• Economic and Environmental Geology
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• v.50 no.4
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• pp.287-302
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• 2017

The carbonate rocks of the Daegi Formation are composed of the limestone at the upper and lower zones, and the dolomite at the middle zone, in which the upper zone has higher CaO content than others. The colors of carbonate rock in the Daegi Formation can be divided into five types; white, light brown, light gray, gray, and dark gray. The white to light gray colored rocks correspond to the high purity limestone with 53.15 ~ 55.64 wt. % CaO, and the light brown colored rocks contain 20.71 ~ 21.67 wt. % MgO. The bleaching of carbonate rocks are not related to CaO composition of the rocks, as light gray rocks tend to be higher in CaO content than those of the white rocks at the lower zone. The pelitic components are also occasionally increased in white limestone than light grey one. $Al_2O_3$ is one of the most difficult content to remove during hydrothermal processes, so the interpretation that the limestone is purified together with hydrothemral bleaching, has little merit. The wide range (over 16 ‰) of ${\delta}^{18}O_{SMOW}$, smaller variation (within 2 ‰) of ${\delta}^{13}C_{PDB}$ are apparent in both the upper and lower zones, which indicate the Daegi Formation had been affected overall by hydrothermal fluids. The K-Ar isotopic age of hydrothermal alteration in the GMI limestone mine is $85.1{\pm}1.7Ma$. Gradual change from grey through light grey to white limestone is accompaned by lower oxygen stable isotope values, which is major evidence that the hydrothermal effect is the main process of the bleaching. Although the Daegi Formation has suffered from hydrothermal activity and increase in whiteness, there is no clear evidence demonstrating the relationship between bleaching and high purity of limestone. The purification of limestone has nothing to do with the hydrothermal activity in this area. Instead, it should be considered that the change of sedimentary environment related to see-level fluctuation which can prevent deposition of pelitic components especially $Al_2O_3$ contrbuted to the formation of the high purity limestone in the upper zone of the Daegi Formation. Considering the evidences such as increase in CaO content of limestone by depth, gradual change from calcite to dolomite at the lower zones, and occurring the high purity limestone at the upper zone, the interpretation of sequence stratigraphic aspect to the formation of the high purity Daegi limestone appears to be more suitable than that of hydrothermal alteration origin.

• 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 Intelligence and Information Systems
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• v.26 no.2
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• pp.57-78
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• 2020

With the development of the Internet, consumers have had an opportunity to check product information easily through E-Commerce. Product reviews used in the process of purchasing goods are based on user experience, allowing consumers to engage as producers of information as well as refer to information. This can be a way to increase the efficiency of purchasing decisions from the perspective of consumers, and from the seller's point of view, it can help develop products and strengthen their competitiveness. However, it takes a lot of time and effort to understand the overall assessment and assessment dimensions of the products that I think are important in reading the vast amount of product reviews offered by E-Commerce for the products consumers want to compare. This is because product reviews are unstructured information and it is difficult to read sentiment of reviews and assessment dimension immediately. For example, consumers who want to purchase a laptop would like to check the assessment of comparative products at each dimension, such as performance, weight, delivery, speed, and design. Therefore, in this paper, we would like to propose a method to automatically generate multi-dimensional product assessment scores in product reviews that we would like to compare. The methods presented in this study consist largely of two phases. One is the pre-preparation phase and the second is the individual product scoring phase. In the pre-preparation phase, a dimensioned classification model and a sentiment analysis model are created based on a review of the large category product group review. By combining word embedding and association analysis, the dimensioned classification model complements the limitation that word embedding methods for finding relevance between dimensions and words in existing studies see only the distance of words in sentences. Sentiment analysis models generate CNN models by organizing learning data tagged with positives and negatives on a phrase unit for accurate polarity detection. Through this, the individual product scoring phase applies the models pre-prepared for the phrase unit review. Multi-dimensional assessment scores can be obtained by aggregating them by assessment dimension according to the proportion of reviews organized like this, which are grouped among those that are judged to describe a specific dimension for each phrase. In the experiment of this paper, approximately 260,000 reviews of the large category product group are collected to form a dimensioned classification model and a sentiment analysis model. In addition, reviews of the laptops of S and L companies selling at E-Commerce are collected and used as experimental data, respectively. The dimensioned classification model classified individual product reviews broken down into phrases into six assessment dimensions and combined the existing word embedding method with an association analysis indicating frequency between words and dimensions. As a result of combining word embedding and association analysis, the accuracy of the model increased by 13.7%. The sentiment analysis models could be seen to closely analyze the assessment when they were taught in a phrase unit rather than in sentences. As a result, it was confirmed that the accuracy was 29.4% higher than the sentence-based model. Through this study, both sellers and consumers can expect efficient decision making in purchasing and product development, given that they can make multi-dimensional comparisons of products. In addition, text reviews, which are unstructured data, were transformed into objective values such as frequency and morpheme, and they were analysed together using word embedding and association analysis to improve the objectivity aspects of more precise multi-dimensional analysis and research. This will be an attractive analysis model in terms of not only enabling more effective service deployment during the evolving E-Commerce market and fierce competition, but also satisfying both customers.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.31 no.2
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• pp.142-152
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• 1995

