• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.29 no.3C
• /
• pp.115-125
• /
• 2009

The preconsolidation pressure has been commonly determined by oedometer test. However, it can also be determined by insitu test, such as piezocone test with theoretical and(or) empirical correlations. Recently, Neural Network (NN) theory was applied and some models were proposed to estimate the preconsolidation pressure or OCR. It was already found that NN model can come over the site dependency and prediction accuracy is greatly improved when compared with present theoretical and empirical models. However, since the optimization process of synaptic weights of NN model is dependent on the initial synaptic weights, NN models which are trained with different initial weights can't avoid the variability on prediction result for new database even though they have same structure and use same transfer function. In this study, Committee Neural Network (CNN) model is proposed to improve the initial weight dependency of multi-layered neural network model on the prediction of preconsolidation pressure of soft clay from piezocone test result. Prediction results of CNN model are compared with those of conventional empirical and theoretical models and multi-layered neural network model, which has the optimized structure. It was found that even though the NN model has the optimized structure for given training data set, it still has the initial weight dependency, while the proposed CNN model can improve the initial weight dependency of the NN model and provide a consistent and precise inference result than existing NN models.

• Analytical Science and Technology
• /
• v.31 no.5
• /
• pp.208-218
• /
• 2018

Autophagy is a cellular process whereby cytosolic materials or organelles are taken up in a double-membrane vesicle structure known as an autophagosome and transported into a lysosome for degradation. Although autophagy has been studied at the genetic, cellular, or biochemical level, systematic ultrastructural quantitative analysis of autophagosomes during the autophagy process by using transmission electron microscopy (TEM) has not yet been reported. In this study, we performed ultrastructural analysis of autophagosomes in wild-type (WT) mouse embryonic fibroblasts (MEFs) and autophagy essential gene (atg5) knockout (KO) MEFs. First, we performed ultrastructural analysis of autophagosomes in WT MEFs compared to atg5 KO MEFs in basal autophagy or starvation-induced autophagy. Although we observed phagopore, early, late autophagosomes, or autolysosomes in WT MEFs, atg5 KO MEFs had immature autophagosomes that showed incomplete closure. Upon starvation, late autophagosomes accumulated in WT MEFs while the number of immature autophagosomes significantly increased in atg5 KO MEF indicating that atg5 plays an important role in the maturation of autophagosomes. Next, we examined autophagosomes in the cell model expressing polyQ-expanded N-terminal fragment of huntingtin. Our TEM analysis indicates that the number of late autophagosomes was significantly increased in the cells expressing the mutant huntingtin, indicating that improving the fusion of autophagosome with lysosome may be effective to enhance autophagy for the treatment of Huntington's disease. Taken together, the results of our study indicate that ultrastructural and quantitative analysis of autophagosomes using TEM can be applied to various human cellular disease models, and that they will provide an important insight for cellular pathogenesis of human diseases associated with autophagy.

• Journal of The Korean Association For Science Education
• /
• v.20 no.4
• /
• pp.582-598
• /
• 2000

There are many bioethical issues in line with the rapid advance of biology. In this situation, it is important for students to make a rational decision on value problem. In this study 'value inquiry in biology education' is defined as 'the process of rational value judgement and wise decision-making in the biology-related value problem' and the model was developed. To develop the model, value inquiry models were reviewed. Value clarification model is helpful for the formation of the personal value as the process of individual value inquiry, but it isn't helpful for clarifying the value conflicts. Value analysis model focuses on the rational solution of value problem through the logical procedure. But it has the limitations that overemphasizing the logical and systematic aspects results in devaluating students' affective aspects. So it is necessary to coordinate psychological and logical aspects of value inquiry. In this regard, the model was developed, including identifying and clarifying value problem, understanding biological knowledge related to conflict situation, considering on the related persons, searching for alternatives, predicting the consequences of each alternative, selecting the alternative, evaluating the alternative, and final value judgement and affirming it. The educational objectives of value inquiry were selected in consideration of the ability to carry out the steps of the developed model. And the selected contents were animal duplication, test-tube baby, genetic engineering, growth hormone injection problem, brain death, organ transplant, animal to be experimented and were organized on the basis of the 6th and the 7th science curriculum. And the suitable instructional models for the value inquiry education were selected: bioethical value clarification decision-making model, group presentation according to the value analysis model, role play and debate, and discussion through web forum. And the interview was considered to be suitable to evaluate the students' value inquiry ability and the rubric was made to evaluate the attainment of the educational objectives for value inquiry.

