• Journal of the Korean Applied Science and Technology
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• v.34 no.4
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• pp.874-882
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• 2017

As the interest on the air-pollution is gradually rising up at home and abroad, automotiv e and fuel researchers have been working on the exhaust emission reduction from vehicles through a lot of approaches, which consist of new engine design, innovative after-treatment systems, using clean (eco-friendly alternative) fuels and fuel quality improvement. This research has brought forward three main issues : evaporative, performance, air pollution. In addition, researcher studied the environment problems of the bio-ethanol, bio-butanol, bio-ETBE (Ethyl Tertiary Butyl Ether), MTBE (Methyl Tert iary Butyl Ether) fuel contained in the fuel as octane number improver. The researchers have many dat a about the health effects of ingestion of octane number improver. However, the data support the con clusion that octane number improver is a potential human carcinogen at high doses. Based on the bio-fuel and octane number improver types (bio-ethanol, bio-butanol, bio-ETBE, MTBE), this paper dis cussed the influence of gasoline fuel properties on the evaporative emission characteristics. Also, this p aper assessed the acceleration and power performance of gasoline vehicle for the bio-fuel property. As a result of the experiment, it was found that all the test fuels meet the domestic exhaust gas standards, and as a result of measurement of the vapor pressure of the test fuels, the bio - ethanol : 15 kPa and the biobutanol : 1.6 kPa. thus when manufacturing E3 fuel, Increasing the biobutanol content reduces evaporation gas and vapor pressure. In addition, Similar accelerating and powering performance was shown for the type of biofuel and when bio-butanol and bio-ethanol were compared accelerated perf ormance was improved by about 3.9% and vehicle power by 0.8%.

• Journal of Distribution Science
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• v.7 no.4
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• pp.47-56
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• 2009

The purpose of this study is to present an alternative improving the efficient and reasonable of the physical distribution system management is influenced by many factors. Therefore, the study depends on the documentary method and survey method to achieve the purpose of this study. The major components of a physical distribution system are refers to as elements, include warehouse·storage system, transportation system, inventory system, physical distribution information system. The factors used in this study are ① factor of product(quality·A/S·added value of product·adaption of product·technical competitive power to other enterprises), ② factor of market(market channel·kinds of customer·physical distribution share), ③ factor of warehouse·storage(warehouse design·size·direction·storage ability·warehouse quality), ④ factor of transportation(promptness·reliability·responsibility·kinds of transportation·cooperation united transportation system·national transportation network), ⑤ factor of packaging (packaging design·material·educating program·pollution degree measure program), ⑥ factor of inventory(ordinary inventory criterion·consistence for inventories record), ⑦ factor of unloaded(unloaded machine·having machine ratio), ⑧ factor of information system (physical distribution quantity analysis·usable computer part), ⑨ factor of physical distribution cost(sales ratio to product) ⑩ factor of physical distribution system(physical distribution center etc). The implication of this study can be summarized as follows: ① In firms that have not adopted a systems integrative approach, physical distribution is a fragmented and often uncoordinated set of activities spread throughout various functions with function having its own set of priorities and measurements. ② The physical distribution is recognized as more an important strategic factor than a simple cost reduction factor, ③ It can be used a strategic competition tool to enterprise.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.81-96
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• 2016

