• Journal of Korean Society of Steel Construction
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• v.22 no.5
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• pp.465-475
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• 2010

In the horizontally curved bridges, girders are subjected to the combined action of vertical bending and torsion due to their curvatures without any eccentric loads. As subjected to bending and torsion, the ultimate strength of steel/concrete composite box girders are limited by the diagonal tensile stress in the deck concrete induced by the St. Venant torsion. To determine the ultimate strength of composite box girders in bending and torsion and their interactions, this study conducted a 3-dimensional FEA and classical strength of materials investigation. Using ABAQUS, the FEA fully utilized advanced nonlinear analysis techniques simulating material/geometrical nonlinearity and post-cracking behaviors. The ultimate strength from numerical data were compared with theoretically derived values. Concurrent compressive stresses in the concrete deck improve the shear-resisting capacity of concrete, thereby resulting in an increased torsional resistance of the composite box girder in positive bending. The proposed interaction equation is very simple yet it provides a rational lower bound in determining the ultimate strength of concrete/steel composite box girders.

• Journal of Navigation and Port Research
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• v.36 no.10
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• pp.787-793
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• 2012

A hull form design technique to enhance the wave-making resistance performance for a medium size high speed Ro-Pax ship was studied introducing an optimization method and an automatic hull form modification method. SQP(sequential quadratic programming) was applied as the optimization algorithm and the geometry of hull surface was represented and modified using the NURBS(Non-Uniform Rational B-Spline). The wave-making resistance performance as an objective function in the optimization procedure was evaluated using the Rankine source panel method in which nonlinearity of the free surface boundary conditions and the trim and sinkage of the ship was fully taken into account. Using the Ro-Pax ship as a base hull, the hull-form optimization method was applied to obtain the hull shape that produced the lower wave-making resistance. To verify the validity of the hull-form optimization method, the numerical results was compared with the model test results.

• Safety and Health at Work
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• v.11 no.3
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• pp.322-334
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• 2020

Background: Spontaneous combustion of coal is one of the factors which causes direct or indirect gas and dust explosion, mine fire, the release of toxic gases, loss of reserve, and loss of miners' life. To avoid these incidents, the prediction of spontaneous combustion is essential. The safety of miner's in the mining field can be assured if the prediction of a coal fire is carried out at an early stage. Method: Adularya Underground Coal Mine which is fully mechanized with longwall mining method was selected as a case study area. The data collected for 2017, by sensors from ten gas monitoring stations were used for the simulation and prediction of a coal fire. In this study, the fuzzy logic model is used because of the uncertainties, nonlinearity, and imprecise variables in the data. For coal fire prediction, CO, O₂, N₂, and temperature were used as input variables whereas fire intensity was considered as the output variable.The simulation of the model is carried out using the Mamdani inference system and run by the Fuzzy Logic Toolbox in MATLAB. Results: The results showed that the fuzzy logic system is more reliable in predicting fire intensity with respect to uncertainties and nonlinearities of the data. It also indicates that the 1409 and 610/2B gas station points have a greater chance of causing spontaneous combustion and therefore require a precautional measure. Conclusion: The fuzzy logic model shows higher probability in predicting fire intensity with the simultaneous application of many variables compared with Graham's index.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.7
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• pp.759-766
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• 2011

Recently, noise reduction in room air conditioners has been one of the important issues as well as cooling efficiency. The rotary compressor is the dominant noise source in an air conditioner. A number of studies have been conducted on reducing compressor noise through improving muffler and resonator design. However the noise from the accumulator, a noise delivering path between compressor and air conditioner, is not fully taken into consideration. The accumulator contains a large inner cavity, and usually generates additional resonance noise during operation. This paper aims to conduct an optimal design for reducing accumulator noise by maximizing the transmission loss within the target frequency range that represents high-order nonlinearity. Design of experiments and radial basis function neural network are used in the context of approximate meta-models, and genetic algorithm is used as an optimization tool.