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

• Economic and Environmental Geology
• /
• v.50 no.4
• /
• pp.287-302
• /
• 2017

The carbonate rocks of the Daegi Formation are composed of the limestone at the upper and lower zones, and the dolomite at the middle zone, in which the upper zone has higher CaO content than others. The colors of carbonate rock in the Daegi Formation can be divided into five types; white, light brown, light gray, gray, and dark gray. The white to light gray colored rocks correspond to the high purity limestone with 53.15 ~ 55.64 wt. % CaO, and the light brown colored rocks contain 20.71 ~ 21.67 wt. % MgO. The bleaching of carbonate rocks are not related to CaO composition of the rocks, as light gray rocks tend to be higher in CaO content than those of the white rocks at the lower zone. The pelitic components are also occasionally increased in white limestone than light grey one. $Al_2O_3$ is one of the most difficult content to remove during hydrothermal processes, so the interpretation that the limestone is purified together with hydrothemral bleaching, has little merit. The wide range (over 16 ‰) of ${\delta}^{18}O_{SMOW}$, smaller variation (within 2 ‰) of ${\delta}^{13}C_{PDB}$ are apparent in both the upper and lower zones, which indicate the Daegi Formation had been affected overall by hydrothermal fluids. The K-Ar isotopic age of hydrothermal alteration in the GMI limestone mine is $85.1{\pm}1.7Ma$. Gradual change from grey through light grey to white limestone is accompaned by lower oxygen stable isotope values, which is major evidence that the hydrothermal effect is the main process of the bleaching. Although the Daegi Formation has suffered from hydrothermal activity and increase in whiteness, there is no clear evidence demonstrating the relationship between bleaching and high purity of limestone. The purification of limestone has nothing to do with the hydrothermal activity in this area. Instead, it should be considered that the change of sedimentary environment related to see-level fluctuation which can prevent deposition of pelitic components especially $Al_2O_3$ contrbuted to the formation of the high purity limestone in the upper zone of the Daegi Formation. Considering the evidences such as increase in CaO content of limestone by depth, gradual change from calcite to dolomite at the lower zones, and occurring the high purity limestone at the upper zone, the interpretation of sequence stratigraphic aspect to the formation of the high purity Daegi limestone appears to be more suitable than that of hydrothermal alteration origin.

• Journal of KIISE:Software and Applications
• /
• v.30 no.5_6
• /
• pp.444-453
• /
• 2003

Recently, a great interest of bio-technology(BT) is concentrated and the image analysis technique for electrophoresis gels is highly requested to analyze genetic information or to look for some new bio-activation materials. For this purpose, the location and quantity of each band in a lane should be measured. In most of existing techniques, the approach of peak searching in a profile of a lane is used. But this peak is improper as the representative of a band, because its location does not correspond to that of the brightest pixel or the center of gravity. Also, it is improper to measure band quantity in most of these approaches because various enhancement processes are commonly applied to original images to extract peaks easily. In this paper, we adopt an approach to measure accumulated brightness as a band quantity in each band region, which Is extracted by not using any process of changing relative brightness, and the gravity center of the region is calculated as a band location. Actually, we first extract lanes with an entropy-based threshold calculated on a gel-image histogram. And then, three other methods are proposed and applied to extract bands. In the MER method, peaks and valleys are searched on a vertical search line by which each lane is bisected. And the minimum enclosing rectangle of each band is set between successive two valleys. On the other hand, in the RG-1 method, each band is extracted by using region growing with a peak as a seed, separating overlapped neighbor bands. In the RG-2 method, peaks and valleys are searched on two vertical lines by which each lane is trisected, and the left and right peaks nay be paired up if they seem to belong to the same band, and then each band region is grown up with a peak or both peaks if exist. To compare above three methods, we have measured the location and amount of bands. As a result, the average errors in band location of MER, RG-1, and RG-2 were 6%, 3%, and 1%, respectively, when the lane length is normalized to a unit value. And the average errors in band amount were 8%, 5%, and 2%, respectively, when the sum of band amount is normalized to a unit value. In conclusion, RG-2 was shown to be more reliable in the accuracy of measuring the location and amount of bands.