The combined bottom trawl and hydroacoustic survey was conducted by using the training ship Oshoro Maru belong to Hokkaido University in November 1989-1992 and the training ship Nagasaki Maru belong to Nagasaki University in April 1994 in the East China Sea, respectively. The aim of the investigations was to collect the target strength data of fish school in relation to the biomass estimation of fish in the survey area. The hydroacoustic survey was performed by using the scientific echo sounder system operating at three frequencies of 25, 50 and 100kHz with a microcomputer-based echo integrator. Fish samples were collected by bottom trawling and during the trawl surveys, the openings of otter board and net mouth were measured. The target strength of fish school was estimated from the relationship between the volume back scattering strength for the depth strata of bottom trawling and the weight per unit volume of trawl catches. A portion of the trawl catches preserved in frozon condition on board, the target strength measurements for the defrosted samples of ten species were conducted in the laboratory tank, and the relationship between target strength and fish weight was examined. In order to investigate the effect of swimbladder on target strength, the volume of the swimbladder of white croaker, Argyrosomus argentatus, sampled by bottom trawling was measured by directly removing the gas in the swimbladder with a syringe on board. The results obtained can be summarized as follows: 1.The relationship between the mean volume back scattering strength (, dB) for the depth strata of trawl hauls and the weight(C, $kg/\textrm{m}^3$) per unit volume of trawl catches were expressed by the following equations : 25kHz : = - 29.8+10Log(C) 50kHz : = - 32.4+10Log(C) 100kHz : = - 31.7+10Log(C) The mean target strength estimates for three frequencies of 25, 50 and 100 kHz derived from these equations were -29.8dB/kg, -32.4dB/kg and -31.7dB/kg, respectively. 2. The relationship between target strength and body weight for the fish samples of ten species collected by trawl surveys were expressed by the following equations : 25kHz : TS = - 34.0+10Log($W^{\frac{2}{3}}$) 100kHz : TS = - 37.8+10Log($W^{\frac{2}{3}}$) The mean target strength estimates for two frequencies of 25 and 100 kHz derived from these equations were -34.0dB/kg, -37.8dB/kg, respectively. 3. The representative target strength values for demersal fish populations of the East China Sea at two frequencies of 25 and 100 kHz were estimated to be -31.4dB/kg, -33.8dB/kg, respectively. 4. The ratio of the equivalent radius of swimbladder to body length of white croaker was 0.089 and the volume of swimbladder was estimated to be approximately 10% of total body volume.

