• Journal of Physiology & Pathology in Korean Medicine
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• v.26 no.4
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• pp.551-556
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• 2012

The database for the oriental medicine had been existed in documentation in past times and it has been developed to the database type for random accesses in the information society. However, the aspects of the database are not so diversified and the database for the bio herbal material exists in widened type dictionary style. It is a situation that the database which handles the in-depth raw herbal medicines is not sufficient in its quantity and quality. Korean wild grape is a deciduous plant categorized into the Vitaceae and it was found experimentally that it has various medical effects. It is one of the medical materials with higher potentiality of academic study and commercialization recently because it has a bigger possibility to be applied into diverse industrial fields including the medical product for health, food and beauty. We constituted the cooperative system among the Muju cluster business group for Korean mountain wild grapes, Physiology Laboratory in Kyung Hee University Oriental Medicine and Medical Classics Laboratory in Kyung Hee University Oriental Medicine with a view to focusing on such potentiality and a database for Korean wild grapes was made a touchstone for establishing the in-depth database for the single bio medical materials. First of all, the literatures based on the North East Asia in ancient times had been categorized into the classical literature (Korean literature published by government organization, Korean classical literature, Chinese classical literature and classical literature fro Korean and Chinese oriental medicine) and modern literature (Modern literature for oriental medicine, modern literature for domestic and foreign herbal medicine) to cover the eastern and western research records and writings related to Korean wild grapes and the text-mining work has been performed through the cooperation system with the Medical Classics Laboratory in Kyung Hee University Oriental Medicine. First of all, the data for the experiment and theory for Korean wild grape were collected for the Medline database controlled by the Parliament Library of USA to arrange the domestic and foreign theses with topic for Korean wild grapes and the network hyperlink function and down load function were mounted for self-thesis searching function and active view based on the collected data. The thesis searching function provides various auxiliary functions and the searching is available according to the diverse searching/queries such as the name of sub species of Korean wild grape, the logical intersection index for the active ingredients, efficacy and elements. It was constituted for the researchers who design the Korean wild grape study to design of easier experiment. In addition, the data related to the patents for Korean wild grape which were collected from European Patent Office in response to the commercialization possibility and the system available for searching and view was established in the same viewpoint. Perl was used for the query programming and MS-SQL for database establishment and management in the designing of this database. Currently, the data is available for free use and the address is as follows. http://163.180.41.43:8011/index.html

• Journal of Soil and Groundwater Environment
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• v.12 no.5
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• pp.39-53
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• 2007

Generally, fractured medium can be described with some key parameters, such as hydraulic conductivities or random field of hydraulic conductivities (continuum model), spatial and statistical distribution of permeable fractures (discrete fracture network model). Investigating the practical applicability of the well-known conceptual models for the description of groundwater flow in fractured media, various types of hydraulic tests were applied to studies on the highly fractured media in Geumsan, Korea. Results from single-hole packer test show that the horizontal hydraulic conductivities in the permeable media are between $7.67{\times}10^{-10}{\sim}3.16{\times}10^{-6}$ m/sec, with $7.70{\times}10^{-7}$ m/sec arithmetic mean and $2.16{\times}10^{-7}$ m/sec geometric mean. Total number of test interval is 110 at 8 holes. The number of completely impermeable interval is 9, and the low permeable interval - below $1.0{\times}10^{-8}$ m/sec is 14. In other words, most of test intervals are permeable. The vertical distribution of hydraulic conductivities shows apparently the good correlation with the results of flowmeter test. But the results from the cross-hole test show some different features. The results from the cross-hole test are highly related to the connectivity and/or the binary properties of fractured media; permeable and impermeable. From the viewpoint of the connection, the application of the general stochastic approach with a single continuum model may not be appropriate even in the moderately or highly permeable fractured medium. Then, further studies on the investigation method and the analysis procedures should be required for the reasonable and practical design of the conceptual model, with which the binary properties, including permeable/impermeable features, can be described.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.105-122
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• 2019

