• Science of Emotion and Sensibility
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• v.13 no.1
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• pp.47-60
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• 2010

Most of the researches about classification usually have used kNN(k-Nearest Neighbor), SVM(Support Vector Machine), which are known as learn-based model, and Bayesian classifier, NNA(Neural Network Algorithm), which are known as statistics-based methods. However, there are some limitations of space and time when classifying so many web pages in recent internet. Moreover, most studies of classification are using uni-gram feature representation which is not good to represent real meaning of words. In case of Korean web page classification, there are some problems because of korean words property that the words have multiple meanings(polysemy). For these reasons, LSA(Latent Semantic Analysis) is proposed to classify well in these environment(large data set and words' polysemy). LSA uses SVD(Singular Value Decomposition) which decomposes the original term-document matrix to three different matrices and reduces their dimension. From this SVD's work, it is possible to create new low-level semantic space for representing vectors, which can make classification efficient and analyze latent meaning of words or document(or web pages). Although LSA is good at classification, it has some drawbacks in classification. As SVD reduces dimensions of matrix and creates new semantic space, it doesn't consider which dimensions discriminate vectors well but it does consider which dimensions represent vectors well. It is a reason why LSA doesn't improve performance of classification as expectation. In this paper, we propose new LSA which selects optimal dimensions to discriminate and represent vectors well as minimizing drawbacks and improving performance. This method that we propose shows better and more stable performance than other LSAs' in low-dimension space. In addition, we derive more improvement in classification as creating and selecting features by reducing stopwords and weighting specific values to them statistically.

• Korean Journal of Cognitive Science
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• v.27 no.3
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• pp.441-467
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• 2016

This fMRI study is aim to investigate effects of competitive environment in cognitive empathic process in human brain. Empathy is known as a crucial factor for human's adaptive behavior in aspects of social cognition and it is almost automatic process, on the other hand competitive situation is psychologically devastated environment to win someone for getting rewards. We hypnotized that reading and understanding of other person's mind are a specific characteristic related to survival evolutionarily, however competition would have an effect on the empathic cognitive process because of mechanisms of competition. To manipulate the competitive atmosphere, one researcher took a role of competitor against participants and they were instructed to get monetary rewards when their performance was better than a competitor. 21 participants(9 males and 12 females) performed to judge the emotional valence of the empathic task consisted of illustrated images with various situation could be experienced in real world as on $1^{st}$ person perspective in both competitive and non-competitive condition, and did same performance with objects stimulus in control condition. In order to examine the competition effects on empathic process,, hemodynamic response were obtained during fMRI session and the imaging data were analyzed to identify brain regions where responses to each condition across the two consecutive runs. Participants' reaction time in competitive condition was faster statistically significant than non-competitive one. Activation for competitive condition increased in the following areas: ACC, mPFC, SMG, thalamus extended caudate and Nacc, parahippocampal gyrus, and for non-competitive condition increased paracingulate gyrus, temporal pole, vmPFC, superior occipital gyrus. As a result of regression analysis using empathic scores as covariance, the rSMG, IFG, fusiform gyrus, thalamus, putamen were correlated with higher empathic levels, and TPJ were correlated with lower empathic scores. We suggest that these observations could mean competitive environment have an effect on neural base of cognitive empathic process.

• Journal of Biomedical Engineering Research
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• v.25 no.5
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• pp.421-430
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• 2004

