• Journal of Korea Water Resources Association
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• v.31 no.4
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• pp.363-373
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• 1998

In this study, random flow field in a nonstationary porous formation is characterized through cross covariances of the velocity with the log conductivity and the head. The hydraulic head and the velocity in saturated aquifers are found through stochastic analysis of a steady, two-dimensional flow field without recharge. Expression for these cross covariances are obtained in quasi-analytic forms all in terms of the parameters which characterize the nonstationary conductivity field and the average head gradient. The cross covariances with a Gaussian correlation function for the log conductivity are presented for two particular cases where the trend is either parallel or perpendicular to the mean head gradient and for separation distances along and across the mean flow direction. The results may be of particular importance in transport predictions and conditioning on field measurements when the log conductivity field is suspected to be nonstationary and also serve as a benchmark for testing nonstationary numerical codes. Keywords : cross covariance, nonstationary conductivity field, saturated aquifer, stochastic analysis.

• Journal of Digital Convergence
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• v.14 no.11
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• pp.267-273
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• 2016

Creation and Sharing of information which is structured data as well as various unstructured data. makes progress actively through the spread of mobile. Recently, Big Data extracts the semantic information from SNS and data mining is one of the big data technique. Especially, the general emotion analysis that expresses the collective intelligence of the masses is utilized using large and a variety of materials. In this paper, we propose the emotion prediction system architecture which extracts the significant keywords from social network paragraphs using n-gram and Korean morphological analyzer, and predicts the emotion using SVM and these extracted emotion features. The proposed system showed 82.25% more improved recall rate in average than previous systems and it will help extract the semantic keyword using morphological analysis.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• v.1 no.1
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• pp.31-40
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• 2020

The rapid development of technologies in unmanned aerial vehicles (UAVs) has led to their use in various areas. UAVs are mainly used for commercial purposes, but their utilization is increasingly important in other areas because their operation cost is less than satellites and aerial imaging. The utilization of UAVs in the environment/ecology area is relatively new. Therefore, identifying the trends of UAV-related spatial information is significant in basic research for UAV utilization. This study quantitatively identified domestic and international research trends related to UAV utilization and analyzed research areas. An attempt was also made to identify upcoming UAV-related topics in the environment/ecology research field using text mining to analyze the bibliographic information of global research literature. Domestic UAV-related studies were classified into seven clusters where basic research on "UAV technology/industry trends" was abundant, and studies on data collection and analysis through UAV remote sensing technology have increased since 2015. Eight clusters were identified for international studies where the most active research area international was "remote sensing technology/data analysis". In addition, Canopy, Classification, Forest, Leaf Area Index, Normalized Difference Vegetation Index, Temperature, Tree, and Atmosphere appeared as the main keywords related to environment and ecology. The appearance frequencies and association strengths were high because the advancement in UAV optical sensor technology and the rapid development of image processing technology enabled the acquisition of data that could not be obtained from existing spatial information. They are recognized as future research topics as related domestic studies have begun corresponding to international research.

• Fire Science and Engineering
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• v.28 no.2
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• pp.91-97
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• 2014

New types of fire accidents appear or the existing types disappeared because of rapidly changing society. We proposed a methodology of extracting new nouns from fire investigation data each of which is an accident report producted by fire investigators. The new nouns could be used in modifying the existing categories for classifying fire accidents. We analysed morphology of the product names and the ignition summaries using the proposed method for the fire accidents classified as the etc sub-category of the category of equipments involved in ignition. In this paper, we found "dryer" as a new sub-category of the agricultural equipment category and "boiler" in the seasonal appliance category from the product names of the fire accidents. We also extracted the new input keywords of "aquarium" and "monitor" in the commercial facilities category and the video, audio apparatus category from the ignition summaries respectively. Using the four subcategories, we reclassified 548 (14.39%) of 3,808 fire accidents assigned to the etc sub-category.

• The Journal of the Convergence on Culture Technology
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• v.6 no.2
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• pp.449-454
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• 2020

This paper proposes a machine learning-based emotion analysis system that detects a user's depression through their SNS posts. We first made a list of keywords related to depression in Korean, then used these to create a training data by crawling Twitter data - 1,297 positive and 1,032 negative tweets in total. Lastly, to identify the best machine learning model for text-based depression detection purposes, we compared RNN, LSTM, and GRU in terms of performance. Our experiment results verified that the GRU model had the accuracy of 92.2%, which is 2~4% higher than other models. We expect that the finding of this paper can be used to prevent depression by analyzing the users' SNS posts.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.5
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• pp.89-95
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• 2020

As the e-learning market expands, interest in customized education for learners based on artificial intelligence is increasing. Customized education for learners requires essential components such as a large amount of data and learning contents for learner analysis, and it requires time and cost efforts to collect such data. In this paper, to enable efficient learner-tailored learning even in small learning groups, unstructured learner data was analyzed using python modules, and a learning algorithm was presented based on this. Through the analysis of the unstructured learning data presented in this paper, it is possible to quantify and measure the unstructured data related to learning, and the accuracy of more than 80% was confirmed when analyzing keywords for providing customized education for learners.

