• 한국노년학
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• v.38 no.3
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• pp.745-766
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• 2018

The purpose of this study is to examine the research trends regarding the published articles in the Journal of Korean Gerongological Society within the past 10 years. This study is based on the article written by Won and Mo (2008). This article classified previously published studies into themes, methods, and application of theory. Out of the total of 187 articles published in the past 10 years, 11 articles were about social change and institution, 94 articles were about social issues, 12 articles were about social problems and deviation, 42 articles were about social culture, 14 papers were about gerontological theory and 13 papers were about residence/architecture. In the last 10 years, the most popular topic was around the various ways aging. New topic that emerged was the effect of IT and technology on the quality of life among the older adults. Other topics that gained interest were age discrimination and prejudice on aging. Trends in research methods showed increased use of qualitative methods. In the future, more research needs to be completed to theorize the results of quantitative research. Furthermore, the use of qualitative research methods needs to be increased in order to understand the lives of older adults in depth. Through more meta analysis, the results of past research articles should be synthesized to get a bigger picture of the Korean older adults.

• Analytical Science and Technology
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• v.31 no.6
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• pp.240-246
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• 2018

The present study analyzed standard samples of three types of aluminum matrix certified reference materials (CRM) using GD-MS. Calibration curves were constructed for 13 elements (Mg, Si, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, Ga, Sn, and Pb), with the slope representing the relative sensitivity factor (RSF). The x- and y-axes of the calibration curve represented ion beam ratio (IBR) and the authenticated value of the standard sample, respectively. In order to evaluate precision and linearity of the calibration curve, RSD and the coefficient of determination were calculated. Curve RSD for every element reflected high precision (within 10 %). For most elements, the coefficient of determination was ${\geq}0.99$, indicating excellent linearity. However, vanadium, nickel, and gallium curves exhibited relatively low linearity (0.90~0.95), likely due to their narrow concentration ranges. Standard RSF was calculated using the slope of the curve generated for three types of CRM. Despite vanadium, nickel, and gallium exhibiting low coefficients of determination, their standard RSF resembled that of the three types of CRM. Therefore, the RSF method may be used for element quantitation. Standard iron matrix samples were analyzed to verify the applicability of the aluminum matrix standard RSF, as well as to calculate the RSD-estimated error of the measured value relative to the actual standard value. Six elements (Al, Si, V, Cr, Mn, and Ni) exhibited an RSD of approximately 30 %, while the RSD of Cu was 77 %. In general, Cu isotopes are subject to interference: $^{63}Cu$ to $^{54}Fe^{2+}-^{36}Ar$ and $^{65}Cu$ to $^{56}Fe-Al^{3+}$ interference. Thus, the influence of these impurities may have contributed to the high RSD value observed for Cu. To reliably identify copper, the resolution should be set at ${\geq}8000$. However, high resolutions are inappropriate for analyzing trace elements, as it lowers ion permeability. In conclusion, quantitation of even relatively low amounts of six elements (Al, Si, V, Cr, Mn, and Ni) is possible using this method.

• Journal of agricultural medicine and community health
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• v.44 no.1
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• pp.11-27
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• 2019

Dementia is major epidemic disease of the 21st century in the world. Dementia is one of the major issues in public health globally. Also in Korea, the estimated prevalence of dementia was 8.7%(0.47 million) in 2010, the number will reach the 1 million mark in 2024, it will become a 15.1%(2.71 million) by 2050. Among Koreans aged 65 or older, 725,000 are estimated to be suffering from dementia in 2017. Against dementia, Korea developed three National Dementia Plans in 2008, 2012, and 2016. The 1st plan was came into effect in 2008 and focused on prevention, early diagnostic, development and coordination of infrastructures and management, and improving awareness. The 2nd plan was launched in 2012, addressed the same priorities but had a stronger focus on supporting family members. In 2012 the Dementia Management Act established a statutory basis for organization of the National Dementia Plans. Under the Dementia Management Act, the government is required to produce a comprehensive plan for dementia every 5 years. The Act also orders that the government should register the dementia patients and collect statistics on epidemiology and the management of the dementia conditions. The Dementia Management Act of Korea required the operation of the National Institute of Dementia and Metropolitan/Provincial Dementia Centers to make and carry out dementia management plans throughout the nation. The Act also mandate to establish Dementia Counselling Centers in every public health center and the National Dementia Helpline. The 3rd National Dementia Plan of 2016 aims to build a dementia friendly community to ensure people with dementia and their carer live well. This plan focus on community-based prevention and management of dementia, convenient and safe diagnosis, treatment, and care for people with dementia, the reduction of the care burden for family care-givers of people with dementia, and support for dementia research through research, statistics and technology. In 2017, Moon's government will introduce the "National Dementia Responsibility System," which guarantees most of the burden caused by dementia. This plan include that the introduction of a ceiling on self-pay for dementia diseases, expansion of the application of dementia care standards through alleviating the support criteria for long-term care insurance for mild dementia, expansion of dementia support centers, expansion of national and public dementia care facilities. In the meantime, Korea has accomplished many accomplishments by establishing many measures related to dementia and promoting related projects in a short time, but there are still many challenges.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.239-251
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• 2019