Clinically, it is almost impossible for a physician to distinguish subtle changes of frequency spectrum by using a stethoscope alone especially in the early stage of thrombus formation. Considering that reliability of mechanical valve is paramount because the failure might end up with patient death, early detection of valve thrombus using noninvasive technique is important. Thus the study was designed to provide a tool for early noninvasive detection of valve thrombus by observing shift of frequency spectrum of acoustic signals with computer aid diagnosis system. A thrombus model was constructed on commercialized mechanical valves using polyurethane or silicon. Polyurethane coating was made on the valve surface, and silicon coating on the sewing ring of the valve. To simulate pannus formation, which is fibrous tissue overgrowth obstructing the valve orifice, the degree of silicone coating on the sewing ring varied from 20％, 40％, 60％ of orifice obstruction. In experiment system, acoustic signals from the valve were measured using microphone and amplifier. The microphone was attached to a coupler to remove environmental noise. Acoustic signals were sampled by an AID converter, frequency spectrum was obtained by the algorithm of spectral analysis. To quantitatively distinguish the frequency peak of the normal valve from that of the thrombosed valves, analysis using a neural network was employed. A return map was applied to evaluate continuous monitoring of valve motion cycle. The in-vivo data also obtained from animals with mechanical valves in circulatory devices as well as patients with mechanical valve replacement for 1 year or longer before. Each spectrum wave showed a primary and secondary peak. The secondary peak showed changes according to the thrombus model. In the mock as well as the animal study, both spectral analysis and 3-layer neural network could differentiate the normal valves from thrombosed valves. In the human study, one of 10 patients showed shift of frequency spectrum, however the presence of valve thrombus was yet to be determined. Conclusively, acoustic signal measurement can be of suggestive as a noninvasive diagnostic tool in early detection of mechanical valve thrombosis.

• Journal of Preventive Medicine and Public Health
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• v.9 no.1
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• pp.55-64
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• 1976

This paper presents the results of a survey for the causes of sensori-neural hearing impairment in Korea, The subjects were 1,676 children of total 2,928 enrolled in 16 Deaf Schools; two schools in each area of Seoul, Busan, Kyoungbook, Kyoungnam, Kyounggi and Chunbug, and each one in Chungnam, Chungbug, Chunnam and Jaeju. The data were collected by questionaire with 28 items distributed to their parents. The filling in the check lists were performed by their class teachers, interviewer, for 18 months from September, 1975 to february, 1976. The questionable or missed problems were reaffirmed. The results obtained were as follows. Most of the reasons, 78.5% were acquired characters that could be developed during pregnant period, the time of delivery and the time of after birth. The pure hereditary reasons except the cases complexed with one or two were only 11.3%. Those who could not be defined with any reasons were 10.2%. Among the acquired causes, 5.8% of total subjects were developed for pregnancy: 3.3%, during delivery; and 69.7%, after birth. In the pregnant period, the drug intoxications were 2.4% of total subjects, several diseases such as influenja, bleeding, surgical operation, venereal diseases and rubella etc. were about one percent, and the accompanied with some symptoms of pregnancy intoxication and traumatic events were 2.4%, During time, the cases with delayed rhythmical pain were 16 persons, the immaturities were 11, the asphyxial cases were nine, the errors of forceps delivery were seven, the cases of low body weight inspite of full term were four, the cases with cesarian section were three, the head injuries were two, and the accompanied with three kinds of above reasons were three. During after birth, the cases with acute communicable diseases were 35.4% of total subjects, the fever unknown origin were 16.1%, the chronic otitis media were 3.7%, the meningitis were 3.5%, the gastric and nutritional diseases were 3.5%, the drug intoxications were 4.8%, the blood diseases were 0.3% and the other causes were 2.2%. Here by acute communicable diseases, some importants were measle, 10.1% of total subjects; meningitis, 7.3%; convulsion with some reasons, 4.9%; poliomyelitis. 3.2%; encephalitis, 2.4%; and mumps, rubella, pertusis, scarlet fever, and small pox were somewhat played a role in. Among 59 cases with train diseases, 53 were concussion by the accidents, such as traffic and falling or sliping down etc., the cerebral paralysis and hydrocephalus were two, respectively. And the blood diseases were severe newjaundice in all five cases. If we were summarized with the above mentioned, most of the hearing impairments were introduced by the combined reasons with familial or hereditary factors and the acquired, than by a simple disease. Among the congenital or hereditary hearing impairments classified to now a day, we suppose that the many cases with the acquired causes during pregnancy, delivery and after birth were complexed. Subsequently, the maternal and child health should be more and more developed in our country, also.

