• Journal of Surface Science and Engineering
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• v.56 no.6
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• pp.437-442
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• 2023

This research aims to enhance the efficiency of Pt/C catalysts due to the limited availability and high cost of platinum in contemporary fuel cell catalysts. Nano-sized platinum particles were distributed onto a carbon-based support via the polyol process, utilizing the metal precursor H₂PtCl₆·6H₂O. Key parameters such as pH, temperature, and RPM were carefully regulated. The findings revealed variations in the particle size, distribution, and dispersion of nano-sized Pt particles, influenced by temperature and pH. Following sodium hydroxide treatment, heat treatment procedures were systematically executed at diverse temperatures, specifically 120, 140, and 160 ℃. Notably, the thermal treatment at 140 ℃ facilitated the production of Pt/C catalysts characterized by the smallest platinum particle size, measuring at 1.49 nm. Comparative evaluations between the commercially available Pt/C catalysts and those synthesized in this study were meticulously conducted through cyclic voltammetry, X-ray diffraction (XRD), and field-emission scanning electron microscopy-energy dispersive X-ray spectroscopy (FE-SEM EDS) methodologies. The catalyst synthesized at 160 ℃ demonstrated superior electrochemical performance; however, it is imperative to underscore the necessity for further optimization studies to refine its efficacy.

• Mass Spectrometry Letters
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• v.15 no.1
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• pp.40-48
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• 2024

Capacity of the analytical/quantitative evaluation techniques to satisfy both qualitative and quantitative considerations for effective extraction of marigold oleoresins/xanthophylls and their potential as anti-mycotic and antioxidant activity was assessed. Accelerated solvent extraction (ASE), Soxhlet extraction (SE), Supercritical fluid extraction (SCFE), Cold extraction (CE), and ultrasonically assisted extraction (USE) techniques were evaluated for extraction of oleoresin/xanthophyll content from Tagetes erecta (Var. Pusa Narangi Genda) with respect to solvent consumption, extraction time, reproducibility, and yield. Followed by the antifungal and antioxidant activity evaluation. The overall yield of Tagetes oleoresin was higher in ASE (64.5 g/kg) followed by SE (57.3 g/kg), USE (50.7 g/kg), SCFE (45.3 g/kg) and CE (31.6 g/kg). The lutein esters represented more than 80% of the constituents. Further, xanthophyll/ lutein content in oleoresin was found to be quite higher in HPLC (r2 = 0.996) analysis than in the AOAC recommended UV spectrophotometer analysis. The oleoresin exhibited moderate antioxidant activity (DPPH assay) and antifungal activity against three phytopathogenic fungi. Based on the various parameters, the reproducibility of ASE was better (0.3-8.0%) than that of SE (0.5-12.9%), SCFE (0.2-9.4%), USE (0.3-12.4%) and CE (0.8-15.3%). ASE with (RSD 1.6%) is preferred being faster, reproducible, uses less solvent, robust and automation allows sequential extraction of the sample in less time.

• The Journal of the Acoustical Society of Korea
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• v.25 no.7
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• pp.312-318
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• 2006

The conventional lip-synch system has a two-step process, speech segmentation and recognition. However, the difficulty of speech segmentation procedure and the inaccuracy of training data set due to the segmentation lead to a significant Performance degradation in the system. To cope with that, the connected vowel recognition method using Head-Body-Tail (HBT) model is proposed. The HBT model which is appropriate for handling relatively small sized vocabulary tasks reflects co-articulation effect efficiently. Moreover the 7 vowels are merged into 3 classes having similar lip shape while the system is optimized by employing a class dependent SCHMM structure. Additionally in both end sides of each word which has large variations, 8 components Gaussian mixture model is directly used to improve the ability of representation. Though the proposed method reveals similar performance with respect to the CHMM based on the HBT structure. the number of parameters is reduced by 33.92%. This reduction makes it a computationally efficient method enabling real time operation.

