• Journal of the Korean Society of Food Science and Nutrition
• /
• v.39 no.3
• /
• pp.376-382
• /
• 2010

It has been shown that BF-7 enhances short- and long-term memory and attention in normal person. BF-7 was addressed to clinical study for children if BF-7 is also effective to children, since accumulated verification of safety and effectiveness is needed for young ages, in special. We administered BF-7 and a placebo control to two different groups of children (7-12 years old, 9.78 on averages). Their memory enhancement was tested with Rey-Kim Memory Test for Children before and after the administration of BF-7 and a placebo, in a double blinded way. The results showed that long- and short-term memories were significantly improved by the administration of BF-7. Interestingly, the degree of memory preservation, the ability of memory application and awareness of complex thing were also significantly improved. These results indicate that BF-7 is a promising substance from natural resource improving learning and memory of children as well as cognitive function of adults

• Proceedings of the Korean Society of Toxicology Conference
• /
• 2006.11a
• /
• pp.95-101
• /
• 2006

Compare to the toxicity tests using experimental animals, the GLP application and compliance in toxicity studies using cell culture systems may be less straightforward elucidated in the two documents published by the OECD Working Croup on GLP 'The Application of the GLP Principles to Short Term Studies (1999)' and 'The Application of the Principles of GLP to in vitro Studies (2004)' The object of this presentation is to show how to interpret the GLP principles and to apply with actual performances in a well known toxicity test using cell culture, chromosome aberration study. The presentation will cover test substance, test system (cell line), study environment management, documentation, quality assurance, and study protocol and report.

• 한국신재생에너지학회:학술대회논문집
• /
• 2010.11a
• /
• pp.90.1-90.1
• /
• 2010

The design and fabrication of a metallic bipolar plate-gasket assembly for polymer electrolyte fuel cells (PEMFCs) is defined. This bipolar plate-gasket assembly was prepared by inserting a previously prepared bipolar plate in the specially designed gasket mold. For this aim, a proprietary fluoro-silicone based rubber was injected directly into the bipolar plate borders. Gaskets obtained like this showed the chemically / physically stable and the good sealibilty in typically operating PEM fuel cell conditions. And also, this bipolar plate-gasket assembly shows lots of advantages with respect to traditional PEMFCs stack assembling systems: useful application to automative stacking due to easy handling, reduced fabrication time, possibility of quality control and failed elements substitution. This bipolar plate-gasket assembly was evaluated in the short fuel cell stack and met the leakage requirement for normal operation both in short-term and in long-term operation. Especially, it was confirmed that this gasket could be applied successfully even in the high pressure FEM fuel cell systems(over 2.0 bar in absolute pressure).

• Asia pacific journal of information systems
• /
• v.7 no.1
• /
• pp.67-83
• /
• 1997

The utility of learning techniques in investment analysis has been demonstrated in many areas, ranging from forecasting individual stocks to entire market indexes. To date, however, the application of artificial intelligence to financial forecasting has focused largely on short predictive horizons. Usually the forecast window is a single period ahead; if the input data involve daily observations, the forecast is for one day ahead; if monthly observations, then a month ahead; and so on. Thus far little work has been conducted on the efficacy of long-term prediction involving multiperiod forecasting. This paper examines the impact of alternative procedures for extended prediction using knowledge discovery techniques. One dimension in the study involves temporal granularity: a single jump from the present period to the end of the forecast window versus a web of short-term forecasts involving a sequence of single-period predictions. Another parameter relates to the numerosity of input variables: a technical approach involving only lagged observations of the target variable versus a fundamental approach involving multiple variables. The dual possibilities along each of the granularity and numerosity dimensions entail a total of 4 models. These models are first evaluated using neural networks, then compared against a multi-input jump model using case based reasoning. The computational models are examined in the context of forecasting the S&P 500 index.

• Journal of Korean Society of Water and Wastewater
• /
• v.28 no.4
• /
• pp.377-389
• /
• 2014

In recent years, Smart Water Grid (SWG) concept has globally emerged over the last decade and also gained significant recognition in South Korea. Especially, there has been growing interest in water demand forecast and this has led to various studies regarding energy saving and improvement of water supply reliability. In this regard, this study aims to develop a nonlinear ensemble model for hourly water demand forecasting which allow us to estimate uncertainties across different model classes. The concepts was demonstrated through application to observed from water plant (A) in the South Korea. Various statistics (e.g. the efficiency coefficient, the correlation coefficient, the root mean square error, and a maximum error rate) were evaluated to investigate model efficiency. The ensemble based model with an cross-validate prediction procedure showed better predictability for water demand forecasting at different temporal resolutions. In particular, the performance of the ensemble model on hourly water demand data showed promising results against other individual prediction schemes.

