• Journal of the Korean Geotechnical Society
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• v.18 no.5
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• pp.37-42
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• 2002

Artificial neural networks are efficient computing techniques that are widely used to solve complex problems in many fields. In this paper a liquefaction potential was estimated by using a back propagation neural network model applicated to cyclic triaxial test data, soil parameters and site investigation data. Training and testing of the network were based on a database of 43 cyclic triaxial test data from 00 sites. The neural networks are trained by modifying the weights of the neurons in response to the errors between the actual output values and the target output value. Training was done iteratively until the average sum squared errors over all the training patterns were minimized. This generally occurred after about 15,000 cycles of training. The accuracy from 72% to 98% was shown for the model equipped with two hidden layers and ten input variables. Important effective input variables have been identified as the NOC,$D_10$ and (N$_1$)$_60$. The study showed that the neural network model predicted a CSR(Cyclic shear stress Ratio) of silty-sand reasonably well. Analyzed results indicate that the neural-network model is more reliable than simplified method using N value of SPT.

• Korean Journal of Social Welfare Studies
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• v.41 no.2
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• pp.5-33
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• 2010

The purpose of this study is to understand the open adoption experience of adoptive families. For this purpose, the data were collected through diverse data collection methods including in-depth interviews with adoptive families who had experiences of open adoption and analyzed using a qualitative case study approach. Data collected from six adoptive families were employed for within-case analysis and cross-case analysis. Each case was carefully examined and summarized using story-telling style in the within-case analysis and major issues for open adoption appeared in each case were described and compared one by one. In the cross-case analysis, all the cases were re-examined keeping the issues appeared in the within-case analysis in mind and eight integrated themes were emerged from it. The eight integrated themes are 'the crucial meeting', 'a clear arrangement', 'an uneasy parallel', 'the other mom', 'who are the real parents?', 'the words never to say', 'the hidden characters', and 'the center of relations or the outsider'. Based on the results of the study, the policies and practical guidelines related to open adoption were suggested. Also, the suggestions for the further studies were made to obtain more abundant information beyond the limitations of the study.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.1-16
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• 2015

Traffic accident is one of the major cause of death worldwide for the last several decades. According to the statistics of world health organization, approximately 1.24 million deaths occurred on the world's roads in 2010. In order to reduce future traffic accident, multipronged approaches have been adopted including traffic regulations, injury-reducing technologies, driving training program and so on. Records on traffic accidents are generated and maintained for this purpose. To make these records meaningful and effective, it is necessary to analyze relationship between traffic accident and related factors including vehicle design, road design, weather, driver behavior etc. Insight derived from these analysis can be used for accident prevention approaches. Traffic accident data mining is an activity to find useful knowledges about such relationship that is not well-known and user may interested in it. Many studies about mining accident data have been reported over the past two decades. Most of studies mainly focused on predict risk of accident using accident related factors. Supervised learning methods like decision tree, logistic regression, k-nearest neighbor, neural network are used for these prediction. However, derived prediction model from these algorithms are too complex to understand for human itself because the main purpose of these algorithms are prediction, not explanation of the data. Some of studies use unsupervised clustering algorithm to dividing the data into several groups, but derived group itself is still not easy to understand for human, so it is necessary to do some additional analytic works. Rule based learning methods are adequate when we want to derive comprehensive form of knowledge about the target domain. It derives a set of if-then rules that represent relationship between the target feature with other features. Rules are fairly easy for human to understand its meaning therefore it can help provide insight and comprehensible results for human. Association rule learning methods and subgroup discovery methods are representing rule based learning methods for descriptive task. These two algorithms have been used in a wide range of area from transaction analysis, accident data analysis, detection of statistically significant patient risk groups, discovering key person in social communities and so on. We use both the association rule learning method and the subgroup discovery method to discover useful patterns from a traffic accident dataset consisting of many features including profile of driver, location of accident, types of accident, information of vehicle, violation of regulation and so on. The association rule learning method, which is one of the unsupervised learning methods, searches for frequent item sets from the data and translates them into rules. In contrast, the subgroup discovery method is a kind of supervised learning method that discovers rules of user specified concepts satisfying certain degree of generality and unusualness. Depending on what aspect of the data we are focusing our attention to, we may combine different multiple relevant features of interest to make a synthetic target feature, and give it to the rule learning algorithms. After a set of rules is derived, some postprocessing steps are taken to make the ruleset more compact and easier to understand by removing some uninteresting or redundant rules. We conducted a set of experiments of mining our traffic accident data in both unsupervised mode and supervised mode for comparison of these rule based learning algorithms. Experiments with the traffic accident data reveals that the association rule learning, in its pure unsupervised mode, can discover some hidden relationship among the features. Under supervised learning setting with combinatorial target feature, however, the subgroup discovery method finds good rules much more easily than the association rule learning method that requires a lot of efforts to tune the parameters.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.151-176
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• 2021