• Journal of Life Science
• /
• v.18 no.10
• /
• pp.1377-1383
• /
• 2008

A bacterium producing non- or partially digestible dextran was isolated from kimchi broth by enrichment culture technique. The bacterium was identified tentatively as Leuconostoc sp. strain SKY. We established the response surface methodology (Box-Behnken design) to optimize the principle parameters such as culture pH, temperature, and yeast extract concentration for maximizing production of dextran. The ranges of parameters were determined based on prior screening works done at our laboratory and accordingly chosen as 5.5, 6.5, and 7.5 for pH, 25, 30, and $35^{\circ}C$ for temperature, and 1, 5, and 9 g/l yeast extract. Initial concentration of sucrose was 100 g/l. The mineral medium consisted of 3.0 g $KH_2PO_4$, 0.01 g $FeSO_4{\cdot}H_2O$, 0.01 g $MnSO_4{\cdot}4H_2O$, 0.2 g $MgSO_4{\cdot}7H_2O$, 0.01 g NaCl, and 0.05 g $CaCO_3$ per 1 liter deionized water. The optimum values of pH and temperature, and yeast extract concentration were obtained at pH (around 7.0), temperature (27 to $28^{\circ}C$), and yeast extract (6 to 7 g/l). The best dextran yield was 60% (dextran/g sucrose). The best dextran productivity was 0.8 g/h-l.

• Asia pacific journal of information systems
• /
• v.24 no.2
• /
• pp.233-253
• /
• 2014

Can the stock market really be predicted? Stock market prediction has attracted much attention from many fields including business, economics, statistics, and mathematics. Early research on stock market prediction was based on random walk theory (RWT) and the efficient market hypothesis (EMH). According to the EMH, stock market are largely driven by new information rather than present and past prices. Since it is unpredictable, stock market will follow a random walk. Even though these theories, Schumaker [2010] asserted that people keep trying to predict the stock market by using artificial intelligence, statistical estimates, and mathematical models. Mathematical approaches include Percolation Methods, Log-Periodic Oscillations and Wavelet Transforms to model future prices. Examples of artificial intelligence approaches that deals with optimization and machine learning are Genetic Algorithms, Support Vector Machines (SVM) and Neural Networks. Statistical approaches typically predicts the future by using past stock market data. Recently, financial engineers have started to predict the stock prices movement pattern by using the SNS data. SNS is the place where peoples opinions and ideas are freely flow and affect others' beliefs on certain things. Through word-of-mouth in SNS, people share product usage experiences, subjective feelings, and commonly accompanying sentiment or mood with others. An increasing number of empirical analyses of sentiment and mood are based on textual collections of public user generated data on the web. The Opinion mining is one domain of the data mining fields extracting public opinions exposed in SNS by utilizing data mining. There have been many studies on the issues of opinion mining from Web sources such as product reviews, forum posts and blogs. In relation to this literatures, we are trying to understand the effects of SNS exposures of firms on stock prices in Korea. Similarly to Bollen et al. [2011], we empirically analyze the impact of SNS exposures on stock return rates. We use Social Metrics by Daum Soft, an SNS big data analysis company in Korea. Social Metrics provides trends and public opinions in Twitter and blogs by using natural language process and analysis tools. It collects the sentences circulated in the Twitter in real time, and breaks down these sentences into the word units and then extracts keywords. In this study, we classify firms' exposures in SNS into two groups: positive and negative. To test the correlation and causation relationship between SNS exposures and stock price returns, we first collect 252 firms' stock prices and KRX100 index in the Korea Stock Exchange (KRX) from May 25, 2012 to September 1, 2012. We also gather the public attitudes (positive, negative) about these firms from Social Metrics over the same period of time. We conduct regression analysis between stock prices and the number of SNS exposures. Having checked the correlation between the two variables, we perform Granger causality test to see the causation direction between the two variables. The research result is that the number of total SNS exposures is positively related with stock market returns. The number of positive mentions of has also positive relationship with stock market returns. Contrarily, the number of negative mentions has negative relationship with stock market returns, but this relationship is statistically not significant. This means that the impact of positive mentions is statistically bigger than the impact of negative mentions. We also investigate whether the impacts are moderated by industry type and firm's size. We find that the SNS exposures impacts are bigger for IT firms than for non-IT firms, and bigger for small sized firms than for large sized firms. The results of Granger causality test shows change of stock price return is caused by SNS exposures, while the causation of the other way round is not significant. Therefore the correlation relationship between SNS exposures and stock prices has uni-direction causality. The more a firm is exposed in SNS, the more is the stock price likely to increase, while stock price changes may not cause more SNS mentions.