As the internet has become widespread and easy to access everywhere, it is common for people to search information via online search engines such as Google and Naver in everyday life. Recent studies have used online search volume of specific keyword as a measure of the internet users' attention in order to predict disease outbreaks such as flu and cancer, an unemployment rate, and an index of a nation's economic condition, and etc. For stock traders, web search is also one of major information resources to obtain data about individual stock items. Therefore, search volume of a stock item can reflect the amount of investors' attention on it. The investor attention has been regarded as a crucial factor influencing on stock price but it has been measured by indirect proxies such as market capitalization, trading volume, advertising expense, and etc. It has been theoretically and empirically proved that an increase of investors' attention on a stock item brings temporary increase of the stock price and the price recovers in the long run. Recent development of internet environment enables to measure the investor attention directly by the internet search volume of individual stock item, which has been used to show the attention-induced price pressure. Previous studies focus mainly on Dow Jones and NASDAQ market in the United States. In this paper, we investigate the relationship between the individual investors' attention measured by the internet search volumes and stock price changes of individual stock items in the KOSDAQ market in Korea, where the proportion of the trades by individual investors are about 90% of the total. In addition, we examine the difference between industries in the influence of investors' attention on stock return. The internet search volume of stocks were gathered from "Naver Trend" service weekly between January 2007 and June 2015. The regression model with the error term with AR(1) covariance structure is used to analyze the data since the weekly prices in a stock item are systematically correlated. The market capitalization, trading volume, the increment of trading volume, and the month in which each trade occurs are included in the model as control variables. The fitted model shows that an abnormal increase of search volume of a stock item has a positive influence on the stock return and the amount of the influence varies among the industry. The stock items in IT software, construction, and distribution industries have shown to be more influenced by the abnormally large internet search volume than the average across the industries. On the other hand, the stock items in IT hardware, manufacturing, entertainment, finance, and communication industries are less influenced by the abnormal search volume than the average. In order to verify price pressure caused by investors' attention in KOSDAQ, the stock return of the current week is modelled using the abnormal search volume observed one to four weeks ahead. On average, the abnormally large increment of the search volume increased the stock return of the current week and one week later, and it decreased the stock return in two and three weeks later. There is no significant relationship with the stock return after 4 weeks. This relationship differs among the industries. An abnormal search volume brings particularly severe price reversal on the stocks in the IT software industry, which are often to be targets of irrational investments by individual investors. An abnormal search volume caused less severe price reversal on the stocks in the manufacturing and IT hardware industries than on average across the industries. The price reversal was not observed in the communication, finance, entertainment, and transportation industries, which are known to be influenced largely by macro-economic factors such as oil price and currency exchange rate. The result of this study can be utilized to construct an intelligent trading system based on the big data gathered from web search engines, social network services, and internet communities. Particularly, the difference of price reversal effect between industries may provide useful information to make a portfolio and build an investment strategy.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.127-142
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• 2016

Deep learning model is a kind of neural networks that allows multiple hidden layers. There are various deep learning architectures such as convolutional neural networks, deep belief networks and recurrent neural networks. Those have been applied to fields like computer vision, automatic speech recognition, natural language processing, audio recognition and bioinformatics where they have been shown to produce state-of-the-art results on various tasks. Among those architectures, convolutional neural networks and recurrent neural networks are classified as the supervised learning model. And in recent years, those supervised learning models have gained more popularity than unsupervised learning models such as deep belief networks, because supervised learning models have shown fashionable applications in such fields mentioned above. Deep learning models can be trained with backpropagation algorithm. Backpropagation is an abbreviation for "backward propagation of errors" and a common method of training artificial neural networks used in conjunction with an optimization method such as gradient descent. The method calculates the gradient of an error function with respect to all the weights in the network. The gradient is fed to the optimization method which in turn uses it to update the weights, in an attempt to minimize the error function. Convolutional neural networks use a special architecture which is particularly well-adapted to classify images. Using this architecture makes convolutional networks fast to train. This, in turn, helps us train deep, muti-layer networks, which are very good at classifying images. These days, deep convolutional networks are used in most neural networks for image recognition. Convolutional neural networks use three basic ideas: local receptive fields, shared weights, and pooling. By local receptive fields, we mean that each neuron in the first(or any) hidden layer will be connected to a small region of the input(or previous layer's) neurons. Shared weights mean that we're going to use the same weights and bias for each of the local receptive field. This means that all the neurons in the hidden layer detect exactly the same feature, just at different locations in the input image. In addition to the convolutional layers just described, convolutional neural networks also contain pooling layers. Pooling layers are usually used immediately after convolutional layers. What the pooling layers do is to simplify the information in the output from the convolutional layer. Recent convolutional network architectures have 10 to 20 hidden layers and billions of connections between units. Training deep learning networks has taken weeks several years ago, but thanks to progress in GPU and algorithm enhancement, training time has reduced to several hours. Neural networks with time-varying behavior are known as recurrent neural networks or RNNs. A recurrent neural network is a class of artificial neural network where connections between units form a directed cycle. This creates an internal state of the network which allows it to exhibit dynamic temporal behavior. Unlike feedforward neural networks, RNNs can use their internal memory to process arbitrary sequences of inputs. Early RNN models turned out to be very difficult to train, harder even than deep feedforward networks. The reason is the unstable gradient problem such as vanishing gradient and exploding gradient. The gradient can get smaller and smaller as it is propagated back through layers. This makes learning in early layers extremely slow. The problem actually gets worse in RNNs, since gradients aren't just propagated backward through layers, they're propagated backward through time. If the network runs for a long time, that can make the gradient extremely unstable and hard to learn from. It has been possible to incorporate an idea known as long short-term memory units (LSTMs) into RNNs. LSTMs make it much easier to get good results when training RNNs, and many recent papers make use of LSTMs or related ideas.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.33-56
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• 2016

