• Journal of Biomedical Engineering Research
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• v.28 no.2
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• pp.235-243
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• 2007

Hepatocellular carcinoma is significant worldwide public health problem with an estimated annually mortality of 1,000,000 people. Radiofrequency (RF) ablation is an interventional technique that in recent years has come to be used for treatment of the hepatocellualr carcinoma, by destructing tumor tissues in high temperatures. Numerous studies have been attempted to prove excellence of RF ablation and to improve its efficiency by various methods. However, the attempts are sometimes paradox to advantages of a minimum invasive characteristic and an operative simplicity in RF ablation. The aim of the current study is, therefore, to suggest an improved RF ablation technique by identifying an optimum RF pattern, which is one of important factors capable of controlling the extent of high temperature region in lossless of the advantages of RF ablation. Three-dimensional finite element (FE) model was developed and validated comparing with the results reported by literature. Four representative Rf patterns (sine, square, exponential, and simulated RF waves), which were corresponding to currents fed during simulated RF ablation, were investigated. Following parameters for each RF pattern were analyzed to identify which is the most optimum in eliminating effectively tumor tissues. 1) maximum temperature, 2) a degree of alteration of maximum temperature in a constant time range (30-40 second), 3) a domain of temperature over $47^{\circ}C$ isothermal temperature (IT), and 4) a domain inducing over 63% cell damage. Here, heat transfer characteristics within the tissues were determined by Bioheat Governing Equation. Developed FE model showed 90-95% accuracy approximately in prediction of maximum temperature and domain of interests achieved during RF ablation. Maximum temperatures for sine, square, exponential, and simulated RF waves were $69.0^{\circ}C,\;66.9^{\circ}C,\;65.4^{\circ}C,\;and\;51.8^{\circ}C$, respectively. While the maximum temperatures were decreased in the constant time range, average time intervals for sine, square, exponential, and simulated RE waves were $0.49{\pm}0.14,\;1.00{\pm}0.00,\;1.65{\pm}0.02,\;and\;1.66{\pm}0.02$ seconds, respectively. Average magnitudes of the decreased maximum temperatures in the time range were $0.45{\pm}0.15^{\circ}C$ for sine wave, $1.93{\pm}0.02^{\circ}C$ for square wave, $2.94{\pm}0.05^{\circ}C$ for exponential wave, and $1.53{\pm}0.06^{\circ}C$ for simulated RF wave. Volumes of temperature domain over $47^{\circ}C$ IT for sine, square, exponential, and simulated RF waves were 1480mm3, 1440mm3, 1380mm3, and 395mm3, respectively. Volumes inducing over 63% cell damage for sine, square, exponential, and simulated RF waves were 114mm3, 62mm3, 17mm3, and 0mm3, respectively. These results support that applying sine wave during RF ablation may be generally the most optimum in destructing effectively tumor tissues, compared with other RF patterns.

• Journal of the Korean Society of Food Science and Nutrition
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• v.38 no.7
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• pp.935-945
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• 2009

This study was performed to find the optimum ratio of ingredients in the Chinese quince jam. The experiment was designed according to the D-optimal design of mixture design, which included 14 experimental points with 4 replicates for three independent variables (Chinese quince paste $45{\sim}60%$, pectin $1.5{\sim}4.5%$, sugar $45.5{\sim}63.5%$). A mathematical analytical tool was employed for the optimization of typical ingredients. The canonical form and trace plot showed the influence of each ingredient in the mixture against final product. By use of F-test, sweetness, pH, L, b, ${\Delta}E$, and firmness were expressed by a linear model, while the spreadmeter value, a, and sensory characteristics (appearance, color, smell, taste, and overall acceptability) were by a quadratic model. The optimum formulations by numerical and graphical method were similar: Chinese quince paste 54.48%, pectin 2.45%, and sugar 53.07%. Optimum ingredient formulation is expected to improve use of Chinese quince and contribute to commercialization of high quality Chinese quince jam.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.12
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• pp.1141-1153
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• 2010

