• KIISE Transactions on Computing Practices
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• v.23 no.3
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• pp.183-187
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• 2017

Smart phone users prefer fast reading and texting. Hence, users frequently use abbreviated sequences of words and phrases. Nowadays, abbreviations are widely used from chat terms to technical terms. Therefore, gathering abbreviations would be helpful to many services, including information retrieval, recommendation system, and so on. However, manually gathering abbreviations needs to much effort and cost. This is because new abbreviations are continuously generated whenever a new material such as a TV program or a phenomenon is made. Thus it is required to generate of abbreviations automatically. To generate Korean abbreviations, the existing methods use the rule-based approach. The rule-based approach has limitations, in that it is unable to generate irregular abbreviations. Another problem is to decide the correct abbreviation among candidate abbreviations generated rules. To address the limitations, we propose a method of generating Korean abbreviations automatically using sequence-to-sequence learning in this paper. The sequence-to-sequence learning can generate irregular abbreviation and does not lead to the problem of deciding correct abbreviation among candidate abbreviations. Accordingly, it is suitable for generating Korean abbreviations. To evaluate the proposed method, we use dataset of two type. As experimental results, we prove that our method is effective for irregular abbreviations.

• Journal of the Korea Society of Computer and Information
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• v.25 no.11
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• pp.27-40
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• 2020

In this paper, we investigate the correlation between the amount of English sentences that Korean English learners (L2ers) are exposed to and their sentence processing patterns by examining what Long Short-Term Memory (LSTM) language models (LMs) can learn about implicit syntactic relationship: that is, the filler-gap dependency. The filler-gap dependency refers to a relationship between a (wh-)filler, which is a wh-phrase like 'what' or 'who' overtly in clause-peripheral position, and its gap in clause-internal position, which is an invisible, empty syntactic position to be filled by the (wh-)filler for proper interpretation. Here to implement L2ers' English learning, we build LSTM LMs that in turn learn a subset of the known restrictions on the filler-gap dependency from English sentences in the L2 corpus that L2ers can potentially encounter in their English learning. Examining LSTM LMs' behaviors on controlled sentences designed with the filler-gap dependency, we show the characteristics of L2ers' sentence processing using the information-theoretic metric of surprisal that quantifies violations of the filler-gap dependency or wh-licensing interaction effects. Furthermore, comparing L2ers' LMs with native speakers' LM in light of processing the filler-gap dependency, we not only note that in their sentence processing both L2ers' LM and native speakers' LM can track abstract syntactic structures involved in the filler-gap dependency, but also show using linear mixed-effects regression models that there exist significant differences between them in processing such a dependency.

• Proceedings of the Korea Contents Association Conference
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• 2005.05a
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• pp.36-50
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• 2005

e-Learning has been mushrooming with wide range of learning groups from pedagogy to andragogy. Despite of increasing e-learning opportunities, many people doubt whether e-learning learners really learn something. The related research papers emphasized that e-Learning would be a failure in terms of understanding of e-learners and intuitive learning activities for activating learner's long-term memory span. The current learning strategies in e-Learning may be based on the traditional classroom, and this results in boring and ineffective learning outcomes. This paper analyzed that how learners have received e-Learning for the last few years from the research and explained what could be the failing aspects of e-Learning. To be successful, e-Learning should consider the e-Learner's individualized teaming style and thinking patterns. When considering of various e-Learning components, the quality of e-learning should not be focused on any specific single factor, but develop every individual factor to the high level of quality. In conclusion, this paper suggest that we need new understand of e-Learning and e-Learner. Also the e-Learning strategies should be examined throughly whether they are on the side of learners and realized how they learn from e-Learning. Finally, we should add enormous imagination into e-Learning for next generation because their teaming patterns significantly differ from their parent's generation.

