• Transactions of The Korea Fluid Power Systems Society
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• v.8 no.2
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• pp.36-41
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• 2011

Analysis of two-dimensional flow in an involute gear pump has been done by using FLUENT. Analysis extended to the turbulent flow includes the mass flow rate with functions of pressure difference between inlet and outlet, rotational velocities of involute gear, and clearances between tip of gear and housing. In general mass flow rate decreases with decreasing rotational velocity, and with increasing clearance and pressure difference. The flow rate efficiency of gear pump, which is defined with the theoretical flow rate, has been presented in terms of the above parameters.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.12 no.1
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• pp.19-26
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• 2000

A numerical model is applied for predicting two-dimensional beach and dune erosion during severe storms. The model uses equation of sediment continuity and dynamic equation, governing the on-offshore sediment transport due to a disequilibrium of wave energy dissipation. And the model also uses sediment transport rate parameter K from dimensional analysis instead of that recommended by Kriebel. During a storm, a beach profile evolves to a form where the depth at the surf zone is related to the distance seaward of the waterline. In general, the erosion in the beach profile is found to be sensitive to equilibrium profile parameter, sediment transport rate parameter, storm surge level and breaking wave height.

• Structural Engineering and Mechanics
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• v.37 no.2
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• pp.215-231
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• 2011

As a brittle and heterogeneous material, concrete behaves differently under different stress conditions and its bulk strength is loading rate dependent. To a large extent, the varying behavioural properties of concrete can be explained by the mechanical failure processes at a mesoscopic level. The development of a computational mesoscale model in a general finite element environment, as presented in the preceding companion paper (Part 1), makes it possible to investigate into the underlying mechanisms governing the bulk-scale behaviour of concrete under a variety of loading conditions and to characterise the variation in quantitative terms. In this paper, we first present a series of parametric studies on the behaviour of concrete material under quasi-static compression and tension conditions. The loading-face friction effect, the possible influences of the non-homogeneity within the mortar and ITZ phases, and the effect of randomness of coarse aggregates are examined. The mesoscale model is then applied to analyze the dynamic behaviour of concrete under high rate loading conditions. The potential contribution of the mesoscopic heterogeneity towards the generally recognized rate enhancement of the material compressive strength is discussed.

• Journal of the Society of Naval Architects of Korea
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• v.48 no.3
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• pp.200-206
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• 2011

The mechanical properties of the most widely used cryogenic materials, i.e. austenitic stainless steel (ASS), aluminum alloy and invar steel, strongly depend on temperatures and strain rates. These phenomena show very complicated non-linear behaviors and cannot be expressed by general constitutive equation. In this study, an unified constitutive equation was proposed to represent the effect of temperature and strain rate on the materials. The proposed constitutive equation has been based on Tomita/Iwamoto and Bodner/Partom model for the expression of 2nd hardening due to martensite phase transformation of ASS. To simulate ductile fracture, modified Bodner/Chan damage model was additionally applied to the model and the model validity was verified by comparison of experimental and simulation results.

• Journal of Bio-Environment Control
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• v.31 no.3
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• pp.255-260
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• 2022

This study aimed to develop a service model that uses a deep learning algorithm for detecting diseases and pests in strawberries through image data. In addition, the pest detection performance of deep learning models was further improved by proposing segmented image data sets specialized in disease and pest symptoms. The CNN-based YOLO deep learning model was selected to enhance the existing R-CNN-based model's slow learning speed and inference speed. A general image data set and a proposed segmented image dataset was prepared to train the pest and disease detection model. When the deep learning model was trained with the general training data set, the pest detection rate was 81.35%, and the pest detection reliability was 73.35%. On the other hand, when the deep learning model was trained with the segmented image dataset, the pest detection rate increased to 91.93%, and detection reliability was increased to 83.41%. This study concludes with the possibility of improving the performance of the deep learning model by using a segmented image dataset instead of a general image dataset.

