• Structural Engineering and Mechanics
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• v.84 no.5
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• pp.605-618
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• 2022

Glass fiber reinforced polymer (GFRP) elastic gridshells consist of long continuous GFRP tubes that form elastic deformations. In this paper, a method for the form-finding of gridshell structures is presented based on the interpretable machine learning (ML) approaches. A comparative study is conducted on several ML algorithms, including support vector regression (SVR), K-nearest neighbors (KNN), decision tree (DT), random forest (RF), AdaBoost, XGBoost, category boosting (CatBoost), and light gradient boosting machine (LightGBM). A numerical example is presented using a standard double-hump gridshell considering two characteristics of deformation as objective functions. The combination of the grid search approach and k-fold cross-validation (CV) is implemented for fine-tuning the parameters of ML models. The results of the comparative study indicate that the LightGBM model presents the highest prediction accuracy. Finally, interpretable ML approaches, including Shapely additive explanations (SHAP), partial dependence plot (PDP), and accumulated local effects (ALE), are applied to explain the predictions of the ML model since it is essential to understand the effect of various values of input parameters on objective functions. As a result of interpretability approaches, an optimum gridshell structure is obtained and new opportunities are verified for form-finding investigation of GFRP elastic gridshells during lifting construction.

• Structural Engineering and Mechanics
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• v.53 no.4
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• pp.745-765
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• 2015

A nonlinear Finite Element (FE) algorithm is proposed to analyze the Reinforced Concrete (RC) columns subjected to Cyclic Loading (CL), Cyclic Oriented Lateral Force and Axial Loading (COLFAL), Monotonic Loading (ML) or Oriented Pushover Force and Axial Loading (OPFAL) in any direction. In the proposed algorithm, the following parameters are considered: uniaxial behavior of concrete and steel elements, the pseudo-plastic hinge produced in the critical sections, and global behavior of RC columns. In the proposed numerical simulation, the column is discretized into two Macro-Elements (ME) located between the pseudo-plastic hinges at critical sections and the inflection point. The critical sections are discretized into Fixed Rectangular Finite Elements (FRFE) in general cases of CL, COLFAL or ML and are discretized into Variable Oblique Finite Elements (VOFE) in the particular cases of ML or OPFAL. For pushover particular case, a fairly fast converging and properly accurate nonlinear simulation method is proposed to assess the behavior of RC columns. The proposed algorithm has been validated by the results of tests carried out on full-scale RC columns.

• Solar Energy
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• v.17 no.2
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• pp.67-74
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• 1997

Spherical shape of phase change material(PCM) has been prepared by using sodium acetate trihydrate as a latent heat storage medium. Gelatin was used as an effective thickener to prevent undesirable phase separation. Sodium pyrophosphate decahydrate was used as nucleator to decrease the degree of supercooling in the thickened phase change material. Spherical PCM particles of 3-3.5 mm in diameter continuously manufactured with molten PCM with those conditions. The particle size of PCM was not affected by the effluent velocity of molten PCM in range of 1.3-1.8 ml/min. DSC, SEM and XRD were also used to characterize the properties of PCM particles.

• Soft Computing and Machine Intelligence
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• v.1 no.1
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• pp.1-10
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• 2021

Analysis and Tracking of bug reports is a challenging field in software repositories mining. It is one of the fundamental ways to explores a large amount of data acquired from defect tracking systems to discover patterns and valuable knowledge about the process of bug triaging. Furthermore, bug data is publically accessible and available of the following systems, such as Bugzilla and JIRA. Moreover, with robust machine learning (ML) techniques, it is quite possible to process and analyze a massive amount of data for extracting underlying patterns, knowledge, and insights. Therefore, it is an interesting area to propose innovative and robust solutions to analyze and track bug reports originating from different open source projects, including Mozilla and Eclipse. This research study presents an ML-based classification model to analyze and track bug defects for enhancing software engineering management (SEM) processes. In this work, Artificial Neural Network (ANN) and Naive Bayesian (NB) classifiers are implemented using open-source bug datasets, such as Mozilla and Eclipse. Furthermore, different evaluation measures are employed to analyze and evaluate the experimental results. Moreover, a comparative analysis is given to compare the experimental results of ANN with NB. The experimental results indicate that the ANN achieved high accuracy compared to the NB. The proposed research study will enhance SEM processes and contribute to the body of knowledge of the data mining field.