• Journal of the korean academy of Pediatric Dentistry
• /
• v.31 no.1
• /
• pp.11-18
• /
• 2004

The eruption of permanent teeth represents the movement in the alveolar bone before appearance in oral cavity, to the occlusal plane after appearance in oral cavity, and additive movement after reaching th the occlusal plane. Tooth eruption is mostly controlled by genetic signals. The eruption stage is divided to preeruptive alveolar stage, alveolar bone stage, mucosal stage according to the process of growth and development. If the disturbance is occured in any stage of eruption, tooth does not erupt. The cause of eruption disturbance are ectopic position of the tooth germ, obstruction of the eruption path and defects in the follicle or PDL. In the treatment of eruption disturbance, surgical procedures are commonly used. There are three kind of surgical procedure ; surgical exposure, surgical repositioning, surgical exposure and traction Surgical exposure is basic procedure. This involves removal of mucosa, bone, lesion that are surrounding the teeth, dental sac when necessary to maintain a patent channel between the crown and the normal eruptive path into the oral cavity. To ensure this patency, many techniques including cementation of a celluloid crown, packing with gutta-percha or zinc oxide-eugenol, or a surgical pack, are used. When surgical exposure is conducted, operators should not expose any part of cervical root cement and not injure periodontium or root of adjunct tooth. After surgical exposure, tooth should be surrounded by keratinized gingiva. There is direct relationship between the extent of development of pathophysiologic aberrations and the intensity of the manipulative injury inflicted on the tooth by surgical treatment, so operator should consider this thing. In these cases, surgical exposure is conducted on Maxillary 1st milars that have a eruption disturbance and improve the eruption disturbance effectively.

• Journal of Radiation Protection and Research
• /
• v.38 no.2
• /
• pp.106-118
• /
• 2013

In this paper, we analyzed the Cognition of irradiated food and its relation with self-efficacy. The most important variables described behaviors based on health choices compared with the choice to choose irradiated food items. According to the survey, 33.1% of respondents said that the reason why irradiated food is considered to be a health risk is because "radiation is dangerous". 27.9% of respondents answered that "eating irradiated food is like eating a radioactive substance", 21.1% said radiated food is comparable to a "genetic variation in food" while 10.1% said "food goes bad during the irradiation process". On this basis, it is reasonable to conclude that respondents have a misunderstanding of irradiated food without reference to the general theory of irradiated knowledge. In this respect, it would be helpful to provide education showing that irradiated food is not related to eating harmful or genetically modified food to help high school students create informed opinions of irradiated food. In terms of relevance with health-specific self-efficacy, experience of acquiring information about irradiated food was marked at r=0.148 (p<0.01), experience of purchasing irradiated food was marked at r=0.077 (p<0.05), experience of eating irradiated food was marked at r=0.113 (p<0.01) while knowledge of irradiated food, attitude towards irradiated food and behavior was marked at r=0.103 (p<0.01), r=0.076 (p<0.05) and r=0.105 (p<0.01) respectively. This shows that self-efficacy is high when one has experience of acquiring information about irradiated food, purchasing or eating irradiated food resulting in a high level of knowledge, attitude and behavior. Education which serves to improve the level of self-efficacy needs to be provided along with an educational program which will increase the public's understanding of irradiated food. It is expected that if this education which increases the level of self-efficacy is provided together with correct information of irradiated food, behavior to choose and eat irradiated food will also improve.