• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.1-19
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• 2019

Artificial intelligence (AI) is one of the main driving forces leading the Fourth Industrial Revolution. The technologies associated with AI have already shown superior abilities that are equal to or better than people in many fields including image and speech recognition. Particularly, many efforts have been actively given to identify the current technology trends and analyze development directions of it, because AI technologies can be utilized in a wide range of fields including medical, financial, manufacturing, service, and education fields. Major platforms that can develop complex AI algorithms for learning, reasoning, and recognition have been open to the public as open source projects. As a result, technologies and services that utilize them have increased rapidly. It has been confirmed as one of the major reasons for the fast development of AI technologies. Additionally, the spread of the technology is greatly in debt to open source software, developed by major global companies, supporting natural language recognition, speech recognition, and image recognition. Therefore, this study aimed to identify the practical trend of AI technology development by analyzing OSS projects associated with AI, which have been developed by the online collaboration of many parties. This study searched and collected a list of major projects related to AI, which were generated from 2000 to July 2018 on Github. This study confirmed the development trends of major technologies in detail by applying text mining technique targeting topic information, which indicates the characteristics of the collected projects and technical fields. The results of the analysis showed that the number of software development projects by year was less than 100 projects per year until 2013. However, it increased to 229 projects in 2014 and 597 projects in 2015. Particularly, the number of open source projects related to AI increased rapidly in 2016 (2,559 OSS projects). It was confirmed that the number of projects initiated in 2017 was 14,213, which is almost four-folds of the number of total projects generated from 2009 to 2016 (3,555 projects). The number of projects initiated from Jan to Jul 2018 was 8,737. The development trend of AI-related technologies was evaluated by dividing the study period into three phases. The appearance frequency of topics indicate the technology trends of AI-related OSS projects. The results showed that the natural language processing technology has continued to be at the top in all years. It implied that OSS had been developed continuously. Until 2015, Python, C ++, and Java, programming languages, were listed as the top ten frequently appeared topics. However, after 2016, programming languages other than Python disappeared from the top ten topics. Instead of them, platforms supporting the development of AI algorithms, such as TensorFlow and Keras, are showing high appearance frequency. Additionally, reinforcement learning algorithms and convolutional neural networks, which have been used in various fields, were frequently appeared topics. The results of topic network analysis showed that the most important topics of degree centrality were similar to those of appearance frequency. The main difference was that visualization and medical imaging topics were found at the top of the list, although they were not in the top of the list from 2009 to 2012. The results indicated that OSS was developed in the medical field in order to utilize the AI technology. Moreover, although the computer vision was in the top 10 of the appearance frequency list from 2013 to 2015, they were not in the top 10 of the degree centrality. The topics at the top of the degree centrality list were similar to those at the top of the appearance frequency list. It was found that the ranks of the composite neural network and reinforcement learning were changed slightly. The trend of technology development was examined using the appearance frequency of topics and degree centrality. The results showed that machine learning revealed the highest frequency and the highest degree centrality in all years. Moreover, it is noteworthy that, although the deep learning topic showed a low frequency and a low degree centrality between 2009 and 2012, their ranks abruptly increased between 2013 and 2015. It was confirmed that in recent years both technologies had high appearance frequency and degree centrality. TensorFlow first appeared during the phase of 2013-2015, and the appearance frequency and degree centrality of it soared between 2016 and 2018 to be at the top of the lists after deep learning, python. Computer vision and reinforcement learning did not show an abrupt increase or decrease, and they had relatively low appearance frequency and degree centrality compared with the above-mentioned topics. Based on these analysis results, it is possible to identify the fields in which AI technologies are actively developed. The results of this study can be used as a baseline dataset for more empirical analysis on future technology trends that can be converged.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.10 no.1
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• pp.45-53
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• 2012

