• Journal of Korean Society on Water Environment
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• v.34 no.6
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• pp.591-601
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• 2018

Optimized BMP plans for controlling water quality using the Pareto trade-off surface curve in upper basin of Namgang Dam is proposed. The proposed alternatives consist of BMP installation scenarios in which the reduction efficiency of non-point pollutants is maximized in a given budget. The multi-objective optimization process for determining the optimal alternatives was performed without direct implementation of a watershed model such as SWAT analysis, thereby reducing the time taken. The shortening of the calculation time further enhances the applicability of the multi-objective optimization technique in preparing regional water quality management alternatives. In this study, different types of BMP are applied depending on the land use conditions. Fertilizer input control and vegetative filter strip are considered as alternatives to applying BMP to the field but only control of fertilizer input can be applied to rice paddies. Fertilizer input control and vegetative filter strip can be installed separately or simultaneously in a hydrologic response unit. Finally, 175 BMP application alternatives were developed for the water quality management of the upper river basin of Namgang dam. The proposed application alternative can be displayed on the map, which has the advantage of clearly defining the BMP installation location.

• Steel and Composite Structures
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• v.42 no.4
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• pp.427-446
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• 2022

Cutouts in the beams or plates are often unavoidable due to inspection, maintenance, ventilation, structural aesthetics purpose, and sometimes to lighten the structures. Therefore, there will be a substantial reduction in the strength of the structure due to the introduction of the cutouts. However, these cutouts can be reinforced with the different patterns of ribs (stiffener) to enhance the strength of the structure. The present study highlights the influence of the elliptical cutout reinforced with a different pattern of ribs on the stability performance of such stiffened composite panels subjected to non-uniform edge loads by employing the Finite element (FE) technique. In the present formulation, a 9-noded heterosis element is used to model the skin, and a 3-noded isoparametric beam element is used to simulate the rib that is attached around a cutout in different patterns. The displacement compatibility condition is employed between the plate and stiffener, and arbitrary orientations are taken care by introducing respective transformation matrices. The effect of shear deformation and rotary inertia are incorporated in the formulation. A new mesh configuration is developed to house the attached ribs around an elliptical cutout with different patterns. Initially, a study is performed on the panels with different stiffener schemes for various ply orientations and for different stiffener depth to width ratios (d_s/b_s) to determine an optimal stiffener configuration. Further, various parametric studies are conducted on an obtained optimal stiffened panel to understand the effect of cutout size, cutout orientation, panel aspect ratio, and boundary conditions. Finally, from the analysis, it can be observed that the arrangement of the stiffener attached to a panel has a major impact on the buckling capacity of the stiffened panel. The stiffener's depth to width ratio also significantly influences the buckling characteristic.

• Smart Structures and Systems
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• v.29 no.1
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• pp.17-28
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• 2022

The cable component of cable-stayed bridges is gradually impacted by weather conditions, vehicle loads, and material corrosion. The stayed cable is a critical load-carrying part that closely affects the operational stability of a cable-stayed bridge. Damaged cables might lead to the bridge collapse due to their tension capacity reduction. Thus, it is necessary to develop structural health monitoring (SHM) techniques that accurately identify damaged cables. In this work, a combinational identification method of three efficient techniques, including statistical analysis, clustering, and neural network models, is proposed to detect the damaged cable in a cable-stayed bridge. The measured dataset from the bridge was initially preprocessed to remove the outlier channels. Then, the theory and application of each technique for damage detection were introduced. In general, the statistical approach extracts the parameters representing the damage within time series, and the clustering approach identifies the outliers from the data signals as damaged members, while the deep learning approach uses the nonlinear data dependencies in SHM for the training model. The performance of these approaches in classifying the damaged cable was assessed, and the combinational identification method was obtained using the voting ensemble. Finally, the combination method was compared with an existing outlier detection algorithm, support vector machines (SVM). The results demonstrate that the proposed method is robust and provides higher accuracy for the damaged cable detection in the cable-stayed bridge.