This paper introduces ONLIS(Overlay Multicast Network for Live IPTV Service), a novel overlay multicast network optimized to deliver live broadcast IPTV stream. We analyzed IPTV reference model of ITU-T IPTV standardization group in terms of network and stream delivery from the source networks to the customer networks. Based on the analysis, we divide IPTV reference model into 3 networks; source network, core network and access network, ION(Infrastructure-based Overlay Multicast Network) is employed for the source and core networks and PON(P2P-based Overlay Multicast Network) is applied to the access networks. ION provides an efficient, reliable and stable stream distribution with very negligible delay while PON provides bandwidth efficient and cost effective streaming with a little tolerable delay. The most important challenge in live P2P streaming is to reduce end-to-end delay without sacrificing stream quality. Actually, there is always a trade-off between delay & stream quality in conventional live P2P streaming system. To solve this problem, we propose two approaches. Firstly, we propose DSPT(Distributed Streaming P2P Tree) which takes advantage of combinational overlay multicasting. In DSPT, a peer doesn't fully rely on SP(Supplying Peer) to get the live stream, but it cooperates with its local ANR(Access Network Relay) to reduce delay and improve stream quality. When RP detects bandwidth drop in SP, it immediately switches the connection from SP to ANR and continues to receive stream without any packet loss. DSPT uses distributed P2P streaming technique to let the peer share the stream to the extent of its available bandwidth. This means, if RP can't receive the whole stream from SP due to lack of SP's uploading bandwidth, then it receives only partial stream from SP and the rest from the ANR. The proposed distributed P2P streaming improves P2P networking efficiency.

• Herbal Formula Science
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• v.22 no.1
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• pp.113-122
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• 2014

Objectives : This study investigated the improvement effects of Pakistani (DF-a) and Chinese Ephedra herba-containing Gangjihwan (DF-b) on nonalcoholic fatty liver disease in a high fat diet-induced obese mouse model. Methods : Eight-week-old C57BL/6N mice were divided into five groups: a normal lean group given a standard diet, an obese control group given a high fat diet, and atorvastatin, DF-a, and DF-b groups given a high fat diet with atorvastatin (10 mg/kg), DF-a (80 mg/kg), and DF-b (80 mg/kg), respectively. After 8 weeks of treatment, body weight gain, blood lipid markers, ALT concentrations, liver weight and histology were examined. Results : 1. Body weight gain was significantly decreased in DF-a, DF-b, and atorvastatin groups compared with control. The extent of decreases was eminent in DF-a group. 2. Circulating concentrations of total cholesterol and LDL-cholesterol were significantly decreased in DF-a, DF-b, and atorvastatin groups compared with control. The decreases were most effective in atorvastatin group. 3. Liver weights were decreased in DF-a, DF-b, and atorvastatin groups compared with control. In particular, liver weight was significantly reduced in DF-b group. 4. Hepatic lipid accumulation was significantly decreased in DF-a, DF-b, and atorvastatin groups compared with control, and the magnitude of which was most effective in DF-b group. 5. Circulating ALT concentrations were decreased in DF-a, DF-b, and atorvastatin groups compared with control, but ALS levels were significantly reduced only in DF-b group. Conclusions : In conclusion, these results suggest that DF-a and DF-b decrease body weight gain, improve blood lipid metabolism, and reduce liver weight and hepatic lipid accumulation, contributing to the improvement of nonalcoholic fatty liver disease. In addition, these effects were similar between Pakistani and Chinese Ephedra herba-containing Gangjihwan.

• 대한공업교육학회지
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• v.37 no.1
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• pp.145-164
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• 2012

