• Journal of Korean Tunnelling and Underground Space Association
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• v.26 no.3
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• pp.281-301
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• 2024

The power cable tunnels which are part of the underground transmission line project, are constructed using the shield TBM method. The disc cutter among the shield TBM components plays an important role in breaking rock mass. Efficient tunnel construction is possible only when appropriate replacement occurs as the wear limit is reached or damage such as uneven wear occurs. A study was conducted to determine the wear conditions of disc cutter using a deep learning algorithm based on real-time measurement data of wear and rotation speed. Based on the results of full-scaled tunnelling tests, it was confirmed that measurement data was obtained differently depending on the wear conditions of disc cutter. Using real-time measurement data, an algorithm was developed to determine disc cutter wear characteristics based on a convolutional neural network model. Distributional patterns of data can be learned through CNN filters, and the performance of the model that can classify uniform wear and uneven wear through these pattern features.

• Korean Chemical Engineering Research
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• v.62 no.3
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• pp.214-224
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• 2024

Valves play an essential role in a chemical plant such as regulating fluid flow and pressure. Therefore, optimal selection of the valve size and type is essential task. Valve size and type have been selected based on theoretical formulas about calculating valve sizing coefficient (Cv). However, this approach has limitations such as requiring expert knowledge and consuming substantial time and costs. Herein, this study developed a model for predicting valve sizes and types using machine learning. We developed models using four algorithms: ANN, Random Forest, XGBoost, and Catboost and model performances were evaluated using NRMSE & R² score for size prediction and F1 score for type prediction. Additionally, a case study was conducted to explore the impact of phases on valve selection, using four datasets: total fluids, liquids, gases, and steam. As a result of the study, for valve size prediction, total fluid, liquid, and gas dataset demonstrated the best performance with Catboost (Based on R², total: 0.99216, liquid: 0.98602, gas: 0.99300. Based on NRMSE, total: 0.04072, liquid: 0.04886, gas: 0.03619) and steam dataset showed the best performance with RandomForest (R²: 0.99028, NRMSE: 0.03493). For valve type prediction, Catboost outperformed all datasets with the highest F1 scores (total: 0.95766, liquids: 0.96264, gases: 0.95770, steam: 1.0000). In Engineering Procurement Construction industry, the proposed fluid-specific machine learning-based model is expected to guide the selection of suitable valves based on given process conditions and facilitate faster decision-making.

• Journal of the Korean Geotechnical Society
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• v.40 no.4
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• pp.69-79
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• 2024

Field geotechnical data are obtained from various field and laboratory tests and are documented in geotechnical investigation reports. For efficient design and construction, digitizing these geotechnical parameters is essential. However, current practices involve manual data entry, which is time-consuming, labor-intensive, and prone to errors. Thus, this study proposes an automatic data extraction method from geotechnical investigation reports using image-based deep learning models and text-mining techniques. A deep-learning-based page classification model and a text-searching algorithm were employed to classify geotechnical investigation report pages with 100% accuracy. Computer vision algorithms were utilized to identify valid data regions within report pages, and text analysis was used to match and extract the corresponding geotechnical data. The proposed model was validated using a dataset of 205 geotechnical investigation reports, achieving an average data extraction accuracy of 93.0%. Finally, a user-interface-based program was developed to enhance the practical application of the extraction model. It allowed users to upload PDF files of geotechnical investigation reports, automatically analyze these reports, and extract and edit data. This approach is expected to improve the efficiency and accuracy of digitizing geotechnical investigation reports and building geotechnical databases.

• Journal of Veterinary Clinics
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• v.24 no.4
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• pp.514-521
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• 2007

The sympathetic nerve block improves the blood flow in the innervated regions. For this region, the sympathetic nerve block has been performed in the neural and cerebral disorders. However, the cerebral blood flow regulation of the cranial cervical ganglion block in dogs have not been well defined and the correlation to the changes in the cerebral circulation and the changes in the electroencephalogram is not well defined in dogs yet. Therefore, we investigated the hypothesis that changes in the EEG could be affected by the changes in cerebral blood flow following the cranial cervical ganglion block in dogs. Twenty five beagle dogs were divided into 3 groups; group I(LCCGB, n=10) underwent left sided cranial cervical ganglion block using the 1% lidocaine, group II(L, n=10) injected the 1% lidocaine into the right or left sided digastricus muscle, group III(N/SCCGB, n=5, served as control) underwent the left sided cranial cervical ganglion block using saline. A statistical difference was not found between the control group and the LCCGB group in the 95% spectral edge frequency(SEF) and the median frequency(MF). In the relative band power, the $\delta$ frequency was decreased during 5-25 min, while the $\alpha$ frequency was increased during the same time(p<0.05). But the $\theta$ frequency and the $\beta$ frequency were not shown the significant changes compared with the control group during the same time(p<0.05). These results suggest that the left cranial cervical ganglion block does not induce the change of the cerebral blood flow and its effect is insignificant.

