• Journal of Korean Association for Spatial Structures
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• v.23 no.3
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• pp.105-113
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• 2023

The study used the whole-life carbon assessment method to conduct a thorough carbon-neutral evaluation of a standard steel structure. To further assess carbon emissions, 11 design-changed models were evaluated, with changes made to the span between beams and columns. The results of the carbon emission assessment showed savings of approximately 13.1% by implementing the stage of the beyond life cycle. Additionally, the evaluation of carbon emissions through design changes revealed a difference of up to 42.2%. These findings confirmed that recycling and structural design changes can significantly reduce carbon emissions by up to 48.6%, making it an effective means of achieving carbon neutrality. It is therefore necessary to apply the stage of beyond life cycle and structural change to reduce carbon emissions.

• Advances in nano research
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• v.14 no.2
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• pp.155-164
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• 2023

Grain size in sheet metals in one of the main parameters in determining formability. Grain size control in industry requires delicate process control and equipment. In the present study, effects of grain size on the formability of steel sheets is investigated. Experimental investigation of effect of grain size is a cumbersome method which due to existence of many other effective parameters are not conclusive in some cases. On the other hand, since the average grain size of a crystalline material is a statistical parameter, using traditional methods are not sufficient for find the optimum grain size to maximize formability. Therefore, design of experiment (DoE) and artificial intelligence (AI) methods are coupled together in this study to find the optimum conditions for formability in terms of grain size and to predict forming limits of sheet metals under bi-stretch loading conditions. In this regard, a set of experiment is conducted to provide initial data for training and testing DoE and AI. Afterwards, the using response surface method (RSM) optimum grain size is calculated. Moreover, trained neural network is used to predict formability in the calculated optimum condition and the results compared to the experimental results. The findings of the present study show that DoE and AI could be a great aid in the design, determination and prediction of optimum grain size for maximizing sheet formability.

• Structural Engineering and Mechanics
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• v.86 no.2
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• pp.249-260
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• 2023

The uncertainties involved in structural performances are of importance when the optimum number and property of seismic retrofit devices are determined. This paper proposes a seismic retrofit design framework for asymmetric soft-first-story buildings, considering uncertainties in the soil condition and seismic retrofit device. The effect of the uncertain parameters on the structural performance is used to find a robust and optimal seismic retrofit solution. The framework finds a robust and optimal seismic retrofit solution by finding the optimal locations and mechanical properties of the seismic retrofit device for different realizations of the uncertain parameters. The structural performance for each realization is computed to evaluate the effect of the uncertainty parameters on the seismic performance. The framework utilizes parallel processing to decrease the computationally intensive nonlinear dynamic analysis time. The framework returns a robust design solution that satisfies the given limit state for every realization of the uncertain parameters. The proposed framework is applied to the seismic retrofit design of a five-story asymmetric soft-first-story case study structure retrofitted with a viscoelastic damper. Robust optimal parameters for retrofitting a structure to satisfy the limit state for the different realizations of the uncertain parameter are found using the proposed framework. According to the performance evaluation results of the retrofitted structure, the developed framework is proved effective in the seismic retrofit of the asymmetric structure with inherent uncertainties.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.36 no.2
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• pp.164-169
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• 2023

Nano Pt particles were dispersed on carbon-based supports by a polyol process for a catalyst application in a polymer electrolyte fuel cell. We tried to optimize the effect of pH on the electrostatic forces between the support and the Pt colloids. We investigated the relationship among the surface charges on the carbon support, the solution pH, and the concentration of a glycolate, and the Pt particle size. The produced catalyst with nano Pt particles on the support was evaluated by the long-term cyclic voltammetry (CV) performance test and compared with the results from a commercial catalyst. Our experimental results reveal that the pH-control can modify the particle size distribution and the dispersion of the nano Pt particles. This resulted in a cost-effective method for the synthesis of highly Pt loaded Pt/C catalysts for fuel cells better than a commercial catalyst system.

• Kyungpook Mathematical Journal
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• v.62 no.1
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• pp.119-132
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• 2022

Virotherapy is an effective method for the treatment of cancer. The oncolytic virus specifically infects the lyse cancer cell without harming normal cells. There is a time delay between the time of interaction of the virus with the tumor cells and the time when the tumor cells become infectious and produce new virus particles. Several types of viruses are used in virotherapy and the delay varies with the type of virus. This delay can play an important role in the success of virotherapy. Our present study is to explore the impact of this delay in cancer virotherapy through a mathematical model based on delay differential equations. The partial success of virotherapy is guarenteed when one gets a stable non-trivial equilibrium with a low level of tumor cells. There exits Hopf-bifurcation by considering the delay as bifurcation parameter. We have estimated the length of delay which preserves the stability of the non-trivial equilibrium point. So when the delay is less than a threshold value, we can predict partial success of virotherapy for suitable sets of parameters. Here numerical simulations are also performed to support the analytical findings.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.2
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• pp.48.1-48.1
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• 2021