The purpose of this study is to develope the teaching materials of career education for the 'Information and Communication Technology and Our Life' unit in the technology education. As preparation phase, in order to choose the suitable contents for career education, we analyzed the technology education curriculum and 'Information and Communication Technology and our Life' unit of technology and home economics. And then we compared and analyzed the existing related researches. After content analysis of the teaching materials for career education, we mapped the contents into career education area. In the 'Design' step of teaching, we extracted the unit design components after analyzing 'Development in Information and Communication Technology' unit of eleven text books used in 2007 revised curriculum In the 'Introduction', 'Activity', 'Arrangement' steps of teaching, by applying the SHIP model, one of career education program model, we develop the teaching materials. Then, we get expert evaluation using questionnaire and improve the suitability of the teaching materials. The results are as followings: First, our teaching materials reflect the development history of information and communication technology well, show the features of career education, and are suitable to middle school students as the teaching materials. Second, our teaching materials can help students to face various jobs related with the development of Information and communication technology, to have more interests and exploring opportunities about 'Information and Communication Technology' subject. Third, our teaching materials can help teachers to use it for the career education of 'Information and Communication Technology and our Life' unit of 2007 revised curriculum in the class time. Our teaching materials can also be used in the extra activity related to career education and the Creative Experience Activities. Furthermore, since 2009 revised curriculum includes the career education unit in the 'Information and Communication Technology' subject, our teaching materials can be used partially as the teaching materials in the future.

• Journal of Preventive Medicine and Public Health
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• v.12 no.1
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• pp.13-23
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• 1979

A study was carried out from October 1977 to September 1978 in order to develope health care delively system which will meet to rural area in Korea. For the study objective a model of health care delivery system of Myun (township) area was developed which is adopted the net-work of village health voluntary worker who will play the role of bridge for communication related with health and illness between families or village people and health subcenter, and :he model health care delivery system net-work was set in the area of Soodong Myun, Yangju Gun. which is the rural health demonstration area of Ewha Womans University since 1972. The activities and attitude of 22 village health voluntary workers were observed and analized. during the study period. The results are as follows; 1. For the field activities of village health voluntary workers. a guide line which is described with specific behavioral objectives was developed and used for not only training of the workers but also evaluation of their field activities. 2. During the study period, the number of 971 village people were served primary health care service by village health voluntary worker and the service was classified largely into symptomatic medications (92%) and preventive measures (8%). 3. Comparative percentage of the number of 894 symptomatic cases cared by village health voluntary workers to 5,695 cases of patient treated by Soodong Health Subcenter during the same period was 15.7%. 4. Annual utility rate of village health voluntary worker by Myun total people was 16.1% but utility rate by Rie was varied from 38.2% to 2.8% which shown there were considerable differences in each Rie. In order to settle the village health care service, the obstructive factors of utility should be detected and their counter measure must be taken. 5. As the health need of village people increases, it is expected that the supplement of drug excluding present sit basic drugs is inevitable, but considering the ability of village health voluntary worker, the selection of additional drugs and education, plan should be carefully studied. 6. It is desirable that a financial resource for supplementary purchase of first aid kit, drugs and materials whould be alloted from village public fund like Saemaeul Women's Club fund, which has already practiced in a few villages in the study area. 7. As pointed out by village health voluntary workers, in order to improve the village health, village leaders should be in the center of it and the cooperation of whole village people is a core of healthful village development, and it is reasonable that the health subcenter backs up these voluntary health activities by village people in techniques. 8. It seems effective that a supplementary education for village health voluntary worker be accomplished by a planned education through regular meetings like worker's monthly meeting and irregular post guide when Myun Health Workers can handle the problems found during the round trip of villages. 9. It is desirable that village health voluntary workers, who are recommended by a civil voluntary organization like Saemael Woman's Club, are charged by natural villagc unit, are given a function of village health care service and used through basic education at health subcenter. 10. It is advisable that the village health voluntary worker's service is compensated not by a form of money, but by other way such as an exemption of medical fee of worker herself or her families in health subcenter can be one method. 11. Daily health activities of each village health voluntary worker should be reported to health subcenter by biweekly or monthly in order to get not only for basic data of the program but also for evaluation the program. It is recomandable that the report form should be simple and clear enough for village health voluntary worker to fill it effectively. 12. Village health care service should be developed into a Saemaeul Movement in which village people actively participate. For this, the appointed function of village health voluntary worker should be absorbed into those of living Environment Betterment Section or Family Planning Section of Saemaeul Women's Club or it is desirable that establish a new section, Village Health Promoting Section and make it involve the appointed functions of those sections mentioned above.