• Journal of Yeungnam Medical Science
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• v.14 no.1
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• pp.168-174
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• 1997

Alphafetoprotein(AFP) is a glycoprotein synthesized by the fetus early in gestation by the yolk sac and later by the gastrointestinal tract and liver. The concentration of AFP is highest in fetal serum and amniotic fluid around 13th week, and 32nd week in maternal serum. Some conditions are associated with abnormal maternal serum AFP concentration. For examples, neural tube defects, omphalocele, renal anomalies are associated with elevated maternal serum AFP and fetal death, chromosomal trisomies are associated with low level of maternal serum AFP. So maternal serum AFP screening plays a significant role in assessing candidates for prenatal diagnosis and prenatal counselling in pregnant women. This study evaluates the normal ranges of AFP using enzyme immunoassay in normal pregnant women. We studied 500 normal pregnant women who visited the Department of Obstetrics & Gynecology, Yeungnam Medical Center, Yeungnam University during the period through January, 1993 to September, 1996. The group of the study were selected randomly at various gestational ages from 8 to 41 weeks. The results were summarized as follows: 1. The lowest level of AFP in our study group was 2.1ng/ml at 8 weeks of gestation. Thereafter serum alpha-fetoprotein concentrations rose rapidly to reach a peak value at 32nd week. 2. The mean levels of AFP in the primipara and multipara were $166.37{\pm}12.06ng/ml$, and $223.78{\pm}14.00ng/ml$, respectively, showing stastiscally significant difference between these two groups(p<0.01). 3. The mean levels of AFP between mothers who delivered male and female babies were $192.96{\pm}13.00ng/ml$, and $194.29{\pm}13.84ng/ml$, respectively, without statistically significant difference(p>0.05). 4. The normal ranges of maternal serum AFP according to each gestational week were evaluated.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.187-204
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• 2016

Document classification based on emotional polarity has become a welcomed emerging task owing to the great explosion of data on the Web. In the big data age, there are too many information sources to refer to when making decisions. For example, when considering travel to a city, a person may search reviews from a search engine such as Google or social networking services (SNSs) such as blogs, Twitter, and Facebook. The emotional polarity of positive and negative reviews helps a user decide on whether or not to make a trip. Sentiment analysis of customer reviews has become an important research topic as datamining technology is widely accepted for text mining of the Web. Sentiment analysis has been used to classify documents through machine learning techniques, such as the decision tree, neural networks, and support vector machines (SVMs). is used to determine the attitude, position, and sensibility of people who write articles about various topics that are published on the Web. Regardless of the polarity of customer reviews, emotional reviews are very helpful materials for analyzing the opinions of customers through their reviews. Sentiment analysis helps with understanding what customers really want instantly through the help of automated text mining techniques. Sensitivity analysis utilizes text mining techniques on text on the Web to extract subjective information in the text for text analysis. Sensitivity analysis is utilized to determine the attitudes or positions of the person who wrote the article and presented their opinion about a particular topic. In this study, we developed a model that selects a hot topic from user posts at China's online stock forum by using the k-means algorithm and self-organizing map (SOM). In addition, we developed a detecting model to predict a hot topic by using machine learning techniques such as logit, the decision tree, and SVM. We employed sensitivity analysis to develop our model for the selection and detection of hot topics from China's online stock forum. The sensitivity analysis calculates a sentimental value from a document based on contrast and classification according to the polarity sentimental dictionary (positive or negative). The online stock forum was an attractive site because of its information about stock investment. Users post numerous texts about stock movement by analyzing the market according to government policy announcements, market reports, reports from research institutes on the economy, and even rumors. We divided the online forum's topics into 21 categories to utilize sentiment analysis. One hundred forty-four topics were selected among 21 categories at online forums about stock. The posts were crawled to build a positive and negative text database. We ultimately obtained 21,141 posts on 88 topics by preprocessing the text from March 2013 to February 2015. The interest index was defined to select the hot topics, and the k-means algorithm and SOM presented equivalent results with this data. We developed a decision tree model to detect hot topics with three algorithms: CHAID, CART, and C4.5. The results of CHAID were subpar compared to the others. We also employed SVM to detect the hot topics from negative data. The SVM models were trained with the radial basis function (RBF) kernel function by a grid search to detect the hot topics. The detection of hot topics by using sentiment analysis provides the latest trends and hot topics in the stock forum for investors so that they no longer need to search the vast amounts of information on the Web. Our proposed model is also helpful to rapidly determine customers' signals or attitudes towards government policy and firms' products and services.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.139-157
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• 2016