• The Journal of the Korea Contents Association
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• v.5 no.5
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• pp.316-324
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• 2005

e-Learning has been mushrooming with wide range of teaming groups from pedagogy to andragogy As e-teaming opportunities increase, many people raise question about whether e-teaming show positive teaming effects. The related research emphasized that e-learning would be a failure in terms of understanding of e-Learners and activating intuitive teaming activities from learner's long-term memory span. The e-teaming strategies based on the traditional classroom and resulted boring and ineffective teaming outcomes, should be changed to provide authentic and effective teaming results. This paper analyzed that how learners have received e-Learning for the last few years from the research and explained what could be the failing aspects in e-Learning. To be successful, e-loaming should consider the e-learner's individualized teaming style and thinking patterns. When considering of various e-Learning components, the quality of e-teaming should not be focused on any specific single factor, but develop every individual factor to be integrated into high level of quality. In conclusion, this paper suggest that it is needed new understandings of e-Loaming and e-Learner. Also the e-Learning strategies should be examined throughly whether they are on the side of learners and realized how they learn from e-Learning. Finally, we should add enormous imagination into e-loaming for next generation because new generation's teaming patterns significantly differ from their parent's generation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.10
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• pp.8-15
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• 2020

A forecasting method using deep learning does not have consistent results due to the differences in the characteristics of the dataset, even though they have the same forecasting models and parameters. For example, the forecasting model X optimized with dataset A would not produce the optimized result with another dataset B. The forecasting model with the characteristics of the dataset needs to be optimized to increase the accuracy of the forecasting model. Therefore, this paper proposes novel optimization steps for outlier removal, dataset classification, and a CNN-LSTM-based hyperparameter tuning process to forecast the daily power usage of a university campus based on the hourly interval. The proposing model produces high forecasting accuracy with a 2% of MAPE with a single power input variable. The proposing model can be used in EMS to suggest improved strategies to users and consequently to improve the power efficiency.

• Journal of Korea Water Resources Association
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• v.51 no.6
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• pp.503-514
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• 2018

In recent, the hydrological regime of the Mekong river is changing drastically due to climate change and haphazard watershed development including dam construction. Information of hydrologic feature like streamflow of the Mekong river are required for water disaster prevention and sustainable water resources development in the river sharing countries. In this study, runoff simulations at the Kratie station of the lower Mekong river are performed using SWAT (Soil and Water Assessment Tool), a physics-based hydrologic model, and LSTM (Long Short-Term Memory), a data-driven deep learning algorithm. The SWAT model was set up based on globally-available database (topography: HydroSHED, landuse: GLCF-MODIS, soil: FAO-Soil map, rainfall: APHRODITE, etc) and then simulated daily discharge from 2003 to 2007. The LSTM was built using deep learning open-source library TensorFlow and the deep-layer neural networks of the LSTM were trained based merely on daily water level data of 10 upper stations of the Kratie during two periods: 2000~2002 and 2008~2014. Then, LSTM simulated daily discharge for 2003~2007 as in SWAT model. The simulation results show that Nash-Sutcliffe Efficiency (NSE) of each model were calculated at 0.9(SWAT) and 0.99(LSTM), respectively. In order to simply simulate hydrological time series of ungauged large watersheds, data-driven model like the LSTM method is more applicable than the physics-based hydrological model having complexity due to various database pressure because it is able to memorize the preceding time series sequences and reflect them to prediction.

• Korean Journal of Community Nutrition
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• v.6 no.5
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• pp.726-733
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• 2001

This study was designed to investigate the effect of iron supplementation on the iron nutritional status and anemia of high school girls in Korea. One hundred thirty-five female students residing in Ulian metropolitan city in Korea diagnosed as having anemia or iron deficiency participated in this study. One or two tablets of iron medicine(80-160 mg Fe as ferrous sulfate/day) were administered to all participants for 3 months. Subjects were evaluated with a questionaire, measurement of hematological indices before and after iron supplementation. The average height and weight of respondents were 161.62 $\pm$ 4.68 cm and 53.87 $\pm$ 6.10 kg, respectively. Daily intakes of energy were 1597.8 $\pm$ 302.35 kcal(76.0% RDA). Iron intakes were 13.72 $\pm$ 4.17 mg (76.3% of RDA) and calcium intakes were 580.74 $\pm$ 177.21(72.5% of RDA) before iron supp]ementation. At baseline, 63% of all participants had depleted store(serum ferritin 12 ug/ml and/or transferrin saturation(TS) < 14%). After iron supplementation, this proportion declined to 19.3%. 55.6% of subjects had 12 ug/m1 of basal ferritin concentration before iron supplementation, and this proportion declined to 16.3% after iron supplementation. The basal hemoglobin(Hb) concentrations were 12.13 $\pm$ 1.01 g/dl and they increased to 12.79 $\pm$ 0.81 g/dl, which showed significant difference artier iron supplementation(p < 0.001). The basal ferritin and TS(%) were 13.24 $\pm$ 11.66 ng/ml, 18.42 $\pm$ 10.12% and they significantly increased to 32.95 $\pm$ 21.14 ng/ml, 33.53 $\pm$ 16.64%, respectively(p < 0.001). The basal total iron binding protein(TIBC) were 467.81 $\pm$ 97.24 ug/dl and they significantly decreased to 325.05 $\pm$ 48.89 ug/dl(p < 0.001) after iron supplementation. The number of tablets administered was positively correlated with serum iron(t = 0.553, p < 0.01), serum ferritin(t = 0.557, p < 0.01), TS(%)(t = 0.588, p < 0.01) and negatively correlated with TIBC(t= -0.409, p <0.01). The anemia symptoms such as ‘Shortening of breath when going upstairs(p < 0.01)’, ‘Tired out easily(p < 0.01)’, ‘Feeling blue(p < 0.001)’, ‘Decreased ability to concentrate(p < 0.01)’, and ‘Poor memory(p < 0.001)’improved significantly after iron supplementation. In this study, daily iron supplementations were efficacious in improving the iron status and anemic symptoms of female high school students. Regular check-ups and nutrition education for adolescents are necessary because of their vulnerability to iron deficiency. Further studies are needed to determine the minimum effective dose of iron and to examine the adverse effect of long-term iron supplementation.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.4
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• pp.153-163
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• 2017