Statistical methods such as moving averages, Kalman filtering, exponential smoothing, regression analysis, and ARIMA (autoregressive integrated moving average) have been used for stock market predictions. However, these statistical methods have not produced superior performances. In recent years, machine learning techniques have been widely used in stock market predictions, including artificial neural network, SVM, and genetic algorithm. In particular, a case-based reasoning method, known as k-nearest neighbor is also widely used for stock price prediction. Case based reasoning retrieves several similar cases from previous cases when a new problem occurs, and combines the class labels of similar cases to create a classification for the new problem. However, case based reasoning has some problems. First, case based reasoning has a tendency to search for a fixed number of neighbors in the observation space and always selects the same number of neighbors rather than the best similar neighbors for the target case. So, case based reasoning may have to take into account more cases even when there are fewer cases applicable depending on the subject. Second, case based reasoning may select neighbors that are far away from the target case. Thus, case based reasoning does not guarantee an optimal pseudo-neighborhood for various target cases, and the predictability can be degraded due to a deviation from the desired similar neighbor. This paper examines how the size of learning data affects stock price predictability through k-nearest neighbor and compares the predictability of k-nearest neighbor with the random walk model according to the size of the learning data and the number of neighbors. In this study, Samsung electronics stock prices were predicted by dividing the learning dataset into two types. For the prediction of next day's closing price, we used four variables: opening value, daily high, daily low, and daily close. In the first experiment, data from January 1, 2000 to December 31, 2017 were used for the learning process. In the second experiment, data from January 1, 2015 to December 31, 2017 were used for the learning process. The test data is from January 1, 2018 to August 31, 2018 for both experiments. We compared the performance of k-NN with the random walk model using the two learning dataset. The mean absolute percentage error (MAPE) was 1.3497 for the random walk model and 1.3570 for the k-NN for the first experiment when the learning data was small. However, the mean absolute percentage error (MAPE) for the random walk model was 1.3497 and the k-NN was 1.2928 for the second experiment when the learning data was large. These results show that the prediction power when more learning data are used is higher than when less learning data are used. Also, this paper shows that k-NN generally produces a better predictive power than random walk model for larger learning datasets and does not when the learning dataset is relatively small. Future studies need to consider macroeconomic variables related to stock price forecasting including opening price, low price, high price, and closing price. Also, to produce better results, it is recommended that the k-nearest neighbor needs to find nearest neighbors using the second step filtering method considering fundamental economic variables as well as a sufficient amount of learning data.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.201-220
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• 2019

Fake news has emerged as a significant issue over the last few years, igniting discussions and research on how to solve this problem. In particular, studies on automated fact-checking and fake news detection using artificial intelligence and text analysis techniques have drawn attention. Fake news detection research entails a form of document classification; thus, document classification techniques have been widely used in this type of research. However, document summarization techniques have been inconspicuous in this field. At the same time, automatic news summarization services have become popular, and a recent study found that the use of news summarized through abstractive summarization has strengthened the predictive performance of fake news detection models. Therefore, the need to study the integration of document summarization technology in the domestic news data environment has become evident. In order to examine the effect of extractive summarization on the fake news detection model, we first summarized news articles through extractive summarization. Second, we created a summarized news-based detection model. Finally, we compared our model with the full-text-based detection model. The study found that BPN(Back Propagation Neural Network) and SVM(Support Vector Machine) did not exhibit a large difference in performance; however, for DT(Decision Tree), the full-text-based model demonstrated a somewhat better performance. In the case of LR(Logistic Regression), our model exhibited the superior performance. Nonetheless, the results did not show a statistically significant difference between our model and the full-text-based model. Therefore, when the summary is applied, at least the core information of the fake news is preserved, and the LR-based model can confirm the possibility of performance improvement. This study features an experimental application of extractive summarization in fake news detection research by employing various machine-learning algorithms. The study's limitations are, essentially, the relatively small amount of data and the lack of comparison between various summarization technologies. Therefore, an in-depth analysis that applies various analytical techniques to a larger data volume would be helpful in the future.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.24 no.1
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• pp.106-127
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• 2019