• Korean Journal of Remote Sensing
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• v.37 no.5_3
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• pp.1475-1490
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• 2021

Detecting oil spill area using statistical characteristics of SAR images has limitations in that classification algorithm is complicated and is greatly affected by outliers. To overcome these limitations, studies using neural networks to classify oil spills are recently investigated. However, the studies to evaluate whether the performance of model shows a consistent detection performance for various oil spill cases were insufficient. Therefore, in this study, two CNNs (Convolutional Neural Networks) with basic structures(Simple CNN and U-net) were used to discover whether there is a difference in detection performance according to the structure of CNN and distribution characteristics of oil spill. As a result, through the method proposed in this study, the Simple CNN with contracting path only detected oil spill with an F1 score of 86.24% and U-net, which has both contracting and expansive path showed an F1 score of 91.44%. Both models successfully detected oil spills, but detection performance of the U-net was higher than Simple CNN. Additionally, in order to compare the accuracy of models according to various oil spill cases, the cases were classified into four different categories according to the spatial distribution characteristics of the oil spill (presence of land near the oil spill area) and the clarity of border between oil and seawater. The Simple CNN had F1 score values of 85.71%, 87.43%, 86.50%, and 85.86% for each category, showing the maximum difference of 1.71%. In the case of U-net, the values for each category were 89.77%, 92.27%, 92.59%, and 92.66%, with the maximum difference of 2.90%. Such results indicate that neither model showed significant differences in detection performance by the characteristics of oil spill distribution. However, the difference in detection tendency was caused by the difference in the model structure and the oil spill distribution characteristics. In all four oil spill categories, the Simple CNN showed a tendency to overestimate the oil spill area and the U-net showed a tendency to underestimate it. These tendencies were emphasized when the border between oil and seawater was unclear.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.1
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• pp.29-41
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• 2019

Terrestrial Gross Primary Production (GPP) is the largest global carbon flux, and forest ecosystems are important because of the ability to store much more significant amounts of carbon than other terrestrial ecosystems. There have been several attempts to estimate GPP using mechanism-based models. However, mechanism-based models including biological, chemical, and physical processes are limited due to a lack of flexibility in predicting non-stationary ecological processes, which are caused by a local and global change. Instead mechanism-free methods are strongly recommended to estimate nonlinear dynamics that occur in nature like GPP. Therefore, we used the mechanism-free machine learning techniques to estimate the daily GPP. In this study, support vector machine (SVM), random forest (RF) and artificial neural network (ANN) were used and compared with the traditional multiple linear regression model (LM). MODIS products and meteorological parameters from eddy covariance data were employed to train the machine learning and LM models from 2006 to 2013. GPP prediction models were compared with daily GPP from eddy covariance measurement in a deciduous forest in South Korea in 2014 and 2015. Statistical analysis including correlation coefficient (R), root mean square error (RMSE) and mean squared error (MSE) were used to evaluate the performance of models. In general, the models from machine-learning algorithms (R = 0.85 - 0.93, MSE = 1.00 - 2.05, p < 0.001) showed better performance than linear regression model (R = 0.82 - 0.92, MSE = 1.24 - 2.45, p < 0.001). These results provide insight into high predictability and the possibility of expansion through the use of the mechanism-free machine-learning models and remote sensing for predicting non-stationary ecological processes such as seasonal GPP.

• The Journal of the Acoustical Society of Korea
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• v.42 no.6
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• pp.603-616
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• 2023

In this study, machine learning-based clustering and classification of residential noise in apartment buildings was conducted using frequency and temporal characteristics. First, a residential noise source dataset was constructed . The residential noise source dataset was consisted of floor impact, airborne, plumbing and equipment noise, environmental, and construction noise. The clustering of residential noise was performed by K-Means clustering method. For frequency characteristics, L_eq and L_max values were derived for 1/1 and 1/3 octave band for each sound source. For temporal characteristics, Leq values were derived at every 6 ms through sound pressure level analysis for 5 s. The number of k in K-Means clustering method was determined through the silhouette coefficient and elbow method. The clustering of residential noise source by frequency characteristic resulted in three clusters for both L_eq and L_max analysis. Temporal characteristic clustered residential noise source into 9 clusters for L_eq and 11 clusters for L_max. Clustering by frequency characteristic clustered according to the proportion of low frequency band. Then, to utilize the clustering results, the residential noise source was classified using three kinds of machine learning. The results of the residential noise classification showed the highest accuracy and f1-score for data labeled with L_eq values in 1/3 octave bands, and the highest accuracy and f1-score for classifying residential noise sources with an Artificial Neural Network (ANN) model using both frequency and temporal features, with 93 % accuracy and 92 % f1-score.