Bankruptcy involves considerable costs, so it can have significant effects on a country's economy. Thus, bankruptcy prediction is an important issue. Over the past several decades, many researchers have addressed topics associated with bankruptcy prediction. Early research on bankruptcy prediction employed conventional statistical methods such as univariate analysis, discriminant analysis, multiple regression, and logistic regression. Later on, many studies began utilizing artificial intelligence techniques such as inductive learning, neural networks, and case-based reasoning. Currently, ensemble models are being utilized to enhance the accuracy of bankruptcy prediction. Ensemble classification involves combining multiple classifiers to obtain more accurate predictions than those obtained using individual models. Ensemble learning techniques are known to be very useful for improving the generalization ability of the classifier. Base classifiers in the ensemble must be as accurate and diverse as possible in order to enhance the generalization ability of an ensemble model. Commonly used methods for constructing ensemble classifiers include bagging, boosting, and random subspace. The random subspace method selects a random feature subset for each classifier from the original feature space to diversify the base classifiers of an ensemble. Each ensemble member is trained by a randomly chosen feature subspace from the original feature set, and predictions from each ensemble member are combined by an aggregation method. The k-nearest neighbors (KNN) classifier is robust with respect to variations in the dataset but is very sensitive to changes in the feature space. For this reason, KNN is a good classifier for the random subspace method. The KNN random subspace ensemble model has been shown to be very effective for improving an individual KNN model. The k parameter of KNN base classifiers and selected feature subsets for base classifiers play an important role in determining the performance of the KNN ensemble model. However, few studies have focused on optimizing the k parameter and feature subsets of base classifiers in the ensemble. This study proposed a new ensemble method that improves upon the performance KNN ensemble model by optimizing both k parameters and feature subsets of base classifiers. A genetic algorithm was used to optimize the KNN ensemble model and improve the prediction accuracy of the ensemble model. The proposed model was applied to a bankruptcy prediction problem by using a real dataset from Korean companies. The research data included 1800 externally non-audited firms that filed for bankruptcy (900 cases) or non-bankruptcy (900 cases). Initially, the dataset consisted of 134 financial ratios. Prior to the experiments, 75 financial ratios were selected based on an independent sample t-test of each financial ratio as an input variable and bankruptcy or non-bankruptcy as an output variable. Of these, 24 financial ratios were selected by using a logistic regression backward feature selection method. The complete dataset was separated into two parts: training and validation. The training dataset was further divided into two portions: one for the training model and the other to avoid overfitting. The prediction accuracy against this dataset was used to determine the fitness value in order to avoid overfitting. The validation dataset was used to evaluate the effectiveness of the final model. A 10-fold cross-validation was implemented to compare the performances of the proposed model and other models. To evaluate the effectiveness of the proposed model, the classification accuracy of the proposed model was compared with that of other models. The Q-statistic values and average classification accuracies of base classifiers were investigated. The experimental results showed that the proposed model outperformed other models, such as the single model and random subspace ensemble model.

• Journal of Life Science
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• v.22 no.4
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• pp.499-507
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• 2012

