• Proceedings of the Korean Powder Metallurgy Institute Conference
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• 2006.09b
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• pp.1082-1083
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• 2006

Carbon nanotube (CNT) reinforced hydroxyapatite (HAp) composites were fabricated by using the spark plasma sintering process with surfactant modified CNT and HAp nano powder. Without the dependency on sintering temperature, the main crystal phase existed with the HAp phase although a few contents of ${\beta}-TCP$ (Tri calcium phosphate) phase were detected. The maximum fracture toughness, $(1.27\;MPa.m^{1/2})$ was obtained in the sample sintered at $1100^{\circ}C$ and on the fracture surface a typical intergranular fracture mode, as well as the pull-out pmhenomenon of CNT, was observed.

• Fire Science and Engineering
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• v.24 no.4
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• pp.92-97
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• 2010

Fire simulation by using a computational fluid dynamics model and examination of the fires at indoor shooting ranges broken out in the past were conducted, to presume causes of the fire at the indoor shooting range in Busan and suggest fire safety measures. On-site investigations and shooting tests on unburned gunpowder were also carried out. No trace of the muzzle spark and spark at the bullet trap was found in CCTV footage, and the impact of a stray bullet failed to ignite gunpowder. Cigarette was therefore presumed to be the most likely source of ignition among the potential sources. It appeared that the explosion in the shooting area was caused by violent burning of the polyurethane sound absorber and unburned gunpowder accumulated on it. The fire safety measures include prohibit of use of profile polyurethane sound absorber, removal of steel components from bullet trap, clean up and control of unburned gunpowder, etc.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.203-208
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• 2022

The occurrence of Type 2 Diabetes Mellitus (T2DM) is hoarding globally. All kinds of Diabetes Mellitus is controlled to disrupt over 415 million grownups worldwide. It was the seventh prime cause of demise widespread with a measured 1.6 million deaths right prompted by diabetes during 2016. Over 90% of diabetes cases are T2DM, with the utmost persons having at smallest one other chronic condition in UK. In valuation of contemporary applications of Big Data (BD) to Diabetes Medicare by sighted its upcoming abilities, it is compulsory to transmit out a bottomless revision over foremost theoretical literatures. The long-term growth in medicine and, in explicit, in the field of "Diabetology", is powerfully encroached to a sequence of differences and inventions. The medical and healthcare data from varied bases like analysis and treatment tactics which assistances healthcare workers to guess the actual perceptions about the development of Diabetes Medicare measures accessible by them. Apache Spark extracts "Resilient Distributed Dataset (RDD)", a vital data structure distributed finished a cluster on machines. Machine Learning (ML) deals a note-worthy method for building elegant and automatic algorithms. ML library involving of communal ML algorithms like Support Vector Classification and Random Forest are investigated in this projected work by using Jupiter Notebook - Python code, where significant quantity of result (Accuracy) is carried out by the models.

• Journal of KIISE
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• v.43 no.1
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• pp.87-95
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• 2016

Recently, due to the development of the Internet and electronic devices, there has been an enormous increase in the amount of available knowledge and information. As this growth has proceeded, studies on large-scale ontological reasoning have been actively carried out. In general, a machine learning program or knowledge engineer measures and provides a degree of confidence for each triple in a large ontology. Yet, the collected ontology data contains specific uncertainty and reasoning such data can cause vagueness in reasoning results. In order to solve the uncertainty issue, we propose an RDFS reasoning approach that utilizes confidence values indicating degrees of uncertainty in the collected data. Unlike conventional reasoning approaches that have not taken into account data uncertainty, by using the in-memory based cluster computing framework Spark, our approach computes confidence values in the data inferred through RDFS-based reasoning by applying methods for uncertainty estimating. As a result, the computed confidence values represent the uncertainty in the inferred data. To evaluate our approach, ontology reasoning was carried out over the LUBM standard benchmark data set with addition arbitrary confidence values to ontology triples. Experimental results indicated that the proposed system is capable of running over the largest data set LUBM3000 in 1179 seconds inferring 350K triples.

• Journal of the Korean Ceramic Society
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• v.39 no.12
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• pp.1183-1189
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• 2002

In order to develope the porous alumina ceramics with high strength, the pore characteristics and compressive strength were investigated in terms of relation to the conditions of spark-plasma sintering and the contents of graphite as a pore precursor. Porous alumina bodies were successfully prepared by spark-plasma sintering and burning out graphite in air. High porous bodies were fabricated by sintering at 1000${\circ}C$ for 3 min under a pressure of 30 MPa, heating rate of 80${\circ}C$/min and on-off pulse type of 12:2. For example, alumina bodies prepared by the addition of 10∼30 vol% graphite showed high porosity of 50∼57%. Also, the open porosity increased with graphite content. The relationship between pore characteristics and graphite contents could be explained by percolation model depending on cluster number and size. Porous alumina bodies prepared by the addition of 10∼30 vol% graphite showed the high compressive strength of 55∼200 MPa. This great improvement in strength was considered to be mainly due to the spark-plasma discharges and the self-heating action between particles.