The metal chloride wastes from a pyrochemical process to recover uranium and transuranic elements has been considered as a problematic waste difficult to apply to a conventional solidification method due to the high volatility and low compatibility with silicate glass. In this study, a dechlorination approach to treat LiCl-KCl waste for final disposal was adapted. In this study, a $SiO_2-Al_2O_3-P_2O_5$ (SAP) inorganic composite as a dechlorination agent was prepared by a conventional sol-gel process. By using a series of SAPs, the dechlorination behavior and consolidation of reaction products were investigated. Different from LiCl waste, the dechlorination reaction occurred mainly at two temperature ranges. The thermogravimetric test indicated that the first reaction range was about $400^{\circ}C$ for LiCl and the second was about $700^{\circ}C$ for KCl. The SAP 1071 (Si/Al/P=1/0.75/1 in molar) was found to be the most favorable SAP as a dechlorination agent under given conditions. The consolidation test revealed that the bulk shape and the densification of consolidated forms depended on the SAP/Salt ratios. The leaching test by PCT-A method was performed to evaluate the durability of consolidated forms. This study provided the basic information on the dechlorination approach. Based on the experimental results, the dechlorination method using a $SiO_2-Al_2O_3-P_2O_5$ (SAP) could be considered as one of alternatives for the immobilization of waste salt.

• The Journal of the Korea Contents Association
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• v.12 no.5
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• pp.311-322
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• 2012

Restoration materials used to investigate effects of fluorine such as enamel strengthening and anti-caries effects in several types of dental restoration materials were five kinds including Ionoseal(VOCO GmbH, Cuxhaven, Germany), Fuji Filling LC(GC Co. Tokyo, Japan), Quadrant Universal LC(CAVEX Holland BV, Netherlands), PermaCem$^{(R)}$(DMG, Hamburg, Germany) and Dyract$^{(R)}$ AP(Dentsply GmbH, Germany), and the amount of fluorine-releasing was measured with ICS-5000 Reagent-FreeTM Ion Chromatography(RFICTM, Dionex, U.S.A.). The results of this study are as follows. 1. In all types of restoration materials, the amount of fluoride-releasing was reduced with time passage and it was declined sharply to show significance in four weeks. Fuji Filling LC(12.445PPM) or resin-reinforced glass ionomer and PermaCem$^{(R)}$(16.121PPM) or compomer were found to release fluorine for a long term(P<.001). 2. Ionoseal(0.887PPM) or glass ionomer and Quadrant Universal LC(0.957PPM) or composite resin released a few fluorine of 1PPM or less than 1PPM after one week, and Dyract$^{(R)}$ AP or compomer released fluorine of 8.631PPM in one week and its amount of releasing decreased dramatically in two and four week by recording 0.175PPM and 0.116PPM, respectively. Therefore, the effect of releasing fluorine in four weeks was observed to be poor (P<.001). 3. Fuji Filling LC or resin-reinforced glass ionomer and PermaCem$^{(R)}$ or compomer released fluorine of 33.372 and 1.902PPM, respectively in one week and their amount of releasing increased to be 36.371 and 18.223PPM, respectively in two weeks. So, their amount of fluorine-releasing recorded the highest levels in two weeks(P<.001).

• Journal of Life Science
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• v.25 no.6
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• pp.680-685
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• 2015

Previously, cellulase and xylanase producing microorganism, Bacillus subtilis NC1, was isolated from soil. Based on the 16S rRNA gene sequence and API 50 CHL test the strain was identified as Bacillus subtilis, and named as B. subtilis NC1. We cloned and sequenced the genes for cellulase and xylanase. Plus, the deduced amino acid sequences from the genes of cellulase and xylanase were determined and were also identified as glycosyl hydrolases family (GH) 5 and 30, respectively. In this study to optimize the medium parameters for cellulase production by B. subtilis NC1 the RSM (response surface methodology) based on CCD (central composite design) model was performed. Three factors, tryptone, yeast extract, and NaCl, for N or C source were investigated. The cellulase activity was measured with a carboxylmethyl cellulose (CMC) plate and the 3,5-dinitrosalicylic acid (DNS) methods. The coefficient of determination (R²) for the model was 0.960, and the probability value (p=0.0001) of the regression model was highly significant. Based on the RSM, the optimum conditions for cellulase production by B. subtilis NC1 were predicted to be tryptone of 2.5%, yeast extract of 0.5%, and NaCl of 1.0%. Through the model verification, cellulase activity of Bacillus subtilis NC1 increased from 0.5 to 0.62 U/ml (24%) compared to the original medium.