• Microbiology and Biotechnology Letters
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• v.51 no.2
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• pp.191-202
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• 2023

Elevated serum cholesterol is a main risk factor for heart disorders. Most probiotic products administered to lower cholesterol are dairy products which are not suitable for lactose-intolerant individuals. In this study, we assessed the cholesterol-lowering efficacy of LAB isolated from traditionally fermented drinks in diet-induced rats and determine their efficacy in the production of non-dairy, probiotic formulations using papaya juice. LAB were isolated from palm wine and corn beer on MRS agar using a pour-plate technique. Identification was carried out using 16S rRNA gene sequencing. A hypercholesterolemia model in which diet-induced Wistar albino rats were assigned into four groups was established. Oral gavage was carried out for 30 days. On the 31^st day, the rats were dissected and the serum lipid profile was analyzed using biochemical kits. A 10⁶ cfu/ml of a 24-h-old culture of selected lactobacilli was used to inoculate papaya juice and incubated at 37℃. Microbial and chemical changes were assessed during papaya fermentation and after four weeks of cold storage. Two selected isolates (Pw1 and Cb4) had in vitro cholesterol reduction of > 80%. These two isolates lowered lipid profile (triglyceride, total cholesterol, LDL-c) significantly, and increased HDL-c levels (p < 0.5) in the rat sera. Phylogenetic analysis showed that Pw1 was 98.86% similar to Limosilactobacillus fermentum, while Cb4 was 99.54% similar to Enteroccocus faecium. Both strains fermented papaya juice with cell viability reaching 8.92 × 10⁸ cfu/ml and 25.3 × 10⁸ cfu/ml respectively, and were still viable after 4 weeks of cold storage.

• Structural Engineering and Mechanics
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• v.89 no.3
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• pp.225-238
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• 2024

In this paper, a quasi-3D hyperbolic shear deformation theory for the bending responses of a functionally graded (FG) porous micro-beam is based on a modified couple stress theory requiring only one material length scale parameter that can capture the size influence. The model proposed accounts for both shear and normal deformation effects through an illustrative variation of all displacements across the thickness and satisfies the zero traction boundary conditions on the top and bottom surfaces of the micro-beam. The effective material properties of the functionally graded micro-beam are assumed to vary in the thickness direction and are estimated using the homogenization method of power law distribution, which is modified to approximate the porous material properties with even and uneven distributions of porosity phases. The equilibrium equations are obtained using the virtual work principle and solved using Navier's technique. The validity of the derived formulation is established by comparing it with the ones available in the literature. Numerical examples are presented to investigate the influences of the power law index, material length scale parameter, beam thickness, and shear and normal deformation effects on the mechanical characteristics of the FG micro-beam. The results demonstrate that the inclusion of the size effects increases the microbeams stiffness, which consequently leads to a reduction in deflections. In contrast, the shear and normal deformation effects are just the opposite.

• Journal of the Korea institute for structural maintenance and inspection
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• v.15 no.4
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• pp.165-174
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• 2011

This research aims at the optimal constitution of sensors required to identify the structural shortcoming of roller-coaster. In this research we analyzed the dynamic characteristics of roller-coaster by three dimensional FE modelling, decided on the appropriate location and number of sensors through optimal transducer theory, abstracted the mathematical value of modal features before and after damage on the basis of optimally placed and numbered sensors. and then presented it as a primary information about the basic structure which would be applied to damage estimation. As a target structure, the roller-coater at Seoul Children's Grand Park was chosen and built as a model reduced by one twentieth in size. In order to consider the Kinetics features particular to the roller-coaster structure, we made an exact three-dimensional FE modelling for the model structure by means of Spline function. As for the proper location and number of sensors, it was done by applying EIM and EOT. We also estimated the damage from the combination of strength, flexibility, and model corelation after abstracting the value of modal features. Finally the optimal transducer theory presented here in this research was proved to be valid, and the structural damage was well identified through changes in strength and flexibility. As a result, we were able to present the optimal constitution of sensors needed for the analysis of dynamic characteristics and the development of techniques in dynamic characteristics, which would ultimately contribute to the development of health monitoring for roller-coaster.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.7A
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• pp.649-658
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• 2006