• Microbiology and Biotechnology Letters
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• v.46 no.4
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• pp.360-371
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• 2018

Extracts and fractions of Anemarrhena asphodeloides Bunge were prepared and their physiological activities and components were analyzed. Antimicrobial activities of the ethyl acetate and aglycone fractions were $78{\mu}g/ml$ and $31{\mu}g/ml$, respectively, for Staphylococcus aureus and $156{\mu}g/ml$ and $125{\mu}g/ml$, respectively, for Pseudomonas aeruginosa. 1,1-Diphenyl-2-picrylhydrazyl free radical scavenging activities ($FSC_{50}$) of 50% ethanol extract, ethyl acetate fraction, and aglycone fraction of A. asphodeloides extracts were $146.2{\mu}g/ml$, $23.19{\mu}g/ml$, and $71.06{\mu}g/ml$, respectively. The total antioxidant capacity ($OSC_{50}$) in an $Fe^{3+}$-EDTA/hydrogen peroxide ($H_2O_2$) system were $17.5{\mu}g/ml$, $1.5{\mu}g/ml$, and $1.4{\mu}g/ml$, respectively. The cytoprotective effect (${\tau}_{50}$) in $^1O_2$-induced erythrocyte hemolysis was 181 min with $4{\mu}g/ml$ of the aglycone fraction. The ${\tau}_{50}$ of the aglycone fraction was approximately 4-times higher than that of (+)-${\alpha}$-tocopherol (${\tau}_{50}$, 41 min). Analysis of $H_2O_2$-induced damage of HaCaT cells revealed that the maximum cell viabilities for the 50% ethanol extract, ethyl acetate fraction, and aglycone fraction were 86.23%, 86.59%, and 89.70%, respectively. The aglycone fraction increased cell viability up to 11.53% at $1{\mu}g/ml$ compared to the positive control treated with $H_2O_2$. Analysis of ultraviolet B radiation-induced HaCaT cell damage revealed up to 41.77% decreased intracellular reactive oxygen species in the $2{\mu}g/ml$ aglycone fraction compared with the positive control treated with ultraviolet B radiation. The findings suggest that the extracts and fractions of A. asphodeloides Bunge have potential applications in the field of cosmetics as natural preservatives and antioxidants.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.219-240
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• 2018

Sentiment analysis, which is one of the text mining techniques, is a method for extracting subjective content embedded in text documents. Recently, the sentiment analysis methods have been widely used in many fields. As good examples, data-driven surveys are based on analyzing the subjectivity of text data posted by users and market researches are conducted by analyzing users' review posts to quantify users' reputation on a target product. The basic method of sentiment analysis is to use sentiment dictionary (or lexicon), a list of sentiment vocabularies with positive, neutral, or negative semantics. In general, the meaning of many sentiment words is likely to be different across domains. For example, a sentiment word, 'sad' indicates negative meaning in many fields but a movie. In order to perform accurate sentiment analysis, we need to build the sentiment dictionary for a given domain. However, such a method of building the sentiment lexicon is time-consuming and various sentiment vocabularies are not included without the use of general-purpose sentiment lexicon. In order to address this problem, several studies have been carried out to construct the sentiment lexicon suitable for a specific domain based on 'OPEN HANGUL' and 'SentiWordNet', which are general-purpose sentiment lexicons. However, OPEN HANGUL is no longer being serviced and SentiWordNet does not work well because of language difference in the process of converting Korean word into English word. There are restrictions on the use of such general-purpose sentiment lexicons as seed data for building the sentiment lexicon for a specific domain. In this article, we construct 'KNU Korean Sentiment Lexicon (KNU-KSL)', a new general-purpose Korean sentiment dictionary that is more advanced than existing general-purpose lexicons. The proposed dictionary, which is a list of domain-independent sentiment words such as 'thank you', 'worthy', and 'impressed', is built to quickly construct the sentiment dictionary for a target domain. Especially, it constructs sentiment vocabularies by analyzing the glosses contained in Standard Korean Language Dictionary (SKLD) by the following procedures: First, we propose a sentiment classification model based on Bidirectional Long Short-Term Memory (Bi-LSTM). Second, the proposed deep learning model automatically classifies each of glosses to either positive or negative meaning. Third, positive words and phrases are extracted from the glosses classified as positive meaning, while negative words and phrases are extracted from the glosses classified as negative meaning. Our experimental results show that the average accuracy of the proposed sentiment classification model is up to 89.45%. In addition, the sentiment dictionary is more extended using various external sources including SentiWordNet, SenticNet, Emotional Verbs, and Sentiment Lexicon 0603. Furthermore, we add sentiment information about frequently used coined words and emoticons that are used mainly on the Web. The KNU-KSL contains a total of 14,843 sentiment vocabularies, each of which is one of 1-grams, 2-grams, phrases, and sentence patterns. Unlike existing sentiment dictionaries, it is composed of words that are not affected by particular domains. The recent trend on sentiment analysis is to use deep learning technique without sentiment dictionaries. The importance of developing sentiment dictionaries is declined gradually. However, one of recent studies shows that the words in the sentiment dictionary can be used as features of deep learning models, resulting in the sentiment analysis performed with higher accuracy (Teng, Z., 2016). This result indicates that the sentiment dictionary is used not only for sentiment analysis but also as features of deep learning models for improving accuracy. The proposed dictionary can be used as a basic data for constructing the sentiment lexicon of a particular domain and as features of deep learning models. It is also useful to automatically and quickly build large training sets for deep learning models.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.139-156
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• 2021