• The Transactions of the Korea Information Processing Society
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• v.13 no.4
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• pp.189-198
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• 2024

Lipreading is one of the important parts of speech recognition, and several studies have been conducted to improve the performance of lipreading in lipreading systems for speech recognition. Recent studies have used method to modify the model architecture of lipreading system to improve recognition performance. Unlike previous research that improve recognition performance by modifying model architecture, we aim to improve recognition performance without any change in model architecture. In order to improve the recognition performance without modifying the model architecture, we refer to the cues used in human lipreading and set other regions such as chin and cheeks as regions of interest along with the lip region, which is the existing region of interest of lipreading systems, and compare the recognition rate of each region of interest to propose the highest performing region of interest In addition, assuming that the difference in normalization results caused by the difference in interpolation method during the process of normalizing the size of the region of interest affects the recognition performance, we interpolate the same region of interest using nearest neighbor interpolation, bilinear interpolation, and bicubic interpolation, and compare the recognition rate of each interpolation method to propose the best performing interpolation method. Each region of interest was detected by training an object detection neural network, and dynamic time warping templates were generated by normalizing each region of interest, extracting and combining features, and mapping the dimensionality reduction of the combined features into a low-dimensional space. The recognition rate was evaluated by comparing the distance between the generated dynamic time warping templates and the data mapped to the low-dimensional space. In the comparison of regions of interest, the result of the region of interest containing only the lip region showed an average recognition rate of 97.36%, which is 3.44% higher than the average recognition rate of 93.92% in the previous study, and in the comparison of interpolation methods, the bilinear interpolation method performed 97.36%, which is 14.65% higher than the nearest neighbor interpolation method and 5.55% higher than the bicubic interpolation method. The code used in this study can be found a https://github.com/haraisi2/Lipreading-Systems.

• 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.

• Investigative Magnetic Resonance Imaging
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• v.9 no.1
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• pp.9-15
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• 2005

Purpose : This study attempted to investigate the effects of supply of highly concentrated $(30\%)$ oxygen on human ability of visuospatial cognition using time course data analysis of functional Magnetic Resonance Imaging (fMRI). Materials and Methods : To select an item set in the visuospatial performance test, two questionnaires with similar difficulty were developed through group testing. A group test was administered to 263 college students. Two types of questionnaire containing 20 questions were developed to measure the ability of visuospatial cognition. Eight college students (right-handed male, average age of 23.5 yrs) were examined for fMRI study. The experiment consisted of two runs of the visuospatial cognition testing, one with $21\%$ level of oxygen and the other with $30\%$ oxygen level. Each run consisted of 4 blocks, each containing control and visuospatial items. Functional brain images were taken from 37 MRI using the single-shot EPI method. Using the subtraction procedure, activated areas in the brain during visuospatial tasks were color-coded by t-score. To investigate the time course data in each activated area from brain images, 4 typical regions (cerebellum, occipital lobe, parietal lobe, and frontal lobe) were selected. Results : The average accuracy was $50.63{\pm}8.63$ and $62.50{\pm}9.64$ for $21\%\;and\;30\%$ oxygen respectively, and a statistically significant difference was found in the accuracy between the two types of oxygen (p<0.05). There were more activation areas observed at the cerebellum, occipital lobe, parietal lobe and frontal lobe with $30\%$ oxygen administration. The rate of increase in the cerebellum, occipital lobe and parietal lobe was $17\%$ and that of the frontal lobe, $50\%$. Especially, there were increase of intensity of BOLD signal at the parietal lobe with $30\%$ oxygen administration. The increase rate of the left parietal lobe was $1.4\%$ and that of the right parietal lobe, $1.7\%$. Conclusion : It is concluded that while performing visuospatial tasks, high concentrations of oxygen administration make oxygen administration sufficient, thus making neural network activate more, and the ability to perform visuospatial tasks increase.