Previous work has characterized myomodulin A (MMA, PMSMLRLamide) and myomodulin E (MME, GLQMLRLamide) purified from the central nervous systems of the sea hare, $Aplysia$ $Kurodai$, using the anterior byssus retractor muscle (ABRM) of the mussel, $Mytilus$ $edulis$. The amino acid sequences of MMA and MME were the same as those of the myomodulin family peptide found in other mollusks. In this study, we synthesized MME, its derivatives, and other neuropeptides to investigate the relationship between the structure and biological activity of MME. The primary structures of MME's derivatives, Des[$Gly^1$]-MME, Des[$Gly^1,Leu^2$]-MME, and Des[$Gly^1,Leu^2,Gln^3$]-MME, were LQMLRLamide, QMLRLamide, and MLRLamide, respectively. MMA and synthetic peptides were tested on ABRM in $M.$ $edulis$ as well as muscle preparations in $Achatina$ $fulica$. MME displayed an inhibitory effect on phasic contraction of the ABRM at $1{\times}10^{-9}$ M or higher. MME also had a relaxing effect on the catch-tension of AMRM at $1{\times}10^{-8}$ M. Both MMA and its analogs stimulated a contractile response on the crop and relaxed the catch-relaxing response on the penial retractor muscle of $A.$ $fulica$. These results suggest that MME and its analogs have modulatory effects on various muscles of mollusks. This study has also laid the groundwork for future neural and circuit modulation studies during animal behavioral changes.

• Korean Journal of Ichthyology
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• v.4 no.1
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• pp.1-13
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• 1992

The gobiid fish, Luciogobius guttalus Gill has an anguilliform with some blackish and reddish brown color in life. It grows up to 90mm in total length. The specimens have been collected from several localities in the southern parts of Korea and Hokkaido, Japan. During the ebb tide, the fish was found in high level of intertidal zone exposed to the air among pebbles in the hollows and slopes of rocks. There are also some other small gobiid fishes comprising 3 species of relative gobies and 1 species of blennioid fish. A total of 5 egg masses were collected from the coast of Haeundae in April to May 1990. Each egg mass was deposited in one layer on the underside of a stone embedded in pebbles and guarded by the male parent. The eggs are club-shaped ranging from 2.71 to 2.80mm in long axis and from 0.65 to 0.74mm in short axis. The eggs were hatched in 98 hours after incubatied at the temperature varying from 19.5 to $25.5^{\circ}C$The newly hatched larvae were from 3.85 to 4.00mm in total length with 35~36 myomeres. In eleven days after hatching, total length reached 5.50mm. The part of the fin-fold of the future dorsal and anal fins became high. In sixteen days after hatching, the lavae averaged 6.20mm in total length and the caudal notochord flex at $45^{\circ}$. The larvae reached the juvenile stage in 48~50 days after hatching and attained 12.80~14.00mm in total length, and all fin-rays was formed. Ossification of the cranium took place at 5.50mm of mean total length in parasphenoid and basioccipital. Ossification of the visceral skeleton occurred in areas where active movements of bones are required, notalbly in the parts of feeding and respiration. Vertebrae began to develop from the anterior end to ossify posteriorly. Neural and haemal spines of vertebrae ossified always prior to the corresponding centra. When larvae reached to about 6.60mm in mean total length (17~18 days after hatching), jaw bones were more repidly ossified than vertebrae and cranium. Ossification of all bones nearly completed when the larvae reached to 13.40mm in mean total length (47~50 days after hatching).

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.71-89
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• 2016

Sentiment analysis is used for identifying emotions or sentiments embedded in the user generated data such as customer reviews from blogs, social network services, and so on. Various research fields such as computer science and business management can take advantage of this feature to analyze customer-generated opinions. In previous studies, the star rating of a review is regarded as the same as sentiment embedded in the text. However, it does not always correspond to the sentiment polarity. Due to this supposition, previous studies have some limitations in their accuracy. To solve this issue, the present study uses a supervised sentiment classification model to measure a more accurate sentiment polarity. This study aims to propose an advanced sentiment classifier and to discover the correlation between movie reviews and box-office success. The advanced sentiment classifier is based on two supervised machine learning techniques, the Support Vector Machines (SVM) and Feedforward Neural Network (FNN). The sentiment scores of the movie reviews are measured by the sentiment classifier and are analyzed by statistical correlations between movie reviews and box-office success. Movie reviews are collected along with a star-rate. The dataset used in this study consists of 1,258,538 reviews from 175 films gathered from Naver Movie website (movie.naver.com). The results show that the proposed sentiment classifier outperforms Naive Bayes (NB) classifier as its accuracy is about 6% higher than NB. Furthermore, the results indicate that there are positive correlations between the star-rate and the number of audiences, which can be regarded as the box-office success of a movie. The study also shows that there is the mild, positive correlation between the sentiment scores estimated by the classifier and the number of audiences. To verify the applicability of the sentiment scores, an independent sample t-test was conducted. For this, the movies were divided into two groups using the average of sentiment scores. The two groups are significantly different in terms of the star-rated scores.