• Korean Journal of Agricultural and Forest Meteorology
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• v.14 no.1
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• pp.1-10
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• 2012

Digital Forecast of the Korea Meteorological Administration (KMA) represents 5 km gridded weather forecast over the Korean Peninsula and the surrounding oceanic regions in Korean territory. Digital Forecast provides 12 weather forecast elements such as three-hour interval temperature, sky condition, wind direction, wind speed, relative humidity, wave height, probability of precipitation, 12 hour accumulated rain and snow, as well as daily minimum and maximum temperatures. These forecast elements are updated every three-hour for the next 48 hours regularly. The objective of this study was to construct Forest Fire Danger Rating Systems on the Korean Peninsula (FFDRS_KORP) based on the daily weather index (DWI) and to improve the accuracy using the digital forecast data. We produced the thematic maps of temperature, humidity, and wind speed over the Korean Peninsula to analyze DWI. To calculate DWI of the Korean Peninsula it was applied forest fire occurrence probability model by logistic regression analysis, i.e. $[1+{\exp}\{-(2.494+(0.004{\times}T_{max})-(0.008{\times}EF))\}]^{-1}$. The result of verification test among the real-time observatory data, digital forecast and RDAPS data showed that predicting values of the digital forecast advanced more than those of RDAPS data. The results of the comparison with the average forest fire danger rating index (sampled at 233 administrative districts) and those with the digital weather showed higher relative accuracy than those with the RDAPS data. The coefficient of determination of forest fire danger rating was shown as $R^2$=0.854. There was a difference of 0.5 between the national mean fire danger rating index (70) with the application of the real-time observatory data and that with the digital forecast (70.5).

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.241-254
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• 2011

Financial time-series forecasting is one of the most important issues because it is essential for the risk management of financial institutions. Therefore, researchers have tried to forecast financial time-series using various data mining techniques such as regression, artificial neural networks, decision trees, k-nearest neighbor etc. Recently, support vector machines (SVMs) are popularly applied to this research area because they have advantages that they don't require huge training data and have low possibility of overfitting. However, a user must determine several design factors by heuristics in order to use SVM. For example, the selection of appropriate kernel function and its parameters and proper feature subset selection are major design factors of SVM. Other than these factors, the proper selection of instance subset may also improve the forecasting performance of SVM by eliminating irrelevant and distorting training instances. Nonetheless, there have been few studies that have applied instance selection to SVM, especially in the domain of stock market prediction. Instance selection tries to choose proper instance subsets from original training data. It may be considered as a method of knowledge refinement and it maintains the instance-base. This study proposes the novel instance selection algorithm for SVMs. The proposed technique in this study uses genetic algorithm (GA) to optimize instance selection process with parameter optimization simultaneously. We call the model as ISVM (SVM with Instance selection) in this study. Experiments on stock market data are implemented using ISVM. In this study, the GA searches for optimal or near-optimal values of kernel parameters and relevant instances for SVMs. This study needs two sets of parameters in chromosomes in GA setting : The codes for kernel parameters and for instance selection. For the controlling parameters of the GA search, the population size is set at 50 organisms and the value of the crossover rate is set at 0.7 while the mutation rate is 0.1. As the stopping condition, 50 generations are permitted. The application data used in this study consists of technical indicators and the direction of change in the daily Korea stock price index (KOSPI). The total number of samples is 2218 trading days. We separate the whole data into three subsets as training, test, hold-out data set. The number of data in each subset is 1056, 581, 581 respectively. This study compares ISVM to several comparative models including logistic regression (logit), backpropagation neural networks (ANN), nearest neighbor (1-NN), conventional SVM (SVM) and SVM with the optimized parameters (PSVM). In especial, PSVM uses optimized kernel parameters by the genetic algorithm. The experimental results show that ISVM outperforms 1-NN by 15.32%, ANN by 6.89%, Logit and SVM by 5.34%, and PSVM by 4.82% for the holdout data. For ISVM, only 556 data from 1056 original training data are used to produce the result. In addition, the two-sample test for proportions is used to examine whether ISVM significantly outperforms other comparative models. The results indicate that ISVM outperforms ANN and 1-NN at the 1% statistical significance level. In addition, ISVM performs better than Logit, SVM and PSVM at the 5% statistical significance level.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.253-266
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• 2018