• Journal of the Society of Naval Architects of Korea
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• v.52 no.6
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• pp.471-477
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• 2015

When a ship sails in a seaway, the resistance on a ship increases due to incident waves and winds. The magnitude of added resistance amounts to about 15–30% of a calm-water resistance. An accurate prediction of added resistance in waves, therefore, is essential to evaluate the performance of a ship in a real sea state and to design an optimum hull form from the viewpoint of the International Maritime Organization (IMO) regulations such as Energy Efficiency Design Index (EEDI) and Energy Efficiency Operational Indicator (EEOI). The present study considers added resistance problem of AFRAMAX-class tankers with the conventional bow and Ax-bow shapes. Added resistance due to waves is successfully calculated using 1) a three-dimensional time-domain seakeeping computations based on a Rankine panel method (three-dimensional panel) and 2) a commercial CFD program (STAR-CCM+). In the hydrodynamic computations of a three-dimensional panel method, geometric nonlinearity is accounted for in Froude-Krylov and restoring forces using simple wave corrections over exact wet hull surface of the tankers. Furthermore, a CFD program is applied by performing fully nonlinear computation without using an analytical formula for added resistance or empirical values for the viscous effect. Numerical computations are validated through four degree-of-freedom model-scale seakeeping experiments in regular head waves at the deep towing tank of Hyundai Heavy Industries.

• Bulletin of the Society of Naval Architects of Korea
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• v.27 no.1
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• pp.24-34
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• 1990

In this paper, the formulation of a new simplified finite element is made to analyze the ultimate strength of damaged tubular members subjected to combined axial force and end moment. A damaged tubular member that has the bending deformation and the local dent is modeled by beam elements. Tangent elastic stiffness matrix of a beam element which contains the effect of the geometric nonlinearity is derived by using the updated Lagrangian approach. Here the contribution of the stiffness in the dented area is neglected since its resistance against the external loads is considered to be small. A fully plastic interaction curve of the element under combined loads taking account of the local dent effect is selected as a yielding criterion at each nodal point. Also tangent elasto-plastic stiffness matrix of the element is formulated by plastic node method. Comparison with the present solution and the existing experimental results is made showing that the present method gives quite an accurate solution.

• Structural Engineering and Mechanics
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• v.32 no.2
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• pp.283-300
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• 2009

The characteristic response of a structure to blast load may be divided into two distinctive phases, namely the direct blast response during which the shock wave effect and localized damage take place, and the post-blast phase whereby progressive collapse may occur. A reliable post-blast analysis depends on a sound understanding of the direct blast effect. Because of the complex loading environment and the stress wave effects, the analysis on the direct effect often necessitates a high fidelity numerical model with coupled fluid (air) and solid subdomains. In such a modelling framework, an appropriate representation of the blast load and the high nonlinearity of the material response is a key to a reliable outcome. This paper presents a series of calibration study on these two important modelling considerations in a coupled Eulerian-Lagrangian framework using a hydrocode. The calibration of the simulated blast load is carried out for both free air and internal explosions. The simulation of the extreme dynamic response of concrete components is achieved using an advanced concrete damage model in conjunction with an element erosion scheme. Validation simulations are conducted for two representative scenarios; one involves a concrete slab under internal blast, and the other with a RC column under air blast, with a particular focus on the simulation sensitivity to the mesh size and the erosion criterion.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.4
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• pp.826-832
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• 2004

A CMOS 8 bit folding and interpolating ADC for an embedded system inside VLSI is presented in this paper. This folding ADC uses the 2 stage architecture for improving of nonlinearity. repeating the folding and interpolating twice. At a proposed structure, a transistor differential pair operates on the second folder. A ADC with 2 stage architecture reduces the number of comparators and resisters. So it is possible to provide small chip size, low power consumption and high operating speed. The design technology is based on fully standard 0.25m double-Poly 2 metal n-well CMOS Process. The simulated Power consumption is 45mW with an applied voltage of 2.5V and sampling frequency of 250MHz. The INL and DNL are within <ㅆㄸㅌ>$\pm$0.2LSB, respectively. The SNDR is approximately 45dB for input frequency of 10MHz.