• Proceedings of the Korean Institute of Surface Engineering Conference
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• 2017.05a
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• pp.128-128
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• 2017

무전해 도금이란 외부의 전원을 사용하지 않고 환원제를 이용하여 금속 피막을 석출시키는 방법이다. 이러한 무전해 도금은 최근에 와서 인쇄회로기판(printed circuit board)등 다양한 전자부품에 적용되고 있다. 최근 급격한 금 가격 상승으로 인해 전자부품용에 사용되는 금의 사용량을 감소시키거나 또는 금을 대체할 수 있는 도금 층에 관한 연구가 활발하게 진행되고 있다. 금을 대체할 수 있는 즉 비용 경감의 가능성이 있는 도금 재료로는 팔라듐이 유력하다. 팔라듐은 금의 약 1/2가격으로 우수한 내식성 및 낮은 접촉저항을 가지기 때문에 접점재료로서 오래전부터 사용되어져왔다. 무전해 팔라듐-인 도금의 착화제, 환원제, 경도, 접촉저항에 대한 연구는 많이 있지만 도금속도에 미치는 인자에 대해서는 명확하지 않은 것이 많다. 따라서 본 연구는 무전해 팔라듐-인 도금의 석출석도에 미치는 에틸렌디아민 농도의 영향에 대하여 조사하였다. 실험방법으로는 환원제로 차아인산을 사용하고 착화제로는 에틸렌디아민을 사용하여 문전해 팔라듐-인 도금액을 제조하였다. 에틸렌디아민의 농도는 각각 5ml/L, 7.5ml/L, 10ml/L, 12ml/L로 하였다. pH는 7.5, 온도는 $45^{\circ}C$로 하여 30분 동안 도금을 실시하였다. 무전해 팔라듐-인 도금속도는 정밀저울로 무게를 측정하였고 ICP-OES을 사용하여 도금층의 농도를 분석, XRF를 사용하여 성분분석과 XRD를 사용해 결정회절을 분석하였다. 또한 SEM을 사용하여 단면 관찰을 하였으며 석출속도에 미치는 에틸렌디아민의 영향을 전기화학 분극곡선을 통해서 고찰을 시도해 보았다.

• Korean Journal of Veterinary Research
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• v.30 no.4
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• pp.487-497
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• 1990

This study was carried out to observe in vitro effect of praziquantel on the tegumental changes of Heterophyopsis continua with scanning electron microscope. Metacercariae were collected from the perch, Lateolabrax japonicus, by artificial digestion technique and fed to 2-week old chickens. Adult worms were recovered from small intestine of chickens 8 days after infection. For working solutions, praziquantel was diluted with TC199 medium at concentration of 0.01, 0.1. 1 and $10{\mu}g/ml$. To each petri dish containing 10ml of solution, 5~10 worms were introduced and incubated at $37^{\circ}C$. For the scanning electron microscopy(SEM), the worms were fixed in cold 2.5% glutaraldehyde, dehydrated in a series of graded ethanol and freeze-dried. Dried specimen was mounted on stub and coated with gold and observed in an SEM. The results were as follows: 1. Severe tegumental alterations were recognized by scanning electron microscope. Bleb formation of tegument was observed in 5 minute group and most pronounced on anterior tegument of worms. The number and size of blebs increased as incubation time prolonged. 2. The surface destruction was more pronounced at ventral margin between the oral and the ventral suckers. 3. The sensory papillae were slightly affected, but destruction of tegumental spine was not recognized. 4. The effect of praziquantel on the worm was found dependent on the concentration and incubation time, however, the effect was more dependent upon the incubation time.