This study aims to understand spatio-temporal variations of macrozoobenthos community in Han River (HRE), Geum River (GRE), and Nakdong River estuaries (NRE) of Korea, sampled by National Survey of Marine Ecosystem. The survey was seasonally performed at a total of 20 stations for three years (2015-2017). Sediment samples were taken three times with van Veen grab of $0.1m^2$) areal size and sieved through a 1 mm pore size mesh on site. A total of 1,008 species were identified with 602 species in HRE, 612 in GRE, and 619 in NRE, showing similar number of species between estuaries. Mean density was $1,357ind./m^2$, showing the high in NRE ($1,357ind./m^2$), mid in GRE ($1,357ind./m^2$), and low in HRE ($1,127ind./m^2$). Mean biomass was $116.8g/m^2$, showing similar variations to density ($174.2g/m^2$ in NRE, $129.0g/m^2$ in GRE, $49.0g/m^2$ in HRE). Polychaeta dominated in number of species and density in three estuaries. Biomass-dominated taxon was Mollusca in HRE and GRE, and Echinodermata in NRE. Polychaetous species dominated all three estuaries over 4% of density, such as Dispio oculata, Heteromastus filiformis and Aonides oxycephala in HRE, Heteromastus filiformis and Scoletoma longifolia in GRE, and Pseudopolydora sp. and Aphelochaeta sp. in NRE, showing various density between estuaries. Community structure was determined by various environmental variables among estuaries such as mean grain size and sorting (HRE), salinity and mean grain size (GRE), and salinity, dissolved oxygen, loss on ignition and mud content (NRE). Our study demonstrates the application of different measures to manage ecosystems in three estuaries. HRE needs to alleviate sedimentary stressors such as sand mining, land-filling, dike construction. Management of GRE should be focused on fresh water control and sedimentary stressors. In NRE, monitoring of dominant benthos and process study on hypoxia occurrence in inner Masan Bay are necessary.

• Journal of Environmental Impact Assessment
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• v.28 no.5
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• pp.492-506
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• 2019

Heavy metal contaminants in the shooting range are mostly present in a warhead circle or a metal fragment present as a particle, these fine metal particles are weathered for a long period of time is very likely that the surface is present as an oxide or carbon oxide. In particular, lead which is a representative contaminant in the shooting range soil, is present as more fine particles because it increases the softness and is stretched well. Therefore, by physical washing experiment, we conducted a degree analysis, concentration of heavy metals by cubic diameter, composition analysis of metallic substances, and assessment of applicability of gravity, magnetism and floating selection. The experimental results FESEM analysis and the measurement results lead to the micro-balance was confirmed thatthe weight goes outless than the soil ofthe same size in a thinly sliced and side-shaped structure according to the dull characteristics it was confirmed that the high specific gravity applicability. In addition, the remediation efficiency evaluation results using a hydrocyclone applied to this showed a cumulative remediation efficiency of 71%,twice 80%, 3 times 91%. On the other hand, magnetic sifting showed a low efficiency of 17%,floating selection -35mesh (0.5mm)target soil showed a relatively high efficiency to 39% -10mesh (2mm) efficiency was only 16%. The target treatment diameter of soil washing should be 2mm to 0.075mm, which is applied to the actual equipment by adding an additional input classification, which would require management as additional installation costs and processes are constructed. As a result, it is found that the soilremediation of shooting range can be separately according to the size of the warhead. The size is larger than the gravel diameter to most 5.56mm, so it is possible to select a specific gravity using a high gravity. However, the contaminants present in the metal fragments were found to be processed by separating using a hydrocyclone of the soil washing according to the weight is less than the soil of the same particle size in a thinly fragmented structure.

• Korean Chemical Engineering Research
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• v.59 no.1
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• pp.127-137
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• 2021

Air emission charge for nitrogen oxide as a precursor of fine dust has been introduced and implemented within the country from 2020. Therefore, the development of economical combustion technology for NOx reduction has got more needed urgently. This study investigated the air-staging effect as a way to reduce the NOx during combustion of domestic unused forest biomass, recently possible to secure REC (Renewable Energy Certification) as a substitute for overseas wood pellets in a 0.1 MWth circulating fluidized bed combustion test-rig. Operating conditions were comparison with and without air-staging, the supply position of tertiary air (6.4 m, 8.1 m, 9.4 m in the combustor) and variation of air-staging ratio (Primary air:Secondary air:Tertiary air=91%:9%:0%, 82%:9%:9%, 73%:9%:18%). NO and CO concentrations in flue gas, profiles of temperature and pressure at the height of the combustion, unburned carbon in sampled fly ash and combustion efficiency on operating conditions were evaluated. As notable results, NO concentration with air-staging application under tertiary air supply at 9.4 m in the combustor reduced 100.7 ppm compared to 148.8 ppm without air-staging while, CO concentration increased from 52.2 ppm without air-staging to 99.8 ppm with air-staging. However, among air-staging runs, when tertiary air supply amount at 6.4 m in the combustor increased by air-staging ratio (Primary air:Secondary air:Tertiary air=73%:9%:18%), NO and CO concentrations decreased the lowest 90.8 ppm and 66.1 ppm, respectively. Furthermore, combustion efficiency at this condition was improved to 99.3%, higher than that (98.3%) of run without air-staging.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.67-86
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• 2020