• Proceedings of the Korea Contents Association Conference
• /
• 2015.05a
• /
• pp.15-16
• /
• 2015

Measuring sensor, equipment, ICT facilities and their software have relatively short life time comparing to constructional structure so that we should exchange or fix them continuously in the process of maintenance and management. In this paper, we propose a novel design of integrated maintenance, management, and measuring monitoring system applying the concept of mobile cloud. For the sake of disaster prevention for constructional structures such as bridge, tunnel, and other traditional buildings in the village of local heritage, we analyze status of these structures in the long term or short term period as well as disaster situations. Collecting data based on mobile cloud and analyzing future expectations based on probabilistic and statistical techniques, we implement our integrated monitoring system for constructional structures to solve these existing problems. Final results of this design and implementation are basically applied to the monitoring system for more than 10,000 structures spread over national land in Korea. In addition, we can specifically apply the monitoring system presented here to a bridge of timber structure in Asan Oeam Village and a traditional house in Andong Hahoe Village to watch them from possible disasters. Total procedure of system design and implementation as well as development of the platform LinkSaaS and application services of monitoring functions implemented on the platform. We prove a good performance of our system by fulfilling TTA authentication test, web accommodation test, and operation test using real measuring data.

• Journal of Environmental Science International
• /
• v.15 no.9
• /
• pp.865-870
• /
• 2006

Recently, Total Organic Carbon (TOC) which can be measured instantly can be used as an organic pollutant index instead of BOD or COD due to the diversity of pollutants and non-degradable problem. The primary purpose of the present study is to reveal the properties of time series data for TOC which have been measured by real-time monitoring in Juam Lake and, in particularly, to understand the long- and short-term characteristics with the extraction of the respective components based on the different return periods. For the purpose, we proposed Discrete Wavelet Transform (DWT) as the methodology. The results from the DWT showed that the different components according to the respective periodicities could be extracted from the time series data for TOC and the variation of each component with respect to time could emerge from the return periods and the respective energy ratios of the decomposed components against the raw data.

• Korean Journal of Remote Sensing
• /
• v.37 no.4
• /
• pp.719-731
• /
• 2021

This study proposes a two-stage hybrid classification model for crop classification using multi-temporal remote sensing images; the model combines feature embedding by using an autoencoder (AE) with a convolutional neural network (CNN) classifier to fully utilize features including informative temporal and spatial signatures. Long short-term memory (LSTM)-based AE (LAE) is fine-tuned using class label information to extract latent features that contain less noise and useful temporal signatures. The CNN classifier is then applied to effectively account for the spatial characteristics of the extracted latent features. A crop classification experiment with multi-temporal unmanned aerial vehicle images is conducted to illustrate the potential application of the proposed hybrid model. The classification performance of the proposed model is compared with various combinations of conventional deep learning models (CNN, LSTM, and convolutional LSTM) and different inputs (original multi-temporal images and features from stacked AE). From the crop classification experiment, the best classification accuracy was achieved by the proposed model that utilized the latent features by fine-tuned LAE as input for the CNN classifier. The latent features that contain useful temporal signatures and are less noisy could increase the class separability between crops with similar spectral signatures, thereby leading to superior classification accuracy. The experimental results demonstrate the importance of effective feature extraction and the potential of the proposed classification model for crop classification using multi-temporal remote sensing images.

• Molecular & Cellular Toxicology
• /
• v.4 no.1
• /
• pp.66-71
• /
• 2008

Long term exposure of Jurkat cells to 2 ATA pressure resulted in the inhibition of cell growth. Under a 2 ATA pressure, the morphological changes in the cells were visualized by electron microscopy. The cells exhibited significant inhibitory responses after three passages. However, short-term exposure study was carried out, 2 ATA pressure may have beneficial effects. The Jurkat cells were exposed to $H_2O_2$ (25 and $50{\mu}M$) in order to induce DNA damage, and then incubated under at either normal pressure or 2 ATA for 1 or 2 hours in order to recover the DNA damage. The extent of DNA damage was determined via Comet assay. More recovery from DNA damage was observed at 2 ATA than at normal pressure. The activity of the DNA repair enzymes, DNA polymerase-$\beta$, was also evaluated at both normal pressure and 2 ATA. The activity of DNA polymerase-$\beta$ was observed to have increased significantly at the 2 ATA than at normal pressure. In conclusion, the effects of hyperbaric pressure from 1 ATA to 2 ATA on biochemical systems can be either beneficial or harmful. Long term exposure to hyperbaric pressure clearly inhibited cell proliferation and caused genotoxic effects, but short-term exposure to hyperbaric pressure proved to be beneficial in terms of bolstering the DNA repair system. The results of the present study have clinical therapeutic application, and might prove to be an useful tool in the study of genotoxicity in the future.

• The Journal of Society for e-Business Studies
• /
• v.22 no.4
• /
• pp.1-19
• /
• 2017

Wikipedia is one of the world's most visited sites for content collaboration. Its success is due to thousands of volunteers' motivation and commitment to contribute their knowledge to Wikipedia. In this paper, we use the Cox regression model to assess the effect of self-loop editing on the promotion of Wikipedia featured articles. We collected 2978 Wikipedia featured article editing history from start of Wikipedia until 2011. We use self-loops as a proxy measure for Wikipedia editors' commitment, and find that self-loop editing has a positive effect on the promotion of featured articles. We further distinguish the self-loop into a short-term self-loop and a long-term self-loop. We find that long-term self-loop editing is more helpful than short-term self-loop editing. This research has been conducted with both theoretical and practical application methods.