• /
• v.8 no.4
• /
• pp.331-344
• /
• 2021

Airframe internal and external specifications are the product of intensive intellectual efforts and technological breakthroughs distinguishing each aircraft manufacturer. Therefore, geometrical information characterizing aircraft primary aerodynamic surfaces remain classified. When attempting to model real aircraft, many members of the aeronautical community depend on their personal expertise and generic design principles to bypass the confidentiality obstacles and sketch real aircraft airfoils, which therefore vary for the same aircraft due to the different designers' initial assumptions. This paper presents a photogrammetric shape prediction method for deriving geometrical properties of real aircraft airframe by utilizing their publicly accessible static and dynamic visual content. The method is based on extracting the visually distinguishable curves at the fairing regions between aerodynamic surfaces and fuselage. Two case studies on B-29 and B-737 are presented showing how to approximate the sectional coordinates of their wing inboard airfoils and proving the good agreement between the geometrical and aerodynamic properties of the replicated airfoils to their original versions. Therefore, the paper provides a systematic reverse engineering approach that will enhance aircraft conceptual design and flight performance optimization studies.

• Genomics & Informatics
• /
• v.21 no.2
• /
• pp.25.1-25.12
• /
• 2023

Adaptation of infections and hosts has resulted in several metabolic mechanisms adopted by intracellular pathogens to combat the defense responses and the lack of fuel during infection. Human tuberculosis caused by Mycobacterium tuberculosis (MTB) is the world's first cause of mortality tied to a single disease. This study aims to characterize and anticipate potential antigen characteristics for promising vaccine candidates for the hypothetical protein of MTB through computational strategies. The protein is associated with the catalyzation of dithiol oxidation and/or disulfide reduction because of the protein's anticipated disulfide oxidoreductase properties. This investigation analyzed the protein's physicochemical characteristics, protein-protein interactions, subcellular locations, anticipated active sites, secondary and tertiary structures, allergenicity, antigenicity, and toxicity properties. The protein has significant active amino acid residues with no allergenicity, elevated antigenicity, and no toxicity.

• Geomechanics and Engineering
• /
• v.15 no.1
• /
• pp.631-643
• /
• 2018

The estimation of the strength and other mechanical parameters characterizing the tensile behavior of granites can play an important role in civil engineering tasks such as design, construction, rehabilitation and repair of existing structures. The purpose of this paper is to apply data mining techniques, such as multiple regression (MR), artificial neural networks (ANN) and support vector machines (SVM) to estimate the mechanical properties of granites. In a first phase, the mechanical parameters defining the complete tensile behavior are estimated based on the tensile strength. In a second phase, the estimation of the mechanical properties is carried out from different combination of the physical properties (ultrasonic pulse velocity, porosity and density). It was observed that the estimation of the mechanical properties can be optimized by combining different physical properties. Besides, it was seen that artificial neural networks and support vector machines performed better than multiple regression model.

• Korean Journal of Agricultural Science
• /
• v.43 no.4
• /
• pp.507-521
• /
• 2016

Landslides are one of the most common natural hazards causing significant damage and casualties every year. In Korea, the increasing trend in landslide occurrence in recent decades, caused by climate change, has set off an alarm for researchers to find more reliable methods for landslide prediction. Therefore, an accurate landslide-susceptibility assessment is fundamental for preventing landslides and minimizing damages. However, analyzing the stability of a natural slope is not an easy task because it depends on numerous factors such as those related to vegetation, soil properties, soil moisture distribution, the amount and duration of rainfall, earthquakes, etc. A variety of different methods and techniques for evaluating landslide susceptibility have been proposed, but up to now no specific method or technique has been accepted as the standard method because it is very difficult to assess different methods with entirely different intrinsic and extrinsic data. Landslide prediction methods can fall into three categories: empirical, statistical, and physical approaches. This paper reviews previous research and surveys three groups of landslide prediction methods.

• Computers and Concrete
• /
• v.17 no.2
• /
• pp.173-188
• /
• 2016

Generally, thermal stress induced by hydration heat causes cracking in mass concrete structures, requiring a thorough control during the construction. The prediction of the thermal stress is currently undertaken by means of numerical analysis despite its lack of reliability due to the properties of concrete varying over time. In this paper, a method for the prediction of thermal stress in concrete structures by adjusting thermal stress measured by a thermal stress device according to the degree of restraint is proposed to improve the prediction accuracy. The ratio of stress in concrete structures to stress under complete restraint is used as the degree of restraint. To consider the history of the degree of restraint, incremental stress is predicted by comparing the degree of restraint and the incremental stress obtained by the thermal stress device. Furthermore, the thermal stresses of wall and foundation predicted by the proposed method are compared to those obtained by numerical analysis. The thermal stresses obtained by the proposed method are similar to those obtained by the analysis for structures with internally as well as externally strong restraint. It is therefore concluded that the prediction of thermal stress for concrete structures with various boundary conditions using the proposed method is suggested to be accurate.

• Computers and Concrete
• /
• v.12 no.2
• /
• pp.187-209
• /
• 2013

This paper presents a study on the prediction of transfer length of 13 mm seven-wire prestressing steel strand in pretensioned prestressed concrete members with rectangular cross-section including several material properties and design and manufacture parameters. To this end, a carefully selected database consisting of 207 different cases coming from 18 different sources spanning a variety of practical transfer length prediction situations was compiled. 16 single input features and 5 combined input features are analyzed. A widely used feedforward neural regression model was considered as a representative of several machine learning methods that have already been used in the engineering field. Classical multiple linear regression was also considered in order to comparatively assess performance and robustness in this context. The results show that the implemented model has good prediction and generalization capacity when it is used on large input data sets of practical interest from the engineering point of view. In particular, a neural model is proposed -using only 4 hidden units and 10 input variables-which significantly reduces in 30% and 60% the errors in transfer length prediction when using standard linear regression or fixed formulas, respectively.

• Tunnel and Underground Space
• /
• v.13 no.6
• /
• pp.413-420
• /
• 2003

Modelling for performance prediction of mechanical excavation is discussed in this paper. Two of the most successful performance prediction models, namely theoretical based CSM model and empirical based NTH model, are discussed and compared. The basic principles of rock cutting with disc cutters, especially Constant Cross Section cutters, are discussed and a theoretical model developed is introduced to provide an estimate of disc cutting forces as a function of rock properties and the cutting geometry. General modelling logic for the performance prediction of mechanical excavation is introduced. CSM computer model developed and currently used at the Earth Mechanics Institute(EMI) of the Colorado School of Mines is discussed. Example of input and output of this model is illustrated for the typical operation by Tunnel Boring Machine(TBM).

• Transactions of the Korean Society of Mechanical Engineers
• /
• v.14 no.6
• /
• pp.1583-1591
• /
• 1990

Life prediction and residual life prediction of structures of machines are one of the most strongly world wide needed problems as requirement in the stage of slowly developing economy which comes after rapidly and highly developing stage. For the purpose of statistical life prediction, fatigue test was conducted under the 4 stress levels, and for each stress level, about 20 specimens are used. The statistical properties of crack growth parameter m and C in the fatigue crack growth law of da/dN=C(.DELTA.K)sup m/, and the relationship between m and C, and the statistical distribution pattern of fatigue crack growth rate can be obtained by experimental results.