• Geomechanics and Engineering
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• v.36 no.5
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• pp.511-525
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• 2024

Underground openings significantly affect the mechanical stability of underground spaces and create damaged zones. This study investigated the acoustic emission (AE) characteristics associated with the formation of damaged zones around circular openings. Uniaxial compression experiments were conducted on three types of rock specimens, namely, granite (GN-1 and GN-2), gabbro (GB), and slate (SL), containing a circular opening. AE and digital image correlation (DIC) techniques were used to monitor and evaluate the damaged zones near the circular openings. The AE characteristics were evaluated using AE parameters, including count, energy, amplitude, average frequency, and RA value. The DIC results revealed that the estimated diameters of the damaged zones of GN-1, GN-2, GB, and SL were 1.66D, 1.53D, 1.49D, and 1.9D, respectively. The average displacements at the surface of the damaged zones for these specimens were 0.814, 0.786, 0.661, and 0.673 mm, respectively, thus demonstrating a strong correlation with Young's modulus. The AE analysis with DIC revealed that tensile failure occurred in the direction parallel to the maximum compression axis as the load increased. Thus, this study provides fundamental data for a comprehensive analysis of damaged zones in underground openings and will facilitate the optimization of rock engineering projects and safety assessments thereof.

• Steel and Composite Structures
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• v.51 no.2
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• pp.117-126
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• 2024

There are many challenges in the construction of large-section tunnels, such as extremely soft rock and fractured zones. In order to solve these problems, the confined concrete support technology is proposed to control the surrounding rocks. The large-scale laboratory test is carried out to clarify mechanical behaviours of the combined confined concrete and traditional I-steel arches. The test results show that the bearing capacity of combined confined concrete arch is 3217.5 kN, which is 3.12 times that of the combined I-steel arch. The optimum design method is proposed to select reasonable design parameters for confined concrete arch. The parametric finite element (FE) analysis is carried out to study the effect of the design factors via optimum design method. The steel pipe wall thickness and the longitudinal connection ring spacing have a significant effect on the bearing capacity of the combined confined concrete arch. Based on the above research, the confined concrete support technology is applied on site. The field monitoring results shows that the arch has an excellent control effect on the surrounding rock deformation. The results of this research provide a reference for the support design of surrounding rocks in large-section tunnels.

• Journal of Korean Association for Spatial Structures
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• v.24 no.3
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• pp.43-51
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• 2024

Dynamic responses of nuclear power plant structure subjected to earthquake loads should be carefully investigated for safety. Because nuclear power plant structure are usually constructed by material of reinforced concrete, the aging deterioration of R.C. have no small effect on structural behavior of nuclear power plant structure. Therefore, aging deterioration of R.C. nuclear power plant structure should be considered for exact prediction of seismic responses of the structure. In this study, a machine learning model for seismic response prediction of nuclear power plant structure was developed by considering aging deterioration. The OPR-1000 was selected as an example structure for numerical simulation. The OPR-1000 was originally designated as the Korean Standard Nuclear Power Plant (KSNP), and was re-designated as the OPR-1000 in 2005 for foreign sales. 500 artificial ground motions were generated based on site characteristics of Korea. Elastic modulus, damping ratio, poisson's ratio and density were selected to consider material property variation due to aging deterioration. Six machine learning algorithms such as, Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Artificial Neural Networks (ANN), eXtreme Gradient Boosting (XGBoost), were used t o construct seispic response prediction model. 13 intensity measures and 4 material properties were used input parameters of the training database. Performance evaluation was performed using metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analysis results show that neural networks present good prediction performance considering aging deterioration.

• Journal of Korean Society of Water and Wastewater
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• v.38 no.4
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• pp.233-242
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• 2024

This study evaluates the potential of various coagulants to enhance the efficiency of total phosphorus removal facilities in a sewage treatment plant. After analyzing the existing water quality conditions of the sewage treatment plant, the coagulant of poly aluminium chloride was experimentally applied to measure its effectiveness. In this process, the use of poly aluminium chloride and polymers in various ratios was explored to identify the optimal combination of coagulants. The experimental results showed that the a coagulants combination demonstrated higher treatment efficiency compared to exclusive use of large amounts of poly aluminium chloride methods. Particularly, the appropriate combination of poly aluminium chloride and polymers played a significant role. The optimal coagulant combination derived from the experiments was applied in a micro flotation method of real sewage treatment plant to evaluate its effectiveness. This study presents a new methodology that can contribute to enhancing the efficiency of sewage treatment processes and reducing environmental pollution. This research is expected to make an important contribution to improving to phosphorus remove efficiency of similar wastewater treatment plant and reducing the ecological impact from using coagulants in the future.