Dimensionality reduction is one of the methods to handle big data in text mining. For dimensionality reduction, we should consider the density of data, which has a significant influence on the performance of sentence classification. It requires lots of computations for data of higher dimensions. Eventually, it can cause lots of computational cost and overfitting in the model. Thus, the dimension reduction process is necessary to improve the performance of the model. Diverse methods have been proposed from only lessening the noise of data like misspelling or informal text to including semantic and syntactic information. On top of it, the expression and selection of the text features have impacts on the performance of the classifier for sentence classification, which is one of the fields of Natural Language Processing. The common goal of dimension reduction is to find latent space that is representative of raw data from observation space. Existing methods utilize various algorithms for dimensionality reduction, such as feature extraction and feature selection. In addition to these algorithms, word embeddings, learning low-dimensional vector space representations of words, that can capture semantic and syntactic information from data are also utilized. For improving performance, recent studies have suggested methods that the word dictionary is modified according to the positive and negative score of pre-defined words. The basic idea of this study is that similar words have similar vector representations. Once the feature selection algorithm selects the words that are not important, we thought the words that are similar to the selected words also have no impacts on sentence classification. This study proposes two ways to achieve more accurate classification that conduct selective word elimination under specific regulations and construct word embedding based on Word2Vec embedding. To select words having low importance from the text, we use information gain algorithm to measure the importance and cosine similarity to search for similar words. First, we eliminate words that have comparatively low information gain values from the raw text and form word embedding. Second, we select words additionally that are similar to the words that have a low level of information gain values and make word embedding. In the end, these filtered text and word embedding apply to the deep learning models; Convolutional Neural Network and Attention-Based Bidirectional LSTM. This study uses customer reviews on Kindle in Amazon.com, IMDB, and Yelp as datasets, and classify each data using the deep learning models. The reviews got more than five helpful votes, and the ratio of helpful votes was over 70% classified as helpful reviews. Also, Yelp only shows the number of helpful votes. We extracted 100,000 reviews which got more than five helpful votes using a random sampling method among 750,000 reviews. The minimal preprocessing was executed to each dataset, such as removing numbers and special characters from text data. To evaluate the proposed methods, we compared the performances of Word2Vec and GloVe word embeddings, which used all the words. We showed that one of the proposed methods is better than the embeddings with all the words. By removing unimportant words, we can get better performance. However, if we removed too many words, it showed that the performance was lowered. For future research, it is required to consider diverse ways of preprocessing and the in-depth analysis for the co-occurrence of words to measure similarity values among words. Also, we only applied the proposed method with Word2Vec. Other embedding methods such as GloVe, fastText, ELMo can be applied with the proposed methods, and it is possible to identify the possible combinations between word embedding methods and elimination methods.

• 한국노년학
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• v.38 no.4
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• pp.811-835
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• 2018

Social relations and cognitive function in old age are closely related to each other, and social relation is classified into structural characteristics and qualitative characteristics reflecting cognitive and emotional evaluation. The concept of social isolation is the focus of attention in relation to the social relations of old age. Social isolation has a multidimensional theoretical structure that is divided into objective dimension such as social network, type of furniture, social participation, and subjective dimension such as lack of perceived social support and loneliness. There is also a close relationship between cognitive function and interpersonal conflict in old age. In this study, we examined the effect of subjective social isolation, which shows the structural characteristics of social relations, and subjective social isolation and interpersonal conflict on the dementia occurrence by age group in the elderly. The data were analyzed by applying a random effect panel logit model using 1,740 panel data from the first year to the third year of KSHAP. The results of the analysis are summarized as follows. First, the cognitive impairment increased sharply with age. Objective and subjective social isolation were both U-shaped distribution with an inflection point of 80 years old. Second, the main effect on the probability of cognitive impairment was statistically significant with objective and subjective social isolation, but the type of interpersonal conflict did not appear to be significant. Third, the results of two-way interaction effect analysis on the probability of cognitive impairment are as follows. The relationship between subjective social isolation and the probability of occurrence of cognitive impairment was significantly different according to the level of conflict with spouse. In addition, the higher the subjective social isolation, the higher the probability of cognitive impairment in the elderly(over 85) than in the young-old(65~74). In addition, as the level of conflict with spouses increases, the probability of cognitive impairment of the oldest-old(aged 85 or older) is drastically lower than that of the young-old(aged 65~74). Based on the results of this study, policy and practical implications for reducing the cognitive impairment of the elderly age group were suggested, and limitations of the study and suggestions for future research were discussed.