Many researchers have focused on developing bankruptcy prediction models using modeling techniques, such as statistical methods including multiple discriminant analysis (MDA) and logit analysis or artificial intelligence techniques containing artificial neural networks (ANN), decision trees, and support vector machines (SVM), to secure enhanced performance. Most of the bankruptcy prediction models in academic studies have used financial ratios as main input variables. The bankruptcy of firms is associated with firm's financial states and the external economic situation. However, the inclusion of qualitative information, such as the economic atmosphere, has not been actively discussed despite the fact that exploiting only financial ratios has some drawbacks. Accounting information, such as financial ratios, is based on past data, and it is usually determined one year before bankruptcy. Thus, a time lag exists between the point of closing financial statements and the point of credit evaluation. In addition, financial ratios do not contain environmental factors, such as external economic situations. Therefore, using only financial ratios may be insufficient in constructing a bankruptcy prediction model, because they essentially reflect past corporate internal accounting information while neglecting recent information. Thus, qualitative information must be added to the conventional bankruptcy prediction model to supplement accounting information. Due to the lack of an analytic mechanism for obtaining and processing qualitative information from various information sources, previous studies have only used qualitative information. However, recently, big data analytics, such as text mining techniques, have been drawing much attention in academia and industry, with an increasing amount of unstructured text data available on the web. A few previous studies have sought to adopt big data analytics in business prediction modeling. Nevertheless, the use of qualitative information on the web for business prediction modeling is still deemed to be in the primary stage, restricted to limited applications, such as stock prediction and movie revenue prediction applications. Thus, it is necessary to apply big data analytics techniques, such as text mining, to various business prediction problems, including credit risk evaluation. Analytic methods are required for processing qualitative information represented in unstructured text form due to the complexity of managing and processing unstructured text data. This study proposes a bankruptcy prediction model for Korean small- and medium-sized construction firms using both quantitative information, such as financial ratios, and qualitative information acquired from economic news articles. The performance of the proposed method depends on how well information types are transformed from qualitative into quantitative information that is suitable for incorporating into the bankruptcy prediction model. We employ big data analytics techniques, especially text mining, as a mechanism for processing qualitative information. The sentiment index is provided at the industry level by extracting from a large amount of text data to quantify the external economic atmosphere represented in the media. The proposed method involves keyword-based sentiment analysis using a domain-specific sentiment lexicon to extract sentiment from economic news articles. The generated sentiment lexicon is designed to represent sentiment for the construction business by considering the relationship between the occurring term and the actual situation with respect to the economic condition of the industry rather than the inherent semantics of the term. The experimental results proved that incorporating qualitative information based on big data analytics into the traditional bankruptcy prediction model based on accounting information is effective for enhancing the predictive performance. The sentiment variable extracted from economic news articles had an impact on corporate bankruptcy. In particular, a negative sentiment variable improved the accuracy of corporate bankruptcy prediction because the corporate bankruptcy of construction firms is sensitive to poor economic conditions. The bankruptcy prediction model using qualitative information based on big data analytics contributes to the field, in that it reflects not only relatively recent information but also environmental factors, such as external economic conditions.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.97-113
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• 2016

Due to the growth of internet data and the rapid development of internet technology, "big data" analysis is actively conducted to analyze enormous data for various purposes. Especially in recent years, a number of studies have been performed on the applications of text mining techniques in order to overcome the limitations of existing structured data analysis. Various studies on sentiment analysis, the part of text mining techniques, are actively studied to score opinions based on the distribution of polarity of words in documents. Usually, the sentiment analysis uses sentiment dictionary contains positivity and negativity of vocabularies. As a part of such studies, this study tries to construct sentiment dictionary which is customized to specific data domain. Using a common sentiment dictionary for sentiment analysis without considering data domain characteristic cannot reflect contextual expression only used in the specific data domain. So, we can expect using a modified sentiment dictionary customized to data domain can lead the improvement of sentiment analysis efficiency. Therefore, this study aims to suggest a way to construct customized dictionary to reflect characteristics of data domain. Especially, in this study, movie review data are divided by genre and construct genre-customized dictionaries. The performance of customized dictionary in sentiment analysis is compared with a common sentiment dictionary. In this study, IMDb data are chosen as the subject of analysis, and movie reviews are categorized by genre. Six genres in IMDb, 'action', 'animation', 'comedy', 'drama', 'horror', and 'sci-fi' are selected. Five highest ranking movies and five lowest ranking movies per genre are selected as training data set and two years' movie data from 2012 September 2012 to June 2014 are collected as test data set. Using SO-PMI (Semantic Orientation from Point-wise Mutual Information) technique, we build customized sentiment dictionary per genre and compare prediction accuracy on review rating. As a result of the analysis, the prediction using customized dictionaries improves prediction accuracy. The performance improvement is 2.82% in overall and is statistical significant. Especially, the customized dictionary on 'sci-fi' leads the highest accuracy improvement among six genres. Even though this study shows the usefulness of customized dictionaries in sentiment analysis, further studies are required to generalize the results. In this study, we only consider adjectives as additional terms in customized sentiment dictionary. Other part of text such as verb and adverb can be considered to improve sentiment analysis performance. Also, we need to apply customized sentiment dictionary to other domain such as product reviews.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.111-123
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• 2013