• Computers and Concrete
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• v.34 no.3
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• pp.279-296
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• 2024

In this study, ensemble machine learning (ML) models are employed to estimate the hardened properties of Self-Compacting Geopolymer Concrete (SCGC). The input variables affecting model development include the content of the SCGC such as the binder material, the age of the specimen, and the ratio of alkaline solution. On the other hand, the output parameters examined includes compressive strength, flexural strength, and split tensile strength. The ensemble machine learning models are trained and validated using a database comprising 396 records compiled from 132 unique mix trials performed in the laboratory. Diverse machine learning techniques, notably K-nearest neighbours (KNN), Random Forest, and Extreme Gradient Boosting (XGBoost), have been employed to construct the models coupled with Bayesian optimisation (BO) for the purpose of hyperparameter tuning. Furthermore, the application of nested cross-validation has been employed in order to mitigate the risk of overfitting. The findings of this study reveal that the BO-XGBoost hybrid model confirms better predictive accuracy in comparison to other models. The R² values for compressive strength, flexural strength, and split tensile strength are 0.9974, 0.9978, and 0.9937, respectively. Additionally, the BO-XGBoost hybrid model exhibits the lowest RMSE values of 0.8712, 0.0773, and 0.0799 for compressive strength, flexural strength, and split tensile strength, respectively. Furthermore, a SHAP dependency analysis was conducted to ascertain the significance of each parameter. It is observed from this study that GGBS, Flyash, and the age of specimens exhibit a substantial level of influence when predicting the strengths of geopolymers.

• Nuclear Engineering and Technology
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• v.56 no.9
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• pp.3740-3749
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• 2024

To reduce the losses caused by aging failure of insulation gate bipolar transistor (IGBT), which is the core components of nuclear power plant rod position indicating and rod control (RPC) system. It is necessary to conduct studies on its life prediction. The selection of IGBT failure characteristic parameters in existing research relies heavily on failure principles and expert experience. Moreover, the analysis and learning of time-domain degradation data have not been fully conducted, resulting in low prediction efficiency as the monotonicity, time correlation, and poor anti-interference ability of extracted degradation features. This paper utilizes the advantages of the stacked denoising autoencoder(SDAE) network in adaptive feature extraction and denoising capabilities to perform adaptive feature extraction on IGBT time-domain degradation data; establishes a long-short-term memory (LSTM) prediction model, and optimizes the learning rate, number of nodes in the hidden layer, and number of hidden layers using the Gray Wolf Optimization (GWO) algorithm; conducts verification experiments on the IGBT accelerated aging dataset provided by NASA PCoE Research Center, and selects performance evaluation indicators to compare and analyze the prediction results of the SDAE-LSTM model, PSOLSTM model, and BP model. The results show that the SDAE-LSTM model can achieve more accurate and stable IGBT life prediction.

• Journal of Korean Society for Quality Management
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• v.52 no.3
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• pp.413-428
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• 2024

Purpose: This study aims to optimize the adhesive strength of epoxy adhesive when applied to tiles, addressing frequent issues of adhesion degradation observed in indoor interiors. The degradation often leads to costly repairs and maintenance, highlighting the need for improved adhesive formulations and application techniques. Methods: Employing the DMAIC (Define, Measure, Analyze, Improve, Control) methodology integral to Six Sigma and utilizing MINITAB for data analysis, this research focused on critical factors like curing time, application method, and mixing ratio. The Taguchi Experimental Design within the Design of Experiments (DOE) framework was applied to determine the impact of these parameters on adhesive strength. Results: The analysis facilitated by Taguchi's method led to notable improvements in adhesive workability and consistency. It identified the optimal combination of factors that significantly increase adhesive strength, evidenced by the improvement in signal-to-noise ratio and I-MR control charts. Conclusion: By applying a structured statistical approach through Six Sigma and the Taguchi method, the study successfully pinpointed optimal conditions for epoxy adhesive application on tiles. This contributes to quality management in the manufacturing and application processes of epoxy adhesives, ensuring enhanced durability and reliability in indoor tiling applications. The findings offer a significant methodological framework for future material optimization research.