Recommender Systems have been huge influence users and business more and more. Recently the importance of E-commerce has been reached rapid growth greatly in world-wide COVID-19 pandemic. Recommender system is the center of E-commerce lively. Top ranked E-commerce managers mentioned that recommender systems have a major influence on customer's purchase such as about 50% of Netflix, Amazon sales from their recommender systems. Most algorithms have been focused on improving accuracy of recommender system regardless of novelty, diversity, serendipity etc. Recommender systems with only high accuracy cannot satisfy business long-term profit because of generating sales polarization. In addition, customers do not experience enjoyment of shopping from only focusing accuracy recommender system because customer's preference is changed constantly. Therefore, recommender systems with various values need to be developed for user's high satisfaction. Reranking is the most useful methodology to realize diversity of recommender system. In this paper, diversity of recommender system is represented through constructing high similarity with users who have different preference using each user's purchased item's category algorithm. It is distinguished from past research approach which is changing the algorithm of recommender system without user's diversity preference level. We tried to discover user's diversity preference level and observed the results how the effect was different according to user's diversity preference level. In addition, graph-based recommender system was used to show diversity through user's network, not collaborative filtering. In this paper, Amazon Grocery and Gourmet Food data was used because the low-involvement product, such as habitual product, foods, low-priced goods etc., had high probability to show customer's diversity. First, a bipartite graph with users and items simultaneously is constructed to make graph-based recommender system. However, each users and items unipartite graph also need to be established to show diversity of recommender system. The weight of each unipartite graph has played crucial role changing Jaccard Distance of item's category. We can observe two important results from the user's unipartite network. First, the user's diversity preference level is observed from the network and second, dissimilar users can be discovered in the user's network. Through the research process, diversity of recommender system is presented highly with small accuracy loss and optimalization for higher accuracy is possible controlling diversity ratio. This paper has three important theoretical points. First, this research expands recommender system research for user's satisfaction with various values. Second, the graph-based recommender system is developed newly. Third, the evaluation indicator of diversity is made for diversity. In addition, recommender systems are useful for corporate profit practically and this paper has contribution on business closely. Above all, business long-term profit can be improved using recommender system with diversity and the recommender system can provide right service according to user's diversity level. Lastly, the corporate selling low-involvement products have great effect based on the results.

• Economic and Environmental Geology
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• v.54 no.6
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• pp.689-698
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• 2021

Since the mine reclamation scheme was implemented from 2007 in Korea, various remediation programs have been decontaminated the pollution associated with mining and 254 mines were managed to reclamation from 2011 to 2015. However, as the total amount of contaminated mine drainage has been increased due to the discovery of potential hazards and contaminated zone, more efficient and economical treatment technology is required. Therefore, in this study, the adsorption properties of arsenic was evaluated according to the adsorbents which were derived from water treatment sludge(Alum based adsorbent, ABA-500) and granular ferric hydroxide(GFH), already commercialized. The alum sludge and GFH adsorbents consisted of aluminum, silica materials and amorphous iron hydroxide, respectively. The point of zero charge of ABA-500 and GFH were 5.27 and 6.72, respectively. The result of the analysis of BET revealed that the specific surface area of GFH(257 m²·g^-1) was larger than ABA-500(126~136 m²·g^-1) and all the adsorbents were mesoporous materials inferred from N₂ adsorption-desorption isotherm. The adsorption capacity of adsorbents was compared with the batch experiments that were performed at different reaction times, pH, temperature and initial concentrations of arsenic. As a result of kinetic study, it was confirmed that arsenic was adsorbed rapidly in the order of GFH, ABA-500(granule) and ABA-500(3mm). The adsorption kinetics were fitted to the pseudo-second-order kinetic model for all three adsorbents. The amount of adsorbed arsenic was increased with low pH and high temperature regardless of adsorbents. When the adsorbents reacted at different initial concentrations of arsenic in an hour, ABA-500(granule) and GFH could remove the arsenic below the standard of drinking water if the concentration was below 0.2 mg·g^-1 and 1 mg·g^-1, respectively. The results suggested that the ABA-500(granule), a low-cost adsorbent, had the potential to field application at low contaminated mine drainage.