The Main-belt asteroid (298) Baptistina (hereafter 'Baptistina') is regarded as an X- (or C-) type asteroid and the largest member of the Baptistina asteroid family. Its basic physical properties play an important role in understanding the rotational evolution and orbital dynamics of the Baptistina family. In this study, we determined the physical characteristics of Baptistina from the optical observations. We conducted BVRI and R band photometric observations from 2017 to 2021 for a total of 47 nights using the 0.5 - 2.0 m-class telescopes. As a result, the color indices of Baptistina were derived as, , and ; this result is consistent with the previous classification of Baptistina as an X- (or C-) type. We also determined absolute magnitude () and slope parameter () by using a simplified version of the IAU H & G function (Bowell et al. 1989) are mag and respectively. We calculated the effective radius of Baptistina of km considering the visual geometric albedo of 0.131 from the NEOWISE data. Using the light-curve inversion method, the sidereal rotation period of 16.224235 h and the 3D shape model with a pole orientation (,) were also determined. In this presentation we will introduce our observations and results, and also discuss about the physical properties of Baptistina asteroid family members such as color indices.

• Journal of the Korean Society for Precision Engineering
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• v.11 no.1
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• pp.138-149
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• 1994

This paper introduces a new monitoring technique which utilizes an adaptive signal processing for feature generation, coupled with a multilayered merual network for pattern recognition. The cutting force signal in face milling operation was modeled by a low order discrete autoregressive model, shere parameters were estimated recursively at each sampling instant using a parameter adaptation algorithm based on an RLS(recursive least square) method with discounted measurements. The influences of the adaptation algorithm parameters as well as some considerations for modeling on the estimation results are discussed. The sensitivity of the extimated model parameters to the tool state(new and worn tool)is presented, and the application of a multilayered neural network to tool state monitoring using the previously generated features is also demonstrated with a high success rate. The methodology turned out to be quite suitable for in-process tool wear monitoring in the sense that the model parameters are effective as tool state features in milling operation and that the classifier successfully maps the sensors data to correct output decision.

• Nuclear Engineering and Technology
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• v.55 no.5
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• pp.1757-1762
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• 2023

Radiological hazards from external exposure of naturally occurring radioactive materials (NORM) scales residues, generated during the extraction process of oil and gas production in southern Algeria, are evaluated. The activity concentrations of ²²⁶Ra, ²³²Th, and ⁴⁰K were measured using high-purity gamma-ray spectrometry (GeHP). Mean activity concentration of ²²⁶Ra, ²³²Th and ⁴⁰K, found in scale samples are 4082 ± 41, 1060 ± 38 and 568 ± 36 Bq kg^-1, respectively. Radiological hazard parameters, such as radium equivalent (Ra_eq), external and internal hazard indices (H_ex, H_in), and gamma index (I_γ) are also evaluated. All hazard parameter values were greater than the permissible and recommended limits and the average annual effective dose value exceeded the dose constraint (0.3 mSv y^-1). However, for occasionally exposed workers, the dose rate of 0.65 ± 0.02 mSv y^-1 is lower than recommended limit of 1 mSv y^-1 for public.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2020.06a
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• pp.206-207
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• 2020

Accurate estimation of concrete strength development at early ages is a critical factor to secure structural stability as well as to speed up the construction process. The temperature generated from the heat of hydration is considered as a key parameter in predicting the early age strength. Conventionally, concrete temperature has been measured by temperature sensors installed inside concrete. However, considering the measurement on building structures with multiple floors, this method requires reinstallation and repositioning of hardware such as sensors, data loggers and routers for data transfer. This makes the temperature monitoring work cumbersome and inefficient. Concrete temperature monitoring by using thermal remote sensing can be an effective alternative to supplement those shortcomings. In this study, image processing was carried out through K-means clustering technique, which is a unsupervised learning method, and the classification results were analyzed accordingly. In the future, research will be conducted on how to automatically recognize concrete among various objects by using deep learning techniques.

• Journal of Powder Materials
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• v.30 no.3
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• pp.223-232
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• 2023

Metal additive manufacturing (AM) has transformed conventional manufacturing processes by offering unprecedented opportunities for design innovation, reduced lead times, and cost-effective production. Aluminum alloy, a material used in metal 3D printing, is a representative lightweight structural material known for its high specific strength and corrosion resistance. Consequently, there is an increasing demand for 3D printed aluminum alloy components across industries, including aerospace, transportation, and consumer goods. To meet this demand, research on alloys and process conditions that satisfy the specific requirement of each industry is necessary. However, 3D printing processes exhibit different behaviors of alloy elements owing to rapid thermal dynamics, making it challenging to predict the microstructure and properties. In this study, we gathered published data on the relationship between alloy composition, processing conditions, and properties. Furthermore, we conducted a sensitivity analysis on the effects of the process variables on the density and hardness of aluminum alloys used in additive manufacturing.