• Wind and Structures
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• v.38 no.1
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• pp.43-58
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• 2024

To ensure the flutter stability of three-tower suspension bridges under skew wind, by using the computational procedure of 3D refined flutter analysis of long-span bridges under skew wind, in which structural nonlinearity, the static wind action(also known as the aerostatic effect) and the full-mode coupling effect etc., are fully considered, the flutter stability of a three-tower suspension bridge-the Taizhou Bridge over the Yangtze River in completion and during the deck erection is numerically investigated under the constant uniform skew wind, and the influences of skew wind and aerostatic effects on the flutter stability of the bridge under the service and construction conditions are assessed. The results show that the flutter critical wind speeds of three-tower suspension bridge under service and construction conditions fluctuate with the increase of wind yaw angle instead of a monotonous cosine rule as the decomposition method proposed, and reach the minimum mostly in the case of skew wind. Both the skew wind and aerostatic effects significantly reduce the flutter stability of three-tower suspension bridge under the service and construction conditions, and the combined skew wind and aerostatic effects further deteriorate the flutter stability. Both the skew wind and aerostatic effects do not change the evolution of flutter stability of the bridge during the deck erection, and compared to the service condition, they lead to a greater decrease of flutter critical wind speed of the bridge during deck erection, and the influence of the combined skew wind and aerostatic effects is more prominent. Therefore, the skew wind and aerostatic effects must be considered accurately in the flutter analysis of three-tower suspension bridges.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.23-45
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• 2020

Big data is creating in a wide variety of fields such as medical care, manufacturing, logistics, sales site, SNS, and the dataset characteristics are also diverse. In order to secure the competitiveness of companies, it is necessary to improve decision-making capacity using a classification algorithm. However, most of them do not have sufficient knowledge on what kind of classification algorithm is appropriate for a specific problem area. In other words, determining which classification algorithm is appropriate depending on the characteristics of the dataset was has been a task that required expertise and effort. This is because the relationship between the characteristics of datasets (called meta-features) and the performance of classification algorithms has not been fully understood. Moreover, there has been little research on meta-features reflecting the characteristics of multi-class. Therefore, the purpose of this study is to empirically analyze whether meta-features of multi-class datasets have a significant effect on the performance of classification algorithms. In this study, meta-features of multi-class datasets were identified into two factors, (the data structure and the data complexity,) and seven representative meta-features were selected. Among those, we included the Herfindahl-Hirschman Index (HHI), originally a market concentration measurement index, in the meta-features to replace IR(Imbalanced Ratio). Also, we developed a new index called Reverse ReLU Silhouette Score into the meta-feature set. Among the UCI Machine Learning Repository data, six representative datasets (Balance Scale, PageBlocks, Car Evaluation, User Knowledge-Modeling, Wine Quality(red), Contraceptive Method Choice) were selected. The class of each dataset was classified by using the classification algorithms (KNN, Logistic Regression, Nave Bayes, Random Forest, and SVM) selected in the study. For each dataset, we applied 10-fold cross validation method. 10% to 100% oversampling method is applied for each fold and meta-features of the dataset is measured. The meta-features selected are HHI, Number of Classes, Number of Features, Entropy, Reverse ReLU Silhouette Score, Nonlinearity of Linear Classifier, Hub Score. F1-score was selected as the dependent variable. As a result, the results of this study showed that the six meta-features including Reverse ReLU Silhouette Score and HHI proposed in this study have a significant effect on the classification performance. (1) The meta-features HHI proposed in this study was significant in the classification performance. (2) The number of variables has a significant effect on the classification performance, unlike the number of classes, but it has a positive effect. (3) The number of classes has a negative effect on the performance of classification. (4) Entropy has a significant effect on the performance of classification. (5) The Reverse ReLU Silhouette Score also significantly affects the classification performance at a significant level of 0.01. (6) The nonlinearity of linear classifiers has a significant negative effect on classification performance. In addition, the results of the analysis by the classification algorithms were also consistent. In the regression analysis by classification algorithm, Naïve Bayes algorithm does not have a significant effect on the number of variables unlike other classification algorithms. This study has two theoretical contributions: (1) two new meta-features (HHI, Reverse ReLU Silhouette score) was proved to be significant. (2) The effects of data characteristics on the performance of classification were investigated using meta-features. The practical contribution points (1) can be utilized in the development of classification algorithm recommendation system according to the characteristics of datasets. (2) Many data scientists are often testing by adjusting the parameters of the algorithm to find the optimal algorithm for the situation because the characteristics of the data are different. In this process, excessive waste of resources occurs due to hardware, cost, time, and manpower. This study is expected to be useful for machine learning, data mining researchers, practitioners, and machine learning-based system developers. The composition of this study consists of introduction, related research, research model, experiment, conclusion and discussion.