• Journal of the Korean Electrochemical Society
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• v.10 no.4
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• pp.283-287
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• 2007

KIER has been developing the anode-supported flat tubular solid oxide fuel cell unit bundle for the intermediate temperature($700{\sim}800^{\circ}C$) operation. Anode-supported flat tubular cells have Ni/YSZ cermet anode support, 8 moi.% $Y_2O_3$ stabilized $ZrO_2(YSZ)$ thin electrolyte, and cathode multi-layer composed of Sr-doped $LaSrMnO_3(LSM)$, LSM-YSZ composite, and $LaSrCoFeO_3(LSCF)$. The prepared anode-supported flat tubular cell was joined with ferritic stainless steel cap by induction brazing process. Current collection for the cathode was achieved by winding Ag wire and $La_{0.6}Sr_{0.4}CoO_3(LSCo)$ paste, while current collection for the anode was achieved by using Ni wire and felt. For making stack, the prepared anode-supported flat tubular cells with effective electrode area of $90\;cm^2$ connected in series with 12 unit bundles, in which unit bundle consists of two cells connected in parallel. The performance of unit bundle in 3% humidified $H_2$ and air at $800^{\circ}C$ shows maximum power density of $0.39\;W/cm^2$ (@ 0.7V). Through these experiments, we obtained basic technology of the anode-supported flat tubular cell and established the proprietary concept of the anode-supported flat tubular cell unit bundle.

• KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
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• v.24 no.2
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• pp.65-71
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• 2018

Paper packs, glass bottles, metal cans, and plastic materials are classified according to packaging material recycling groups that are Extended Producer Responsibility (EPR). In the case of waste paper pack, the compressed cartons are dissociated to separate polyethylene films and other foreign substance, and then these are washed, pulverized and dried to produce toilet paper. Glass bottle for recycling is provided to the bottle manufacturers after the process of collecting the waste glass bottle, removing the foreign substance, sorting by color, crushing, raw materializing process. Waste glass recycling technology of Korea is largely manual, except for removal of metal components and low specific gravity materials. Metal can is classified into iron and aluminum cans through an automatic sorting machine, compressed, and reproduced as iron and aluminum through a blast furnace. In the case of composite plastic material, the selected compressed product is crushed and then recycled through melt molding and refined products are produced through solid fuel manufacturing steps through emulsification and compression molding through pyrolysis. In the recycling process of paper packs, glass bottles, metal cans, and plastic materials, the influx of recycled materials and other substances interferes with the recycling process and increases the recycling cost and time. Therefore, the government needs to improve the legal system which is necessary to use materials and structure that are easy to recycle from the design stage of products or packaging materials.

• Journal of the Korean Wood Science and Technology
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• v.44 no.1
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• pp.85-95
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• 2016

In this study, furfural, which is one of the value-added chemicals, was produced from the hydrolyzate of Quercus mongolica using dilute acid pretreatment, and the optimal pretreatment condition was determined by Response Surface Methodology (RSM) to obtain high yield of furfural. Based on Central Composite Design, the pretreatment experiment was designed with parameters such as reaction temperature ($X_1$), acid concentration ($X_2$), and reaction time ($X_3$) as independent variables, while dependent variable was furfural concentration (Y), and furfural yield (Z) was shown as percentage of Y per a dry weight basis. According to results of RSM, it was confirmed that reaction temperature ($X_1$) was the most influence factor and reaction temperature ($X_1$)-acid concentration ($X_2$) was the most significant interaction factor on furfural yield. Also, the optimal condition for the highest furfural yield was predicted at reaction temperature of $184^{\circ}C$, acid concentration of 1.17%, and reaction time of 5 min by RSM, and expected maximum yield of furfural was 6.37%. Experimentally, the maximum yield of furfural produced at above optimal condition was 6.21%, and it was considerably similar with the predicted value, and therefore the model for furfural production from the hydrolyzate of Quercus mongolica during dilute acid pretreatment could be built using RSM.

