• Journal of Korea Water Resources Association
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• v.46 no.4
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• pp.401-412
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• 2013

Water footprint of a product and service is the volume of freshwater used to produce the product, measured in the life cycle or over the full supply chain. Since water footprint assessment helps us to understand how human activities and products relate to water scarcity and pollution, it can contribute to seek a sustainable way of water use in the consumption perspective. For the introduction of WFP scheme, it is indispensable to construct water inventory/accounting for the assessment, but there is no database in Korea to cover all industry sectors. Therefore, the aim of the study is to develop water footprint inventory within a nation at 403 industrial sectors using Input-Output Analysis. Water uses in the agricultural sector account for 79% of total water, and industrial sector have higher indirect water at most sectors, which is accounting for 82%. Most of the crop water is consumptive and direct water except rice. The greatest water use in the agricultural sectors is in rice paddy followed by aquaculture and fruit production, but the greatest water use intensity was not in the rice. The greatest water use intensity was 103,263 $m^3$/million KRW for other inedible crop production, which was attributed to the low economic value of the product with great water consumption in the cultivation. The next was timber tract followed by iron ores, raw timber, aquaculture, water supply and miscellaneous cereals like corn and other edible crops in terms of total water use intensity. In holistic view, water management considering indirect water in the industrial sector, i.e. supply chain management in the whole life cycle, is important to increase water use efficiency, since more than 56% of total water was indirect water by humanity. It is expected that the water use intensity data can be used for a water inventory to estimate water footprint of a product for the introduction of water footprint scheme in Korea.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.195-211
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• 2010

An exhibition is defined as market events for specific duration to present exhibitors' main product range to either business or private visitors, and it also plays a key role as effective marketing channels. Especially, as the effect of the opinions of the visitors after the exhibition impacts directly on sales or the image of companies, exhibition organizers must consider various needs of visitors. To meet needs of visitors, ubiquitous technologies have been applied in some exhibitions. However, despite of the development of the ubiquitous technologies, their services cannot always reflect visitors' preferences as they only generate information when visitors request. As a result, they have reached their limit to meet needs of visitors, which consequently might lead them to loss of marketing opportunity. Recommendation systems can be the right type to overcome these limitations. They can recommend the booths to coincide with visitors' preferences, so that they help visitors who are in difficulty for choices in exhibition environment. One of the most successful and widely used technologies for building recommender systems is called Collaborative Filtering. Traditional recommender systems, however, only use neighbors' evaluations or behaviors for a personalized prediction. Therefore, they can not reflect visitors' dynamic preference, and also lack of accuracy in exhibition environment. Although there is much useful information to infer visitors' preference in ubiquitous environment (e.g., visitors' current location, booth visit path, and so on), they use only limited information for recommendation. In this study, we propose a booth recommendation methodology using Sequential Association Rule which considers the sequence of visiting. Recent studies of Sequential Association Rule use the constraints to improve the performance. However, since traditional Sequential Association Rule considers the whole rules to recommendation, they have a scalability problem when they are adapted to a large exhibition scale. To solve this problem, our methodology composes the confidence database before recommendation process. To compose the confidence database, we first search preceding rules which have the frequency above threshold. Next, we compute the confidences of each preceding rules to each booth which is not contained in preceding rules. Therefore, the confidence database has two kinds of information which are preceding rules and their confidence to each booth. In recommendation process, we just generate preceding rules of the target visitors based on the records of the visits, and recommend booths according to the confidence database. Throughout these steps, we expect reduction of time spent on recommendation process. To evaluate proposed methodology, we use real booth visit records which are collected by RFID technology in IT exhibition. Booth visit records also contain the visit sequence of each visitor. We compare the performance of proposed methodology with traditional Collaborative Filtering system. As a result, our proposed methodology generally shows higher performance than traditional Collaborative Filtering. We can also see some features of it in experimental results. First, it shows the highest performance at one booth recommendation. It detects preceding rules with some portions of visitors. Therefore, if there is a visitor who moved with very a different pattern compared to the whole visitors, it cannot give a correct recommendation for him/her even though we increase the number of recommendation. Trained by the whole visitors, it cannot correctly give recommendation to visitors who have a unique path. Second, the performance of general recommendation systems increase as time expands. However, our methodology shows higher performance with limited information like one or two time periods. Therefore, not only can it recommend even if there is not much information of the target visitors' booth visit records, but also it uses only small amount of information in recommendation process. We expect that it can give real?time recommendations in exhibition environment. Overall, our methodology shows higher performance ability than traditional Collaborative Filtering systems, we expect it could be applied in booth recommendation system to satisfy visitors in exhibition environment.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.185-202
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• 2012