Semantic similarity/relatedness measure between two concepts plays an important role in research on system integration and database integration. Moreover, current research on keyword recommendation or tag clustering strongly depends on this kind of semantic measure. For this reason, many researchers in various fields including computer science and computational linguistics have tried to improve methods to calculating semantic similarity/relatedness measure. This study of similarity between concepts is meant to discover how a computational process can model the action of a human to determine the relationship between two concepts. Most research on calculating semantic similarity usually uses ready-made reference knowledge such as semantic network and dictionary to measure concept similarity. The topological method is used to calculated relatedness or similarity between concepts based on various forms of a semantic network including a hierarchical taxonomy. This approach assumes that the semantic network reflects the human knowledge well. The nodes in a network represent concepts, and way to measure the conceptual similarity between two nodes are also regarded as ways to determine the conceptual similarity of two words(i.e,. two nodes in a network). Topological method can be categorized as node-based or edge-based, which are also called the information content approach and the conceptual distance approach, respectively. The node-based approach is used to calculate similarity between concepts based on how much information the two concepts share in terms of a semantic network or taxonomy while edge-based approach estimates the distance between the nodes that correspond to the concepts being compared. Both of two approaches have assumed that the semantic network is static. That means topological approach has not considered the change of semantic relation between concepts in semantic network. However, as information communication technologies make advantage in sharing knowledge among people, semantic relation between concepts in semantic network may change. To explain the change in semantic relation, we adopt the cognitive semantics. The basic assumption of cognitive semantics is that humans judge the semantic relation based on their cognition and understanding of concepts. This cognition and understanding is called 'World Knowledge.' World knowledge can be categorized as personal knowledge and cultural knowledge. Personal knowledge means the knowledge from personal experience. Everyone can have different Personal Knowledge of same concept. Cultural Knowledge is the knowledge shared by people who are living in the same culture or using the same language. People in the same culture have common understanding of specific concepts. Cultural knowledge can be the starting point of discussion about the change of semantic relation. If the culture shared by people changes for some reasons, the human's cultural knowledge may also change. Today's society and culture are changing at a past face, and the change of cultural knowledge is not negligible issues in the research on semantic relationship between concepts. In this paper, we propose the future directions of research on semantic similarity. In other words, we discuss that how the research on semantic similarity can reflect the change of semantic relation caused by the change of cultural knowledge. We suggest three direction of future research on semantic similarity. First, the research should include the versioning and update methodology for semantic network. Second, semantic network which is dynamically generated can be used for the calculation of semantic similarity between concepts. If the researcher can develop the methodology to extract the semantic network from given knowledge base in real time, this approach can solve many problems related to the change of semantic relation. Third, the statistical approach based on corpus analysis can be an alternative for the method using semantic network. We believe that these proposed research direction can be the milestone of the research on semantic relation.

• Journal of the Korean Society of Food Science and Nutrition
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• v.33 no.6
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• pp.921-929
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• 2004

Dietary intake of whole grains, vegetable and fruit is known to reduce the degenerative chronic diseases, such as cancer and cardiovascular diseases. Antioxidative and antimutagenic effects of the ethanol extract of Korean Millet, Buckwheat, Sorghum and Job's tears were examined by inhibition against iron-induced linoleate per-oxidation, DPPH (1,l-diphenyl-2-picrylhydrazyl) radical generation and MDA-BSA (malondialdehyde-bovine serum albumin) conjugation, and Ames test using Salmonella. Buckwheat showed the strongest antioxidative effect in three different systems among these four grains, but it showed the lowest antimutagenic effect. Sorghum was the second to Buckwheat in iron-induced linoleate peroxidation inhibition activity and DPPH radical scavenging activity, and showed very good direct-antimutagenic effect in 2-Nitrofluorene treated Salmonella Typhimurium TA98 and indirect-antimutagenic effect in 2-Anthramine treated Salmonella Typhimurium TA98 and TA100 with hepatic S9 mixture. Millet showed the strongest antimutagenic effect in Salmonella Typhimurium TA98 and TA 100 with or without S9. Buckwheat contained the highest total flavonoids and polyphenols, 1.14 mg/g and 3.71 mg/g, respectively. Total flavonoid content in these four grains was negatively correlated with $IC_{50}$/ for DPPH radical scavenging antioxidative effect significantly (r=-0.9924, p=0.0076), but not with antimutagenic effect.