• Journal of the Korea Convergence Society
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• v.12 no.12
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• pp.153-159
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• 2021

The purpose of this study is to determine the demage of tooth surface changes according to exposure time of commercially available green grape juice and pomegranate juice. Extracted healthy human premolar enamel surfaces were used. Control group immersed in phosphate-buffered saline (PBS) and 10 ml of commercially available green grape juice and pomegranate juice applied experimental group was divided into 7 groups. The pH of the experimental juice was measured, and the change and micrographics of the surface were confirmed through a Scanning Electron Microscope (SEM). It was found that the more the immersion time between the tooth surface and acid juice, such as damage to the tooth surface, has a greater effect on the surface damage. Based on the results of this study, it is necessary to reduce the number of drinking times and retention time in the oral cavity.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2000.07a
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• pp.270-273
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• 2000

For the well-preparation of the superconducting wire by electrophoresis, the control of the cracking on the YBCO, BSCCO superconductor deposited on Ag wire in acetone and buthanol solution with PEG(poly-ethylenglycol) was investigated with XRD and SEM analysis. After deposition, drying and heat treatment process, the cracks on the deposited surface of YBCO and BSCCO samples was clearly removed and decreased, which was perpared in suspension with addition of PEG from 1 to 3ml. However, in the case of the addition rate of PEG in acetone suspension was exceeded in 3ml, BSCCO superconductor deposited on Ag wire was slightly melted at 90$0^{\circ}C$ which was the same heat treatment condition of other samples with different additin rate of PEG. In the process of electrophoretic deposition, drying and heat treatment, PEG added into the suspension solution as a binder was very useful to prepare the crack-free thick film-wire of YBCO and BSCCO.

• Korean Chemical Engineering Research
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• v.59 no.3
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• pp.455-466
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• 2021

Micro and nanoparticles of Finasteride were prepared by gas-antisolvent method. The influence of process parameters such as pressure (100, 130 and 160 bar), temperature (308, 318 and 328 K) and solute concentrations (10, 25 and 40 mg/ml) on mean particle size was studied by Box-Behnken design. As ANOVA results indicated, the highest influence in production of smaller particles was attributed to the pressure. Optimum condition leading to the smallest particle size was as follows: initial solute concentration, 10 mg/ml; temperature, 308 K and pressure, 160 bar. The particles were evaluated with FTIR, SEM, DLS, XRD as well as DSC. The analyses revealed a size decrease in the precipitated Finasteride particles (232.4 nm, on mean) via gas-antisolvent method, as compared to the original particles (55.6 ㎛).

• Structural Engineering and Mechanics
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• v.89 no.3
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• pp.283-300
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• 2024

Machine learning (ML) models based on artificial neural network (ANN) and decision tree (DT) were developed for estimation of axial capacity of concrete columns reinforced with fiber reinforced polymer (FRP) bars. Between the design codes, the Canadian code provides better formulation compared to the Australian or American code. For empirical models based on elastic modulus of FRP, Hadhood et al. (2017) model performed best. Whereas for empirical models based on tensile strength of FRP, as well as all empirical models, Raza et al. (2021) was adjudged superior. However, compared to the empirical models, all ML models exhibited superior performance according to all five performance metrics considered. The performance of ANN and DT models were comparable in general. Under the present setup, inclusion of the transverse reinforcement information did not improve the accuracy of estimation with either ANN or DT. With selective use of inputs, and a much simpler ANN architecture (4-3-1) compared to that reported in literature (Raza et al. 2020: 6-11-11-1), marginal improvement in correlation could be achieved. The metrics for the best model from the study was a correlation of 0.94, absolute errors between 420 kN to 530 kN, and the range being 0.39 to 0.51 for relative errors. Though much superior performance could be obtained using ANN/DT models over empirical models, further work towards improving accuracy of the estimation is indicated before design of FRP reinforced concrete columns using ML may be considered for design codes.