.Since the value of information has been realized in the information society, the usage and collection of information has become important. A facial expression that contains thousands of information as an artistic painting can be described in thousands of words. Followed by the idea, there has recently been a number of attempts to provide customers and companies with an intelligent service, which enables the perception of human emotions through one's facial expressions. For example, MIT Media Lab, the leading organization in this research area, has developed the human emotion prediction model, and has applied their studies to the commercial business. In the academic area, a number of the conventional methods such as Multiple Regression Analysis (MRA) or Artificial Neural Networks (ANN) have been applied to predict human emotion in prior studies. However, MRA is generally criticized because of its low prediction accuracy. This is inevitable since MRA can only explain the linear relationship between the dependent variables and the independent variable. To mitigate the limitations of MRA, some studies like Jung and Kim (2012) have used ANN as the alternative, and they reported that ANN generated more accurate prediction than the statistical methods like MRA. However, it has also been criticized due to over fitting and the difficulty of the network design (e.g. setting the number of the layers and the number of the nodes in the hidden layers). Under this background, we propose a novel model using Support Vector Regression (SVR) in order to increase the prediction accuracy. SVR is an extensive version of Support Vector Machine (SVM) designated to solve the regression problems. The model produced by SVR only depends on a subset of the training data, because the cost function for building the model ignores any training data that is close (within a threshold ${\varepsilon}$) to the model prediction. Using SVR, we tried to build a model that can measure the level of arousal and valence from the facial features. To validate the usefulness of the proposed model, we collected the data of facial reactions when providing appropriate visual stimulating contents, and extracted the features from the data. Next, the steps of the preprocessing were taken to choose statistically significant variables. In total, 297 cases were used for the experiment. As the comparative models, we also applied MRA and ANN to the same data set. For SVR, we adopted '${\varepsilon}$-insensitive loss function', and 'grid search' technique to find the optimal values of the parameters like C, d, ${\sigma}^2$, and ${\varepsilon}$. In the case of ANN, we adopted a standard three-layer backpropagation network, which has a single hidden layer. The learning rate and momentum rate of ANN were set to 10%, and we used sigmoid function as the transfer function of hidden and output nodes. We performed the experiments repeatedly by varying the number of nodes in the hidden layer to n/2, n, 3n/2, and 2n, where n is the number of the input variables. The stopping condition for ANN was set to 50,000 learning events. And, we used MAE (Mean Absolute Error) as the measure for performance comparison. From the experiment, we found that SVR achieved the highest prediction accuracy for the hold-out data set compared to MRA and ANN. Regardless of the target variables (the level of arousal, or the level of positive / negative valence), SVR showed the best performance for the hold-out data set. ANN also outperformed MRA, however, it showed the considerably lower prediction accuracy than SVR for both target variables. The findings of our research are expected to be useful to the researchers or practitioners who are willing to build the models for recognizing human emotions.

• Journal of Genetic Medicine
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• v.7 no.2
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• pp.125-132
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• 2010

