• 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.

• Journal of KIISE:Computing Practices and Letters
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• v.7 no.5
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• pp.402-415
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• 2001

Advantages of flash memories are their shock resistance and fast read speed, which is much faster than that of a HDD. Because of these characteristics, they are increasingly used in the traditional household electric appliance and portable handset and therefore, development of file systems which use them as storage medium is increasingly needed. But they have two problems as storage medium. First, data stored in them cannot be overwritten: it must be erased before new data can be stored. Unfortunately, this erase operation usually takes about one second. Consequently, updating data in flash memories takes long time. In this paper, their problem is solved by using a data update mechanism like LFS(Log-structured File System). Second, their erase operations are restricted. We propose novel cleaning policy in order to increase the life cycle. We implemented FAT file system, which is suitable to small storage medium and solved problems, which usually happen in implementing FAT. We evaluated the performance of sequential writes and random writes on our implemented flash file system.

• Journal of Korea Port Economic Association
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• v.27 no.3
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• pp.41-64
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• 2011

This article investigates the interrelationships in daily returns using fractionally integrated error correction term and volatilities using constant conditional correlation and dynamic conditional correlation GARCH with asymmetries between Capesize and Panamax markets. Our findings are as follows. First, for the fractionally cointegrated error correction model, there is a unidirectional relationship in returns from the Panamax market to the Capesize market, but a bidirectional causal relationship prevails for the traditional error correction models. Second, the coefficients for the error correction term are all statistically significant. Of particular interest are the signs of the estimates for the error correction term, which are all negative for the Capesize return equation and all positive for the Panamax return. Third, there are bidirectional volatility spillovers between both markets and the direction of the information flow seems to be stronger from Panamax to Capesize. Fourth, the coefficients for the asymmetric term are all significantly positive in the Capesize market, but the Panamax market does not have a significant effect. However, the coefficients for the asymmetric term are all significant, implying that the leverage effect does exist in the Capesize and Panamax markets.

• Journal of Ginseng Research
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• v.42 no.4
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• pp.401-411
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• 2018

Longevity in medicine can be defined as a long life without mental or physical deficits. This can be prevented by Alzheimer's disease (AD). Current conventional AD treatments only alleviate the symptoms without reversing AD progression. Recent studies demonstrated that Panax ginseng extract improves AD symptoms in patients with AD, and the two main components of ginseng might contribute to AD amelioration. Ginsenosides show various AD-related neuroprotective effects. Gintonin is a newly identified ginseng constituent that contains lysophosphatidic acids and attenuates AD-related brain neuropathies. Ginsenosides decrease amyloid ${\beta}$-protein ($A{\beta}$) formation by inhibiting ${\beta}$- and ${\gamma}$-secretase activity or by activating the nonamyloidogenic pathway, inhibit acetylcholinesterase activity and $A{\beta}$-induced neurotoxicity, and decrease $A{\beta}$-induced production of reactive oxygen species and neuro-inflammatory reactions. Oral administration of ginsenosides increases the expression levels of enzymes involved in acetylcholine synthesis in the brain and alleviates $A{\beta}$-induced cholinergic deficits in AD models. Similarly, gintonin inhibits $A{\beta}$-induced neurotoxicity and activates the nonamyloidogenic pathway to reduce $A{\beta}$ formation and to increase acetylcholine and choline acetyltransferase expression in the brain through lysophosphatidic acid receptors. Oral administration of gintonin attenuates brain amyloid plaque deposits, boosting hippocampal cholinergic systems and neurogenesis, thereby ameliorating learning and memory impairments. It also improves cognitive functions in patients with AD. Ginsenosides and gintonin attenuate AD-related neuropathology through multiple routes. This review focuses research demonstrating that ginseng constituents could be a candidate as an adjuvant for AD treatment. However, clinical investigations including efficacy and tolerability analyses may be necessary for the clinical acceptance of ginseng components in combination with conventional AD drugs.

• 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 Society of Computer and Information
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• v.15 no.8
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• pp.13-21
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• 2010

One of the most distinguished features of the DHP association rules mining algorithm is that it counts the support of hash key combinations composed of k items at phase k-1, and uses the counted support for pruning candidate large itemsets to improve performance. At this time, it is desirable for each hash key combination to have a separate count variable, where it is impossible to allocate the variables owing to memory shortage. So, the algorithm uses a direct hashing mechanism in which several hash key combinations conflict and are counted in a same hash bucket. But the direct hashing mechanism is not efficient because the distribution of hash key combinations is unvalanced by the characteristics sourced from the mining process. This paper proposes a mapped perfect hashing function which maps the region of hash key combinations into a continuous integer space for phase 3 and maximizes the efficiency of direct hashing mechanism. The results of a performance test experimented on 42 test data sets shows that the average performance improvement of the proposed hashing mechanism is 7.3% compared to the existing method, and the highest performance improvement is 16.9%. Also, it shows that the proposed method is more efficient in case the length of transactions or large itemsets are long or the number of total items is large.

• Journal of Korea Water Resources Association
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• v.47 no.11
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• pp.1051-1060
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• 2014

This study is attempted to realize an improved computation performance by combining the MPI (Message Passing Interface) Technique, a standard model of the parallel programming in the distributed memory environment, with the DHM(Diffusion Hydrodynamic Model), a inundation analysis model. With parallelizing inundation model, it compared with the existing calculation method about the results of applications to complicate and required long computing time problems. In addition, it attempted to prove the capability to estimate inundation extent, depth and speed-up computing time due to the flooding in protected lowlands and to validate the applicability of the parallel model to the actual flooding analysis by simulating based on various inundation scenarios. To verify the model developed in this study, it was applied to a hypothetical two-dimensional protected land and a real flooding case, and then actually verified the applicability of this model. As a result of this application, this model shows that the improvement effectiveness of calculation time is better up to the maximum of about 41% to 48% in using multi cores than a single core based on the same accuracy. The flood analysis model using the parallel technique in this study can be used for calculating flooding water depth, flooding areas, propagation speed of flooding waves, etc. with a shorter runtime with applying multi cores, and is expected to be actually used for promptly predicting real time flood forecasting and for drawing flood risk maps etc.