Recently, Software Development was applied to new-approach methods as a various form : client-server system and web-programing, object-orient concept, distributed development with a network environments. On the other hand, it be concerned about the distributed development technology and increasing of object-oriented methodology. These technology is spread out the software quality and improve of software production, reduction of the software develop working. Futures, we considered about the distributed software development technique with a many workstation. In this paper, we discussed optimal release problem based on a stochastic differential equation model for the distributed Software development environments. In the past, the software reliability applied to quality a rough guess with a software development process and approach by the estimation of reliability for a test progress. But, in this paper, we decided to optimal release times two method: first, SRGM with an error counting model in fault detection phase by NHPP. Second, fault detection is change of continuous random variable by SDE(stochastic differential equation). Here, we decide to optimal release time as a minimum cost form the detected failure data and debugging fault data during the system test phase and operational phase. Especially, we discussed to limitation of reliability considering of total software cost probability distribution.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.12
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• pp.403-410
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• 2020

In addition to the research on noise removal and super-resolution using the data restoration (Output result) function of Autoencoder, research on the performance improvement of clustering using the dimension reduction function of autoencoder are actively being conducted. The clustering function and data restoration function using Autoencoder have common points that both improve performance through the same learning. Based on these characteristics, this study conducted an experiment to see if the autoencoder model designed to have excellent data recovery performance is superior in clustering performance. Skip connection technique was used to design autoencoder with excellent data recovery performance. The output result performance and clustering performance of both autoencoder model with Skip connection and model without Skip connection were shown as graph and visual extract. The output result performance was increased, but the clustering performance was decreased. This result indicates that the neural network models such as autoencoders are not sure that each layer has learned the characteristics of the data well if the output result is good. Lastly, the performance degradation of clustering was compensated by using both latent code and skip connection. This study is a prior study to solve the Hanja Unicode problem by clustering.

• Smart Media Journal
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• v.12 no.1
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• pp.9-16
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• 2023

Recently, a convolutional neural network (CNN) based system is being developed to overcome the limitations of human resources in the apple quality classification of farmhouse. However, since convolutional neural networks receive only images of the same size, preprocessing such as sampling may be required, and in the case of oversampling, information loss of the original image such as image quality degradation and blurring occurs. In this paper, in order to minimize the above problem, to generate a image patch based graph of an original image and propose a random walk-based positional encoding method to apply the graph transformer model. The above method continuously learns the position embedding information of patches which don't have a positional information based on the random walk algorithm, and finds the optimal graph structure by aggregating useful node information through the self-attention technique of graph transformer model. Therefore, it is robust and shows good performance even in a new graph structure of random node order and an arbitrary graph structure according to the location of an object in an image. As a result, when experimented with 5 apple quality datasets, the learning accuracy was higher than other GNN models by a minimum of 1.3% to a maximum of 4.7%, and the number of parameters was 3.59M, which was about 15% less than the 23.52M of the ResNet18 model. Therefore, it shows fast reasoning speed according to the reduction of the amount of computation and proves the effect.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.6
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• pp.1249-1256
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• 2019

In this paper, we propose a new algorithm to remove acoustic feedback and noise in hearing aids. Instead of using the conventional FIR structure, this algorithm is a deep learning algorithm using neural network adaptive prediction filter to improve the feedback and noise reduction performance. The feedback canceller first removes the feedback signal from the microphone signal and then removes the noise using the Wiener filter technique. Noise elimination is to estimate the speech from the speech signal containing noise using the linear prediction model according to the periodicity of the speech signal. In order to ensure stable convergence of two adaptive systems in a loop, coefficient updates of the feedback canceller and noise canceller are separated and converged using the residual error signal generated after the cancellation. In order to verify the performance of the feedback and noise canceller proposed in this study, a simulation program was written and simulated. Experimental results show that the proposed deep learning algorithm improves the signal to feedback ratio(: SFR) of about 10 dB in the feedback canceller and the signal to noise ratio enhancement(: SNRE) of about 3 dB in the noise canceller than the conventional FIR structure.