This research is based on the previous research that personally preferred safe distance, rotating angle and speed are differentiated. Thus, we use machine learning model for recognizing maneuvering modes trained per personal or per similar driving pattern groups, and we evaluate automatic driving according to maneuvering modes. By utilizing driving knowledge, we subdivided 8 kinds of longitudinal modes and 4 kinds of lateral modes, and by combining the longitudinal and lateral modes, we build 21 kinds of maneuvering modes. we train the labeled data set per time stamp through RNN, LSTM and Bi-LSTM models by the trips of drivers, which are supervised deep learning models, and evaluate the maneuvering modes of automatic driving for the test data set. The evaluation dataset is aggregated of living trips of 3,000 populations by VTTI in USA for 3 years and we use 1500 trips of 22 people and training, validation and test dataset ratio is 80%, 10% and 10%, respectively. For recognizing longitudinal 8 kinds of maneuvering modes, RNN achieves better accuracy compared to LSTM, Bi-LSTM. However, Bi-LSTM improves the accuracy in recognizing 21 kinds of longitudinal and lateral maneuvering modes in comparison with RNN and LSTM as 1.54% and 0.47%, respectively.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.310-318
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• 2018

Recently, the importance of big data analysis is increasing as a large amount of data is generated by various devices connected to the Internet with the advent of Internet of Things (IoT). Especially, it is necessary to analyze various large-scale IoT streaming sensor data generated in real time and provide various services through new meaningful prediction. This paper proposes a real-time indoor PM10 concentration prediction LSTM model based on streaming data generated from IoT sensor using AWS. We also construct a real-time indoor PM10 concentration prediction service based on the proposed model. Data used in the paper is streaming data collected from the PM10 IoT sensor for 24 hours. This time series data is converted into sequence data consisting of 30 consecutive values from time series data for use as input data of LSTM. The LSTM model is learned through a sliding window process of moving to the immediately adjacent dataset. In order to improve the performance of the model, incremental learning method is applied to the streaming data collected every 24 hours. The linear regression and recurrent neural networks (RNN) models are compared to evaluate the performance of LSTM model. Experimental results show that the proposed LSTM prediction model has 700% improvement over linear regression and 140% improvement over RNN model for its performance level.

• The Journal of the Korea Contents Association
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• v.19 no.9
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• pp.308-320
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• 2019

This study aims at investigating the major symptoms that help family carers detect mental illness in elderly patients. Another purpose of this study is to empirically verify the major factors determining the utilization of mental health services with a focus on family carers. The results of this study are as follows. First, the most commonly detected symptoms that caused the family carers to suspect mental illness in the elderly patients were memory decline and other forms of cognitive function decline. Second, the determinants of the elderly's utilization of mental health services included the patient's long-term care insurance level, the age of the family carer, the period of care, the level stress associated with the provision of care felt by the carer, his understanding of geriatric mental illness, and the level of perception about community mental health services. Based on these findings, this study suggests policies and practical implications for the early detection of and response to elderly mental health problems and the utilization of related services from the viewpoint of the family carers of the elderly.