• Food Science and Preservation
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• v.19 no.4
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• pp.470-476
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• 2012

Enzymatic action and microbial growth degrade the quality of fresh-cut lettuce. Browning, a bad smell, and softening during storage are the major forms of quality deterioration. Health-oriented consumers tend to avoid foods treated with chemicals to maintain their freshness. This study was conducted to evaluate the change in the quality of fresh-cut lettuce with combined low-temperature blanching (LB) and ultrasonication (US). The optimum condition was selected using the response surface methodology (RSM), through a regression analysis with the following independent variables; the ultrasonication time (X1), blanching temperature (X2), blanching time (X3), and dependent variable; ${\Delta}E$ value (y). It was found that the condition with the lowest ${\Delta}E$ value occurred with combined 90s US and $45^{\circ}C$ 90s LB (US+LB). The combined treatment group (US+LB) was stored at $10^{\circ}C$ for 9 days with the control group and each single-treatment group, with low-temperature blanching and ultrasonication. Overall, the US+LB group had a significantly high $L^*$ value, which indicates significantly low $a^*$, $b^*$, ${\Delta}E$, browning index, PPO, and POD activity values, and a low total bacteria count (p < 0.05). The US+LB group also had the highest sensory score (except for aroma and texture; p > 0.05).

• Food Engineering Progress
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• v.13 no.1
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• pp.8-15
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• 2009

This study was investigated on optimal conditions of the functional activities of ${\beta}$-glucan which was extracted from rice bran (RB) and rice germ (RG) using response surface methodology. The extraction temperature was varied in the $80-100^{\circ}C$, the extraction time between 2-10 min, and the ethanol concentration was in the interval of 30-70%. A central composite design was applied to investigate the effects of independent variables of extraction temperature ($X_1$), extraction time ($X_2$) and ethanol concentration ($X_3$) on dependent variables such as electron donating ability of RB ($Y_1$), electron donating ability of RG ($Y_2$), total phenolics of RB ($Y_3$), total phenolics of RG ($Y_4$), ${\beta}$-glucan contents of RB ($Y_5$) and ${\beta}$-glucan contents of RG ($Y_6$). As a result, the highest $Y_1$ level was 84.02% at $92.60^{\circ}C$, 2.75 min and 60.41% in saddle point. This value was affected by extraction temperature (P<0.05). The value of $Y_2$ was found to be the highest at $87.52^{\circ}C$, 2.23 min and 54.40% in saddle point. The highest $Y_3$ level was $98.56^{\circ}C$, 6.69 min and 40.26% in saddle point, and this extraction was greatly influenced by extraction temperature (P<0.01) and ethanol concentration (P<0.05). The value of $Y_4$ was found to be highest at $95.73^{\circ}C$, 9.19 min and 53.67% in minimum point. The value of $Y_5$ was found to be the highest at $96.23^{\circ}C$, 7.70 min and 63.69% in saddle point. The value of $Y_6$ was found to be highest at $87.82^{\circ}C$, 2.10 min and 50.03% in minimum point, and this extraction was greatly influenced by extraction time (P<0.01).

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1255-1272
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• 2023

Satellite-based chlorophyll-a concentration, produced as a long-term time series, is crucial for global climate change research. The production of data without gaps through the merging of time-synthesized or multi-satellite data is essential. However, studies related to satellite-based chlorophyll-a concentration in the waters around the Korean Peninsula have mainly focused on evaluating seasonal characteristics or proposing algorithms suitable for research areas using a single ocean color sensor. In this study, a merging dataset of remote sensing reflectance from the geostationary sensor GOCI-II and polar-orbiting sensors (MODIS, VIIRS, OLCI) was utilized to achieve high spatial coverage of chlorophyll-a concentration in the waters around the Korean Peninsula. The spatial coverage in the results of this study increased by approximately 30% compared to polar-orbiting sensor data, effectively compensating for gaps caused by clouds. Additionally, we aimed to quantitatively assess accuracy through comparison with global chlorophyll-a composite data provided by Ocean Colour Climate Change Initiative (OC-CCI) and GlobColour, along with in-situ observation data. However, due to the limited number of in-situ observation data, we could not provide statistically significant results. Nevertheless, we observed a tendency for underestimation compared to global data. Furthermore, for the evaluation of practical applications in response to marine disasters such as red tides, we qualitatively compared our results with a case of a red tide in the East Sea in 2013. The results showed similarities to OC-CCI rather than standalone geostationary sensor results. Through this study, we plan to use the generated data for future research in artificial intelligence models for prediction and anomaly utilization. It is anticipated that the results will be beneficial for monitoring chlorophyll-a events in the coastal waters around Korea.