This paper proposes a methodology applying sequence tagging methodology to improve the performance of NER(Named Entity Recognition) used in QA system. In order to retrieve the correct answers stored in the database, it is necessary to switch the user's query into a language of the database such as SQL(Structured Query Language). Then, the computer can recognize the language of the user. This is the process of identifying the class or data name contained in the database. The method of retrieving the words contained in the query in the existing database and recognizing the object does not identify the homophone and the word phrases because it does not consider the context of the user's query. If there are multiple search results, all of them are returned as a result, so there can be many interpretations on the query and the time complexity for the calculation becomes large. To overcome these, this study aims to solve this problem by reflecting the contextual meaning of the query using Bidirectional LSTM-CRF. Also we tried to solve the disadvantages of the neural network model which can't identify the untrained words by using ontology knowledge based feature. Experiments were conducted on the ontology knowledge base of music domain and the performance was evaluated. In order to accurately evaluate the performance of the L-Bidirectional LSTM-CRF proposed in this study, we experimented with converting the words included in the learned query into untrained words in order to test whether the words were included in the database but correctly identified the untrained words. As a result, it was possible to recognize objects considering the context and can recognize the untrained words without re-training the L-Bidirectional LSTM-CRF mode, and it is confirmed that the performance of the object recognition as a whole is improved.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.21-44
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• 2018

In recent years, the rapid development of internet technology and the popularization of smart devices have resulted in massive amounts of text data. Those text data were produced and distributed through various media platforms such as World Wide Web, Internet news feeds, microblog, and social media. However, this enormous amount of easily obtained information is lack of organization. Therefore, this problem has raised the interest of many researchers in order to manage this huge amount of information. Further, this problem also required professionals that are capable of classifying relevant information and hence text classification is introduced. Text classification is a challenging task in modern data analysis, which it needs to assign a text document into one or more predefined categories or classes. In text classification field, there are different kinds of techniques available such as K-Nearest Neighbor, Naïve Bayes Algorithm, Support Vector Machine, Decision Tree, and Artificial Neural Network. However, while dealing with huge amount of text data, model performance and accuracy becomes a challenge. According to the type of words used in the corpus and type of features created for classification, the performance of a text classification model can be varied. Most of the attempts are been made based on proposing a new algorithm or modifying an existing algorithm. This kind of research can be said already reached their certain limitations for further improvements. In this study, aside from proposing a new algorithm or modifying the algorithm, we focus on searching a way to modify the use of data. It is widely known that classifier performance is influenced by the quality of training data upon which this classifier is built. The real world datasets in most of the time contain noise, or in other words noisy data, these can actually affect the decision made by the classifiers built from these data. In this study, we consider that the data from different domains, which is heterogeneous data might have the characteristics of noise which can be utilized in the classification process. In order to build the classifier, machine learning algorithm is performed based on the assumption that the characteristics of training data and target data are the same or very similar to each other. However, in the case of unstructured data such as text, the features are determined according to the vocabularies included in the document. If the viewpoints of the learning data and target data are different, the features may be appearing different between these two data. In this study, we attempt to improve the classification accuracy by strengthening the robustness of the document classifier through artificially injecting the noise into the process of constructing the document classifier. With data coming from various kind of sources, these data are likely formatted differently. These cause difficulties for traditional machine learning algorithms because they are not developed to recognize different type of data representation at one time and to put them together in same generalization. Therefore, in order to utilize heterogeneous data in the learning process of document classifier, we apply semi-supervised learning in our study. However, unlabeled data might have the possibility to degrade the performance of the document classifier. Therefore, we further proposed a method called Rule Selection-Based Ensemble Semi-Supervised Learning Algorithm (RSESLA) to select only the documents that contributing to the accuracy improvement of the classifier. RSESLA creates multiple views by manipulating the features using different types of classification models and different types of heterogeneous data. The most confident classification rules will be selected and applied for the final decision making. In this paper, three different types of real-world data sources were used, which are news, twitter and blogs.