• The Journal of Internal Korean Medicine
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• v.41 no.3
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• pp.350-361
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• 2020

Objective: This study aimed to report the therapeutic effect of acupuncture on chemotherapy-induced peripheral neuropathy (CIPN). Methods: The articles were sourced from databases including PubMed, EMBASE, Cochrane Library, CNKI, CiNii, WHO ICTRP, JSOM, KMBASE, KISS, NDSL, and OASIS as of July 2019. The main search keywords were peripheral neuropathy and acupuncture, and only randomized controlled trials using acupuncture for therapeutic purposes were included. Cochrane's risk of bias was used to assess the risk of bias, and the Review Manager 5.3 program was used for meta-analysis. Results: Six studies with a total 394 participants were included. When combined treatment of acupuncture and usual care was compared with usual care alone, quality of life improved more significantly in the combination treatment group (SMD=-2.71, 95% CI: -5.01 to -0.41, P=0.02, I²=97%). The CIPN pain score was lower among the combination treatment group, but not to a significant degree (SMD=-2.55, 95% CI: -5.14 to 0.04, P<0.05, I²=98%). There were no severe side effects in any studies. Conclusion: Acupuncture combined with usual care may be considered to safely relieve CIPN pain and improve quality of life for cancer patients. However, as there are few randomized controlled trials studying the effect of acupuncture on CIPN, further well-designed research is needed.

• The Journal of Korean Obstetrics and Gynecology
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• v.30 no.3
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• pp.29-39
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• 2017

Objectives: The purpose of this study is to investigate the effectiveness of acupuncture and moxibustion for mastitis. Methods: We used ten databases including Embase, PubMed, and CNKI to investigate the literatures of mastitis using the keywords "mastitis" and "acupuncture", "breast inflammation" and "acupuncture", "mastitis" and "needle", "breast inflammation" and "needle", "mastitis" and "moxibustion". We excluded studies which included treatments that could affect the judgement of the effectiveness of acupuncture treatment, and the control group was limited to antibiotics. The quality of the paper was evaluated by Risk of Bias. A meta-analysis was performed using a "Review manager" to see the effectiveness of acupuncture and moxibustion compared to antibiotics. Results: Only six RCT were finally selected. Five studies use the total effective rate as a evaluating index, and it was significantly higher than that of the control group in two studies. In other three studies, acupuncture showed similar effectiveness to antibiotics. Meta-analysis was performed through three studies, there was no statistically significant difference in total effective rate between acupuncture and antibiotic treatment. Conclusions: Analysis of six RCT showed that acupuncture and moxibustion seem to have many possibilities as one of the treatments for mastitis. However the number of documents is not sufficient, more research should be done to obtain reliable information.

• The Journal of Korean Society for School & Community Health Education
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• v.18 no.2
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• pp.27-42
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• 2017

Objectives: The purpose of this study was to investigate the characteristics of smoking prevention programs and their effectiveness for Korean school-aged children and adolescents by using systematic review and meta-analysis. Methods: Electronic searches were performed in RISS, NAL, DBPia, KISS using keywords according to inclusion criteria. 21 studies published from 2003 to the first half of 2017 that dealt with effects of smoking prevention programs for school-aged children and adolescents were selected for systematic review. Results: All 21 studies were quasi-experimental research designs. More than half of the programs(66.7%) were conducted for male and female. Most of the programs were conducted more than once a week(71.4%). 14 studies(66.7%) did not report using a theoretical model. Five dependent variables(knowledge of smoking, attitude toward smoking, non-smoking intention, self-assertiveness, and self-efficacy) were selected to measure the effectiveness of the smoking prevention programs. Knowledge of smoking was the most effective at hedge's g=0.673. Self-efficacy and self-assertiveness variables were statistically significant at hedge's g=0.461 and hedge's g=0.279, respectively. Effect sizes of attitude toward smoking and non-smoking intention were not statistically significant compared to the control group. As a result of the moderator effect analysis on the knowledge of smoking variable, the statistically significant variables were 'gender of participants'(p<0.01) and 'duration of the program'(p<0.001). Conclusions: The results of this study using systematic review and meta-analysis will be evidence-based data for researchers conducting smoking prevention programs in school-aged children and adolescents.

• Clinical and Experimental Pediatrics
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• v.64 no.7
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• pp.341-346
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• 2021

Background: The results differ among published studies regarding exposure to meconium and the risk of developing autism spectrum disorders (ASDs). Purpose: The present study pooled all of the epidemiologic studies retrieved from broader databases on the association between meconium exposure and risk of developing ASD in children. Methods: The Web of Science, PubMed, Scopus, and Google Scholar databases were searched without language restrictions for articles published between their inception to February 20, 2020, using relevant keywords. The pooled odds ratios (ORs) and their 95% confidence intervals (CIs) were calculated as random-effect estimates of the associations among studies. A subgroup analysis was conducted to explore any potential sources of heterogeneity among studies. Results: The pooled estimate of OR reported a weakly significant association between meconium exposure and ASD development in children (OR, 1.13; 95% CI, 1.03-1.24). There was low heterogeneity among the articles reporting risk for ASD among children (I²=19.3%; P=0.259). The results of subgroup analysis based on meconium exposure showed a significant association between a meconium-stained neonate and ASD development (OR, 1.18; 95% CI, 1.11-1.24). Couclusion: Meconium exposure was weakly associated with an increased risk of ASD. However, more evidence based on large prospective cohort studies is required to provide conclusive evidence about whether meconium exposure is associated with an increased risk of ASD development.