• Korean Journal of Hospice Care
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• v.8 no.1
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• pp.1-12
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• 2008

Perpose: The purpose of this study was to investigate the perception of death between Korean and Chinese nursing students. And it will help develop curriculum for preparing death, the quality of hospice care, as well as nursing education and practice. Methods: Data was collected from 492 nursing students participated(248 Korean and 244 Chinese) by questionnaire designed for examining Death Orientation (Thorson & Powell, 1988). They were analyzed using Cronbach's Alpha coefficients, factor analysis, t-test, ANOVA and regression analysis (SPSS; win 12.0 version) Results: More than half of the Korean nursing students followed a religion (58.5%) while the majority of Chinese nursing students did not follow a religion (93.9%). In the view of the afterlife, nursing students in China had two views. 'I really don't know what happens after a person dies (30.3%)' and ‘There is no afterlife and death is the end (29.5%)’. On the other hand the Korean nursing students’ answer were, 'After dying, a person goes to heaven or hell (27.3%)' and 'I really don't know what happens after a person dies. (22.9%)' The study also found that the average of 25 items in Death Orientation is 2.36points of nursing students in Korea and 2.50points of nursing students in China. This means that the concern, anxiety and fear were of the middle level for the Chinese Students and were higher than Korean students (t=3.51, p=.000). In the low factor of death orientation, those in Korea had higher 'anxiety of burden to family' than those in China (t=-3.50, p=.001). The nursing students in China had higher 'anxiety of the unknown (t=4.96, p=.000)', 'fear of suffering (t=6.88, p=.000), 'fear of extinction body and life (t=5.20, p=.000), 'fear of lost self-control(t=2.12, p=.034)', and 'anxiety of future existence and nonexistence (t=2.33, p=.020)' than those in Korea. There was no statistically significant difference for the 'concern of body and fear of identity lost' category. The death orientation of Korean nursing students had statistically significant differences according to age (t=3.20, p=.002), religion (t=2.56, p=.011), and afterlife (F=4.64, p=.000). The contribution of Death Orientation had a statistically significant difference, the afterlife variable (0.735, p=0.001). The death orientation of Chinese nursing students did not have any statistically significant differences. Conclusion: In conclusion, there were differences in death orientation between Korean and Chinese nursing students. In particular, those who believed in afterlife showed acceptance of death. The results of this study suggest that nursing curricula should include education program on death and spiritual nursing. Additional studies are needed to establish death education in China with careful considerations on Chinese policies, cultures and social systems.

• Journal of the Korean Society of Food Science and Nutrition
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• v.35 no.9
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• pp.1127-1132
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• 2006

In our previous study, a novel herb mixture (HIM-I) of Angelica gigas radix, Cnidium officinale rhizoma, and Paeonia japonica radix was developed to protect the intestinal and immune systems and promote its recovery against radiation damage. A new herbal composition (HemoHIM) with the high immune modulating activity was developed from HIM-I. HIM-I was fractionated into ethanol fraction (HIM-I-E) and polysaccharide fraction (HIM-I-P). And HemoHIM was prepared by adding HIM-I-P to HIM-I. HemoHIM showed more effective than HIM-I in immune modulation as well as radioprotection. The present study is designed to investigate the protective effects of HIM-I, HIM-I-P, and HemoHIM on hydrogen peroxide $(H_2O_2)$ induced apoptosis of human promyelocytic leukemia (HL-60) cells. It was shown that $H_2O_2$ treatment reduced the viability of cells, and increased appearance of DNA ladders, hypodiploid (subG1) cells, and phosphatidylserine translocation level. Pretreatment of HemoHIM significantly reduced the cytotoxic effect induced by $H_2O_2$, associated with reducing the translocation of phosphatidylserine, hypodiploid cells and DNA ladders. HemoHIM appeared to be more protective than HIM-I against $H_2O_2$ induced apoptosis whereas, it exhibited similar activity to HIM-I-P. These results indicated that HemoHIM might be an useful agent for protection against oxidative stress $(H_2O_2)-induced$ apoptosis as well as immune modulation, especially since it is a relatively nontoxic natural product.