As the information technology industry develops, various kinds of data are being created, and it is now essential to process them and use them in the industry. Analyzing and utilizing various digital data collected online and offline is a necessary process to provide an appropriate experience for customers in the industry. In order to create new businesses, products, and services, it is essential to use customer data collected in various ways to deeply understand potential customers' needs and analyze behavior patterns to capture hidden signals of desire. However, it is true that research using data analysis and UX methodology, which should be conducted in parallel for effective service development, is being conducted separately and that there is a lack of examples of use in the industry. In thiswork, we construct a single process by applying data analysis methods and UX methodologies. This study is important in that it is highly likely to be used because it applies methodologies that are actively used in practice. We conducted a survey on the topic to identify and cluster the associations between factors to establish customer classification and target customers. The research methods are as follows. First, we first conduct a factor, regression analysis to determine the association between factors in the happiness data survey. Groups are grouped according to the survey results and identify the relationship between 34 questions of psychological stability, family life, relational satisfaction, health, economic satisfaction, work satisfaction, daily life satisfaction, and residential environment satisfaction. Second, we classify clusters based on factors affecting happiness and extract the optimal number of clusters. Based on the results, we cross-analyzed the characteristics of each cluster. Third, forservice definition, analysis was conducted by correlating with keywords related to happiness. We leverage keyword analysis of the thumb trend to derive ideas based on the interest and associations of the keyword. We also collected approximately 11,000 news articles based on the top three keywords that are highly related to happiness, then derived issues between keywords through text mining analysis in SAS, and utilized them in defining services after ideas were conceived. Fourth, based on the characteristics identified through data analysis, we selected segmentation and targetingappropriate for service discovery. To this end, the characteristics of the factors were grouped and selected into four groups, and the profile was drawn up and the main target customers were selected. Fifth, based on the characteristics of the main target customers, interviewers were selected and the In-depthinterviews were conducted to discover the causes of happiness, causes of unhappiness, and needs for services. Sixth, we derive customer behavior patterns based on segment results and detailed interviews, and specify the objectives associated with the characteristics. Seventh, a typical persona using qualitative surveys and a persona using data were produced to analyze each characteristic and pros and cons by comparing the two personas. Existing market segmentation classifies customers based on purchasing factors, and UX methodology measures users' behavior variables to establish criteria and redefine users' classification. Utilizing these segment classification methods, applying the process of producinguser classification and persona in UX methodology will be able to utilize them as more accurate customer classification schemes. The significance of this study is summarized in two ways: First, the idea of using data to create a variety of services was linked to the UX methodology used to plan IT services by applying it in the hot topic era. Second, we further enhance user classification by applying segment analysis methods that are not currently used well in UX methodologies. To provide a consistent experience in creating a single service, from large to small, it is necessary to define customers with common goals. To this end, it is necessary to derive persona and persuade various stakeholders. Under these circumstances, designing a consistent experience from beginning to end, through fast and concrete user descriptions, would be a very effective way to produce a successful service.