The biggest reason for using a deep learning model in image classification is that it is possible to consider the relationship between each region by extracting each region's features from the overall information of the image. However, the CNN model may not be suitable for emotional image data without the image's regional features. To solve the difficulty of classifying emotion images, many researchers each year propose a CNN-based architecture suitable for emotion images. Studies on the relationship between color and human emotion were also conducted, and results were derived that different emotions are induced according to color. In studies using deep learning, there have been studies that apply color information to image subtraction classification. The case where the image's color information is additionally used than the case where the classification model is trained with only the image improves the accuracy of classifying image emotions. This study proposes two ways to increase the accuracy by incorporating the result value after the model classifies an image's emotion. Both methods improve accuracy by modifying the result value based on statistics using the color of the picture. When performing the test by finding the two-color combinations most distributed for all training data, the two-color combinations most distributed for each test data image were found. The result values were corrected according to the color combination distribution. This method weights the result value obtained after the model classifies an image's emotion by creating an expression based on the log function and the exponential function. Emotion6, classified into six emotions, and Artphoto classified into eight categories were used for the image data. Densenet169, Mnasnet, Resnet101, Resnet152, and Vgg19 architectures were used for the CNN model, and the performance evaluation was compared before and after applying the two-stage learning to the CNN model. Inspired by color psychology, which deals with the relationship between colors and emotions, when creating a model that classifies an image's sentiment, we studied how to improve accuracy by modifying the result values based on color. Sixteen colors were used: red, orange, yellow, green, blue, indigo, purple, turquoise, pink, magenta, brown, gray, silver, gold, white, and black. It has meaning. Using Scikit-learn's Clustering, the seven colors that are primarily distributed in the image are checked. Then, the RGB coordinate values of the colors from the image are compared with the RGB coordinate values of the 16 colors presented in the above data. That is, it was converted to the closest color. Suppose three or more color combinations are selected. In that case, too many color combinations occur, resulting in a problem in which the distribution is scattered, so a situation fewer influences the result value. Therefore, to solve this problem, two-color combinations were found and weighted to the model. Before training, the most distributed color combinations were found for all training data images. The distribution of color combinations for each class was stored in a Python dictionary format to be used during testing. During the test, the two-color combinations that are most distributed for each test data image are found. After that, we checked how the color combinations were distributed in the training data and corrected the result. We devised several equations to weight the result value from the model based on the extracted color as described above. The data set was randomly divided by 80:20, and the model was verified using 20% of the data as a test set. After splitting the remaining 80% of the data into five divisions to perform 5-fold cross-validation, the model was trained five times using different verification datasets. Finally, the performance was checked using the test dataset that was previously separated. Adam was used as the activation function, and the learning rate was set to 0.01. The training was performed as much as 20 epochs, and if the validation loss value did not decrease during five epochs of learning, the experiment was stopped. Early tapping was set to load the model with the best validation loss value. The classification accuracy was better when the extracted information using color properties was used together than the case using only the CNN architecture.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.149-171
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• 2020