• Journal of Powder Materials
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• v.12 no.6 s.53
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• pp.422-427
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• 2005

The $Al_2O_3$ with various phases were prepared by simple ex-situ hydrolysis and spark plasma sintering (SPS) process of Al powder. The nano bayerite $(\beta-Al(OH)_3)$ phase was derived by hydrolysis of commercial powder of Al with micrometer size, whereas the bohemite (AlO(OH)) phase was obtained by hydrolysis of nano Al powder synthesized by pulsed wire evaporation (PWE) method. Compaction as well as dehydration of both nano bayerite and bohemite was carried out simultaneously by SPS method, which is used to fabricate dense powder compacts with a rapid heating rate of $100^{\circ}C$ per min. under the pressure of 50MPa. After compaction treatment in the temperature ranges from $100^{\circ}C\;to\; 1100^{\circ}C$, the bayerite and bohemite phases change into various alumina phases depending on the compaction temperatures. The bayerite shows phase transition of $Al(OH)_3{\to}{\eta}-Al_2O_3{\to}{\theta}-Al_2O_3{\to}\alpha-Al_2O_3$ sequences. On the other hand, the bohemite experiences the phase transition from AlO(OH) to ${\gamma}-Al_2O_3\;at\;350^{\circ}C.$ It shows AlO(OH) ${\gamma}-Al_2O_3{\to}{\delta}-Al_2O_3{\to}{\alpha}-Al_2O_3$ sequences. The ${\gamma}-Al_2O_3$ compacted at $550^{\circ}C$ shows a high surface area $(138m^2/g)$.

• Journal of Powder Materials
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• v.23 no.3
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• pp.207-212
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• 2016

In this study, Fe-Cu-Ni-Mo-C low alloy steel powder is consolidated by spark plasma sintering (SPS) process. The internal structure and the surface fracture behavior are studied using field-emission scanning electron microscopy and optical microscopy techniques. The bulk samples are polished and etched in order to observe the internal structure. The sample sintered at $900^{\circ}C$ with holding time of 10 minutes achieves nearly full density of 98.9% while the density of the as-received conventionally sintered product is 90.3%. The fracture microstructures indicate that the sample prepared at $900^{\circ}C$ by the SPS process is hard to break out because of the presence of both grain boundaries and internal particle fractures. Moreover, the lamellar pearlite structure is also observed in this sample. The samples sintered at 1000 and $1100^{\circ}C$ exhibit a large number of tiny particles and pores due to the melting of Cu and aggregation of the alloy elements during the SPS process. The highest hardness value of 296.52 HV is observed for the sample sintered at $900^{\circ}C$ with holding time of 10 minutes.

• Journal of the Korean Ceramic Society
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• v.42 no.8 s.279
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• pp.567-574
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• 2005

Densification of SiC powder with additives of total amount of2, 4, 8 $wt\%$ Al-B-C was carried out by Spark Plasma Sintering (SPS). The unique features of the process are the possibilities of a very fast heating rate and a short holding time to obtain fully dense materials. The heating rate and applied pressure were kept at $100^{\circ}C/min$ and 40 MPa, while the sintering temperature and holding time varied from 1700 - $1800^{\circ}C$ for 10 - 40 min, respectively. The SPS-sintered specimens with different amount of Al-B-C at $1800^{\circ}C$ reached near-theoretical density. The $3C{\rightarrow}6H,\;15R{\rightarrow}4H$ phase transformation of SiC was enhanced by increasing the additive amount. The microstructure of SiC sintered up to $1750^{\circ}C$ consisted of fine equiaxed grains. In contrast, the growth of large elongated grains in small matrix grains was shown in sintered bodies at $1800^{\circ}C$, and the plate-like grains interlocking microstructure had been developed by increasing the holding time at $1800^{\circ}C$. The grain growth rate decreases with increasing amount of Al-B-C in SiC starting powder, however, the both of volume fraction and aspect ratio of large grains in sintered body increased.

• Journal of Internet Computing and Services
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• v.18 no.1
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• pp.27-35
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• 2017

In near future, IoT data is expected to be a major portion of Big Data. Moreover, sensor data is expected to be major portion of IoT data, and its' research is actively carried out currently. However, processed results may not be trusted and used if outlier data is included in the processing of sensor data. Therefore, method for detection and deletion of those outlier data before processing is studied in this paper. Moreover, we used Spark which is memory based distributed processing environment for fast processing of big sensor data. The detection and deletion of outlier data consist of four stages, and each stage is implemented with Mapper and Reducer operation. The proposed method is compared in three different processing environments, and it is expected that the outlier detection and deletion performance is best in the distributed Spark environment as data volume is increasing.

• Journal of Advanced Marine Engineering and Technology
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• v.39 no.4
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• pp.368-373
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• 2015

As a preliminary study for the development of the gas fueled marine engine, prediction of indicated performances was carried out for a spark-ignition engine using commercial software, GT-POWER. The optimized models through a previous study were applied for the simulation of the intake and exhaust systems in a SI engine. The Spark-Ignition Wiebe model was used to calculate the burn rate in the cylinders and the modified Woschni model was used to calculate the heat transfer to the walls. The predicted performances, such as air delivery, cylinder pressures and indicated mean effective pressures under a range of operating conditions showed good agreement with the experiments.