• Journal of Korea Water Resources Association
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• v.33 no.5
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• pp.537-550
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• 2000

In this study, Neural Networks models were used to forecast daily streamflow at Jindong station of the Nakdong River basin. Neural Networks models consist of CASE 1(5-5-1) and CASE 2(5-5-5-1). The criteria which separates two models is the number of hidden layers. Each model has Fletcher-Reeves Conjugate Gradient BackPropagation(FR-CGBP) and Scaled Conjugate Gradient BackPropagation(SCGBP) algorithms, which are better than original BackPropagation(BP) in convergence of global error and training tolerance. The data which are available for model training and validation were composed of wet, average, dry, wet+average, wet+dry, average+dry and wet+average+dry year respectively. During model training, the optimal connection weights and biases were determined using each data set and the daily streamflow was calculated at the same time. Except for wet+dry year, the results of training were good conditions by statistical analysis of forecast errors. And, model validation was carried out using the connection weights and biases which were calculated from model training. The results of validation were satisfactory like those of training. Daily streamflow forecasting using Neural Networks models were compared with those forecasted by Multiple Regression Analysis Mode(MRAM). Neural Networks models were displayed slightly better results than MRAM in this study. Thus, Neural Networks models have much advantage to provide a more sysmatic approach, reduce model parameters, and shorten the time spent in the model development.

• The Journal of the Korea Contents Association
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• v.18 no.9
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• pp.294-307
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• 2018

The aim of this study is to find out how married women who had had an abortions experience a change through Family Constellation. The consequent changes will occur in the perception of an abortion experience and in the relation between married women themselves and their family. The participants in the study were 9 married women aged 40~60 who have experienced an abortion, and data collection was carried out over 3 periods which were before, right after and one month after the Family Constellation experience through individual in-depth interviews. The chief methodology of this study is based on the one by Colaizzi's phenomenological research, and by using the methodology to analyze the data 15 theme collections and 3 categories were deducted. According to the result of the analysis, the experience of abortion was having negative influence on the whole spectrum of the life of married women who experienced an abortion even though they did not consciously recall the relevant experiences. In the married women's Family Constellation, the agent visualizes the restrained relation which is hidden in unconsciousness and thereby offers an opportunity for married women to untie "knot". Moreover through this opportunity, married women are able to have new perception of their abortion experience and the relation between their family. Furthermore, not only will they be able to recover the relationship with their family, but also emotional stability.

• The Journal of Korean Academic Society of Nursing Education
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• v.9 no.2
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• pp.264-275
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• 2003

The purpose of this study were to develop a smoking preventive education program for elementary students and evaluate it's effectiveness. This study was a quasi experimental study under the nonequivalent control group with pretest-posttest design. The subjects of this study were 62 who are attending elementary school(31 for each group), 2 different district elementary school. The subjects were matched by grade, similar in anti-smoking educational background of smoking, as well as their residence and income level of their families. The instruments used in this study was 18 criterion referenced test items modeled by Dick & Carey that were developed by researchers for evaluating the subjects' knowledge and attitude about smoking. A pretest was administered a week before treatment The program given to the experimental group is composed of the texts explaining the poisonous substances in tobacco, social and cultural harmfulness of smoking to the body and psychology, indirect smoking, smoking of pregnant women, motives of smoking, refusal skills of smoking; and for the subjects' understanding and the better results of study - pictures, role play, discussion, text through computer based multi-media, puzzle searching for hidden pictures, cross-word puzzle, and finally compensation. The data were collected for 50 days form mid- September to the end of October in the year of 2000, composed of formative evaluation, pre-test and summative evaluation via 2 sessions. Accordingly, the collected data were analysed by t-test, paired t-test, repeated measure ANOVA by the SAS program. This research summarize the findings as follows; 1. There was a significant difference in knowledge between the experimental group(after 1 wks t=10.4680, p=.0001; after 4 wks t= 9.310, p=.0001) and control group(after 1 wks t=0.0420, p= .9669; after 4 wks t= -0.378 p=.7079) in between the results of 1 and 4 week after education in summative evaluation (F=27.45, P=.0001). 2. There was non statistical significant difference in attitude between the experimental group (after 1 wks t=1.2292, p=0.2286 ; after 4 wks t=1.330, p=0.1935) and control group (after 1 wks t=0.1819, p=0.8569 ; after 4 wks t=0.2970, p=0.7685) in between the results of 1 and 4 week after education in summative evaluation(F=0.71, P=0.494). To sum up, the statistics of conclusive analysis evaluative for the children under school age of the 'knowledge acquisition' about smoking harmfulness. On the other hand, as there was already sound attitude about smoking, the evaluation of attitude was non significant difference between control group and experimental group, just there was partially significant difference.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.2
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• pp.39-57
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• 2018

