• Proceedings of the Korean Institute of Surface Engineering Conference
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• 2018.06a
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• pp.74-74
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• 2018

Ti-6Al-4V alloys have been widely used as orthopedic materials because of their excellent corrosion resistance and mechanical properties. However, it does not bind directly to the bone, so it requires a surface modification. This problem can be solved by nanotube and micropore formation. Plasma electrolytic oxidation (PEO) treatment for micropore, which combines high-voltage spark and electrochemical oxidation, is a new way of forming a ceramic coating on light metals such as titanium and its alloys. This method has excellent reproducibility and can easily control the shape and size of the Ti alloy. In this study, formation of bioactive surface by PEO-treatment after $2^{nd}$ ATO technique of Ti-6Al-4V alloy was invesgated by various instrument. Nanotube oxide surface structure was formed on the surface by anodic oxidation treatment in 0.8 wt.% NaF and 1M $H_3PO_4$ electrolytes. After nanotube formation, nanotube layer was removed by ultrasonic cleaning. PEO-treatment was carried out at 280V for 3 minutes in the electrolytic solution containing the bioactive substance (Mg, Zn, Mn, Sr, and Si). The surface of Ti-6Al-4V alloy was observed by field emission scanning electron microscopy (FE-SEM, S-4800 Hitachi, Japan). An energy dispersive X-ray spectrometer (EDS, Inca program, Oxford, UK) was used to analyze the spectra of physiologically active Si, Mn, Mg, Zn, and Sr ions. The PEO film formed on the Ti-6Al-4V alloy surface was characterized using an X-ray diffractometer (TF-XRD, X'pert Philips, Netherlands). It is confirmed that bioactive ions play an essential role in the normal bone growth and metabolism of the human skeletal tissues.

• Journal of Information Processing Systems
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• v.15 no.6
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• pp.1306-1325
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• 2019

As the world is moving towards digitization, data is generated from various sources at a faster rate. It is getting humungous and is termed as big data. The financial sector is one domain which needs to leverage the big data being generated to identify financial risks, fraudulent activities, and so on. The design of predictive models for such financial big data is imperative for maintaining the health of the country's economics. Financial data has many features such as transaction history, repayment data, purchase data, investment data, and so on. The main problem in predictive algorithm is finding the right subset of representative features from which the predictive model can be constructed for a particular task. This paper proposes a correlation-based method using submodular optimization for selecting the optimum number of features and thereby, reducing the dimensions of the data for faster and better prediction. The important proposition is that the optimal feature subset should contain features having high correlation with the class label, but should not correlate with each other in the subset. Experiments are conducted to understand the effect of the various subsets on different classification algorithms for loan data. The IBM Bluemix BigData platform is used for experimentation along with the Spark notebook. The results indicate that the proposed approach achieves considerable accuracy with optimal subsets in significantly less execution time. The algorithm is also compared with the existing feature selection and extraction algorithms.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.12
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• pp.511-517
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• 2016

Current oil-type potential transformers for trains are filled with insulating oil, which could have problems like explosions due to rising inner pressure during train operation. Therefore, mold and dry-type potential transformers are being developed to prevent explosions. One problem in manufacturing mold-type transformers is preventing void formation around the coiled core inside the mold during epoxy filling, which could cause an electrical spark. Micro voids can remain in the resin after filling, and macro voids can occur due to the structure shape. A transformer that is being developed has a cavity at the junction of the core and the coil for better performance, and when highly viscous epoxy flows inside the cavity channel, macro voids can form inside it. Therefore, in this study, the free-surface flow of the mold filling procedure was analyzed numerically by applying the VOF method. The results were used to understand the phenomena of void formation inside the cavity and to modify the process conditions to reduce voids.

• Nuclear Engineering and Technology
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• v.54 no.9
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• pp.3250-3259
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• 2022

A new approach to the use of rice straw as a difficult-to-recycle agricultural waste was proposed. Potassium aluminosilicate was obtained by spark plasma sintering as an effective material for subsequent immobilization of ¹³⁷Cs into a solid-state matrix. The sorption properties of potassium aluminosilicate to ¹³⁷Cs from aqueous solutions were studied. The effect of the synthesis temperature on the phase composition, microstructure, and rate of cesium leaching from samples obtained at 800-1000 ℃ and a pressure of 25 MPa was investigated. It was shown that the positive dynamics of compaction was characteristic of glass ceramics throughout the sintering. Glass ceramics RS-(K,Cs)AlSi₃O₈ obtained by the SPS method at 1000 ℃ for 5 min was characterized by a high density of ~2.62 g/cm³, Vickers hardness ~ 2.1 GPa, compressive strength ~231.3 MPa and the rate of cesium ions leaching of ~1.37 × 10^-7 g cm^-2·day^-1. The proposed approach makes it possible to safe dispose of rice straw and reduce emissions into the atmosphere of microdisperse amorphous silica, which is formed during its combustion and causes respiratory diseases, including cancer. In addition, the obtained is perspective to solve the problem of recycling long-lived ¹³⁷Cs radionuclides formed during the operation of nuclear power plants into solid-state matrices.