The period at university is to make decision about getting an actual job. As our society develops rapidly and highly, jobs are diversified, subdivided, and specialized, and students' job preparation period is also getting longer and longer. This study analyzed the log data of college students to see how the various activities that college students experience inside and outside of school might have influences on employment. For this experiment, students' various activities were systematically classified, recorded as an activity data and were divided into six core competencies (Job reinforcement competency, Leadership & teamwork competency, Globalization competency, Organizational commitment competency, Job exploration competency, and Autonomous implementation competency). The effect of the six competency levels on the employment status (employed group, unemployed group) was analyzed. As a result of the analysis, it was confirmed that the difference in level between the employed group and the unemployed group was significant for all of the six competencies, so it was possible to infer that the activities at the school are significant for employment. Next, in order to analyze the impact of the six competencies on the qualitative performance of employment, we had ANOVA analysis after dividing the each competency level into 2 groups (low and high group), and creating 6 groups by the range of first annual salary. Students with high levels of globalization capability, job search capability, and autonomous implementation capability were also found to belong to a higher annual salary group. The theoretical contributions of this study are as follows. First, it connects the competencies that can be extracted from the school experience with the competencies in the Human Resource Management field and adds job search competencies and autonomous implementation competencies which are required for university students to have their own successful career & life. Second, we have conducted this analysis with the competency data measured form actual activity and result data collected from the interview and research. Third, it analyzed not only quantitative performance (employment rate) but also qualitative performance (annual salary level). The practical use of this study is as follows. First, it can be a guide when establishing career development plans for college students. It is necessary to prepare for a job that can express one's strengths based on an analysis of the world of work and job, rather than having a no-strategy, unbalanced, or accumulating excessive specifications competition. Second, the person in charge of experience design for college students, at an organizations such as schools, businesses, local governments, and governments, can refer to the six competencies suggested in this study to for the user-useful experiences design that may motivate more participation. By doing so, one event may bring mutual benefits for both event designers and students. Third, in the era of digital transformation, the government's policy manager who envisions the balanced development of the country can make a policy in the direction of achieving the curiosity and energy of college students together with the balanced development of the country. A lot of manpower is required to start up novel platform services that have not existed before or to digitize existing analog products, services and corporate culture. The activities of current digital-generation-college-students are not only catalysts in all industries, but also for very benefit and necessary for college students by themselves for their own successful career development.

• The Journal of Korean Society for Radiation Therapy
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• v.26 no.2
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• pp.257-264
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• 2014

Purpose : Photoneutron dose in high-energy photon radiotherapy at linear accelerator increase the risk for secondary cancer. The purpose of this investigation is to evaluate the dose variation of photoneutron with different treatment method, flattening filter, dose rate and gantry angle in radiation therapy with high-energy photon beam ($E{\geq}8MeV$). Materials and Methods : TrueBeam $ST{\time}TM$(Ver1.5, Varian, USA) and Korea Tissue Equivalent Proportional Counter (KTEPC) were used to detect the photoneutron dose out of the high-energy photon field. Complex Patient plans using Eclipse planning system (Version 10.0, Varian, USA) was used to experiment with different treatment technique(IMRT, VMAT), condition of flattening filter and three different dose rate. Scattered photoneutron dose was measured at eight different gantry angles with open field (Field size : $5{\time}5cm$). Results : The mean values of the detected photoneutron dose from IMRT and VMAT were $449.7{\mu}Sv$, $2940.7{\mu}Sv$. The mean values of the detected photoneutron dose with Flattening Filter(FF) and Flattening Filter Free(FFF) were measured as $2940.7{\mu}Sv$, $232.0{\mu}Sv$. The mean values of the photoneutron dose for each test plan (case 1, case 2 and case 3) with FFF at the three different dose rate (400, 1200, 2400 MU/min) were $3242.5{\mu}Sv$, $3189.4{\mu}Sv$, $3191.2{\mu}Sv$ with case 1, $3493.2{\mu}Sv$, $3482.6{\mu}Sv$, $3477.2{\mu}Sv$ with case 2 and $4592.2{\mu}Sv$, $4580.0{\mu}Sv$, $4542.3{\mu}Sv$ with case 3, respectively. The mean values of the photoneutron dose at eight different gantry angles ($0^{\circ}$, $45^{\circ}$, $90^{\circ}$, $135^{\circ}$, $180^{\circ}$, $225^{\circ}$, $270^{\circ}$, $315^{\circ}$) were measured as $3.2{\mu}Sv$, $4.3{\mu}Sv$, $5.3{\mu}Sv$, $11.3{\mu}Sv$, $14.7{\mu}Sv$, $11.2{\mu}Sv$, $3.7{\mu}Sv$, $3.0{\mu}Sv$ at 10MV and as $373.7{\mu}Sv$, $369.6{\mu}Sv$, $384.4{\mu}Sv$, $423.6{\mu}Sv$, $447.1{\mu}Sv$, $448.0{\mu}Sv$, $384.5{\mu}Sv$, $377.3{\mu}Sv$ at 15MV. Conclusion : As a result, it is possible to reduce photoneutron dose using FFF mode and VMAT method with TrueBeam $ST{\time}TM$. The risk for secondary cancer of the patients will be decreased with continuous evaluation of the photoneutron dose.