Purpose: Preimplantation genetic diagnosis (PGD), also known as embryo screening, is a pre-pregnancy technique used to identify genetic defects in embryos created through in vitro fertilization. PGD is considered a means of prenatal diagnosis of genetic abnormalities. PGD is used when one or both genetic parents has a known genetic abnormality; testing is performed on an embryo to determine if it also carries the genetic abnormality. The main advantage of PGD is the avoidance of selective pregnancy termination as it imparts a high likelihood that the baby will be free of the disease under consideration. The application of PGD to genetic practices, reproductive medicine, and genetic counseling is becoming the key component of fertility practice because of the need to develop a custom PGD design for each couple. Materials and Methods: In this study, a survey on the contents of genetic counseling in PGD was carried out via direct contact or e-mail with the patients and specialists who had experienced PGD during the three months from February to April 2010. Results: A total of 91 persons including 60 patients, 49 of whom had a chromosomal disorder and 11 of whom had a single gene disorder, and 31 PGD specialists responded to the survey. Analysis of the survey results revealed that all respondents were well aware of the importance of genetic counseling in all steps of PGD including planning, operation, and follow-up. The patient group responded that the possibility of unexpected results (51.7%), genetic risk assessment and recurrence risk (46.7%), the reproduction options (46.7%), the procedure and limitation of PGD (43.3%) and the information of PGD technology (35.0%) should be included as a genetic counseling information. In detail, 51.7% of patients wanted to be counseled for the possibility of unexpected results and the recurrence risk, while 46.7% wanted to know their reproduction options (46.7%). Approximately 96.7% of specialists replied that a non-M.D. genetic counselor is necessary for effective and systematic genetic counseling in PGD because it is difficult for physicians to offer satisfying information to patients due to lack of counseling time and specific knowledge of the disorders. Conclusions: The information from the survey provides important insight into the overall present situation of genetic counseling for PGD in Korea. The survey results demonstrated that there is a general awareness that genetic counseling is essential for PGD, suggesting that appropriate genetic counseling may play a important role in the success of PGD. The establishment of genetic counseling guidelines for PGD may contribute to better planning and management strategies for PGD.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.95-110
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• 2013

Recently, the amount of unstructured data being generated through a variety of social media has been increasing rapidly, resulting in the increasing need to collect, store, search for, analyze, and visualize this data. This kind of data cannot be handled appropriately by using the traditional methodologies usually used for analyzing structured data because of its vast volume and unstructured nature. In this situation, many attempts are being made to analyze unstructured data such as text files and log files through various commercial or noncommercial analytical tools. Among the various contemporary issues dealt with in the literature of unstructured text data analysis, the concepts and techniques of opinion mining have been attracting much attention from pioneer researchers and business practitioners. Opinion mining or sentiment analysis refers to a series of processes that analyze participants' opinions, sentiments, evaluations, attitudes, and emotions about selected products, services, organizations, social issues, and so on. In other words, many attempts based on various opinion mining techniques are being made to resolve complicated issues that could not have otherwise been solved by existing traditional approaches. One of the most representative attempts using the opinion mining technique may be the recent research that proposed an intelligent model for predicting the direction of the stock index. This model works mainly on the basis of opinions extracted from an overwhelming number of economic news repots. News content published on various media is obviously a traditional example of unstructured text data. Every day, a large volume of new content is created, digitalized, and subsequently distributed to us via online or offline channels. Many studies have revealed that we make better decisions on political, economic, and social issues by analyzing news and other related information. In this sense, we expect to predict the fluctuation of stock markets partly by analyzing the relationship between economic news reports and the pattern of stock prices. So far, in the literature on opinion mining, most studies including ours have utilized a sentiment dictionary to elicit sentiment polarity or sentiment value from a large number of documents. A sentiment dictionary consists of pairs of selected words and their sentiment values. Sentiment classifiers refer to the dictionary to formulate the sentiment polarity of words, sentences in a document, and the whole document. However, most traditional approaches have common limitations in that they do not consider the flexibility of sentiment polarity, that is, the sentiment polarity or sentiment value of a word is fixed and cannot be changed in a traditional sentiment dictionary. In the real world, however, the sentiment polarity of a word can vary depending on the time, situation, and purpose of the analysis. It can also be contradictory in nature. The flexibility of sentiment polarity motivated us to conduct this study. In this paper, we have stated that sentiment polarity should be assigned, not merely on the basis of the inherent meaning of a word but on the basis of its ad hoc meaning within a particular context. To implement our idea, we presented an intelligent investment decision-support model based on opinion mining that performs the scrapping and parsing of massive volumes of economic news on the web, tags sentiment words, classifies sentiment polarity of the news, and finally predicts the direction of the next day's stock index. In addition, we applied a domain-specific sentiment dictionary instead of a general purpose one to classify each piece of news as either positive or negative. For the purpose of performance evaluation, we performed intensive experiments and investigated the prediction accuracy of our model. For the experiments to predict the direction of the stock index, we gathered and analyzed 1,072 articles about stock markets published by "M" and "E" media between July 2011 and September 2011.