• Journal of Korea Water Resources Association
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• v.56 no.5
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• pp.311-323
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• 2023

In order to reduce disaster damage by localized heavy rains, floods, and urban inundation, it is important to know in advance whether natural disasters occur. Currently, heavy rain watch and heavy rain warning by the criteria of the Korea Meteorological Administration are being issued in Korea. However, since this one criterion is applied to the whole country, we can not clearly recognize heavy rain damage for a specific region in advance. Therefore, in this paper, we tried to reset the current criteria for a special weather report which considers the regional characteristics and to predict the damage caused by rainfall after 1 hour. The study area was selected as Gyeonggi-province, where has more frequent heavy rain damage than other regions. Then, the rainfall inducing disaster or hazard-triggering rainfall was set by utilizing hourly rainfall and heavy rain damage data, considering the local characteristics. The heavy rain damage prediction model was developed by a decision tree model and a random forest model, which are machine learning technique and by rainfall inducing disaster and rainfall data. In addition, long short-term memory and deep neural network models were used for predicting rainfall after 1 hour. The predicted rainfall by a developed prediction model was applied to the trained classification model and we predicted whether the rain damage after 1 hour will be occurred or not and we called this as 1ST-Model. The 1ST-Model can be used for preventing and preparing heavy rain disaster and it is judged to be of great contribution in reducing damage caused by heavy rain.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.47-67
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• 2017

Steel plate faults is one of important factors to affect the quality and price of the steel plates. So far many steelmakers generally have used visual inspection method that could be based on an inspector's intuition or experience. Specifically, the inspector checks the steel plate faults by looking the surface of the steel plates. However, the accuracy of this method is critically low that it can cause errors above 30% in judgment. Therefore, accurate steel plate faults diagnosis system has been continuously required in the industry. In order to meet the needs, this study proposed a new steel plate faults diagnosis system using Simultaneous MTS (S-MTS), which is an advanced Mahalanobis Taguchi System (MTS) algorithm, to classify various surface defects of the steel plates. MTS has generally been used to solve binary classification problems in various fields, but MTS was not used for multiclass classification due to its low accuracy. The reason is that only one mahalanobis space is established in the MTS. In contrast, S-MTS is suitable for multi-class classification. That is, S-MTS establishes individual mahalanobis space for each class. 'Simultaneous' implies comparing mahalanobis distances at the same time. The proposed steel plate faults diagnosis system was developed in four main stages. In the first stage, after various reference groups and related variables are defined, data of the steel plate faults is collected and used to establish the individual mahalanobis space per the reference groups and construct the full measurement scale. In the second stage, the mahalanobis distances of test groups is calculated based on the established mahalanobis spaces of the reference groups. Then, appropriateness of the spaces is verified by examining the separability of the mahalanobis diatances. In the third stage, orthogonal arrays and Signal-to-Noise (SN) ratio of dynamic type are applied for variable optimization. Also, Overall SN ratio gain is derived from the SN ratio and SN ratio gain. If the derived overall SN ratio gain is negative, it means that the variable should be removed. However, the variable with the positive gain may be considered as worth keeping. Finally, in the fourth stage, the measurement scale that is composed of selected useful variables is reconstructed. Next, an experimental test should be implemented to verify the ability of multi-class classification and thus the accuracy of the classification is acquired. If the accuracy is acceptable, this diagnosis system can be used for future applications. Also, this study compared the accuracy of the proposed steel plate faults diagnosis system with that of other popular classification algorithms including Decision Tree, Multi Perception Neural Network (MLPNN), Logistic Regression (LR), Support Vector Machine (SVM), Tree Bagger Random Forest, Grid Search (GS), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The steel plates faults dataset used in the study is taken from the University of California at Irvine (UCI) machine learning repository. As a result, the proposed steel plate faults diagnosis system based on S-MTS shows 90.79% of classification accuracy. The accuracy of the proposed diagnosis system is 6-27% higher than MLPNN, LR, GS, GA and PSO. Based on the fact that the accuracy of commercial systems is only about 75-80%, it means that the proposed system has enough classification performance to be applied in the industry. In addition, the proposed system can reduce the number of measurement sensors that are installed in the fields because of variable optimization process. These results show that the proposed system not only can have a good ability on the steel plate faults diagnosis but also reduce operation and maintenance cost. For our future work, it will be applied in the fields to validate actual effectiveness of the proposed system and plan to improve the accuracy based on the results.