• Korean Chemical Engineering Research
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• v.53 no.6
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• pp.723-729
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• 2015

The moment analysis of cyclic adenosine monophosphate (cAMP) was performed using chromatograms that were obtained with the pulse input method from an octadecyl silica (ODS) high-performance liquid chromatography (HPLC) column. The general rate (GR) model was employed to calculate the first absolute moment and the second central moment. Three important coefficients for moment analysis, which are molecular diffusivity ($D_m$), external mass transfer coefficient ($k_f$), and intra-particle diffusivity ($D_e$), were estimated by the Wilke-Chang equation, Wilson-Geankoplis equation, and comparing van Deemter equation to theoretical plate number equation, respectively. Experiments were conducted by various conditions of flow rates, methanol volume ratio of the mobile phase, and solute concentration. After the moment analysis, results were organized by van Deemter plots. Also van Deemter coefficients were compared each other to effect $H_{ax}$, $H_f$, and $H_d$ on height equivalent to a theoretical plate (HETP, $H_{total}$). The value of intraparticle diffusion ($H_d$) was the primary factor which makes for HETP whereas external mass transfer ($H_f$) was disregardable factor.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.1186-1190
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• 1995

This paper is focused on the formulation of explicit closed-form functions describing the performance measures of the general flexible manufacturing system (FMS)according to the strategy of material handling system(MHS). the performance measures such as the production rate, the production lead-time and the utilization rate of the general FMS are expressed, respectively, as the explicit closed-form functions of the part processing time, the service rate of the material handling system (MHS) and the number of machine tools in the FMS. For this, the gensral FMS is presented as a generalized stochastic Petri net model, then, the moment generating function (MGF) based approach is applied to obtain the steady-state probabity formulation. Based on the steady-state formulation, the explicit closed-form functions for performance measures of the probability FMS can be obtained. Finally, the analytical results are compared with the Petri net simulation results to verify the validity of the suggested method. The paper is of significance in the sense that it provides a comprehensive formula for performance measures of the FMS even to the industry engineers and academic reademic resuarchers who have no background on Markov chain analysis method or Petrinet modeling

• Asia pacific journal of information systems
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• v.16 no.2
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• pp.69-84
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• 2006

Importance of Today's diffusion of integrated hospital information systems is that various and huge amount of data is being accumulated in their database systems. Many researchers have studied utilizing such hospital data. While most researches were conducted mainly for medical diagnosis, there have been insufficient studies to develop medical care cost prediction model, especially using machine learning techniques. In this research, therefore, we built a medical care cost prediction model for cancer patients using CBR (Case-Based Reasoning), one of the machine learning techniques. Its performance was compared with those of Neural Networks and Decision Tree models. As a result of the experiment, the CBR prediction model was shown to be the best in general with respect to error rate and linearity between real values and predicted values. It is believed that the medical care cost prediction model can be utilized for the effective management of limited resources in hospitals.

• Proceedings of the Korean Society For Composite Materials Conference
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• 2002.05a
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• pp.203-206
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• 2002

The present study proposes a theoretical model for predicting the matrix crack density growth of each layer in composite laminates subjected to thermo-mechanical loads. Each layer with matrix cracks is treated as an equivalent continuum of degraded elastic stiffnesses which are functions of the matrix crack density in each slyer. The energy release rate as a function of the degraded elastic stiffnesses is then calculated for each layer as functions of thermo-mechanical loads externally applied to the laminate. The matrix crack densities of each layer in general laminates are predicted as functions of the thermo-mechanical loads applied to a number of laminates. Comparisons of the present study with experimental data in the open literatures are also provided.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2000.04a
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• pp.9-12
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• 2000

The importance of the role of plastic spin in the rate-dependent response of materials at large deformations is the main objective of this work. After a brief presentation of a general consitutive framework for visco-rigid plasticity at large strains an isotropic/kinematic hardening and a visco-rigid plastic model are used to analyze the stress-strain response under simple shear. A clear understanding of the role of plastic spin is achieved by obtaining numerical analyzed results for different stress values in which the plastic spin consititutive parameters interrelaste with the strain rate and other more conventional model constants, Especially this paper is concerned with introducing behaviors of Al7075