• Journal of Yeungnam Medical Science
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• v.8 no.1
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• pp.107-126
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• 1991

The purpose of the study was to assess the morbidity pattern and the medical care utilization behavior of the urban residents in the poor area. The study population included 2,591 family members of 677 households in the poor area of Daemyong 8 Dong, Nam-Gu, Taegu and 2,686 family members of 688 households, near the poor area in the same Dong, were interviewed as a control group. On this study the household interview method was applied. Well-trained interviewers visited every household in the designated area and individually interviewed heads of households or housewives for general information, morbidity condition, and medical care utilization with a structured questionnaire. Individuals were interviewed from 1 to 30 December 1988. The major results were summarized as follows : The proportion of the people below 5 years of age was 4.2% of the total study population and 5.5% were above 65 years of age in the poor area. This was slightly higher than in the control area. The average monthly income of a household in the poor area was 403,000 won versus 529,000 won in the control area. Fifty-eight percent of the residents in the poor area and sixty-one percent in the control area were medical security beneficiaries, but the proportion of medical aid beneficiaries was 7.8% in the poor area and 4.6% in the control area. The 15-day period morbidity rate of acute illnesses was 57.1 per 1,000 in the poor area and 24.2 per 1,000 in the control area. Respiratory disease is the most common acute illness in both areas. The most frequently utilized medical facility was the pharmacy among the patients with acute illnesses in the poor area. Among them 58.1% visited pharmacy initially while 38.4% of the patients in the control area visited a clinic. Among persons with illnesses during the 15 days 8.8% in the poor area and 4.6% in the control area did not seek any medical facility. Mean duration of utilization of medical facilities was 3.5 days in the poor area and 3.3 days in the control area. Initially of the medical facilities in Daemyong 8 Dong, The pharmacy in the poor area and the clinic in the control area were most commonly utilized. The most common reason for visiting the hospital was 'regular customers' in the poor area and 'geographical accessibility' in the control area. The one year period morbidity rate of chronic illness in the poor area was 83.0 per 1,000 population and 28.0 per 1,000 in the control area. Disease of nervous system was the most common chronic illness in the poor area while cardiovascular disease in male and gastrointestinal disease in female were most prevalent in the control area. The most frequently utilized medical facility was the pharmacy among the patients with chronic illnesses in the poor area. Among them 24.2% visited the pharmacy initially while 34.7% of the patients in the control area visited the out-patient department of the hospital within a 15-day period. Among the patients with chronic illnesses 34.9% in the poor area and 16.0% in the control area did not seek any medical facility. Mean duration of utilization of medical facilities was 9.2 days in the poor area and 9.9 days in the control area within a 15-day period. Initially of the medical facilities in Daemyong 8 Dong, the pharmacy in the poor area and the hospital in the control area were most commonly utilized. The most common reason for visiting the hospital, clinic, health center or pharmacy in the poor area was 'geographical accessibility' while the reason for visiting herb clinic was 'good result' and 'reputation' in both areas.