The purpose of this study was to construct an artificial intelligence model that learns and estimates the relationship between vehicle queue length and link travel time in urban areas. The vehicle queue length estimation model is modeled by three models. First of all, classify whether vehicle queue is a link overflow and estimate the vehicle queue length in the link overflow and non-overflow situations. Deep learning model is implemented as Tensorflow. All models are based DNN structure, and network structure which shows minimum error after learning and testing is selected by diversifying hidden layer and node number. The accuracy of the vehicle queue link overflow classification model was 98%, and the error of the vehicle queue estimation model in case of non-overflow and overflow situation was less than 15% and less than 5%, respectively. The average error per link was about 12%. Compared with the detecting data-based method, the error was reduced by about 39%.

• Journal of the Korea Convergence Society
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• v.9 no.1
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• pp.419-431
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• 2018

In this paper, we propose a novel method for monitoring mood trend of Twitter users by analyzing their daily tweets for a long period. Then, to more accurately understand their tweets, we analyze all types of content in tweets, i.e., texts and emoticons, and images, thus develop a multimodal sentiment analysis method. In the proposed method, two single-modal analyses first are performed to extract the users' moods hidden in texts and images: a lexicon-based and learning-based text classifier and a learning-based image classifier. Thereafter, the extracted moods from the respective analyses are combined into a tweet mood and aggregated a daily mood. As a result, the proposed method generates a user daily mood flow graph, which allows us for monitoring the mood trend of users more intuitively. For evaluation, we perform two sets of experiment. First, we collect the data sets of 40,447 data. We evaluate our method via comparing the state-of-the-art techniques. In our experiments, we demonstrate that the proposed multimodal analysis method outperforms other baselines and our own methods using text-based tweets or images only. Furthermore, to evaluate the potential of the proposed method in monitoring users' mood trend, we tested the proposed method with 40 depressive users and 40 normal users. It proves that the proposed method can be effectively used in finding depressed users.

• 한국지구물리탐사학회:학술대회논문집
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• 2004.08a
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• pp.157-179
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• 2004

The application of magnetic method for archaeoprospection has been carried out through two archaeological areas in Egypt, Abydos and Abu Sir, In order to find out tile ancient Egyptian archaeological features. The magnetic work at the selected archaeological site of Abydos area was carried out by gradiometer survey, while magnetic work at the selected archaeological site of Abu Sir area was carried out by gradiometer survey and magnetic susceptibility measurements. A gradiometer survey with raster of 0.5 m/0.5 m has been carried out on a surface area of $9600 m^2$ at Abydos area to relocate the buried Solar Boats. The magnetic data were processed using Geoplot software to treat the field noises and enhance the quality of the obtained images. The final magnetic images indicate the existence of 12 Solar Boats as well as tombs, remains of ancient rooms and walls. All of them are expected to belong to the Middle Kingdom, particularly from the 18th to 20th Dynasties. Two magnetic tools have been applied over a selected site of $25600 m^2$ at Abu Sir area in order to detect the hidden archaeological features nearby the Sun Temple. The acquisition of the magnetic data was initiated by the measurements of the topsoil magnetic susceptibility of 272 samples collected from the whole studied area, and then followed by the gradiometer survey to measure tile vertical gradient of the geomagnetic field over an area of $14400 m^2$. The magnetic susceptibility results show the presence of high concentration at the middle part of the study area with a little extension to the south western side, with maximum value of about $36{\times}10^5$ SI. They may indicate the proximity of ritual monuments. Also, they offered the site of interest for carrying out a gradiometer survey. The gradiometer results show tile existence of numerous distributed archaeological features made of mud-bricks with different shapes and sizes. They may indicate tombs, burial rooms, dissected walls; all of them are expected to belong to the 5th Dynasty of pharaohs, who used to build their buildings by mud bricks. The depth of the expected buried archaeological features has been estimated from tihe gradiometer. It is around 1.2m for deep features and 0.42 m for shallow features.